Abrégé

A method for identifying images of any kind of object, applied in an electronic device, obtains images to be classified, and obtains classification models and target models. Image segmentation on the images is carried out and target images are obtained; target images are input into each of the input layers and target feature maps are generated; target values according to size of each target feature map are generated; a convolution operation on the target feature maps generates feature values and a target vector of each target model is generated according to average pooling layers, feature values, and target values. The target vector is input into classification layers of a target model and label categories and probability values of categories obtained, enabling classification of images according to the label categories and the probability values of categories.

Classes IPC  ?

• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
• G06V 10/32 - Normalisation des dimensions de la forme
• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques

Abrégé

A cleaning device includes a base, a positioning component, and a cleaning component, all mounted on the base. The positioning component includes a fixture and a side pushing member, the side pushing member located alongside the fixture. The side pushing member is used to push a workpiece into a limiting area of the fixture, where the fixture secures the workpiece in place. The cleaning component includes an exhaust member and a suction member. The exhaust member blows gas into contaminated holes, the gas is then sucked by the suction member.

Classes IPC  ?

• A47L 5/14 - Caractéristiques de structures des aspirateurs à pompes à air ou compresseurs à moteur, p.ex. fonctionnant par le vide créé par le moteur d'un véhicule nettoyant par ventilation, combiné également avec un nettoyage par aspiration
• A47L 9/02 - Buses
• A47L 9/24 - Tuyaux flexibles; Leurs pièces d'assemblage
• A47L 9/28 - Montage de l'équipement électrique, p.ex. adaptation ou fixation à l'aspirateur; Commande des aspirateurs par des moyens électriques
• B08B 9/00 - Nettoyage d'objets creux par des procédés ou avec un appareillage spécialement adaptés à cet effet

Abrégé

A transporting device can transport at least one product, the transporting device includes a mounting frame, a driving mechanism, a transmitting mechanism including a plurality of bent portions, a plurality of guiding mechanisms, and a supporting mechanism. Each guiding mechanism includes a rotating wheel and a guiding plate. Each bent portion is connected to the rotating wheel. The guiding plate is connected to the rotating wheel. The supporting mechanism can support the product. The driving mechanism is further connected to the rotating wheel and can drive the transmitting mechanism to rotate to drive the supporting mechanism to move. The driving mechanism is further connected to the guiding plate, the guiding plate and the rotating wheel can synchronously rotate to drive the supporting mechanism to pass through the bent portions. The present disclosure further provides a heating device.

Classes IPC  ?

• B65G 19/24 - Fixation des impulseurs à l'élément de traction
• B65G 17/12 - Transporteurs comportant un élément de traction sans fin, p.ex. une chaîne transmettant le mouvement à une surface porteuse de charges continue ou sensiblement continue, ou à une série de porte-charges individuels; Transporteurs à chaîne sans fin dans lesquels des chaînes constituent la surface portant la charge comprenant une série de porte-charges individuels fixés ou normalement fixés à l'élément de traction
• B65G 19/20 - Chaînes, cordes ou câbles de traction

Abrégé

A package structure includes a bonding substrate, an integrated circuit, and a heat sink metal. The integrated circuit includes an active region facing the bonding substrate. The heat sink metal is located between the bonding substrate and the active region of the integrated circuit. The heat sink metal is electrically insulated with the integrated circuit.

Classes IPC  ?

• H01L 23/367 - Refroidissement facilité par la forme du dispositif
• H01L 23/00 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide

Abrégé

A method for collision warning implemented in an electronic device includes fusing obtained radar information and image information; recognizing at least one obstacle in a traveling direction of a vehicle according to the fused radar information and image information; determining motion parameters of the at least one obstacle and the vehicle according to the radar information and the image information; and calculating a collision time between the vehicle and the at least one obstacle according to the motion parameters, and issuing a collision warning.

Classes IPC  ?

• G08G 1/16 - Systèmes anticollision
• G01S 7/40 - Moyens de contrôle ou d'étalonnage
• G01S 7/41 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe utilisant l'analyse du signal d'écho pour la caractérisation de la cible; Signature de cible; Surface équivalente de cible
• G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p.ex. sonar, chercheur de direction
• G01S 13/931 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour prévenir les collisions de véhicules terrestres
• G06T 7/20 - Analyse du mouvement
• G06T 7/579 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir du mouvement
• G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
• G06V 10/80 - Fusion, c. à d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes

Abrégé

A head-mounted device comprises a main body, a monitor-base, a monitor, and an adjusting component. The main body is mounted on a user's head. The monitor moves with the monitor-base. An eyes-monitor distance is defined between the user's eyes and the monitor. A visual range is defined by a range seen by the user's eyes through the monitor. The adjusting component is configured for pulling or pushing the monitor-base towards the user's eyes to prevent the eyes-monitor distance from increasing and to prevent the visual range from decreasing. When the user wears a pair of glasses between the user's eyes and the monitor, and the glasses block the monitor causing the eyes-monitor distance to be larger than a preset distance, the adjusting component pulls or pushes the monitor-base until the monitor contacts the glasses, to prevent the eyes-monitor distance from increasing. An augmented reality device is also disclosed.

Classes IPC  ?

• G06F 1/16 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES - Détails non couverts par les groupes et - Détails ou dispositions de structure

Abrégé

A graphene heating chip includes a substrate, an insulating layer, a graphene film, and a plurality of electrodes. The substrate has two opposite a first surface and a second surface, and the substrate defines a through hole. The insulating layer is suspended on the substrate. The insulating layer covering the through hole and not in direct contact with the first surface is defined as a window, and a plurality of grooves are formed on the window. The graphene film covers the window, and the graphene film includes a first graphene film portion and a second graphene film portion, and the first graphene film portion and the second graphene film portion are spaced apart from each other. The plurality of electrodes are located on the surface of the insulating layer away from the substrate. The present application also provides a method for making the graphene heating chip.

Classes IPC  ?

• H05B 3/14 - Eléments chauffants caractérisés par la composition ou la nature des matériaux ou par la disposition du conducteur caractérisés par la composition ou la nature du matériau conducteur le matériau étant non métallique
• H05B 3/03 - Chauffage par résistance ohmique - Détails Électrodes

Abrégé

A graphene heating chip includes a substrate, an insulating layer, a graphene film, and a plurality of electrodes. The substrate has two opposite a first surface and a second surface, and the substrate defines a through hole. The insulating layer is suspended on the substrate. The insulating layer covering the through hole and not in direct contact with the first surface is defined as a window, and a plurality of grooves are formed on the window. The graphene film covers the window, and the graphene film includes a first graphene film portion and a second graphene film portion, and the first graphene film portion and the second graphene film portion are spaced apart from each other. The plurality of electrodes are located on the surface of the insulating layer away from the substrate. The present application also provides a method for calibrating a temperature of the graphene heating chip.

Classes IPC  ?

• G01K 15/00 - Test ou étalonnage des thermomètres
• G01R 27/02 - Mesure de résistances, de réactances, d'impédances réelles ou complexes, ou autres caractéristiques bipolaires qui en dérivent, p.ex. constante de temps
• H05B 3/03 - Chauffage par résistance ohmique - Détails Électrodes
• H05B 3/14 - Eléments chauffants caractérisés par la composition ou la nature des matériaux ou par la disposition du conducteur caractérisés par la composition ou la nature du matériau conducteur le matériau étant non métallique

Abrégé

A magnetically-isolated wireless charging assembly enabling temporary attachment by magnetism to a device requiring charge by induction includes a housing defining an accommodating space, a plurality of magnetic members disposed within the accommodating space, a wireless charging receiver covering the plurality of magnetic members, and a wireless charging receiver. The wireless charging receiver is disposed on a side of the magnetic shielding member facing the plurality of magnetic members and the magnetic shielding member prevents the magnetic field of the magnetic members extending through or beyond the shielding member.

Classes IPC  ?

• H02J 50/70 - Circuits ou systèmes pour l'alimentation ou la distribution sans fil d'énergie électrique mettant en œuvre la réduction des champs de fuite électriques, magnétiques ou électromagnétiques
• H01F 27/36 - Blindages ou écrans électriques ou magnétiques
• H02J 50/00 - Circuits ou systèmes pour l'alimentation ou la distribution sans fil d'énergie électrique
• H02J 50/10 - Circuits ou systèmes pour l'alimentation ou la distribution sans fil d'énergie électrique utilisant un couplage inductif

Abrégé

A distance measurement method is provided and includes: in response to a distance detection instruction, determining a first position where a pupil of a user is located. An included angle between the pupil and an electronic device is determined based on the first position and a second position where the electronic device is located. Once a first distance between the pupil and the electronic device is determined, a second distance between a waveguide sheet in a pair of smart glasses worn by the user and the pupil is calculated according to the included angle and the first distance.

Classes IPC  ?

• G01S 5/18 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de position; Localisation par coordination de plusieurs déterminations de distance utilisant des ondes ultrasonores, sonores ou infrasonores
• G02B 27/01 - Dispositifs d'affichage "tête haute"
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur

Abrégé

A semiconductor device includes a substrate, an epitaxial layer, a well region, a source region, a base region, a first JFET region, a second JFET region, a gate dielectric layer and a gate layer. The epitaxial layer is at a side of the substrate. The well region is in the epitaxial layer. The source region is in the well region. The base region is in the well region and adjacent to the source region. The first JFET region is adjacent to the well region. The second JFET region is in the first JFET region. A doping concentration of the second JFET region is higher than a doping concentration of the first JFET region. The gate dielectric layer is at a side of the epitaxial layer away from the substrate. The gate layer is at a side of the gate dielectric layer away from the epitaxial layer.

Classes IPC  ?

• H01L 29/78 - Transistors à effet de champ l'effet de champ étant produit par une porte isolée
• H01L 29/06 - Corps semi-conducteurs caractérisés par les formes, les dimensions relatives, ou les dispositions des régions semi-conductrices
• H01L 29/10 - Corps semi-conducteurs caractérisés par les formes, les dimensions relatives, ou les dispositions des régions semi-conductrices avec des régions semi-conductrices connectées à une électrode ne transportant pas le courant à redresser, amplifier ou commuter, cette électrode faisant partie d'un dispositif à semi-conducteur qui comporte trois électrodes ou plus
• H01L 29/66 - Types de dispositifs semi-conducteurs

Abrégé

A mouse device is disclosed. The mouse device is coupled to a computer device. The mouse device includes a first button, a second button, and a processor. The processor is configured to generate at least one signal according to a first state of the first button and a second state of the second button at the same time, and the processor is configured to transmit the at least one signal to the computer device, so that the computer device performs one of a copy operation and a cut operation according to the at least one signal.

Classes IPC  ?

• G06F 3/04812 - Techniques d’interaction fondées sur l’aspect ou le comportement du curseur, p.ex. sous l’influence de la présence des objets affichés
• G06F 3/0354 - Dispositifs de pointage déplacés ou positionnés par l'utilisateur; Leurs accessoires avec détection des mouvements relatifs en deux dimensions [2D] entre le dispositif de pointage ou une partie agissante dudit dispositif, et un plan ou une surface, p.ex. souris 2D, boules traçantes, crayons ou palets
• G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés

Abrégé

A method for detecting image sizes using a computer device is provided. In the method, the computer device obtains a pre-trained image processing model and an input image, establishes a stack according to a number of network layers of the pre-trained image processing model. Output feature maps are obtained by processing the input image using the pre-trained image processing model. Image sizes of the output feature maps are detected. The image sizes are written into the stack in sequence, and the network layers deleted iteratively in an order from large to small according to an execution sequence of the network layers of the pre-trained image processing model until the stack is full. By performing the method, output sizes of the input image can be obtained, and a detection efficiency of image sizes can be improved.

Classes IPC  ?

• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06T 1/60 - Gestion de mémoire
• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos

Abrégé

A machine learning method includes: distinguishing foregrounds and backgrounds of a first image to generate a first mask image; cropping the first image to generate second and third images; cropping the first mask image to generate second and third mask images, wherein a position of the second mask image and a position of the third mask image correspond to a position of the second image and a position of the third image, respectively; generating a first feature vector group of the second image and a second feature vector group of the third image by a model; generating a first matrix according to the first and second feature vector groups; generating a second matrix according to the second and third mask images; generating a function according to the first and second matrices; and adjusting the model according to the function.

Classes IPC  ?

• G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan
• G06T 7/11 - Découpage basé sur les zones

Abrégé

A reshaping method for metal product includes acquiring position data information of a workpiece; converting the position data information into coordinate information, and fitting the coordinate information to obtain a surface contour curve of the workpiece; comparing the surface contour curve with a standard contour curve to generate a comparison result; obtaining reshaping information of the workpiece based on the comparison result; and controlling the reshaping system to reshape the workpiece. The disclosure acquires position data information of the workpiece by measuring, converts the position data information into coordinate information, fits the coordinate information to obtain the surface contour curve of the workpiece, compares the surface contour curve with the standard contour curve to obtain deformation situation, and reshapes the workpiece according to the reshaping information based on the deformation situation, which improves efficiency of workpiece reshaping and reducing labor costs. An electronic device is also provided.

Classes IPC  ?

• G05B 19/4093 - Commande numérique (CN), c.à d. machines fonctionnant automatiquement, en particulier machines-outils, p.ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'u caractérisée par la programmation de pièce, p.ex. introduction d'une information géométrique dérivée d'un dessin technique, combinaison de cette information avec l'information d'usinage et de matériau pour obtenir une information de commande, appelée
• G05B 19/29 - Commande numérique (CN), c.à d. machines fonctionnant automatiquement, en particulier machines-outils, p.ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'u caractérisée par systèmes de commande de positionnement ou de commande de contournage, p.ex. pour commander la position à partir d'un point programmé vers un autre point ou pour commander un mouvement le long d'un parcours continu programmé utilisant un dispositif de mesure numérique absolue pour commande point par point
• G05B 19/4099 - Usinage de surface ou de courbe, fabrication d'objets en trois dimensions 3D, p.ex. fabrication assistée par ordinateur

Abrégé

The present application provides a method of preparing lithium-friendly colloid paint. The method comprises functionalizing a carbon nanotube material to obtain a plurality of carbon nanotubes with functional groups; dispersing the of carbon nanotube material with functional groups in a solution containing nitrogen molecules to from the dispersion liquid to obtain a carbon nanotube precursor; heat-treating the carbon nanotube precursors to obtain a plurality of nitrogen-doped carbon nanotubes; dispersing the plurality of nitrogen-doped carbon nanotubes in an organic solvent, and adding a dispersant obtain a nitrogen-doped carbon nanotube solution precursor; and providing a polymer material colloid and a lithium salt, and uniformly mixing the nitrogen-doped carbon nanotube solution precursor, the lithium salt and the polymer material colloid.

Classes IPC  ?

• C09D 5/02 - Peintures émulsions
• H01M 4/38 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'éléments simples ou d'alliages
• H01M 4/62 - Emploi de substances spécifiées inactives comme ingrédients pour les masses actives, p.ex. liants, charges

Abrégé

A display device includes a first substrate, a plurality of light-emitting diodes, a first wavelength conversion layer and a metasurface. The light-emitting diodes are arranged on the first substrate, in which the light-emitting diodes emit a first color light, and the light-emitting diodes includes a first light-emitting diode, a second light-emitting diode and a third light-emitting diode. The first wavelength conversion layer is on the first light-emitting diode, and configured to convert the first color light emitted from the first light-emitting diode into a second color light, in which the second color light is different from the first color light. The metasurface is above the first wavelength conversion layer, and configured to reflect the first color light and pass the second color light.

Classes IPC  ?

• H01L 33/50 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de conversion de la longueur d'onde
• H01L 25/075 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant tous d'un type prévu dans le même sous-groupe des groupes , ou dans une seule sous-classe de , , p.ex. ensembles de diodes redresseuses les dispositifs n'ayant pas de conteneurs séparés les dispositifs étant d'un type prévu dans le groupe

Abrégé

The present disclosure provides a bolometer including a substrate, a reflecting mirror on the substrate, and a temperature sensing unit above the reflecting mirror. The temperature sensing unit includes a first insulating layer, a thermistor on the first insulating layer, a second insulating layer on the thermistor, an electrode layer in the second insulating layer and right above the thermistor, and a metal meta-surface in the second insulating layer and right above the electrode layer. The electrode layer includes a plurality of electrodes separated from each other. A projection region of the metal meta-surface on the thermistor is equal to or larger than the thermistor.

Classes IPC  ?

• G01J 5/08 - Dispositions optiques
• G01J 5/02 - Pyrométrie des radiations, p.ex. thermométrie infrarouge ou optique - Détails structurels
• G01J 5/0813 - Miroirs plans; Lames de phase parallèles
• G01J 5/20 - Pyrométrie des radiations, p.ex. thermométrie infrarouge ou optique en utilisant des détecteurs électriques de radiations en utilisant des éléments résistants, thermorésistants ou semi-conducteurs sensibles aux radiations, p.ex. des dispositifs photoconducteurs

Abrégé

An optical assembly and projection device using the same. The optical assembly configured for converting light defined by a light path. A color wheel includes at least two color circles, the at least two color circles are arranged concentrically and each includes a plurality of color zones. A first drive assembly connect to the color wheel, the first drive assembly is configured for driving the color wheel to rotate, and the plurality of color zones of each of the at least two color circles are illuminated by the light in the light path. A second drive assembly connect to the first drive assembly, the second drive assembly is configured for driving the color wheel in a first direction, and each of the at least two color circles is illuminated a light beam defined by the light path, the first direction intersects with a directional extension of the light path.

Classes IPC  ?

• H04N 9/31 - Dispositifs de projection pour la présentation d'images en couleurs

Abrégé

A semiconductor device includes a first contact layer, a second contact layer, an active layer, a photonic crystal layer, a passivation layer, a first electrode and a second electrode. The first contact layer has a first surface and a second surface opposite to each other. Microstructures are located on the second surface. The second contact layer is located below the first surface. The active layer is located between the first contact layer and the second contact layer. The photonic crystal layer is located between the active layer and the second contact layer. The passivation layer is located on the second contact layer. The first electrode is located on the passivation layer and is electrically connected the first surface of the first contact layer. The second electrode is located on the passivation layer and is electrically connected to the second contact layer.

Classes IPC  ?

• H01S 5/042 - Excitation électrique
• H01S 5/028 - Revêtements
• H01S 5/11 - Structure ou forme du résonateur optique comprenant une structure de bande photonique interdite

Abrégé

A method for monitoring eye strain implemented in an electronic device includes obtaining an infrared thermal image of eyes of a target user; analyzing the infrared thermal image of the eyes, and obtaining a temperature value of the eyes; monitoring whether the temperature value of the eyes is greater than a preset threshold of the eye temperature; and in response that the temperature value of the eyes is greater than the preset threshold of the eye temperature, outputting a prompt as to eye strain.

Classes IPC  ?

• A61B 3/10 - Appareils pour l'examen optique des yeux; Appareils pour l'examen clinique des yeux du type à mesure objective, c. à d. instruments pour l'examen des yeux indépendamment des perceptions ou des réactions du patient
• A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
• A61B 5/01 - Mesure de la température de parties du corps
• G06T 7/12 - Découpage basé sur les bords
• G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
• G06T 7/68 - Analyse des attributs géométriques de la symétrie
• G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
• G09G 5/10 - Circuits d'intensité
• G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
• G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance

Abrégé

The present application provides a graphics card expansion device. The graphics card expansion device includes a main chassis and an expansion chassis. The expansion chassis comprises: at least one graphics card storage box, the at least one graphics card storage box is equipped with at least one graphics card; at least one second power source, the at least second power source provides power to at least one graphics card in the at least one graphics card storage box; at least one adapter card, the at least one graphics card storage box communicates with the motherboard in the main box through the at least one adapter card.

Classes IPC  ?

• G06F 1/18 - Installation ou distribution d'énergie

Abrégé

A method of making solid oxidesolid oxide electrolyte membrane comprises steps (S1)-(S5). Step (S1), mixing a high molecular polymer and a first solvent to form a first mixed slurry; and homogenizing the first mixed slurry, to obtain a reagent A. Step (S2), mixing an oxide powder, a dispersant and a second solvent to form a second mixed slurry, treating the second mixed slurry, to obtain a reagent B. Step (S3), adding a protective agent into the reagent B to form a third mixed slurry, and homogenizing the third mixed slurry to obtain a reagent C. Step (S4), mixing the reagent A and the reagent C to form a fourth mixed slurry, and treating the fourth mixed slurry a fifth mixed slurry; and homogenizating the fifth mixed slurry to form a solid electrolyte slurry. And step (S5), producing the solid oxidesolid oxide electrolyte membrane by a coating process.

Classes IPC  ?

• H01M 10/0562 - Matériaux solides

Abrégé

The present application provides a logic gate device. The logic gate device includes a gate electrode, a gate insulating layer, a bottom electrode, a two-dimensional semiconductor layer, a first top electrode and a second electrode. The gate insulating layer is located on the gate electrode. The bottom electrode is located on the gate insulating layer. The two-dimensional semiconductor layer is located on the bottom electrode and simultaneously covers the gate insulating layer. The first top electrode and the second electrode are located on the two-dimensional semiconductor layer. The bottom electrode, the two-dimensional semiconductor layer and the gate insulating layer form an air gap, and the air gap is distributed at both sides of the bottom electrode. The gate electrode is configured to connect a gate voltage, and the first top electrode and the second top electrode are configured to connect a signal input terminal.

Classes IPC  ?

• H01L 29/68 - Types de dispositifs semi-conducteurs commandables par le seul courant électrique fourni ou par la seule tension appliquée, à une électrode qui ne transporte pas le courant à redresser, amplifier ou commuter
• H03K 19/08 - Circuits logiques, c. à d. ayant au moins deux entrées agissant sur une sortie; Circuits d'inversion utilisant des éléments spécifiés utilisant des dispositifs à semi-conducteurs
• H01L 29/24 - Corps semi-conducteurs caractérisés par les matériaux dont ils sont constitués comprenant, à part les matériaux de dopage ou autres impuretés, uniquement des matériaux semi-conducteurs inorganiques non couverts par les groupes , ,  ou
• H01L 29/76 - Dispositifs unipolaires

Abrégé

The present application provides an inverter. The inverter includes a gate electrode, a gate insulating layer, a bottom electrode, a two-dimensional semiconductor layer, a first top electrode and a second electrode. The gate insulating layer is located on the gate electrode. The bottom electrode is located on the gate insulating layer. The two-dimensional semiconductor layer is located on the bottom electrode and simultaneously covers the gate insulating layer. The first top electrode and the second electrode are located on the two-dimensional semiconductor layer. The bottom electrode, the two-dimensional semiconductor layer and the gate insulating layer form air gaps, and the air gaps are distributed at both sides of the bottom electrode. The gate electrode is configured to connect with a signal input terminal, the bottom electrode is configured to connect with a signal output terminal.

Classes IPC  ?

• H01L 29/778 - Transistors à effet de champ avec un canal à gaz de porteurs de charge à deux dimensions, p.ex. transistors à effet de champ à haute mobilité électronique HEMT
• H01L 29/417 - Electrodes caractérisées par leur forme, leurs dimensions relatives ou leur disposition relative transportant le courant à redresser, à amplifier ou à commuter
• H01L 29/423 - Electrodes caractérisées par leur forme, leurs dimensions relatives ou leur disposition relative ne transportant pas le courant à redresser, à amplifier ou à commuter
• H02M 1/088 - Circuits spécialement adaptés à la production d'une tension de commande pour les dispositifs à semi-conducteurs incorporés dans des convertisseurs statiques pour la commande simultanée de dispositifs à semi-conducteurs connectés en série ou en parallèle
• H02M 7/5387 - Transformation d'une puissance d'entrée en courant continu en une puissance de sortie en courant alternatif sans possibilité de réversibilité par convertisseurs statiques utilisant des tubes à décharge avec électrode de commande ou des dispositifs à semi-conducteurs avec électrode de commande utilisant des dispositifs du type triode ou transistor exigeant l'application continue d'un signal de commande utilisant uniquement des dispositifs à semi-conducteurs, p.ex. onduleurs à impulsions à un seul commutateur dans une configuration en pont

Abrégé

The present invention relates to a Fourier transform spectrometer applicable in an array of coherent light sources, comprising: a light source, a light transmission device, a control circuit, a detector, an amplifying circuit, and a computer. The control circuit is electrically connected to the light transmission device for control the on-off of light in the light transmission device. The amplifying circuit is connected with the detector for recording and amplifying the photoelectric signal obtained by the detector. The computer is connected with the amplifying circuit. The computer is equipped with a spectral analysis software is used to perform Fourier transform. The Fourier transform spectrometer based on the coherent light source array further includes a coherent light source array. The light transmission device is used to transmit the light emitted by the light source to the coherent light source array.

Classes IPC  ?

• G01N 21/35 - Couleur; Propriétés spectrales, c. à d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p.ex. spectrométrie d'absorption atomique en utilisant la lumière infrarouge

Abrégé

A method for training a depth recognition model implemented in an electronic device includes determining test objects from test images, and obtaining a first image and a second image; calculating a test projection slope of each test object according to coordinates of each pixel point of each test object in the test images; generating a threshold range according to the plurality of test projection slopes; recognizing a type of terrain corresponding to a position of each initial object; adjusting an initial ground area in the first image, and obtaining a target ground area in the first image; generating a target height loss of a preset depth recognition network, an initial depth image corresponding to the first image, and the target ground area; and adjusting the preset depth recognition network according to the target height loss and a depth loss, and obtaining a depth recognition model for recognizing depth of images.

Classes IPC  ?

• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G06T 7/60 - Analyse des attributs géométriques

Abrégé

A deep recognition model training method applied to an electronic device is provided. The method includes obtaining a ground plane area by segmenting a first image using a ground plane segmentation network. A projection image of the first image is generated based on the first image, an initial depth image corresponding to the first image, and a pose matrix. A target height loss of a depth recognition network is generated, and a depth loss of the depth recognition network is obtained according to a gradient loss between the initial depth image and the first image and a photometric loss between the projection image and the first image. A depth recognition model is obtained by adjusting the depth recognition network based on the depth loss and the target height loss.

Classes IPC  ?

• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06V 20/64 - Objets tridimensionnels
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo

Abrégé

A field effect transistor includes a gate electrode, an insulating layer, a source electrode, a drain electrode, and a channel layer. The insulating layer is located on the surface of the gate electrode, and the channel layer is located on the surface of the insulating layer away from the gate electrode. The source electrode and the drain electrode are spaced apart from each on the surface of the channel layer away from the insulating layer. The source electrode and the drain electrode are one-dimensional structures. The present application further provides a method for making the field effect transistor.

Classes IPC  ?

• H01L 29/775 - Transistors à effet de champ avec un canal à gaz de porteurs de charge à une dimension, p.ex. FET à fil quantique
• H01L 29/06 - Corps semi-conducteurs caractérisés par les formes, les dimensions relatives, ou les dispositions des régions semi-conductrices
• H01L 29/786 - Transistors à couche mince
• H01L 29/66 - Types de dispositifs semi-conducteurs

Abrégé

A well cover assembly includes a monitoring device and a well cover. The well cover includes a first protrusion and a second protrusion. A through hole is defined on the well cover and surrounded by the second protrusion. The monitoring device can be installed on the well cover. The monitoring device includes a monitoring terminal and a fixing bracket. The fixing bracket includes a first bracket and a second bracket. One end of the first bracket is fixed to the monitoring terminal, and another end can protrude from the through hole. The second bracket is connected to the end of the first bracket protruding from the through hole, the second bracket can extend from the end of the first bracket toward an edge of the second protrusion and toward the first protrusion. The second bracket further can clamp with each of the second protrusion and the first protrusion.

Classes IPC  ?

• E02D 29/14 - Couvercles pour regards ou analogues; Châssis de couvercles

Abrégé

A laser detection system includes a light source module, an optical isolator, a scanner, and a detector. The light source module is configured for emitting a first laser having a first polarization direction. The optical isolator is on an optical path of the first laser configured to emit a second laser by transmitting the first laser from the light source module and prevent the second laser from transmitting toward the light source module. The scanner is on an optical path of the second laser and configured for reflecting the second laser to project a reference light to the target to be tested. The detector is configured to receive detection light reflected by the target to be tested and obtain position information of the target to be tested according to the detection light.

Classes IPC  ?

• G01S 17/931 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour prévenir les collisions de véhicules terrestres
• G01S 7/481 - Caractéristiques de structure, p.ex. agencements d'éléments optiques

Abrégé

A micro LED display device includes a light emitting layer, a color conversion layer located on a light emitting surface of the light emitting layer, and a Bragg reflection element located on the color conversion layer. The Bragg reflection element includes a plurality of layer pairs. Each of the layer pairs includes at least one first layer and at least one second layer alternately stacked. The layer pairs include a top layer pair, a bottom layer pair and a plurality of middle layer pairs between the top layer pair and the bottom layer pair. A thickness of the first layer of each of the middle layer pairs is smaller than a thickness of the second layer of each of the middle layer pairs. A refractive index of the first layer is greater than a refractive index of the second layer.

Classes IPC  ?

• H01L 33/60 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de mise en forme du champ optique Éléments réfléchissants
• H01L 25/075 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant tous d'un type prévu dans le même sous-groupe des groupes , ou dans une seule sous-classe de , , p.ex. ensembles de diodes redresseuses les dispositifs n'ayant pas de conteneurs séparés les dispositifs étant d'un type prévu dans le groupe
• H01L 33/50 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de conversion de la longueur d'onde

Abrégé

A field effect transistor includes a gate electrode, an insulating layer, a source electrode, a drain electrode, and a channel layer. The insulating layer is located on the surface of the gate electrode, and the channel layer is located on the surface of the insulating layer away from the gate electrode. The source electrode and the drain electrode are spaced apart from each on the surface of the channel layer away from the insulating layer. The source electrode and the drain electrode are one-dimensional structures. The present application further provides a method for making the field effect transistor and a method for measuring an interface resistance of the field effect transistor.

Classes IPC  ?

• G01R 31/26 - Test de dispositifs individuels à semi-conducteurs

Abrégé

The present application provides a flame-retardant electrolyte. The flame-retardant electrolyte includes a specific solvent; a lithium salt; and a fluorinated solvent. The specific solvent is carbonate solvent, ether solvent, succinonitrile, sulfolane, tetraglyme, ionic liquid or a combination thereof. Or, the flame-retardant electrolyte comprises 1-Butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl)imide, lithium salt, and carbonate solvent.

Classes IPC  ?

• H01M 10/0569 - Matériaux liquides caracterisés par les solvants
• H01M 10/0525 - Batteries du type "rocking chair" ou "fauteuil à bascule", p.ex. batteries à insertion ou intercalation de lithium dans les deux électrodes; Batteries à l'ion lithium
• H01M 10/0568 - Matériaux liquides caracterisés par les solutés

Abrégé

A manufacturing method of a semiconductor device includes the following steps. A base region is formed in a substrate. A protective layer is formed on the substrate and covers the base region. First and second sacrificial layers are formed on the substrate and cover the protective layer. A source region, a well region, and a junction field effect transistor (JFET) region are formed in the substrate. When the source region, the well region, and the JFET region are formed in sequence, the source region and the well region are formed by the first sacrificial layer, and the JFET region is formed by the second sacrificial layer. When the JFET region, the well region, and the source region are formed in sequence, the JFET region is formed by the first sacrificial layer, and the well region and the source region are formed by the second sacrificial layer.

Classes IPC  ?

• H01L 29/66 - Types de dispositifs semi-conducteurs
• H01L 21/266 - Bombardement par des radiations ondulatoires ou corpusculaires par des radiations d'énergie élevée produisant une implantation d'ions en utilisant des masques
• H01L 29/78 - Transistors à effet de champ l'effet de champ étant produit par une porte isolée
• H01L 29/10 - Corps semi-conducteurs caractérisés par les formes, les dimensions relatives, ou les dispositions des régions semi-conductrices avec des régions semi-conductrices connectées à une électrode ne transportant pas le courant à redresser, amplifier ou commuter, cette électrode faisant partie d'un dispositif à semi-conducteur qui comporte trois électrodes ou plus

Abrégé

A mask member for a deposition mask is provided. The deposition mask has a resin film in which a pattern of opening portions is formed. The mask member includes a resin film and a reflective film. The reflective film is on a surface of the resin film. The reflective film is adapted to reflect light having wavelengths of laser light.

Classes IPC  ?

• B23K 26/362 - Gravure au laser
• H10K 71/16 - Dépôt d'une matière active organique en utilisant un dépôt physique en phase vapeur [PVD], p. ex. un dépôt sous vide ou une pulvérisation cathodique
• B23K 26/38 - Enlèvement de matière par perçage ou découpage
• C23C 14/04 - Revêtement de parties déterminées de la surface, p.ex. au moyen de masques

Abrégé

A method of manufacturing a light-emitting device includes a number of operations. A light-emitting element is formed. A simulation of a metasurface is performed. The metasurface is formed based on the simulation of the metasurface. The metasurface is disposed on a light-emitting side of the light-emitting element. Performing the simulation of the metasurface includes establishing a metasurface model of the metasurface, in which the metasurface model has a plurality of unit cells, and phase compensation values of the unit cells are periodically distributed with a supercell period length in a deflection direction. The phase compensation values of the unit cell are adjusted and the light source is set to simulate the multiple transmittances of the metasurface model under different phase compensation values. The phase compensation values at a peak value of transmittance are selected as process parameters of the metasurface.

Classes IPC  ?

• H01L 33/58 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de mise en forme du champ optique
• H01S 5/183 - Lasers à émission de surface [lasers SE], p.ex. comportant à la fois des cavités horizontales et verticales comportant uniquement des cavités verticales, p.ex. lasers à émission de surface à cavité verticale [VCSEL]

Abrégé

A method for reducing the error of depth estimation model comprises: obtaining a plurality of monocular images and a point cloud data of each of the plurality of monocular images, wherein each of the plurality of monocular images comprises an object frame image and a reference frame image; reconstructing the object frame image to obtain a reconstructed frame image according to the reference frame image and a first depth estimation model; determining a reconstructed error between the object frame image and the reconstructed frame image; and obtaining an inertia probability of each pixel of the object frame image according to speed information of the point cloud data and pixel information of the object frame image. This application provides more accurate depth estimation results for dynamic scenes. An electronic device and a non-transitory storage recording the method are also disclosed.

Classes IPC  ?

• G06T 7/521 - Récupération de la profondeur ou de la forme à partir de la projection de lumière structurée
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques

Abrégé

A method for detecting defect in image is provided. The method obtains a number of original images, determines a first reference image from the original images, and performs a histogram matching on the original images excluding the first reference image according to the first reference image, to obtain a plurality of matched images. The method further generates a synthesized image according to pixel intensities of the matched images and pixel intensities of the first reference image; and uses the synthesized image as a second reference image to perform an image comparison with a test image, to generate a result of defect detection. A related device and a related non-transitory storage medium are also provided.

Classes IPC  ?

• G06V 10/98 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos Évaluation de la qualité des motifs acquis
• G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
• G06V 10/50 - Extraction de caractéristiques d’images ou de vidéos en utilisant l’addition des valeurs d’intensité d’image; Analyse de projection

Abrégé

An image depth prediction method acquires image frames of containing a dynamic object by a monocular camera, extracts a continuous of object frames and reference frames from the image frames, reconstructs the object frames to obtain reconstructed frames according to the reference frames and a preset depth estimation model, obtains a reconstruction error between the object frames and the reconstructed frames, processes the image frames to obtain point cloud data and instance segmentation data, fuses the point cloud data with the instance segmentation data to obtain mask data, obtains a loss function according to the reconstruction error and the mask data, and trains the depth estimation model based on the loss function until the loss function converges. The method can obtain more accurate depth estimation results for dynamic scenes. An electronic device and a non-transitory storage recording the method are also disclosed.

Classes IPC  ?

• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image

Abrégé

A recognition method includes the following steps. A text is analyzed by a language recognition network to generate an entity feature, a relation feature and an overall feature. An input image is analyzed by an object detection network to generate candidate regions. Node features, aggregated edge features and compound features are generated by an enhanced cross-modal graph attention network according to the entity feature, the relation feature, the candidate regions and the overall feature. The entity feature and the relation feature are matched to the node features and the aggregated edge features to generate the first scores. The overall feature is matched to the compound features to generate second scores. Final scores corresponding to the candidate regions are generated according to the first scores and the second scores.

Classes IPC  ?

• G06F 40/295 - Reconnaissance de noms propres
• G06V 30/146 - Alignement ou centrage du capteur d’image ou du champ d’image
• G06V 30/19 - Reconnaissance utilisant des moyens électroniques

Abrégé

An object location method to obtain location information of an object based on positioning light comprises: emitting a first positioning light, to scan a preset region where the object is located to obtain a characteristic value of each of multiple standard regions in the preset region, wherein the preset region comprises multiple standard regions; determining a basic region where the object is located according to the characteristic value of each standard region, wherein each of the standard region comprises multiple basic regions; and emitting a second positioning light to the basic regions where the object is located and to obtain coordinates of the object. An electronic device and a non-transitory storage medium are also provided.

Classes IPC  ?

• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/145 - Caractéristiques optiques de l’appareil qui effectue l’acquisition ou des dispositifs d’éclairage Éclairage spécialement adapté à la reconnaissance de formes, p.ex. utilisant des réseaux
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques

Abrégé

A horizontal grinder rotor includes a grinding ring, multiple blades, and a center body. The grinding ring, the multiple blades and the central body are formed by 3D printing. The multiple blades are located between the grinding ring and the central body, and the multiple blades are arranged at intervals. The multiple blades are arranged in pairs and symmetrical with respect to the central body, and the horizontal grinder rotor further comprises one or more pins, the one or more pins are provided on at least one side of each of the multiple blades.

Classes IPC  ?

• B02C 17/20 - Organes de désagrégation
• B33Y 80/00 - Produits obtenus par fabrication additive

Abrégé

A method for adjusting a head-mounted device comprises: a pupil detecting phase for detecting two pupils of user; a deviation judging phase for judging whether the head-mounted device is worn with or without deviation; a display guiding phase displaying a guiding picture by a first monitor and a second monitor to guide the user to adjust the wearing of the head-mounted device; a pupillary distance detecting phase for measuring the pupillary distance of the user; and an adjusting phase for defining a first range corresponding to the pupillary distance, and adjusting the distance between the first monitor and the second monitor within the first range, so that the first monitor is aligned with one pupil, and the second monitor is aligned with the other pupil, increasing the visual range of the user, thereby improving the user's experience. A head-mounted device using the method is also disclosed.

Classes IPC  ?

• G02B 27/01 - Dispositifs d'affichage "tête haute"
• G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,

Abrégé

An image depth recognition method held in a storage medium and running in a disclosed electronic device acquires images to be recognized and two original images. An original image is recognized through a depth recognition network to obtain an initial depth image, and a pose absolute value matrix is generated based on the two processed original images and a pose network, the pose network and the initial depth image generating an initial projection image. The processed two original images are recognized according to the pose absolute value matrix and the preset threshold matrix and adjusted based on errors between the initial depth image, the target image, and the target projection image. The depth recognition network obtains a depth recognition model, and the depth information of the image can be recognized. The method can improve the accuracy of the depth recognition of the image.

Classes IPC  ?

• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques

Abrégé

The present application relates to image processing and provides a method for training an image depth recognition model, a method for recognizing image depth, and an electronic device. The method obtains static objects, dynamic objects, a dynamic position by performing an instance segmentation on the first image and the second image. A target dynamic object and a feature dynamic object are selected from the dynamic objects and the dynamic objects. A target image and a target projection image are generated according to the target dynamic object and the feature dynamic object. A depth recognition model is trained based on the target image, and the target projection image. The to-be-recognized image is recognized by the depth recognition model.

Classes IPC  ?

• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06T 7/11 - Découpage basé sur les zones
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
• G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques

Abrégé

A method of forming a semiconductor device includes forming a P-type heavily doped region in a substrate. A sacrificial layer is formed on the substrate and covers the P-type heavily doped region. The sacrificial layer is patterned, so that sidewalls of the sacrificial layer are above the substrate inside the P-type heavily doped region. An N-type heavily doped region adjacent to the P-type heavily doped region is formed in the substrate by using the sacrificial layer as mask. A wet etching process is performed to retract the sidewalls of the sacrificial layer to the substrate inside the N-type heavily doped region. A P-type lightly doped region is formed in the substrate by using the sacrificial layer as mask. The P-type lightly doped region is adjacent to the N-type heavily doped region, and is in contact with bottoms of the P-type heavily doped region and the N-type heavily doped region.

Classes IPC  ?

• H01L 29/06 - Corps semi-conducteurs caractérisés par les formes, les dimensions relatives, ou les dispositions des régions semi-conductrices
• H01L 29/66 - Types de dispositifs semi-conducteurs
• H01L 29/78 - Transistors à effet de champ l'effet de champ étant produit par une porte isolée

Abrégé

A method for detecting defect of images applied in an electronic device inputs flawless sample training images into an autoencoder, and calculates first latent feature by a coding layer of the autoencoder, and calculates first reconstructed images by a decoding layer, and calculates a first reconstruction error by a first preset error function. The electronic device trains the discriminator according to the flawless sample training images and first reconstructed images, and calculates an adversarial learning error, and calculates a sample error, determines an error threshold based on the sample error, and obtains testing sample images, and calculates second latent feature of the testing sample images by the coding layer, and calculates the second reconstructed images of the testing sample images by the decoding layer, and calculate a difference between the testing sample images and the second reconstructed images, thus a detection result of the testing sample images is determined.

Classes IPC  ?

• G06V 10/98 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos Évaluation de la qualité des motifs acquis
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06N 3/094 - Apprentissage antagoniste

Abrégé

A method for managing driving applied in an electronic device which assesses distances to objects in a path of autonomous driving obtains RGB images of a scene in front of a vehicle, processes the RGB images based on a trained depth estimation model, and obtain depth images corresponding to the RGB images. The depth images are converted to 3D point cloud maps, 3D regions of interest from the 3D point cloud maps are determined according to a size of the vehicle, and the 3D regions of interest are converted into 2D regions of interest according to internal parameters of a camera. The 2D regions of interest are analyzed for obstacles. Driving continues when the 2D regions of interest have no obstacles, the vehicle is controlled to issue an alarm when obstacles are discovered.

Classes IPC  ?

• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
• G06T 7/11 - Découpage basé sur les zones

Abrégé

An assistance method of safe driving applied in a vehicle-mounted electronic device obtains RGB images of scene in front of a vehicle, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images into three-dimensional (3D) point cloud maps, determines 3D regions of interest therein, and obtains position and size information of objects in the 3D regions of interest. When the position information satisfies a first preset condition and/or the size information satisfies a second preset condition, the presence of obstacles in the 3D regions of interest is determined and controls the vehicle to issue an alarm. When the position information does not satisfy the first preset condition and/or the size information does not satisfy the second preset condition, the 3D regions of interest are determined as obstacle-free, and permitting the vehicle to continue driving.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
• G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
• B60W 30/09 - Entreprenant une action automatiquement pour éviter la collision, p.ex. en freinant ou tournant
• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention

Abrégé

A method for detecting a product for defects implemented in an electronic device includes detecting images of a product for defects by a first defect detection model in a preset period, and obtaining a detection result; when a ratio of the number of negative sample images is greater than a preset threshold, training an autoencoder model; obtaining historical positive sample images of the product, inputting the history positive sample images into the trained autoencoder model, and calculating a latent feature; inputting the latent feature of each history positive sample image into a decoding layer of the trained autoencoder model, and calculating newly added positive sample images; training the first defect detection model and obtain a second defect detection model; and inputting images of a product to be detected to the second defect detection model, and obtaining a detection result of the product.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
• G06F 17/16 - Calcul de matrice ou de vecteur

Abrégé

A method for obtaining depth images implemented in an electronic device includes obtaining a first image and a second image; obtaining a predicted depth map of the first image, and calculating a first error value of the predicted depth map; determining a first transformation matrix between the first image and the second image; obtaining an instance segmentation image and obtaining a first mask image and a second mask image; obtaining a target transformation matrix; converting the predicted depth map into a first point cloud image, converting the first point cloud image into a second point cloud image, and converting the second point cloud image into a third image; calculating a second error value between the second image and the third image; obtaining a target deep learning network model; and inputting at least one image into the target deep learning network model, and obtaining at least one depth image.

Classes IPC  ?

• G06T 7/579 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir du mouvement
• G06T 7/174 - Découpage; Détection de bords impliquant l'utilisation de plusieurs images
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras

Abrégé

A device to detect unauthorized opening of a casing is disposed in the upper or lower casing of an apparatus. The device includes a base plate with vibrator, carrier, and sensor on the lower case, and receiving grooves. Balls of a lesser number than the number of receiving grooves are installed in the grooves in a predetermined arrangement by the manufacturer. A waveguide element and magnet, fixed on the upper casing, is in contact with the balls, and when the waveguide is vibrated, the number and locations of the balls govern the form of a setting signal derived therefrom at the factory. If the upper casing is separated from the lower casing, the waveguide element separates from the carrier, the magnetic element gathers the balls, the predetermined arrangement being thereby lost, when tested by an authorized repair engineer.

Classes IPC  ?

• G01H 17/00 - Mesure des vibrations mécaniques ou des ondes ultrasonores, sonores ou infrasonores non prévue dans les autres groupes de la présente sous-classe

Abrégé

A method for assisting safer riving applied in a vehicle-mounted electronic device obtains RGB images of a scene in front of a vehicle when engine is running, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images to three-dimensional (3D) point cloud maps. A curvature of the driving path of the vehicle is calculated, and 3D regions of interest of the vehicle are extracted from the 3D point cloud maps according to a size of the vehicle and the curvature or deviation from straight ahead. The 3D regions of interest are analyzed for presence of obstacles. When no obstacles are present, the vehicle is controlled to continue driving, when presence of at least one obstacle is determined, an alarm is issued.

Classes IPC  ?

• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
• B60W 40/072 - Courbure de la route
• B60W 40/105 - Vitesse
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06T 7/50 - Récupération de la profondeur ou de la forme

Abrégé

A method for warning collision of vehicle is provided. The method obtains a pre-detected video. The pre-detected video includes a number of video frames, and the video frames are continuous. The method detects one or more vehicles in each of the video frames and determines a movement state of each of the vehicles via an optical flow method. The method detects one or more lane lines in each of the video frames to determine lane information, and determines whether to provide a collision warning according to the lane information and the movement state of each of the vehicles. A related vehicle and a non-transitory storage medium are provided.

Classes IPC  ?

• G08G 1/16 - Systèmes anticollision
• G08G 1/056 - Détection du mouvement du trafic pour le comptage ou la commande avec des dispositions pour distinguer la direction de circulation
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
• G06T 7/215 - Découpage basé sur le mouvement
• G06T 7/194 - Découpage; Détection de bords impliquant une segmentation premier plan-arrière-plan
• G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments

Abrégé

A near-eye display device includes a camera module, a display module and a rotating module. The camera module is configured for obtaining an image information along a direction of a first optical axis. The display module is configured for transmitting an image light of virtual information to a human eye along a second optical axis. The rotating module is configured for controlling the rotation of the camera module to change a position of the first optical axis, so as to switch the near-eye display device between a state of close-up view and a state of distant view. In the state of distant view, an angle between the first optical axis and the second optical axis is 25°. In the state of close-up view, the angle between the first optical axis and the second optical axis is less than or equal to 13°.

Classes IPC  ?

• G02B 27/01 - Dispositifs d'affichage "tête haute"
• G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,

Abrégé

The present disclosure provides a red micro LED display panel applied in a separated panel display device. The red micro LED display panel includes a driving back plane, an electrode array, a UV micro LED, and a red quantum dot layer. The electrode array is located on the driving back plane. The UV micro LED is located on the electrode array. The red quantum dot layer is located on the UV micro LED.

Classes IPC  ?

• H01L 33/38 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les électrodes ayant une forme particulière
• H01L 33/10 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les corps semi-conducteurs ayant une structure réfléchissante, p.ex. réflecteur de Bragg en semi-conducteur
• H01L 33/50 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de conversion de la longueur d'onde
• H01L 27/15 - Dispositifs consistant en une pluralité de composants semi-conducteurs ou d'autres composants à l'état solide formés dans ou sur un substrat commun comprenant des composants semi-conducteurs avec au moins une barrière de potentiel ou une barrière de surface, spécialement adaptés pour l'émission de lumière

Abrégé

This application provides a method for optimizing a depth estimation model. The method includes obtaining a video of an object and capturing a first image and a second image from the video. An initial depth estimation model is obtained. An updated depth estimation model is obtained by performing an optimization process on the initial depth estimation model, and the optimization process is repeatedly performed on the updated depth estimation model. Once the updated depth estimation model meets predetermined requirements, the updated depth estimation model meeting predetermined requirements is determined as a target depth estimation model.

Classes IPC  ?

• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p.ex. sonar, chercheur de direction

Abrégé

A method of making a silicon-carbon pre-lithium composite anode material is provided. The method includes: nanoizing silicon materials to obtain nano-silicon particles, adding carbon materials and polymer into the nano-silicon particles for homogenization treatment to obtain a silicon-carbon composite; providing a pre-lithium nanomaterial; mixing the silicon-carbon composite and the pre-lithium nanomaterial to granulate to obtain a silicon-carbon pre-lithium composite precursor; and sintering the silicon-carbon pre-lithium composite precursor.

Classes IPC  ?

• H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
• H01M 4/583 - Matériau carboné, p.ex. composés au graphite d'intercalation ou CFx
• H01M 4/38 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'éléments simples ou d'alliages
• H01M 4/62 - Emploi de substances spécifiées inactives comme ingrédients pour les masses actives, p.ex. liants, charges
• H01M 4/04 - Procédés de fabrication en général

Abrégé

A method for training a depth estimation model is provided. The method includes obtaining a first left image and a first right image. A disparity map is obtained by inputting the first left image into a depth estimation model. A second right image is obtained by adding the first left image to the disparity map. The first left image is converted into a third right image. A mask image is obtained by performing a binarization processing on a pixel value of each of pixel points of the third right image. Once a loss value of the depth estimation model is obtained by calculating a mean square error of pixel values of all corresponding pixel points of the first right image, the second right image, and the mask image, a depth estimation model is iteratively trained according to the loss value.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo

Abrégé

A method for training an autoencoder implemented in an electronic device includes obtaining a stereoscopic image as the vehicle is in motion, the stereoscopic image includes a left image and a right image; generating a stereo disparity map according to the left image; generating a predicted right image according to the left image and the stereo disparity map; and calculating a first mean square error between the predicted right image and the right image.

Classes IPC  ?

• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo

Abrégé

A vehicle-borne method for recognizing the illumination state of traffic lights even against a backlighting of strong sunlight or other light source obtains a first image of a set of traffic lights in a road traffic environment. A segmentation map is acquired by dividing a first region from the first image, and an illumination region in the segmentation map is extracted by marking RGB pixels in the region which are of a preset threshold in brightness according to a training model. A lit color of the set of traffic lights is recognized according to a position of the illumination region in the segmentation map. By utilizing the method, accuracy of recognition of illumination state of traffic lights is improved.

Classes IPC  ?

• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
• G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p.ex. utilisant un modèle de réflectance ou d’éclairage
• G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06T 7/11 - Découpage basé sur les zones

Abrégé

A method acquires a first image and a second image of a target object being inputted into the depth estimation network for outputting a depth image. A pixel posture conversion relationship between the first image and the second image is obtained. The pixel posture conversion relationship includes a position relationship between each first pixel in the first image and a second pixel in the second image, which correspond to a same part of the target object. A restored image is generated based on the depth image, the pixel posture conversion relationship, and pre-obtained camera parameters. A loss of the depth estimation network is determined based on a difference between the first image, the depth image, the restored image, and the second image for adjusting the parameters of the depth estimation network. A training apparatus, and an electronic device applying the method are also disclosed.

Classes IPC  ?

• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images

Abrégé

An image inference method is provided by the present disclosure. The method includes determining a target collocation scheme for an image according to an inference request and a preset weight table, the target collocation scheme including a hardware accelerator in an idle state and an estimated time duration of inferring the image. A usage state of the hardware accelerator in the target collocation scheme is updated to be an in use state, and the image is inferred according to the target collocation scheme. When the inferring of the image is completed, the usage state of the hardware accelerator is updated from the in use state to be the idle state. Once an actual time duration of inferring the image is obtained, the estimated time duration is updated to be the actual time duration.

Classes IPC  ?

• G06N 5/02 - Représentation de la connaissance; Représentation symbolique

Abrégé

An image defect detection method applied to an electronic device is provided. The method includes dividing a detecting image into a plurality of detecting areas, generating a detection accuracy value for each detecting area based on a defective image and a non-defective image, and obtaining a plurality of detection accuracy values. An autoencoder is selected for each detection accuracy value. A model corresponding to each detecting area is obtained by training the autoencoder based on the non-defective image. A plurality of reconstructed image blocks is obtained by inputting each of a plurality of detecting blocks into the corresponding model, and a reconstruction error value between each reconstructed image block and the corresponding detecting block is obtained. A detection result of a product contained in the image to be detected is obtained based on the reconstruction error value corresponding to each detecting block.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
• G06T 7/11 - Découpage basé sur les zones
• G06V 10/28 - Quantification de l’image, p.ex. seuillage par histogramme visant à discriminer entre les formes d’arrière-plan et d’avant-plan
• G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques

Abrégé

A method of making lithium-ion battery anode comprising step (S1)-step (S3). step (S1): providing a nano-silicon material, and coating a positively charged carbonizable polymer on a surface of the nano-silicon material, to obtain a nano-silicon coated with the positively charged carbonizable polymer. Step (S2): adding CNTs and the nano-silicon coated with the positively charged carbonizable polymer to a solvent; and performing an ultrasonic dispersion to obtain a dispersion. And step (S3): vacuum filtering the dispersion, to obtain a composite film of the CNTs and the nano-silicon coated with positively charged carbonizable polymer.

Classes IPC  ?

• H01M 4/38 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'éléments simples ou d'alliages
• H01M 4/62 - Emploi de substances spécifiées inactives comme ingrédients pour les masses actives, p.ex. liants, charges
• H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
• H01M 4/60 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs de composés organiques
• H01M 4/04 - Procédés de fabrication en général

Abrégé

A lithium-ion battery anode is provided. The lithium-ion battery anode comprises a carbon nanotube three-dimensional network structure formed by a plurality of carbon nanotubes intertwined with each other. A plurality of nano-silicon particles coated with amorphous carbon, dispersed in the carbon nanotube three-dimensional network structure, and adhered to surfaces of the plurality of carbon nanotubes. The amorphous carbon is obtained by calcining a positively charged carbonizable polymer. And a carbon nanotube functional layer located on two opposite surfaces of the carbon nanotube three-dimensional network structure, to make the carbon nanotube three-dimensional network structure located between two carbon nanotube functional layers. The carbon nanotube functional layer comprises at least two super-aligned carbon nanotube films stacked and crossed with each other.

Classes IPC  ?

• H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
• H01M 4/133 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif Électrodes pour accumulateurs à électrolyte non aqueux, p.ex. pour accumulateurs au lithium; Leurs procédés de fabrication Électrodes à base de matériau carboné, p.ex. composés d'intercalation du graphite ou CFx
• H01M 4/134 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif Électrodes pour accumulateurs à électrolyte non aqueux, p.ex. pour accumulateurs au lithium; Leurs procédés de fabrication Électrodes à base de métaux, de Si ou d'alliages
• H01M 4/38 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'éléments simples ou d'alliages
• H01M 4/587 - Matériau carboné, p.ex. composés au graphite d'intercalation ou CFx pour insérer ou intercaler des métaux légers
• C01B 32/158 - Nanotubes de carbone

Abrégé

A multi-scale autoencoder generation method applied to an electronic device is provided. The method includes acquire product images and acquire an annotation of each product image. Latent spaces of a plurality of scales are constructed. Autoencoders are obtained according to the latent spaces and an image size of the product image. Learners are obtained by training each autoencoder based on non-defective images. Reconstructed images are obtained by inputting the product images into the learners. Detection results are obtained by detecting whether each product image has defects according to the reconstructed images. Similar images for each learner are determined based on a comparison result between each detection result and a corresponding annotation result. Once a correct rate of each learner is obtained according to the similar images, a learner from the plurality of learner is determined as a multi-scale autoencoder according to the correct rate of each learner.

Classes IPC  ?

• G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
• H04N 19/60 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée
• H04N 19/33 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant des techniques hiérarchiques, p.ex. l'échelonnage dans le domaine spatial

Abrégé

A method for identifying road vehicles or other objects which are in motion against those which are not moving applied in an in-vehicle device of an assisted vehicle which is being driven shoots a first image of a target vehicle and a second later image of the target vehicle, determines a first mask area of the target vehicle from the first image, and determines a second mask of the target vehicle from the second image based on an instance segmentation algorithm. An Intersection over Union (IoU) is calculated between the first mask area and the second mask area and a determination made as to whether a dynamic class object mask area of the target vehicle according to the IoU should be generated. A dynamic class object mask area of the target vehicle is generated when the target vehicle is found to be a moving vehicle.

Classes IPC  ?

• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention

Abrégé

A method of detecting moving objects is provided. The method obtains point cloud data set of a target scene and an image of the target scene. The method detects one or more stationary object areas from the image. The method records point cloud data, which corresponds to the stationary object areas in the point cloud data set, to be first point cloud data. The method determines a velocity range of the first point cloud data according to the first point cloud data. The method further determines whether one or more moving objects are present in the target scene according to second point cloud data and the velocity range of the first point cloud data. The second point cloud data is the point cloud data of the point cloud data set excluding the first point cloud data. A related electronic device and a non-transitory storage medium are provided.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• G06T 7/20 - Analyse du mouvement
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 1/60 - Gestion de mémoire
• G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline

Abrégé

A defect detecting apparatus and method are provided. The defect detecting apparatus receives an image to be tested. The defect detecting apparatus detects the image to be tested through a defect detecting model to generate an anomaly score corresponding to the image to be tested, and the defect detecting model is generated based on the training of a generative adversarial network and a plurality of normalized loss functions. The defect detecting apparatus compares the anomaly score with an anomaly score threshold to determine whether the image to be tested is a defective image.

Classes IPC  ?

• G06T 7/00 - Analyse d'image

Abrégé

A method for obtaining depth images for improved driving safety applied in an electronic device processes first and immediately-following second images which have been captured and processes each to obtain two sets of predicted depth maps. The electronic device determines a transformation matrix of a camera between first and second images and converts the first predicted depth maps into first point cloud maps, and second predicted depth maps into second point cloud maps. The first point cloud maps are converted into third point cloud maps, and the second point cloud maps into fourth point cloud maps. First and fourth point cloud maps are matched and first and second error values are calculated, thereby obtaining a target deep learning network model. Images to be detected are input into the target deep learning network model and depth images are obtained.

Classes IPC  ?

• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
• G06V 10/98 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos Évaluation de la qualité des motifs acquis

Abrégé

A method for estimating depth implemented in an electronic device includes obtaining a first image; inputting the first image into a depth estimation model, and obtaining a first depth image; obtaining a depth ratio factor, the depth ratio factor indicating a relationship between a relative depth and a depth of each pixel in the first depth image; and obtaining depth information of the first depth image according to the first depth image and the depth ratio factor.

Classes IPC  ?

• G06T 7/521 - Récupération de la profondeur ou de la forme à partir de la projection de lumière structurée
• G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
• G01S 13/08 - Systèmes pour mesurer la distance uniquement
• G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p.ex. sonar, chercheur de direction

Abrégé

A method for processing images to remove distortions of various types without requiring retraining for per-type processing models, applied in an electronic device, establishes a distortion removal model, and inputs distorted images in the distortion removal model. General distortions are removed by the distortion removal model and the restored images are input into a deep learning model for final image processing. The deep learning model of the present disclosure finally corrects distortion in the images so that distortion-free images are obtained.

Classes IPC  ?

• G06T 5/00 - Amélioration ou restauration d'image

Abrégé

A method for training a depth estimation model comprise acquires a first image and a second image being inputted into the depth estimation model. The depth estimation model outputs a first depth image. A posture conversion relationship between the first image and the second image is extracted by a posture estimation model. A restored image is generated based on the first depth image, the posture conversion relationship, and pre-obtained camera parameters. A similarity between the restored image and the first image is calculated to obtain a two-dimension loss image. A first similarity of pixel points of each weak texture region are determined based on the two-dimension loss image. A ratio of the first similarity for adjusting the parameters of the depth estimation model is decreased and a first loss value is obtained. A training apparatus and an electronic device applying the method are also disclosed.

Classes IPC  ?

• G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
• G06V 10/54 - Extraction de caractéristiques d’images ou de vidéos relative à la texture

Abrégé

A training method includes following steps. A converted image group is generated by a first generator included in the first stage generative adversarial network (GAN) according to a first input image group. A reconstructed image group is generated by a second generator included in the first stage GAN according to the converted image group. The first stage GAN is updated according the reconstructed image group and the first input image group. A sampling block is added into a first stage trained GAN to form a second stage GAN. Progressively training and growing the second stage GAN network at multiple stages according to a second input image group to a last input image group to generate a last stage trained GAN.

Classes IPC  ?

• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image

Abrégé

A method for generating distorted images is applied in an electronic device, obtains first pixel coordinates of undistorted images and a first pixel value of the first pixel coordinates, and selects an arbitrary distortion center coordinate. The distance between the coordinate of the distortion center and each first pixel coordinate is calculated, and second pixel coordinates corresponding to the first pixel coordinates are calculated according to distortion coefficient, the first pixel coordinates, and the distance. The first pixel value of each first pixel coordinates is taken as the second pixel value of each second pixel coordinates, and distorted images from undistorted images are generated for training purposes according to the second pixel coordinates and the second pixel values.

Classes IPC  ?

• G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”

Abrégé

A method and system for generating depth in monocular images acquires multiple sets of binocular images to build a dataset containing instance segmentation labels as to content; training an work using the dataset with instance segmentation labels to obtain a trained autoencoder network; acquiring monocular image, the monocular image is input into the trained autoencoder network to obtain a first disparity map and the first disparity map is converted to obtain depth image corresponding to the monocular image. The method combines binocular images with instance segmentation images as training data for training an autoencoder network, monocular images can simply be input into the autoencoder network to output the disparity map. Depth estimation for monocular images is achieved by converting the disparity map to a depth image corresponding to the monocular image. An electronic device and a non-transitory storage are also disclosed.

Classes IPC  ?

• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
• G06V 10/80 - Fusion, c. à d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
• H04N 13/106 - Traitement de signaux d’images

Abrégé

A detection system determines to-be-test products to be qualified or not. The detection system includes a first detection module, a sensing module, and a control module. The first detection module includes a first photographing apparatus and a first light source. The first light source being a plane light source emits light and illuminates the to-be-tested product while the first photographing apparatus captures an image of the to-be-tested product. The control module controls the first light source to emit light and control the first photographing apparatus to capture the image of the to-be-tested product when receiving the sensing signal generated by the sensing module. The control module further analyzes the captured image for determining the to-be-tested product to be qualified or not.

Classes IPC  ?

• H04N 23/56 - Caméras ou modules de caméras comprenant des capteurs d'images électroniques; Leur commande munis de moyens d'éclairage
• G06T 7/00 - Analyse d'image
• H04N 23/90 - Agencement de caméras ou de modules de caméras, p. ex. de plusieurs caméras dans des studios de télévision ou des stades de sport
• G03B 15/05 - Combinaisons d'appareils photographiques avec flash électronique; Flash électronique

Abrégé

A method for detecting road conditions applied in an electronic device obtains images of a scene in front of a vehicle, and inputs the images into a trained semantic segmentation model. The electronic device inputs the images into a backbone network for feature extraction and obtains a plurality of feature maps, inputs the feature maps into the head network, processes the feature maps by a first segmentation network of the head network, and outputs a first recognition result. The electronic device further processes the feature maps by a second segmentation network of the head network, and outputs a second recognition result, and determines whether the vehicle can continue to drive on safely according to the first recognition result and the second recognition result.

Classes IPC  ?

• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• B60W 40/06 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés à l'état de la route
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

A method for detection of three-dimensional (3D) objects on or around a roadway by machine learning, applied in an electronic device, obtains images of road, inputs the images into a trained object detection model, and determines categories of objects in the images, two-dimensional (2D) bounding boxes of the objects, and parallax (rotation)angles of the objects. The electronic device determines object models and 3D bounding boxes of the object models and determines distance from the camera to the object models according to size of the 2D bounding boxes, image information of the detection images, and focal length of the camera. The positions of the object models in a 3D space can be determined according to the rotation angles, the distance, and the 3D bounding boxes, and the positions of the object models are taken as the position of the objects in the 3D space.

Classes IPC  ?

• G06V 20/64 - Objets tridimensionnels
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras

Abrégé

An image recognition method applied to an electronic device is provided. The method includes constructing a first semantic segmentation network. In response that an initial labeled result of one of a plurality of initial labeled images does not match a preset labeled result, a target image corresponding to the one of the plurality of initial labeled images and a target labeled result of the target image are obtained. A second semantic segmentation network is obtained by training the first semantic segmentation network based on a plurality of the target images and the target labeled result of each target image, and a labeled result of an image to be recognized is obtained by inputting the image to be recognized into the second semantic segmentation network.

Classes IPC  ?

• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion

Abrégé

An image feature matching method is provided by the present disclosure. The method includes determining a first weak texture area of a first image and a second weak texture area of a second image based on an edge detection algorithm. First feature points of the first weak texture area and second feature points of the second weak texture area are extracted. The first feature points and the second feature points are matched by determining a target point for each of the first feature points from the second feature points. Once a position difference value between each first feature point and the corresponding target point is determined, a matching point for each first feature point is determined according to the position difference value between the each first feature point and the corresponding target point.

Classes IPC  ?

• G06T 7/529 - Récupération de la profondeur ou de la forme à partir de la texture
• G06T 7/13 - Détection de bords
• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
• G06T 7/90 - Détermination de caractéristiques de couleur

Abrégé

A method for training a depth estimation model implemented in an electronic device includes obtaining a first image pair from a training data set; inputting the first left image into the depth estimation model, and obtaining a disparity map; adding the first left image and the disparity map, and obtaining a second right image; calculating a mean square error and cosine similarity of pixel values of all corresponding pixels in the first right image and the second right image; calculating mean values of the mean square error and the cosine similarity, and obtaining a first mean value of the mean square error and a second mean value of the cosine similarity; adding the first mean value and the second mean value, and obtaining a loss value of the depth estimation model; and iteratively training the depth estimation model according to the loss value.

Classes IPC  ?

• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo

Abrégé

A method for inspecting product defects implemented in an electronic device includes determining a category of a product, and obtaining golden sample images of the product; selecting at least one inspection tool by a preset application according to the category of the product; creating labeling information of the golden sample image of the product according to the selected inspection tools; and obtaining at least one image of a product to be inspected, and inspecting the at least one image of the product to be inspected according to the labeling information of the golden sample images of the product.

Classes IPC  ?

• G06T 7/00 - Analyse d'image

Abrégé

A method for detecting a product for defects implemented in an electronic device, the method obtains an image of a product to be detected, obtains a reconstructed image by inputting the image to be detected into a pre-trained autoencoder, generates a difference image from the image and the reconstructed image, obtains a number of feature absolute values by performing clustering processing on the difference image; generates a target image according to the number of feature absolute values, the difference image, and a preset value; and determines a defect detection result by detecting the target image for defects.

Classes IPC  ?

• G01N 21/88 - Recherche de la présence de criques, de défauts ou de souillures
• G06N 3/0455 - Réseaux auto-encodeurs; Réseaux encodeurs-décodeurs

Abrégé

A remote collaboration method applied to a remote device is provided. In the method, the remote device receives a first image transmitted by a wearable device and determines a reference area based on the first image. The remote device further determines a position of a target object relative to the reference area and generates the indication information according to the position of the target object relative to the reference area and transmits the indication information to the wearable device. The method can provide a user with the indication information of the position of the target object in the eyesight through remote collaboration between the remote device and the wearable device, thereby improving the efficiency and accuracy of determining the position of the target object.

Classes IPC  ?

• G06T 11/00 - Génération d'images bidimensionnelles [2D]
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06F 3/14 - Sortie numérique vers un dispositif de visualisation

Abrégé

A method for detecting three-dimensional (3D) objects in relation to autonomous driving is applied in an electronic device. The device obtains detection images and depth images, =inputs the detection images into a trained object detection model to determine categories of objects in the detection images and two-dimensional (2D) bounding boxes of the objects. The device determines object models of the objects and 3D bounding boxes of the object models according to the object categories, and calculates point cloud data of the objects selected and distances from the depth camera to the object models. The device determines angles of rotation of the object models of the objects according to the object models of the objects and the point cloud data, and can determine respective positions of the objects in 3D space according to the distance from the depth camera to the object models, the rotation angles, and the 3D bounding boxes.

Classes IPC  ?

• G06V 20/64 - Objets tridimensionnels
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/772 - Détermination de motifs de référence représentatifs, p.ex. motifs de valeurs moyennes ou déformants; Génération de dictionnaires
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos

Abrégé

A method for detecting three-dimensional (3D) objects in roadway is applied in an electronic device. The device inputs training images into a semantic segmentation model, and performs convolution operations and pooling operations on the training images and obtains feature maps. The electronic device performs up-sampling operations on the feature maps to obtain first images, classifies pixels on the first images, calculates and optimizes a classification loss and obtains a trained semantic segmentation model. The device inputs the detection images into the trained semantic segmentation model, determines object models of the objects, point cloud data and distances from the depth camera to the object models, determines rotation angles of the object models according to the point cloud data and the object models, and determines positions of the object models in 3D space according to the distances, the rotation angles, and positions of the objects.

Classes IPC  ?

• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
• G06V 20/64 - Objets tridimensionnels
• G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos

Abrégé

A method for detecting medical images implemented in an electronic device includes obtaining at least one image to be detected; obtaining a reconstructed image by inputting the at least one image to be detected as a target image into a pre-trained variational autoencoder model; determining a target area according to pixel values of pixels in the reconstructed image and the target image; obtaining a feature area and a lesion category of the feature area by inputting the target image into a pre-trained convolutional neural network model; when there is a feature area corresponding to the target area in the target image, determining a lesion area and a corresponding lesion category based on the target area and the feature area, and generating a detection result of the image to be detected.

Classes IPC  ?

• G06T 7/00 - Analyse d'image

Abrégé

A method for detecting an object utilizing a monocular camera obtains an image showing an object and determining a pixel coordinate of the object in the image and determining a spatial position of the object in the image, based on the pixel coordinate of the object in the image and a preset coordinate transformation relationship or a preset depth prediction model. The image showing the object is obtained through the monocular camera, and the pixel coordinates of the object in the image are determined. According to the pixel coordinates of the object in the image and the preset coordinates transformation relationship or the preset depth prediction model, the spatial position of the object in the image is determined, providing efficient and accurate detection. An electronic device and a non-transitory storage recording the method are also disclosed.

Classes IPC  ?

• G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
• B60R 11/04 - Montage des caméras pour fonctionner pendant la marche; Disposition de leur commande par rapport au véhicule

Abrégé

The present application provides a carbonate-based electrolyte. The carbonate-based electrolyte includes a carbonate, an ether, a lithium salt, a lithium nitrate, and a planar macrocyclic compound. The present application further provides a method for making the carbonate-based electrolyte and a lithium metal battery using the carbonate-based electrolyte.

Classes IPC  ?

• H01M 10/0567 - Matériaux liquides caracterisés par les additifs
• H01M 10/0569 - Matériaux liquides caracterisés par les solvants

Abrégé

A driving assembly and a transfer device to allow an adjustable length of wheelbase for varying sizes of loads comprises a driving device, a driving wheel connected to the driving device rotatable by the driving device, a cantilever assembly comprising a cantilever configured for mounting to the bearing platform, and a driven wheel rotatably connected to one end of the cantilever. The other end of the cantilever is detachably connected to a fixed part of the driving device. The transfer device comprises a bearing platform and two driving assemblies.

Classes IPC  ?

• B60B 33/00 - Roues à pivot en général
• B60B 19/12 - Roues en forme de rouleau
• B60B 19/00 - Roues non prévues ailleurs ou ayant des caractéristiques précisées dans l'un des sous-groupes du présent groupe

Abrégé

A nanocrystal array, a laser device, and a display device are provided. The nanocrystal array includes a plurality of nanorods arranged in an array. Each nanorod includes a nanorod buffer layer, a first type semiconductor layer, a tunnel junction layer, a second type semiconductor layer, a multi-quantum well, and another first type semiconductor layer successively stacked on each other. The laser device and the display device include the nanocrystal array. The present disclosure may reduce the laser threshold and increase output power, and further improve the resolution and image quality of the display device.

Classes IPC  ?

• H01S 5/34 - Structure ou forme de la région active; Matériaux pour la région active comprenant des structures à puits quantiques ou à superréseaux, p.ex. lasers à puits quantique unique [SQW], lasers à plusieurs puits quantiques [MQW] ou lasers à hétérostructure de confinement séparée ayant un indice progressif [GRINSCH]
• H01S 5/343 - Structure ou forme de la région active; Matériaux pour la région active comprenant des structures à puits quantiques ou à superréseaux, p.ex. lasers à puits quantique unique [SQW], lasers à plusieurs puits quantiques [MQW] ou lasers à hétérostructure de confinement séparée ayant un indice progressif [GRINSCH] dans des composés AIIIBV, p.ex. laser AlGaAs
• H01S 5/042 - Excitation électrique
• H01S 5/02 - Lasers à semi-conducteurs - Détails ou composants structurels non essentiels au fonctionnement laser
• H01S 5/183 - Lasers à émission de surface [lasers SE], p.ex. comportant à la fois des cavités horizontales et verticales comportant uniquement des cavités verticales, p.ex. lasers à émission de surface à cavité verticale [VCSEL]

Abrégé

An element-doped silicon-carbon composite negative electrode material is provided. The negative electrode material comprises a plurality of element-doped silicon-carbon composite negative electrode material particles, and each them comprises an element-doped silicon nanoparticle, a first carbon coating layer and a second carbon coating layer. The element-doped silicon nanoparticle is a core, and the first carbon coating layer is coated on the element-doped silicon nanoparticle, the second carbon coating layer covers the first carbon coating layer. The dopant element comprises at least one of a group IIIA element, a group VA element and a transition metal element. A method of preparing the element-doped silicon-carbon composite negative electrode material is further provided.

Classes IPC  ?

• H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
• H01M 4/38 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'éléments simples ou d'alliages
• H01M 4/583 - Matériau carboné, p.ex. composés au graphite d'intercalation ou CFx
• H01M 4/04 - Procédés de fabrication en général

Abrégé

A method, a device, and a non-transitory computer readable medium for detecting label on curved container, includes obtaining a first image, the first image comprising a label on the curved container; obtaining a plurality of reference points in the first image; obtaining a first width of the curved container and first position data of the label according to the plurality of reference points; determining whether the curved container is rotated according to the first width and a predetermined standard width; when the curved container rotated being determined, predicting a second width of the curved container according to the firs position data and a predetermined regression equation; and determining whether the label is offset according to the first width and the second width.

Classes IPC  ?

• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G06T 7/60 - Analyse des attributs géométriques

Abrégé

A data processing device includes a detection unit, a memory and a processor. The processor performs: performing a short-term Fourier transform on photoplethysmographic data to generate a first spectrogram matrix; performing numerical split processing on the first spectrogram matrix to generate a first spectrum matrix, where the first spectrum matrix includes multiple first spectrum arrays arranged in sequence from left to right; inputting the first spectrum arrays into a transformer model from left to right to generate multiple second spectrum arrays in sequence, and transforming the second spectrum arrays into a second spectrum matrix according to an order in which the second spectrum arrays are generated; performing numerical combining processing on the second spectrum matrix to generate a second spectrogram matrix; and performing an inverse short-term Fourier transform on the second spectrogram matrix to generate arterial blood pressure data.

Classes IPC  ?

• A61B 5/021 - Mesure de la pression dans le cœur ou dans les vaisseaux sanguins
• A61B 5/024 - Mesure du pouls ou des pulsations cardiaques
• A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus

Abrégé

An obstacle avoidance mechanism applied to a sweeping robot, the sweeping robot comprises a body, a baffle and a road wheel, the baffle is arranged on an exterior side of the body, the baffle is arranged at a first distance from the body, the obstacle avoidance mechanism comprises a shield module and a photosensitive module. When the baffle collides with the an obstacle object, the light path of the photosensitive module is blocked by the shield module, so as to send out a first signal to make the road wheel stop moving forward or turn, and realize the obstacle avoidance function of the sweeping robot and enhance its service life.

Classes IPC  ?

• A47L 11/40 - Machines à nettoyer les planchers, les tapis, les meubles, les murs ou leurs revêtements - Éléments ou parties constitutives des machines non prévus dans les groupes , ou non limités à un de ces groupes, p.ex. poignées, dispositions des interrupteurs, bords, amortisseurs ou leviers
• G05D 1/02 - Commande de la position ou du cap par référence à un système à deux dimensions