Abstract: The disclosure relates to anomaly detection in an industrial environment including multiple industrial units and systems, generating huge volume of data. The conventional methods rely only on sensor data alone. The techniques of handling missing data plays a crucial role in determining the performance of industrial anomaly detection system. Further, imputation of missing data could cause error in computation, thus affecting the accuracy of the industrial anomaly detection system. The present disclosure addresses the problems associated with missing data by utilizing a masking technique. Further, the present disclosure utilizes quantitative and qualitative metadata associated with industrial system along with the sensor data to improve anomaly detection performance. Furthermore, the present disclosure includes a model recommendation system which provides transfer learning based utilization of existing models for similar industrial systems.

Abstract: The quality of user experience (UX) of an information visualization depends on multiple diverse aspects. These include qualitative, quantitative, and contextual parameters that are unmeasurable and incomparable. Hence, measuring the UX of a visualization is challenging. The disclosure herein relates to a system and method that collects, processes, and analyzes a multiple diverse parameters to measure and profile the UX of a visualization. To accomplish this, the system collects data regarding the usage, effectiveness, and user perception of the visualization. The system creates a quantitative and comparable version of all these parameters to measure holistically the UX of the visualization.

Abstract: This disclosure relates to a system and method to autonomously manage hybrid IT infrastructure. An end-to-end, integrated, and autonomous IT infrastructure is suggested to offload the repetitive business as usual (BAU) operational tasks, thereby reducing operational cost, noise, and chaos, improve resiliency, thus improving availability of the business. The autonomous IT infrastructure leads to bring in efficiency to customer business, to reduce incident reduction, optimize cost and to provide insight into any future IT infrastructure need. Herein, one or more key characteristics that make the IT infrastructure autonomous includes auto sensing an environment of the infrastructure, learning the infrastructure behavior, predicting one or more events, determining a course of action, and performing one or more actions with minimal or no human intervention.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: Sustainability measurement is critical to determine whether industry performance is heading in intended direction. State of the art systems in the field of sustainability measurement fail to consider many parameters which are indicative of the sustainability of industries. The disclosure herein generally relates to industry monitoring, and, more particularly, to a method and system for sustainability measurement in an industrial environment. The system calculates similarity score which indicates similarity between different sentences and indicators, and used the calculated similarity scores and extracted features to classify the sentences as belonging to specific classes. This information is in turn used for measuring sustainability of organization from which input data have been collected.

Abstract: Embodiments provide a system and method for statistical subject identification. The system takes texts, videos, audios, and images as input for which subject needs to be identified. The system pre-process input data and generates n-grams and pre-processed text strings by removing stopwords, punctuations, selective POS tags and lemmatization. Frequency distribution of n-grams are computed, and weightage of n-grams is assigned. For each n-gram, sum of weights across all text strings is computed and a maximum weightage is identified. The computed value as a result of taking a ratio of two, is assigned to each of the n-grams. Values computed for the n-grams have a non-normal distribution, when observed statistically. Thus, the n-gram values are transformed to confidence value following a normal distribution. The system maps the n-gram domains using a domain lexicon. Finally, these domains are aggregated and converged for subject identification based on a pre-annotated mapping dictionary.

Abstract: This disclosure relates generally to a method and a system for profiling of metagenome samples. Most state of-art techniques for metagenomic profiling use homology-based, curated database of identified marker sequences generated after complex and costly pre-processing. The disclosed method and system for profiling of metagenome samples are a non-homology based, a non-marker based and an alignment free strain level profiling tools for microbe profiling. The disclosure works with a several k-mer based indexing techniques for constructing a compact and comprehensive multi-level indexing, wherein the multi-level indexing includes a L1-Index and a L2-Index. The multi-level indexing is used for profiling metagenomics by abundance estimation, wherein the abundance estimation includes a relative abundance and an absolute abundance.

Abstract: A system and method for recommending intelligent railway network access plans and modified access plans based on current status of execution of tasks and train timetables. Herein, the system and method need to consider multiple variables including train timetable changes, activity efficiencies etc. to derive an overall optimum access management solution for the railway network. They can interface with existing operational timetables and cost management systems by using existing data sets for operations and cost data. The cost data is manipulated to identify fixed and variable costs and variability of variable costs with access duration and activity bundling. A trade-off between cost and value is considered which results in a longer continuous time window being available for maintenance, less visits to same location to complete a maintenance task, less time spent in setup/unwind activities and higher labour utilization.

Abstract: In the world of digital advertising, optimally allocating an advertisement campaign within a fixed pre-defined budget for an advertising duration aimed at maximizing number of conversions is very important for an advertiser. Embodiments of present disclosure provides a robust and easily generalizable method of optimal allocation of advertisement campaign by formulating it as a constrained Markov Decision Process (MDP) defined by agent state comprising user state and advertiser state, action space comprising a plurality of ad campaigns, state transition routine and a cumulative reward model which rewards maximum total conversions in an advertising duration. The cumulative reward model is trained in conjunction with a deep Q-network for solving the MDP to optimally allocate advertisement campaign for an advertising duration within a constrained budget.

Abstract: This disclosure relates generally to a method and system for online handwritten signature verification providing a simpler low cost system. The method comprises extracting signature data for the subject from a sensor array for the predefined time window at regular predefined time instants. Further, differentiating the matrix row wise and column wise to generate a row difference matrix and a column difference matrix. Further, determining an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix. Further, determining a plurality of signature parameters based on the row difference matrix and the column difference matrix.