Metal corrosion is a ubiquitous phenomenon costing the global economy, trillions of dollars annually. Conventional corrosion inhibitor compounds are either not so effective or require huge amount of inhibitor for adequate protection. The present disclosure addresses the problem of acid corrosion of iron and iron alloys which is relevant for a wide variety of industries such as oil and gas production and acid pickling etc. The technical solution provided in the present disclosure is a new corrosion inhibitor composition including Naphthalene-1-thiocarboxamide for iron and iron alloys in HCl media. This is specifically relevant for acid well stimulation in the oil and gas industries.
C23F 11/04 - Inhibiting corrosion of metallic material by applying inhibitors to the surface in danger of corrosion or adding them to the corrosive agent in markedly acid liquids
C09K 8/54 - Compositions for in situ inhibition of corrosion in boreholes or wells
The present disclosure addresses unresolved problems of task scheduling by adding redundancy in a collection task schedule generated by a ground control station. Embodiments of the present disclosure provide a randomization based redundant collection task scheduling for mitigating occlusions in sensing by small satellite constellations. The randomization based redundant collection task scheduling algorithm receives as input a set of region of observations tessellated into sub-regions, and then collection opportunities for each sub-region is computed based on satellite tracks data. Further, a sub-set of collection opportunities is determined from all the possible collection tasks for each sub-region to further generate the collection task schedule for each of the region of observation. Number of collections opportunities is controlled by a decay function which holds number of redundant collections to a bound, thereby increasing chance of good collection without investing too much resource in redundancy to mitigate occlusions.
Machine Learning approaches in literature for determining optimal solver-preconditioner-smoother for solving matrix equations in computer modelling of any systems are directly dependent on matrix property calculation as an intermediate step. However, in CFD domain, this matrix system is generated from simulation input parameters. Also, part of simulation parameter's relation with the matrix equations can be derived from the theory. Embodiments of the present disclosure provide a method and system for prediction of fastest solver combination for solution of matrix equations during CFD simulations. The system trains a Machine Learning (ML) model using a set of relevant input parameters, based on domain knowledge of a CFD problem of interest, as a plurality of input features. The ML model is a multi-class classification model for the prediction of solver combination taking the CFD simulation parameters as an input.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
4.
METHOD AND SYSTEM FOR GENERATION OF DESCRIPTIVE COPY OF GROCERY PRODUCTS
E-commerce industry is currently expanding rapidly, worldwide. A process of generating product copy for grocery items, which is very challenging as food items, do not have features in common, unlike fashion products. A data associated with one or more grocery products is received as an input. The data is processed to obtain one or more sorted similar grocery products. One or more relevant attributes and allergen information associated with the one or more sorted similar grocery products are extracted. A vocabulary model is created based the one or more relevant attributes and the allergen information associated with the one or more sorted similar grocery products. The vocabulary model is validated based on one or more assigned weights on training data. The one or more descriptive copies associated with grocery products are generated by mapping the validated vocabulary model with the training data.
This disclosure relates generally to method and system for generating key performance indicator prediction model for multi-cloud applications. The disclosed method determines an optimized resource model and a predictive cost structure for one or more multi-cloud applications. The method receives a composite usage request to obtain a current resource consumption metrics and a cost structure for each cloud application identifier (ID). Further, a set of cloud provider API endpoints are invoked to obtain a plurality of usage tracking metrics. Further, a plurality of views are generated for each cloud application ID by processing every record associated with each API response file with allocated resource data. Then, a KPI prediction model is generated by leveraging autoregressive integrated moving average on the KPI time series data to determine an optimized resource model and a cost structure.
This is a method and a system for designing a personalized therapeutic intervention for an individual. Each individual has a unique composition of microbiome in the gut, one probiotic or dietary regimen may not have same efficacy in different individuals. The disclosure recommends a personalized therapeutic intervention for improving gut health of an individual by designing personalized therapeutics and diet based on functions of microbiome. The personalized therapeutic intervention is recommended based on several steps including generating a set of knowledge bases, identifying a change in the gut health of an individual by monitoring the gut samples and recommending a personalized therapeutic intervention. The personalized therapeutic intervention comprises at least one of a prebiotic, a probiotic and an optimized diet, wherein the optimized diet is estimated based on optimizing a gut food score, where the gut food score is computed based on the change in the gut health of an individual.
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
This disclosure relates generally to long-form answer extraction and, more particularly, to long-form answer extraction based on combination of sentence index generation techniques. Existing answer extractions techniques have achieved significant progress for extractive short answers; however, less progress has been made for long form questions that require explanations. Further the state-of-art long-answer extractions techniques result in poorer long-form answers or not address sparsity which becomes an issue longer contexts. Additionally, pre-trained generative sequence-to-sequence models are gaining popularity for factoid answer extraction tasks. Hence the disclosure proposes a long-form answer extraction based on several steps including training a set of generative sequence-to-sequence models comprising a sentence indices generation model and a sentence index spans generation. The trained set of generative sequence-to-sequence models is further utilized for model long-form answer extraction based on a union of several sentence index generation techniques comprising a sentence indices and a sentence index spans.
Simulation of dynamic physical systems is done using iterative solvers. However, this iterative process is a time consuming and compute intensive process and, for a given set of simulation parameters, the solution does not always converge to a physically meaningful solution, resulting in huge waste of man hours and computation resource. Embodiments herein provide a method and system for stabilizing a diverged numerical simulation and accelerating a converged numerical simulation by changing one or more control parameters. An automatic monitoring mechanism of residue history (to interpret convergence or divergence) and a subsequent control logic to auto-tune the under-relaxation factor would help in stabilizing a diverging simulation and reaching faster convergence by accelerating converging simulation.
The present disclosure provides a method for surface wear inspection using millimeter wave radar. The system initially receives a plurality of uncompressed raw Synthetic Aperture Radar (SAR) images. Further, a plurality of reconstructed SAR images are generated based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm (RDA). Further, a master image and a slave image are selected from the reconstructed SAR images and corresponding anchor points are assigned. Further a plurality of fine level and coarse level shift coordinates are computed based on the corresponding anchor points. Further, a net shift value is computed based on the plurality of fine level and coarse level shift coordinates. The master and the slave images are aligned based on the net shift value and the interferogram is generated. The interferogram is further analyzed to profile the corresponding deformation pertaining to the surface under test.
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
G01S 7/41 - RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisation; Target signature; Target cross-section
10.
OPTIMAL DEPLOYMENT OF EMBEDDINGS TABLES ACROSS HETEROGENEOUS MEMORY ARCHITECTURE FOR HIGH-SPEED RECOMMENDATIONS INFERENCE
Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources. Dynamic organization of embedding tables changes location of embedding, hence needs an efficient mechanism to track if a required embedding is present in the fast memory with its current address for faster look-up, which is performed using spline-based learned index.
This disclosure relates generally to program synthesis for weakly-supervised multimodal question answering using filtered iterative back-translation (FIBT). Existing approaches for chart question answering mainly address structural, visual, relational, or simple data retrieval queries with fixed-vocabulary answers. The present disclosure implements a two-stage approach where, in first stage, a computer vision pipeline is employed to extract data from chart images and store in a generic schema. In second stage, SQL programs for Natural Language (NL) queries are generated in dataset by using FIBT. To adapt forward and backward models to required NL queries, a Probabilistic Context-Free Grammar is defined, whose probabilities are set to be inversely proportional to SQL programs in training data and sample programs from it. Compositional similarity-based filtration strategy employed on the NL queries generated for these SQL programs enables synthesizing, filtering, and appending NL query-SQL program pairs to training data, iteratively moving towards required NL query distribution.
Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score. Finally, a longform technical question and answer dataset is generated based on the combined plurality of questions and optimal answers.
Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to pre-empt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
14.
METHODS AND SYSTEMS FOR PREDICTING DIFFICULTY OF LONG FORM TECHNICAL QUESTIONS USING WEAK SUPERVISION
Technical interviewing is important for organizations for assessing a candidate to make hiring decision. For effective technical interviewing, predicting difficulty of long form technical questions is crucial. The present disclosure provides systems and methods for predicting difficulty of long form technical questions using weak supervision from textbooks. Further, zero shot pre-trained large language models and unsupervised template-based technique are used for generating questions. Furthermore, a difficulty score is assigned to the generated questions based on context difficulty and task difficulty. The context difficulty for the generated questions is computed using hierarchical structure of the textbooks, and the task difficulty is computed by determining a similarity between the generated questions and Bloom's taxonomy levels. In the present disclosure, few supervised question difficulty prediction models are trained by means of weak supervision using the generated questions and corresponding difficulty scores and further evaluated for prediction performance using a gold-standard question difficulty dataset.
Performance of a machine learning (ML) model in production, is heavily dependent on underlying distribution of data or underlying process generating labels from attributes. Any change in either one or both impacts the ML model performance heavily and inhibits knowledge of true labels. This in turn affects ML model uncertainty. Thus, performance monitoring of ML models in production becomes necessary. Embodiments of the present disclosure provide estimates operating model accuracy at production stage by constructing the correlations between the model accuracy, model uncertainty and deviation of the distributions in absence of ground truth. In the method of present disclosure, the model performance of the machine learning (ML) model deployed in production is estimated in absence of ground truths. Moreover, this can be done without retraining the model, thus saving computational costs and resources. The method of the present disclosure can be used and performed in real time.
The present disclosure provides a system and method for delay prediction for scheduled public transport. A multi-architectural deep learning approach has been used to predict the delays of a queried vehicle in the scheduled public transport. For this, historical operational data is transformed into temporal, and spatiotemporal data. While, the spatial data is obtained from geographical information. The system uses different combinations of neural networks architectures. A regressor model uses three separate kinds of architecture. One component is the Fully Connected Neural Network (FCNN), which is good at learning from static features, the second is the Long Short Term Memory (LSTM) network which is good at learning from temporal features, and the third is the 3D Convolutional Neural Network (3DCNN) which is good at learning from spatiotemporal features. Learned encoding from each are fed to another FCNN to produce the predicted delay value.
The disclosure generally relates to an FPGA-based online 3D bin packing. Online 3D bin packing is the process of packing boxes into larger bins-Long Distance Containers (LDCs) such that the space inside each LDC is used to the maximum extent. The use of deep reinforcement learning (Deep RL) for this process is effective and popular. However, since the existing processor-based implementations are limited by Von-Neumann architecture and take a long time to evaluate each alignment for a box, only a few potential alignments are considered, resulting in sub-optimal packing efficiency. This disclosure describes an architecture for bin packing which leverages pipelining and parallel processing on FPGA for faster and exhaustive evaluation of all alignments for each box resulting in increased efficiency. In addition, a suitable generic purpose processor is employed to train the neural network within the algorithm to make the disclosed techniques computationally light, faster and efficient.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
18.
METHOD AND SYSTEM FOR EEG MOTOR IMAGERY CLASSIFICATION
This disclosure relates generally to the field of Electroencephalogram (EEG) classification, and, more particularly, to method and system for EEG motor imagery classification. Existing deep learning works employ the sensor-space for EEG graph representations wherein the channels of the EEG are considered as nodes and connection between the nodes are either predefined or are based on certain heuristics. However, these representations are ineffective and fail to accurately capture the underlying brain's functional networks. Embodiments of present disclosure provide a method of training a weighted adjacency matrix and a Graph Neural Network (GNN) to accurately represent the EEG signals. The method also trains a graph, a node, and an edge classifier to perform graph classification (i.e. motor imagery classification), node and edge classification. Thus, representations generated by the GNN can be additionally used for node and edge classification unlike state of the art methods.
Existing approaches in pharmaceutical pricing, fail to provide visibility on nature of pricing followed by pharma players such as pharmacy benefit managers (PBMs), manufacturer, distributor, insurer, and so on, due to involvement of many players in pharma value chain and their complicated pricing strategies. The disclosure herein generally relates to decision support system for pharmaceutical pricing, and, more particularly, to a method and system for providing visibility on nature of pricing followed by different entities of pharma players. The system, by performing pricing analysis, extracts a magnitude of interrelationship between the plurality of entities in the pharmaceutical domain to form a pharmaceutical pricing guide. The pharmaceutical pricing guide is further processed to maximize a measured quality of the pharmaceutical pricing guide in real time and used to choose entities of pharma players associated with retail pharmacy.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
20.
METHOD AND SYSTEM FOR AMBIENT INTELLIGENCE BASED USER INTERACTION
This disclosure relates generally to system and method for ambient intelligence based user interaction. Prior methods for touchless user interaction are sensitive to ambient temperature in a lab environment, susceptible to noise from metallic surfaces and ambient radio waves and are dependent on ambient lighting. Embodiments of the present disclosure provides a multi-modal sensor fusion method which captures touchless gestures from a user or a group of users with their physical context information fused and tagged to these gestures for user interaction. Further pose graphs are generated for user interaction systems using a data association technique and Gaussian mixture model technique. The disclosed method provides a hands-free interface to operate instruments in a smart space, using principles of ambient intelligence.
Model-based image reconstruction (MBIR) methods using convolutional neural networks (CNNs) as priors have demonstrated superior image quality and robustness compared to conventional methods. Studies have explored MBIR combined with supervised and unsupervised denoising techniques for image reconstruction in magnetic resonance imaging (MRI) and positron emission tomography (PET). Unsupervised methods like the deep image prior (DIP) have shown promising results and are less prone to hallucinations. However, since the noisy image is used as a reference, strategies to prevent overfitting are unclear. Recently, Bayesian DIP (BDIP) networks that model uncertainty tend to prevent overfitting without requiring early stopping. However, BDIP has not been studied with data-fidelity term for image reconstruction. Present disclosure provides systems and method that implement a MBIR framework with a modified BDIP. Specifically, an uncertainty-based penalty is included to the BDIP to improve reconstruction across iterations.
Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral/multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.
Embodiments herein provide a method and system for a hyperspectral artificial vision for machines. The system receives a hyperspectral signal of a target material as an input to a neural network model. The system initializes by selecting the number of primitive layers to be used. The system iteratively cycles through all training data (pixels) and updating weights for each unsuccessful material class prediction. Model with two primitives serves as baseline, after which the system adds another primitive layer and repeats the training procedure. The system keeps repeating these processes until obtains convergence. Where the system come to a halt, the system obtains the optimal number of primitives for the given materials. The generated new color pixel is used as a discriminator to aid in locating the target material. The new artificial color is a mixture of weighted chromatic primitives which are optimized for sensitivity/(Spectral Response Functions) SRFs.
Conversational systems are intelligent machines that can understand language and conversing with a customer in writing or verbally. Embodiments herein provide a method for generating a universal conversational system using an ensemble of chatbots and a universal conversational system that adopts wisdom of crowd manifesting as an ensemble of chatbots. The ensemble of chatbots takes responses from NER and rule based conversational models. The knowledge based conversation models where complex queries that require question and answer, and the ensemble of generative knowledge chatbots are relying on a pre-trained models. The pre-trained models are complemented by domain specific training to answer queries that fall outside rule-based chatbot or knowledge graph-based conversation bot capability. The universal conversational system capable of building online virtuous automated learning loop where the models learn from each other and also from human response as wisdom of crowd.
G06F 40/40 - Processing or translation of natural language
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
26.
METHOD AND SYSTEM FOR EVALUATING CLINICAL EFFICACY OF MULTI-LABEL MULTI-CLASS COMPUTATIONAL DIAGNOSTIC MODELS
The present invention relates to the field of evaluating clinical diagnostic models. Conventional metrics does not consider context dependent clinical principles and is unable to capture critically important features that ought to be present in a diagnostic model. Thus, present disclosure provides a method and system for evaluating clinical efficacy of multi-label multi-class computational diagnostic models. Diagnosis for a given dataset of diagnostic samples is obtained from the diagnostic model which is then classified as wrong, missed, over or right diagnosis, based on which a first penalty is calculated. A second penalty is calculated for each diagnostic sample using a contradiction matrix. The first and second penalties are summed up to compute a pre-score for each diagnostic sample. Finally, the diagnostic model is evaluated using a metric that is based on sum of pre-scores, and scores from a perfect and a null multi-label multi-class computational diagnostic model.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
27.
METHOD AND SYSTEM FOR DOCUMENT STRUCTURE BASED UNSUPERVISED LONG-FORM TECHNICAL QUESTION GENERATION
The present disclosure a method for document structure based unsupervised long-form technical question generation. Initially, the system receives a textbook document. Further, a PDF metadata is extracted from the textbook document using a Natural Language Processing (NLP) technique. Further, a plurality of structures from the textbook document based on the PDF metadata using an NLP based filtering technique. Further, a plurality of index based question templates and Table of Contents (TOC) based question templates are obtained from a plurality of predefined question templates using the plurality of structures. Further, the generated plurality of long-form technical questions are generated using the obtained index and TOC based question templates. The plurality of long-form technical questions are further evaluated by the system using plurality of metrics. Further, the generated plurality of long-form technical questions are used to finetune a supervised question generation model for generating optimal questions from document structure.
This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.
This disclosure relates generally to system and method for classification of sensitive date using federated semi-supervised learning. Federated learning has emerged as a privacy-preserving technique to learn one or more machine learning (ML) models without requiring users to share their data. In federated learning, data distribution among clients is imbalanced resulting with limited data in some clients. The method includes extracting a training dataset from one or more data sources and pre-processing the training dataset into a machine readable form based on associated data type. Further, a federated semi-supervised learning model is iteratively trained based on a model contrastive and distillation learning to classify sensitive data from the unlabeled dataset. Then, sensitive data from a user query is received as input which are classified using the federated semi-supervised learning model.
Most techniques to estimate the service life of coatings are experimental in nature, cost expensive and are computationally heavy. Present disclosure provides systems and methods that predict the combined effects of crack path propagation and zones of delamination, that form on coating material and its surface due to weathering. The system of the present disclosure implemented a combined Finite Element Method (FEM) and Monte Carlo based simulation approach to capture the effects of delamination and crack propagation, respectively. The crack paths are predicted using a probabilistic model, considering crack propagation, branching, and keeping a record of crack age. Stress distribution computations are performed using FEM to understand stress concentration zones and delamination behavior with time, which is methodically also combined with the time sequence of cracking as well.
Monitoring the progression of a wound is critical, as it involves repeated clinical trips and lab tests over days. An artificial intelligence (AI) based system and method for analyzing wounds on a person is provided. The system is configured to take an image of the wound taken from a camera of a person. This image is then provided to the physician after the analysis and physician is able to provide a feedback to the person in terms of a healing index. In the analysis part, the system provides a fully automatized wound segmentation and quantify the parameters that assist wound care professionals. An Al based estimation module is provided, implemented with morphological operations, connected component analysis, and shape analysis, improving accuracy and providing the wound parameter and metrics such as area, perimeter, circle diameter, major and minor axis length of an ellipse.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
32.
SYSTEMS AND METHODS FOR SIMULATING GARMENTS ON TARGET BODY POSES
Garments in their natural form are represented by meshes, where vertices (entities) are connected (related) to each other through mesh edges. Earlier methods largely ignored this relational nature of garment data while modeling garments and networks. Present disclosure provides a particle-based garment system and method that learn to simulate template garments on the target arbitrary body poses by representing physical state of garment vertices as particles, expressed as nodes in a graph, and dynamics (velocities of garment vertices) is computed through a learned message-passing. The system and method exploit this relational nature of garment data and network implemented to enforce strong relational inductive bias in garment dynamics thereby accurately simulating garments on the target body pose conditioned on body motion and fabric type at any resolution without modification even for loose garments, unlike existing state-of-the-art (SOTA) methods.
Portable ECG monitors available in market have the disadvantage that the ECG data they provide as input aren't directly interpretable and requires medical knowledge for the users. The disclosure herein generally relates to Electrocardiogram (ECG), and, more particularly, to a method and system for generating 2d representation of electrocardiogram (ECG) signals. The system provides a mechanism for determining variability between a plurality of segments of an ECG data measured, and uses the information on the determined variability to generate the 2D representation corresponding to the ECG signal. The system further provides means to generate a data model that can be further used for processing real-time ECG data for generating corresponding interpretations. This allows a user to obtain the interpretations as output.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
34.
DICTIONARY BASED TEMPORALLY COMPRESSED SYNTHETIC APERTURE RADAR IMAGE RECONSTRUCTION
This disclosure relates generally to Synthetic Aperture Radar (SAR) reconstruction and finds wide application in remote sensing. Conventional approaches either involve huge computational requirement for processing or require specialized hardware along with many additional Radio Frequency (RF) components. The present disclosure provides two approaches for temporally sampling a received pulse compressed signal at two sub-sampling factors, wherein both methods involve frugal hardware implementation. Reconstruction approach of the art is based on the principle of difference ruler and is not suitable for SAR image reconstruction due to the large measurements and image dimensions. In accordance with the present disclosure, the reconstruction problem is framed as an inverse imaging problem by suitably using a forward model and employing an approach like Alternating Direction Method of Multipliers (ADMM) for solving this model which allows use of readily available Plug and Play (PnP) priors.
The present invention relates to a method and system for Phaseless Passive Synthetic Aperture Radar (PPSAR) imaging. Existing method for image reconstruction requires large number of measurements for satisfactory PPSAR image reconstruction. However, this leads to provisioning of more on-board storage and/or a high-speed data link between a mobile platform and a ground station. These requirements are undesirable in practice as PPSAR image reconstruction systems are deployed on resource constrained platforms. The present disclosure uses a regularized Wirtinger Flow (rWF) based approach that uses appropriate regularizers to facilitate the PPSAR image reconstruction with fewer measurements. Further the PPSAR image reconstruction is achieved using Alternating Direction Method of Multipliers (ADMM) by employing standard denoisers such as Total Variation (TV), Block-matching and 3D filtering (BM3D) and, Deep Image Prior (DIP). Further the present disclosure considers an actual location of transmitter for PPSAR imaging that yields better image reconstruction.
A fully automated and reliable picking of a diverse range of unseen objects in clutter is a challenging problem. The present disclosure provides an optimum grasp pose selection to pick an object from a bin. Initially, the system receives an input image pertaining to a surface. Further, a plurality of sampled grasp poses are generated in a random configuration. Further, a depth difference value is computed for each of a plurality of pixels corresponding to each of the plurality of sampled grasp poses. Further, a binary map is generated for each of the plurality of sampled grasp poses and a plurality of subregions are obtained. Further, a plurality of feasible grasp poses are selected based on the plurality of subregions and a plurality of conditions. Further, the plurality of feasible grasp poses are refined and an optimum grasp pose is obtained based on a Grasp Quality Score (GQS).
The disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.
Existing approaches for switching between different hardware accelerators in a heterogeneous accelerator approach have the disadvantage that complete potential of the heterogeneous hardware accelerators do not get used as the switching relies on load on the accelerators or a random switching in which entire task gets reassigned to a different hardware accelerator. The disclosure herein generally relates to data model training, and, more particularly, to a method and system for data model training using heterogeneous hardware accelerators. In this approach, the system switches between hardware accelerators when a measured accuracy of the data model after any epoch is below a threshold of accuracy.
State of the art Acoustic Models (AM), which are trained using data from one environment, may fail to adapt to another environment, and as a result, application is restricted. The disclosure herein generally relates to speech signal processing, and, more particularly, to a method and system for Automatic Speech Recognition (ASR) using Multi-task Learned Embeddings (MTL). In this approach, MTL embeddings are extracted from an MTL neural network that has been trained using feature vectors from a plurality of speech files. The MTL embeddings are then used for generating an acoustic model, which maybe then used for the purpose of Automatic Speech Recognition, along with the feature vectors and the MTL embeddings.
This disclosure relates generally to a method and system for latency optimized heterogeneous deployment of convolutional neural network (CNN). State-of-the-art methods for optimal deployment of convolutional neural network provide a reasonable accuracy. However, for unseen networks the same level of accuracy is not attained. The disclosed method provides an automated and unified framework for the convolutional neural network (CNN) that optimally partitions the CNN and maps these partitions to hardware accelerators yielding a latency optimized deployment configuration. The method provides an optimal partitioning of the CNN for deployment on heterogeneous hardware platforms by searching network partition and hardware pair optimized for latency while including communication cost between hardware. The method employs performance model-based optimization algorithm to optimally deploy components of a deep learning pipeline across right heterogeneous hardware for high performance.
State of the art approaches used to address security aspects in serverless platforms perform workflow validations on an end to end flow, however, this cannot prevent attacks targeted at intermediate function calls in the workflow. Further, the existing systems store policy data in insecure manner, which causes security issues. The disclosure herein generally relates to serverless clouds, and, more particularly, to a method and system for privacy-preserving workflow validations in serverless clouds. The system stores policy data in a secured/encrypted manner. The system also performs validations at different levels, at a first level to allow/deny access at an ingress point, and at a second level to allow/deny access at critical intermediate points. This approach thus provides safety against attacks that may have been initiated post initial validation, and offers added data security.
One of the biggest challenges faced by oil and gas companies is to monitor such long pipelines for leak events and generate false leak event alarms during routine pipe maintenance. A data associated with a first sensing unit is processed to obtain an instant timing information (T0) of a leak event in a conduit at a test environment. A data associated with a second sensing unit is processed to obtain a transient signal associated with the leak event at a specific band. An accelerometer data is filtered to obtain a band passed filtered accelerometer signal (Accelbpf). The Accelbpf is truncated in a time domain from the T0 to a duration Td of the leak event to obtain a temporal template signal (Acceltemplate). A leak event of a real-time conduit is dynamically detected at a physical environment based on Acceltemplate when a cross-correlation value is greater than a threshold value (∝).
G01M 3/24 - Investigating fluid tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations
G01M 3/28 - Investigating fluid tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for valves
44.
METHOD AND SYSTEM FOR CALIBRATING MACHINE LEARNING MODELS IN FULLY HOMOMORPHIC ENCRYPTION APPLICATIONS
The present disclosure provides a technique to evaluate encrypted Machine Learning (ML) models. Conventional methods are unable to provide a holistic approach to evaluate encrypted ML models. Initially, the system receives an encrypted ML model. The ML model can be an unencrypted ML model trained with encrypted data or an encrypted ML model trained with encrypted data, or an encrypted ML model trained with unencrypted data. Further, a plurality of evaluation functions pertaining to the ML model to be calibrated are identified using a pattern matching technique. Further, an approximated function is generated for each of the plurality of evaluation functions using a corresponding approximation technique. After generating a plurality of approximated functions, an Expected Calibration Error (ECE) value is computed based on the plurality of approximated functions. Finally, the ML model is calibrated based on the computed ECE value. The ML model is perfectly calibrated if the computed ECE value is equal to zero.
The present disclosure is of a method for automated authoring of purposive models from Natural Language (NL) documents. Conventional model extractors to automatically extract models from NL documents are specific to a metamodel, do not consider document structure and are not configurable. Initially, the system receives a plurality of Natural Language (NL) documents, a metamodel, a plurality of pattern trees corresponding to the metamodel, and a configurable domain dictionary. Each of the plurality of pattern trees includes a plurality of pattern elements. Further, a document information is generated from each of the plurality of NL documents using a document information reading technique. Finally, a plurality of purposive models are generated for each of the plurality of NL documents by interpreting a corresponding document information based on the plurality of pattern trees and the metamodel using a pattern interpretation technique.
This disclosure relates generally to method and system for jointly pruning and hardware acceleration of pre-trained deep learning models. The present disclosure enables pruning a plurality of DNN models layers using an optimal pruning ratio. The method processes a pruning request to transform the plurality of DNN models and the plurality of hardware accelerators into a plurality of pruned hardware accelerated DNN models based on at least one user option. The first pruning search option executes a hardware pruning search technique to perform search on each DNN model and each processor based on at least one of a performance indicator and an optimal pruning ratio. The second pruning search option executes an optimal pruning search technique, to perform search on each layer with corresponding pruning ratio. The layer assignment sequence technique creates a static load distributor by partitioning the optimal layer of the DNN model into a plurality of layer sequences and assigning each layer sequence to corresponding processing element of hardware accelerators.
Accuracy of transcript is of foremost importance in Automatic Speech Recognition (ASR). State of the art system mostly rely on spelling correction based contextual improvement in ASR, which is generally a static vocabulary based biasing approach. Embodiments of the present disclosure provide a method and system for visual context aware ASR. The method provides biasing using shallow fusion biasing approach with a modified beam search decoding technique, which introduces a non-greedy pruning strategy to allow biasing at the sub-word level. The biasing algorithm brings in the visual context of the robot to the speech recognizer based on a dynamic biasing vocabulary, improving the transcription accuracy. The dynamic biasing vocabulary, comprising objects in a current environment accompanied by their self and relational attributes, is generated using a bias prediction network that explicitly adds label to objects, which are detected and captioned via a state of the art dense image captioning network.
The present disclosure detects a pupil of an eye using circle formation based scoring method. The conventional approaches fail to provide an accurate and reliable biometric authentication due to the usage of simple thresholding based statistical methods and iris dependent segmentation methods. The present disclosure utilizes a circle plotting approach and selects the optimum circle using several parameters. The present disclosure can generate a pupil boundary that fits the pupil region inside an iris perfectly. Initially, the system receives an input image of an eye. After removing reflections, a core point of the reflection free image is identified. Further, a plurality of points are obtained based on a sudden gradient change. and a plurality of circles are plotted. Further, an optimum circle is identified using a score based optimum circle selection method. Finally, the pupil associated with the input image is identified based on the optimum circle.
In retail, macro space optimization is carried out at individual stores to allocate optimum space for each category. Each retailer has many stores and macro space optimization is experimented with different objectives such as expand space, reduce space and constant space of a store individually. Thus, the number of space recommendations analyzed at corporate level increases extremely high and making it difficult to bring key inferences out of these recommendations and creating challenges in implementation of results such as creation of planograms and floor plans. Embodiments of the present disclosure provide a method and system for identifying underlying patterns that reside in space recommendations across stores and creating drastically reduced number of floor plans and planograms in accordance with the identified set of patterns unlike large number of floor plans or planograms generated by state of the art space planning systems.
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation, spatial and temporal correlations, etc. The developing world in particular is plagued by additional factors like lack of lane discipline, excess vehicles, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. Present disclosure provides system and method that implement recurrent neural networks (RNNs) for BATP (in real-time), wherein the system incorporates information pertaining to spatial and temporal correlations and seasonal correlations. More specifically, a Gated Recurrent Unit (GRU) based Encoder-Decoder (ED) model with one or more bi-directional layers at the decoder is implemented for BATP based on relevant additional synchronized inputs (from previous trips) at each step of the decoder. The system further captures congestion influences on travel time prediction.
Fault diagnosis in industries typically involves identification of key variables/sensors bearing fault signature, classification of detected fault into known fault classes and detecting root causes/sources of the fault. This disclosure relates to a method and system for a deep learning based causal inference in a multivariate time series data of abnormal events and failures in industrial manufacturing processes and equipment. The system generates causal networks for non-linear and non-stationary multivariate time series data. The causal network learns for a dynamic non-stationary and nonlinear complex process or system fault using observed data without any prior process knowledge. The causal networks of faults are identified in real-time using a deep learning-based causal network learning technique. The system identifies causal connections and temporal lag information among variables to generate a directed causal graph of fault called the causal network, which is used to identify fault propagation paths and root cause variables.
This disclosure relates generally to method to determine an optimal set of atom centered symmetry functions. One or more parameters associated with one or more atom centered symmetry functions (ACSFs) are received. An initial set of ACSFs is generated by varying the one or more parameters. A histogram with a prespecified bin size is constructed to obtain a distribution of value of each of the initial set of ACSFs. A pruned list of ACSFs is obtained based on width and maximum value of the distribution of the value of initial set of ACSFs. The pruned list of ACSFs is sorted in decreasing order of spread to obtain a sorted list of ACSFs. An optimal set of one or more shortlisted ACSFs is determined by traversing through the sorted list of ACSFs. A high dimensional neural network potential is trained based on the optimal set of one or more shortlisted ACSFs.
This disclosure relates to methods and systems for high resolution and scalable crop yield forecasting by first developing a first crop yield forecasting model to generate coarse resolution yield maps and further dynamically selecting a set of pixels from the coarse resolution yield maps. The coarse resolution yield maps, satellite, weather and soil related data are fed as input to a second crop yield forecasting to generate high resolution crop yield forecasting maps. Further, domain knowledge about crop growth stages, economically important crop growth stages and weather based triggers are identified to quantify extent of change in crop yield. This helps in crop yield forecasting during real time adverse weather conditions. Finally, an adjusted crop yield model is obtained after adjusting losses incurred due to the real time adverse weather conditions to obtain accurate high resolution crop yield forecasting maps. The method of present disclosure is inexpensive, light-weight, and scalable.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
54.
METHODS AND SYSTEMS FOR DERIVING A BEHAVIOR KNOWLEDGE MODEL FOR DATA ANALYTICS
This disclosure relates generally to methods and systems for deriving a behavior knowledge model for data analytics. The current automated technical solutions for monitoring the health status or behavior pattern, that apply a domain knowledge for the data analytics are very limited. Hence the conventional techniques for monitoring the health status or behavior pattern are manual, application centric and inaccurate. The present disclosure automatically leverages relevant domain knowledge and the sensor data for building a behavior knowledge model which further enhanced by the deviations identified using a machine leaning model. The present disclosure facilitates development a knowledge-driven simulator that generates sensor data sets for typical resident behavior, based on definable activity patterns and pattern influencers of interest (e.g., diabetes, nocturia).
This disclosure relates generally to the field of taxonomic profiling of microbial organisms such as bacteria, and, more particularly, to system and method for simultaneous interpretation of taxonomic distribution and replication rates of microbes constituting microbial communities. The present disclosure extracts bacterial genomic DNA from a plurality of bacterial organisms comprised in collected microbiome sample. Maps the plurality of the DNA sequence fragment reads to a precomputed reference sequence database of a plurality of all available completely sequenced bacterial genomes. Based on the mapping, the read coverage is measured at the genomic locations of the phylogenetic marker genes, wherein measured read coverage is used for interpretation of taxonomic distribution of the plurality of bacterial organisms. A plurality of slopes is obtained by fitting a linear function. The present disclosure interprets a replication rate for each of the plurality of bacterial organisms identified from the collected microbiome sample.
C12Q 1/689 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
The present disclosure describes an automated docking system for charging chargeable mobile devices. Conventionally, docking systems utilize expensive technology like wireless charging leading to higher cost. The system of the present disclosure provides a cost effective generic docking station with internet of things (IoT) interface for heterogeneous chargeable mobile devices which leads to precise docking. The generic design of the docking station enables docking different category of the chargeable mobile devices such as fork type, unit load type, at same docking station with different charging currents. IoT enabled docking station communicates with the chargeable mobile devices and charges them based on battery health, quick/slow charge, category, task duration and/or the like. The present disclosure brings a design flexibility to the docking station and contact pads, so that the flexible design adjusts itself in translation and rotation axis for certain degrees of freedom if there is an error in docking.
H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
G06F 1/16 - Constructional details or arrangements
A47L 9/28 - Installation of the electric equipment, e.g. adaptation or attachment to the suction cleaner; Controlling suction cleaners by electric means
57.
METHOD AND SYSTEM FOR REINFORCEMENT LEARNING AND DUAL CHANNEL ACTION EMBEDDING BASED ROBOTIC NAVIGATION
The present disclosure provides a Reinforcement Learning (RL) based architecture to efficiently learn action embeddings in low dimensional space. In conventional methods, the embeddings are learnt with the sole objective of improving policy learning, and there are no specific requirements on the quality of the embeddings. Initially, the system receives a goal to be reached by a mobile robot and a current location of the mobile robot is obtained. Simultaneously current transition dynamics associated with the plurality of directional actuators are obtained using a Reinforcement Learning (RL) technique. Further, a plurality of embeddings is computed based on the current location of the mobile robot and the current transition dynamics using a trained Dual Channel Training (DCT) based autoencoder decoder model. Finally, a displacement vector for current navigation of the mobile robot is computed based on the computed plurality of embeddings using the RL technique.
Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
The disclosure generally relates to scene graph generation. Scene graph captures rich semantic information of an image by representing objects and their relationships as nodes and edges of a graph and has several applications including image retrieval, action recognition, visual question answering, autonomous driving, robotics. However, to leverage scene graphs, computationally efficient scene graph generation methods are required, which is very challenging to generate due presence of a quadratic number of potential edges and computationally intensive/non-scalable techniques for detecting the relationship between each object pair using the traditional approach. The disclosure proposes a combination of edge proposal neural network and the Graph neural network with spatial message passing (GNN-SMP) along with several techniques including a feature extraction technique, object detection technique, un-labelled graph generation technique and a scene graph generation technique to generate scene graphs.
This disclosure relates generally to multi-port multi-functional meta-surface coplanar antenna system. Conventional electronic or mechanical solutions for beam steering incur high installation costs with less performance speed and bulk structures. The present disclosure provides multi-port multi-functional meta-surface coplanar antenna system for beam steering control. The disclosed antenna system enables radiator to have a performance diversity application through beam steering functionalities. The disclosed antenna system provides a minimal design complexity and minimal usage of active or passive lumped components. The disclosed system comprises Gradient Refractive Index Meta-surface (GRIM) and the antenna disposed on the same side of a substrate. Beam steering control is performed using port excitations and controlling the phase between the concerned ports externally.
H01Q 15/00 - Devices for reflection, refraction, diffraction or polarisation of waves radiated from an antenna, e.g. quasi-optical devices
H01Q 3/26 - Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the distribution of energy across a radiating aperture
61.
METHOD AND SYSTEM FOR SEMI-SUPERVISED DOMAIN ADAPTATION BASED UNIVERSAL LESION DETECTION
The present disclosure detects lesions in different datasets using a semi-supervised domain adaptation manner with very few labeled target samples. Conventional approaches suffer due to domain-gap between source and target domain. Initially, the system receives an input image, and extracts a plurality of multi-scale feature maps from the input image. Further, a classification map is generated based on the plurality of multi-scale feature maps. Further, a 4D vector corresponding to each of a plurality of foreground pixels is computed. Further, an objectness score corresponding the plurality of foreground pixels is computed. After computing the objectness score, a centerness score is computed for each of the plurality of foreground pixels using a single centerness network. Further, an updated objectness score is computed for each of the plurality of foreground. Finally, a plurality of multi-sized lesions in the input image are detected using a trained few-shot adversarial lesion detector network.
G06V 10/77 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
62.
SYSTEMS AND METHODS FOR ANALYSING SOFTWARE PRODUCTS
Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before going to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a software product and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components and generated components in the software product under consideration. License attributes of the OSS components are mapped suitably, and a final attribute is derived for each OSS component embedded in the product under consideration.
Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before going to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a software product and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components and generated components in the software product under consideration. License attributes of the OSS components are mapped suitably, and a final attribute is derived for each OSS component embedded in the product under consideration.
This disclosure relates generally to identification and mitigation of bias while training deep learning models. Conventional methods do not provide effective methods for bias identification, and they require pre-defined concepts and rules for bias mitigation. The embodiments of the present disclosure train an auto-encoder to produce a generalized representation of an input image by decomposing into a set of latent embedding. The set of latent embedding are used to learn the shape and color concepts of the input image. The feature specialization is done by training an auto-encoder to reconstruct the input image using the shape embedding modulated by color embedding. To identify the bias, permutation invariant neural network is trained for classification task and attribution scores corresponding to each concept embedding are computed. The method also performs de-biasing the classifier by training it with a set of counterfactual images generated by modifying the latent embedding learned by the auto-encoder.
Existing semi-supervised and unsupervised approaches for intent discovery require an estimate of the number of new intents present in the user logs. The present disclosure receives labeled utterances from known intents and update parameters of a pre-trained language model (PLM). Representation learning and clustering is performed iteratively using labeled and unlabeled utterances from known intents and unlabeled utterances from unknown intents to fine-tune PLM and a plurality of clusters is generated. Cluster merger algorithm is executed iteratively on generated plurality of clusters. A query cluster is obtained by randomly selecting one cluster from the plurality of clusters and by obtaining a corresponding plurality of nearest neighbors based on a cosine-similarity. A response for merging the query cluster and corresponding plurality of nearest neighbors is obtained, and a new cluster is created. The corresponding cluster representation is recalculated and each of the new cluster is interpreted as an intent.
Embodiments disclosed herein model lifelong intent detection as a class-incremental learning where a new set of intents/classes are added at each incremental step. To address the issue of catastrophic forgetting during lifelong intent detection (LID), an incremental learner is provided with Prompt Augmented Generative Replay, wherein unlike existing approaches that store real samples in replay memory, only concept words obtained from old intents are stored, which reduces memory consumption and speeds up incremental training still enabling not forgetting the old intents. Joint training of an incremental learner is carried out for LID and a pseudo-labeled utterance generation with objective is to classify a user utterance into one of multiple pre-defined intents by minimizing a total Loss function comprising a LID loss function, a Labeled Utterance Generation loss function, a Supervised Contrastive Training loss function, and a Knowledge Distillation loss function.
A method and system for synthesizing smartphone cue-cards representing socio-technical system (STS) service design prototyping based on a plurality of constraints has been provided. The system creates just in time, a dynamic visual design for senior and junior designers for displaying of patterns as digital Cue Cards based on human configured time constraint and novelty constraints all the while avoiding repeatability for the designer. Smartphone is utilized as a mechanism to host multiple cue cards and an interaction model which allows multiple cue-card-connectedness to jump across constraints of local-maxima/minima towards a global-maxima/minima. The method allows young designers get access to better long term memory (LTM) and senior designer getting access to faster LTM. The present disclosure helps senior designer get faster access to new patterns to reduce time for novel new interventions.
Traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. Method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. In this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. The training data is then used for generating a GNN data model, which is further used for recommending optimum combination of quantum circuits.
This disclosure relates generally to recommending an optimal VM instance. The increased use of Deep Learning (DL) models in several domains has resulted in an increased demand for hardware configurations to enable heavy computations and faster performance to support the DL techniques. However, the identification of the optimal hardware configuration for the DL requirement is challenging and requires a considerable amount of time and expertise, considering the highly configurable model configuration of DL techniques. The disclosed optimal selection of VM comprises several techniques including benchmarking, using benchmarked results for building an approximation function and use a Bayesian Optimizer (BO) technique to iterate through the search space and generate recommendations of VM configurations, that effectively address the challenges arising due to the dynamic nature of cloud services—pricing and hardware configuration, large number of VM available across regions and cloud service providers and estimating for different types of training code.
The present invention relates to a method and system for early detection of COVID-19. Existing methods require data from multiple sensors for training a prediction model whose output is considered as final prediction which is actually the prediction for a particular day or time instance. However, this prediction doesn't detect actual infection of COVID-19 since it requires monitoring the change in health of the user over consecutive days. Embodiments of present disclosure overcome these challenges by a prediction model for COVID-19 which requires only data from Photoplethysmography (PPG) sensor seamlessly collected from a wearable device still able to provide accurate COVID-19 prediction with application of a post processing technique on the predictions of the prediction model. Since COVID-19 symptoms have an effect on heartrate and oxygen saturation which are effectively captured by PPG sensor data, studying these dynamics during infection period gives insights to perform early detection of COVID-19.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
71.
METHOD AND SYSTEM FOR DETERMINING PROGRESSION OF ATRIAL FIBRILLATION BASED ON HEMODYNAMIC METRICS
The present invention relates to a method and system for determining progression of atrial fibrillation (AF) based on hemodynamic metrics. In conventional CFD models, effect of the AF on a cardiovascular system is not modeled and evaluation of associated hemodynamic metrics and its effect on a Left Atrium (LA) dynamics is not considered. The method and system for determining progression of the AF based on the hemodynamic metrics, analyzes the effect of the AF on cardiovascular parameters of the LA and a left Ventricle (LV), for AF variations. A 3D-CFD model is modelled from a plurality of scan images of a heart of a subject and the AF variations are incorporated in a zero-dimensional (OD) lumped cardiovascular hemodynamic model along with a novel rhythm generator that are used for extracting a plurality of LA hemodynamic metrics of wall shear stress (WSS) that are possible indicators for progression of the AF.
This disclosure addresses the unresolved problems of tackling object disambiguation task for an embodied agent. The embodiments of present disclosure provide a method and system for disambiguation of referred objects for embodied agents. With a phrase-to-graph network disclosed in the system of the present disclosure, any natural language object description indicating the object disambiguation task can be converted into a semantic graph representation. This not only provides a formal representation of the referred object and object instances but also helps to find an ambiguity in disambiguating the referred object using a real-time multi-view aggregation algorithm. The real-time multi-view aggregation algorithm processes multiple observations from an environment and finds the unique instances of the referred object. The method of the present disclosure demonstrates significant improvement in qualifying ambiguity detection with accurate, context-specific information so that it is sufficient for a user to come up with a reply towards disambiguation.
G06F 40/40 - Processing or translation of natural language
G06T 15/00 - 3D [Three Dimensional] image rendering
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
73.
METHOD AND SYSTEM FOR DEEP LEARNING BASED IMAGE FEATURE EXTRACTION
The present disclosure provides a model for deep learning based image feature extraction considering a range of useful negative features. Conventional methods are either considering only positive features or considering all negative features along with positive features which leads to bias in feature extraction. The present disclosure overcomes the problem of the conventional methods using a bounded Rectified Linear activation Unit (B-ReLU) activation function based Bounded-Rectifier Network (B-RectNet). Initially, the present disclosure receives an image pertaining to an object. Further, the received image is preprocessed to remove a plurality of anomalies associated with the image a preprocessing technique. Further, a plurality of image features are extracted based on the preprocessed image using a trained B-RectNet. The bounded ReLU activation function filters a plurality of negative features based on a lower negative bound value and an upper negative bound value before inputting a plurality of feature values to a subsequent layer.
G06V 10/77 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
Organizations/manufacturers have used scenarios to make business decisions. It has been difficult to apply scenarios in dealing with tactical opportunities due to lack of integration of changing inputs for consistent decision making. Conventionally, tools are cumbersome and depend on pre-structured and individually validated data requiring significant expert involvement. Present disclosure manages decision scenarios and optimizes total sourcing cost by obtaining various inputs and retrieving decision scenarios from a database. An optimization technique and/or a simulation technique is performed on the decision scenarios to obtain the total sourcing cost that is based on a quantity filled for each source-entity-destination combination and a corresponding unit lane cost. A decision scenario is selected from the pre-defined decision scenarios based on the total sourcing cost and an ordering schedule for associated demands is created accordingly. The selected decision scenario is further fine-tuned such that the total sourcing cost reaches close to a target cost.
Web application generation using conventional predefined template and data model approach is a technical limitation for varying user requirements. Embodiments herein provide a method and system for automated generation of web applications based on wireframe metadata generated from user requirements for a web application received in unstructured data format. The user requirements comprising a description document, and at least one of actor, requirements, use case number, use case name, and data to be collected is parsed to generate the wireframe metadata using NLP and wireframe generation rules. The wireframe metadata provides data structure of webpage details of webpages of the web application comprising a page name, a menu name, field details, a field component type, an action to be performed and an additional information. From the wireframe metadata data models are created dynamically using data modelling rules in accordance with user requirements along with task lists to generate web application.
Nanomaterials are an important class of materials for sunscreens. Conventional metal oxide nanoparticles are toxic in nature and leave white patches on skin after application. This disclosure provides a sunscreen composition containing biodegradable antimicrobial polymer nanoparticles as UV filter. The sunscreen composition comprising chitosan nanoparticles, at least one flavoring agent, at least one coloring agent, at least one stabilizer, and at least one preservative, in a defined form, wherein size of the chitosan nanoparticles is ranging from 200 to 900 nm. The size of the chitosan nanoparticles ranging from 200 to 400 nm for air medium, 500 to 700 nm for water medium and 700 to 900 nm for ethanol medium. The chitosan polymer is derived from chitin, a glucosamine polymer. The sunscreen composition is available in the defined form selected from a group consisting of a skin cream, a skin lotion, a powder, a gel, and a sprayable liquid.
State of the art techniques used for document processing and particularly for handling processing of images for data extraction have the disadvantage that they have large computational load and memory footprint. The disclosure herein generally relates to text processing, and, more particularly, to a method and system for generating a data model for text extraction from documents. The system prunes a pretrained base model using a Lottery Ticket Hypothesis (LTH) algorithm, to generate a LTH pruned data model. The system further trims the LTH pruned data model to obtain a structured pruned data model, which involves discarding filters that have filter sparsity exceeding a threshold of filter sparsity. The structured pruned data model is then trained from a teacher model in a Knowledge Distillation algorithm, wherein a resultant data model obtained after training the structured pruned data model forms the data model for text detection.
This disclosure relates generally to methods and systems for automated image segmentation of an anatomical structure such as heart. Most of the techniques in literature are using 2-D or slice by-slice data due to lightweight and need of less data for training. These networks lack 3-D contextual information. Further, the conventional techniques are inaccurate and inefficient in the 3-D image segmentation till the last slice of the image. The present disclosure solves automated 3-D image segmentation of the anatomical structure such as heart, by proposing a new Generative Adversarial Network (GAN) based architecture for the 3-D segmentation, with a patch-based extraction technique and a class-weighted generalized dice loss. The proposed 3-D GAN based architecture is capable of storing the 3-D contextual information for the image segmentation of the anatomical structure, with high accuracy.
G06T 15/00 - 3D [Three Dimensional] image rendering
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
79.
METHOD AND SYSTEM FOR NAVIGATION OF ROBOT FROM ONE AREA TO ANOTHER AREA
A system and method for navigation of a robot from a first area to a second area in a facility is provided. The present disclosure is providing robot navigation using the ‘Areagoal’ Navigation technique. ‘Areagoal’ class of problem is divided into two subtasks: identifying the area; and navigation from one area to another. The robot starts in first location and goes out of the current area if it is not in the target area. If there are multiple openings from the first area, it needs to select the most statistically close one to the target area and go there. If the target area is not reached, it backtracks to an earlier viable branch position to continue the target area search. The system takes input from RGB-D camera and odometer, while the output is action space (left, right, forward) with goal of moving to target area.
Hardly any work in literature attempts employing Function-as-a-Service (FaaS) or serverless architecture to accelerate the training or re-training process of meta-learning architectures. Embodiments of the present disclosure provide a method and system for meta learning using distributed training on serverless architecture. The system, interchangeably referred to as MetaFaaS, is a meta-learning based scalable architecture using serverless distributed setup. Hierarchical nature of gradient based architectures is leveraged to facilitate distributed training on the serverless architecture. Further, a compute-efficient architecture, efficient Adaptive Learning of hyperparameters for Fast Adaptation (eALFA) for meta-learning is provided. The serverless architecture based training of models during meta learning enables unlimited scalability and reduction of training time by using optimal number of serverless instances. An analytical model for gradient based meta learning architectures that predicts training time required for the number of FaaS instances is provided which further enables estimating the cost incurred during training models in meta-learning.
This disclosure relates generally to optimal intraday scheduling of aggregated Distributed Energy Resources (DERs). Owing to their stochastic nature, DERs aggregators are more suited to participate in intraday electricity markets. The current works on DER aggregators trading in intraday markets do not satisfactorily model the different aspects. The disclosure is an optimal trading strategy for aggregators managing heterogeneous DERs to participate in intraday markets. The intraday market is modelled using a joint price-volume dynamics distribution and an optimal bidding strategy is disclosed for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation while allowing for energy exchanges within the DER pool. Further the optimal bidding strategy of aggregators in an intraday market is a MINLP problem, which is solved by converting the complex non-linearities in the problem into a coupled MILP—simple maximization set-up, which is then solved in an iterative fashion.
The present invention relates to the field of electrolyte design. Existing methods focus on optimizing the electrolyte composition on a stand-alone basis with respect to its properties and validating battery performance experimentally which is a time-consuming process. Thus, embodiments of present disclosure provide an automated method and system for identifying electrolyte composition for optimal battery performance. The system receives certain input parameters and computes transport properties using the input. Then, a feasible electrolyte composition is identified from a material database based on deviation index metric. The identified electrolyte composition is then optimized based on the input by considering the deviation index and battery performance metrics such as capacity fade and internal heat generation. Simulation case studies performed show that the method is capable of identifying a new electrolyte from the material database as well as identify optimal concentration of same electrolyte which results in better performance of the battery.
H01M 10/48 - Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
H01M 10/056 - Accumulators with non-aqueous electrolyte characterised by the materials used as electrolytes, e.g. mixed inorganic/organic electrolytes
G06F 30/20 - Design optimisation, verification or simulation
83.
METHOD AND SYSTEM FOR GENERATING A DATA MODEL FOR PREDICING DATA TRANSFER RATE
Heterogeneous cloud storage services offered by different cloud service providers have unique deliverable performance. One key challenge is to find the maximum achievable data transfer rate from one cloud service to another. The disclosure herein generally relates to cloud computing, and, more particularly, to a method and system for parameter tuning in cloud network. The system obtains optimum value of parameters of a source cloud and a destination cloud in a cloud pair, by performing a parameter tuning. The optimum value of parameters and corresponding data transfer rate is used as a training data to generate a data model. The data model processes real-time information with respect to cloud pairs, and predicts corresponding data transfer rate.
State of the art approaches of exception management are reactive, manual, and intuition-driven. Command center teams often react to exceptions. They also use BI tools which only provide statistical observation, but fail to mine domain-aware insights and actionable recommendations. The disclosure herein generally relates to analyzing process exceptions, and, more particularly, to a method and system for generating a data model to analyze and predict process exceptions. The system generates a data model by using information on rules, associated properties and exceptions, as training data. The data model is further used to process information on different rules to identify exceptions, and then to generate recommendations in response to identified exceptions.
Present disclosure relates to method and synchronization system for synchronizing plurality of events associated with one or more processes in assembly line for tracing entity. Initially, information related to plurality of events associated with one or more processes from one or more devices is received, where each of the plurality of events comprises respective first timestamp. Upon receiving, the first timestamp between each of the plurality of events is synchronized by performing first level and second level synchronization. The synchronization converts first timestamp into second timestamp with respect to common reference timestamp. Further, the synchronization system may identify one or more defects based on quality value assigned to entity. Thus, the present disclosure helps the synchronization system to efficiently trace entity when a defect is identified and replaces/repairs the entity.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
86.
SYSTEMS AND METHOD FOR DETERMINING HYGIENE IN ENTERPRISE DOCUMENTS WITH RESPECT TO REGULATORY OBLIGATIONS
Enterprises need assurance that their internal documents like policies, procedures, controls, standard operating procedures (SOPS) are adherent to the regulatory obligations. In conventional practice this requires a manual effort where legal, business and IT experts collaborate for assuring completeness and consistency in enterprise documents with respect to regulatory obligations, thereby establishing regulatory hygiene. Governance risk and compliance (GRC) frameworks help experts with collaboration but do not provide automation aids necessary to reduce the analysis and synthesis burden. Present disclosure provides system and method for determining hygiene in the plurality of enterprise documents with respect to the plurality of regulatory obligations by extracting concept ontology models from multiple enterprise documents and multiple regulations and enabling navigation across multiple documents via the ontology. The system further reasons out how this form of navigation or creating a common navigable ontology enables establishing hygiene in enterprise documents with respect to regulatory obligations.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
Owing to their stochastic nature, Distributed Energy Resources (DERs) are more suited to participate in short-term or intraday electricity markets. However, it is very difficult for an asset owner to manage their operation when interacting with markets and create operation schedules. Present disclosure provides systems and methods that for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation in the DER pool. The system models an optimal bidding problem of aggregators in an intraday market as a mixed-integer non-linear programming (MINLP) problem. The MINLP problem is converted to an NLP problem by an optimization model and integer variables are relaxed to solve the NLP problem and obtain (i) an optimal intraday operating schedule for one or more DERs, and (ii) an intraday bid associated with the DERs for a plurality of delivery slots to be traded in an intraday market.
State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.
This disclosure relates generally to methods and systems for designing an optimal dual-band metamaterial polarization converter for refractive index sensing applications. Most of the existing techniques for designing the metamaterial-based polarization converters operating at very high frequency range limits the sensing performance and increases fabrication complexity. In the design of the optimal dual-band metamaterial polarization converter, first a circular split-ring resonator (SRR) as a unit cell is designed. Secondly, the two capacitive gaps of the top layer, are aligned at 180 degrees with respect to each other and at 45 degrees with respect to X-axis and Y-axis. Lastly, step-by-step tuning the one or more key design parameters of the SRR, is performed until an optimum frequency response is obtained, to obtain the optimal dual-band metamaterial polarization converter.
State of the art methods require size of DL model, or its gradients be less than maximum data item size of storage used as a communication channel for model training with serverless platform. Embodiments of the present disclosure provide method and system for training large DL models via serverless architecture using communication channel when the gradients are larger than maximum size of one data item allowed by the channel. Gradients that are generated by each worker during current training instance, are chunked into segments and stored in the communication channel. Corresponding segments of each worker are aggregated by aggregators and stored back. Each of the aggregated corresponding segments are read by each worker to generate an aggregated model to be used during successive training instance. Optimization techniques are used for reading-from and writing-to the channel resulting in significant improvement in performance and cost of training.
Quality of weld images in with bad lighting condition and specific image color formats add constraints to existing automated weld inspection systems. Embodiments herein provide a method and system based on Domain Knowledge Infused Adaptive-Network-based Fuzzy Inference System (DKI-ANFIS) for weld quality inspection. The DKI-ANFIS inspects the quality of weld joint using domain driven quality inspection techniques. A segmentation algorithm is used to extract the weld joint in form of fractals followed by an unsupervised technique to extract useful geometrical features from the fractals. These geometrical features are used for quality index generation. A weld inspection model comprising the DKI-ANFIS is used for determining the quality of the weld joint. DKI-ANFIS modifies layers of ANFIS by infusing layer of domain knowledge to give better results even if there is a class imbalance in the data or the data is skewed or there is only a short corpus of data available.
Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. Existing solutions do not have a way to accurately estimate the scale of magnification of the object in the shelf image, so unable to distinguish between size variants of the same product. A system and method for facilitating planogram compliance for inventory management in a retail store have been provided. The scales are calculated with use of a vector convergence technique followed by a center clustering which automatically removes outliers. Initially disclosure comprises calculation of scales and centers, then generation of region proposals using those scales and centers, Next, classification of the regions proposed and generation of similarity scores, and on the basis of similarity scores conflict resolution is performed among overlapped region proposals using non-maximal suppression.
Optical images in remote sensing are contaminated by cloud cover and bad weather conditions and are only available during the daytime. Whereas SAR images are completely cloud free, independent of weather conditions and can be acquired both during the day and at night. However, due to the speckle effect and side looking imaging mechanism of SAR images, they are not easily interpretable by untrained people. To address this issue, the present disclosure provides a method and system for LULC guided SAR visualization, wherein a GAN is trained to translate SAR images to optical images for visualization. A given SAR image is fed into a first generator of the GAN to obtain LULC map which is then concatenated with the SAR image and fed into a second generator of the GAN to generate an optical image. The LULC map provides semantic information required for generation of more realistic optical image.
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
G01S 7/41 - RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisation; Target signature; Target cross-section
94.
METHOD AND SYSTEM FOR TIME SENSITIVE PROCESSING OF TCP SEGMENTS INTO APPLICATION LAYER MESSAGES
This disclosure relates to time sensitive processing of TCP segments into application layer messages in FPGA. Certain applications such as “stock market” or “ticket booking system” require a time sensitive ordering of the transaction, as the timing of arrival of transaction (packet) will impact the result, wherein the time sensitive ordering occurs when a first packet reaching the application network is processed first or the processing of packets by the server is guaranteed in the order of packets received. However, the existing systems do not honor the time due to the layered network stack. The disclosure is a design and implementation of a middleware framework on FPGA platform which delivers messages to the application in the order in which they arrive. The disclosure enables time sensitive analysis of each message of the TCP segment based on the session-based information to re-assemble the plurality of messages in a time-sensitive queue.
State of art techniques for color regeneration are complex and fail to provide color control. Embodiments of the present disclosure provide a method and system for generating color variants for fashion apparels by providing a Fashion Apparel Regeneration-Generative Adversarial Network (FAR-GAN) to generate color variants of the fashion apparels. The FAR-GAN utilizes a two-step encoding process to encapsulate both an input image and an edge-map information along with a target color embedding branch to manipulate the color information present in the fashion apparel present in the input image that is to be changed to a desired target color. Furthermore, the color and structural information is disentangled by controlling them using a color consistency loss. The FAR-GAN can be trained end-to-end without incorporating complex multi-step process.
A widening gap between the emerging technology curve and technology adoption curve of businesses constitute the technology debt for an enterprise. Embodiments herein provide a method and system to identify technology debts and propose recommendations which may remediate the risk associated with technology debts. The system enables enterprises to take stock of their IT landscape across application and infrastructure that are running the risk of becoming obsolete. The system analyses various critical dimensions of an IT environment across various infrastructure and application components to arrive at the technology debts associated with each domain. The system provides a risk scoring mechanism that combines risk scoring parameters, past impact due to the identified technology debt, obsolescence component percentage in every technology area, security vulnerabilities present in the technology landscape, and critically of the application workload running on obsolescent technology component. The system makes recommendations to mitigate risk associated with identified technology debts.
The embodiments herein provide a method and system for an augmented reality based indoor navigation. The system creates an augmented reality (AR) based fingerprinting to perform the dynamic routing. A floor wise plan of the building is uploaded. The floor plan is converted to a floor plan graph. Then cloud anchors are created and placed across the floor. An anchor is a fixed position and orientation in the real world as recognized by any Augmented Reality (AR) device. Each time an anchor is placed, its position information is fingerprinted on the floorplan graph and that fingerprinting is saved in the database. When user scans for nearest QR code to identify their current position, the user location is identified and the planogram details, fingerprinting and pre-processed floorplan matrix are obtained from database. When user selects his destination, dynamic routing is performed to obtain shortest/optimized path viewed through augmented reality fingerprinting.
G06T 11/20 - Drawing from basic elements, e.g. lines or circles
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G01C 21/20 - Instruments for performing navigational calculations
G06K 7/14 - Methods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
98.
METHOD AND SYSTEM FOR ADVERSARIAL MULTI-ARCHITECTURE BASED DELAY PREDICTION IN SCHEDULED TRANSPORTATION NETWORKS
The present disclosure predicts a delay associated with a vehicle. Conventional methods are mainly mathematical based and machine learning based networks are not predicting delay accurately. Initially, the present disclosure Initially, the system receives a user query comprising an expected delay of a target vehicle in at least one target station. Further, a real time data associated with the user query in a predefined horizon is obtained. Further, a spatial feature vector, a temporal feature vector and spatiotemporal features are extracted based on the real time data using a feature extraction technique. Finally, the expected is predicted based on the plurality of features using a trained adversarial regression model, wherein the trained adversarial regression model comprises a critic network and a regressor network. The regressor network is trained with a plurality of architectures and a best architecture with minimum Mean Absolute Error (MAE) is selected for delay prediction.
G06F 16/9537 - Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
99.
ESTIMATING BLOOD PRESSURE OF A SUBJECT USING AN ECG DRIVEN CARDIOVASCULAR MODEL
This disclosure relates generally to in-silico modeling of hemodynamic patterns of physiologic blood flow. Conventional cardiovascular hemodynamic models depend on neuromodulation schemes (baroreflex autoregulation) and threshold parameters of neuromodulation correlate with physical activities. Thus these models may not work practically for a large set of people due to dependency on prior knowledge of these parameters. The present disclosure enables estimating blood pressure of a subject by estimating cardiac parameters based on the morphology of ECG signal associated with the subject and hence activation delays in cardiac chambers of the in-silico model is reproduced purposefully. In accordance with the present disclosure, the blood pressure of the subject can be estimated using only the ECG signal even if the signal is missed for some time instance(s) or is noisy.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/366 - Detecting abnormal QRS complex, e.g. widening
Migrating application from on premise HPC cluster to serverless platform is tedious task and involves significant amount of human efforts as cloud infrastructure needs to be created, data along with libraries and application code need to be copied from on-premise to cloud, and application need to be made compliant for execution on cloud. Present disclosure provides method and system for performing automated migration of high performance computing application to serverless platform. The system first check cloud readiness of application based on operation qualification parameters of application. In case application is found to be cloud ready, the system determines whether application can be executed on serverless platform based on execution time of the application and permissible limits defined for application in service level agreements. Once the application is found to be executable on the serverless platform, the system performs automatic migration of the application to serverless platform using infrastructure automation engine.
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