Tata Consultancy Services Patente

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There is a need to address the problem of compressed scanning in SAR acquisitions for microwave imaging. Embodiments of the present disclosure provide microwave imaging with multiple-input and multiple-output synthetic aperture radar using compressed multi-coset range migration technique. The present disclosure reduces the SAR acquisition time by compressed scanning, where a compressed scanning acquisition setup captures radar measurements to obtain a microwave image of a target scene by intermittently skipping blocks during SAR acquisition. Further, reconstruction of a microwave image of a target scene is performed using 2D Fourier Transform (FT) of the radar measurements based on a multi-coset range-migration framework. The multi-coset range migration framework makes use of intermittent scanning and formulates a compressed sensing-based architecture by leveraging block-sparsity constraints. Subsequently, a denoising convolutional neural network (DnCNN) is used to enhance and denoise the reconstructed microwave image to obtain a high-resolution image of the target scene.

This disclosure relates generally to system and method to track performance of an AI application during development and production lifecycle. Libraries and tools that are used to develop an enterprise AI application are rapidly expanding across vendors and open-source community. These libraries are coming up with new features and breaking changes with new versions making it difficult to application developer and enterprise runtime executor. The method of the present disclosure receives an enterprise artificial intelligence (AI) application and corresponding software environment as input generate a logger plug-in using one or more fine-tuned large language models (LLMs) based on the plurality of instructions provided by the one or more instructors. Additionally, the logger plug-in is utilized to validate correctness of errors and then the logger plug-in is executed with the enterprise AI application to log and track performance of the enterprise AI application.

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Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.

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Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA. Predicting potential NPAs includes obtaining current season satellite data, data received from the fields via sensors, determining crop protocol being followed, forecast weather, estimating cumulative growth, feedback received from fields, projected yield, estimating crop losses, and then performance score is generated for predicting crops as potential NPA.

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Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

State of art techniques relate to telepresence robots expressing internal states using emotional expressions and corresponding text messages are based on the perceived input from the co-located user or the participant, and not related to the teleoperator. Embodiments of the present disclosure provide a method and system for expressing telepresence robot internal states using combination of multiple modalities. The telepresence robot expresses its own internal states in the form of a plurality of emotional expressions and corresponding text messages to the teleoperator, using a robot internal state predictor model. Unlike the state of art techniques which express an emotion as its internal state, the disclosed method expresses the internal state as the emotional expression with respect to a task processing of the telepresence robot.

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This disclosure relates generally to system and method for molecular property prediction. The method utilizes a set-pooling aggregation operator to derive a graph-level representation of a complete input molecular graph to assist in inductive learning tasks. The method includes iteratively down-sampling the molecular graph into a coarsened molecular graph, and determining adjacency matrix and feature matrix of the coarsened molecular graph. The method then includes computing an average of the hidden state node attributes of the coarsened graph obtained after preforming the iterations to obtain a graph level representation vector of the molecular graph. Using a linear layer from the graph level representation vector the molecular properties are determined.

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Providing the right mechanism to apply blockchain technology with flexible or dynamic credit disbursement while also addressing valuation of the agricultural assets that are dynamic in nature is required. A method and a system for estimating flexible credit eligibility and disbursement schedule using NFTs of agricultural assets is disclosed. A detailed blockchain based approach is provided for evaluating credit collateral of the NFTs representing the agricultural assets, also interchangeably referred to as physical farm assets or agri-assets. Valuation of the credit eligibility is based on flexibility provided to the farmer on deciding the farm assets to be used, current status of the farmer selected agricultural assets and credit equivalent value of the each of NFTed asset selected by the farmer for the season/year. The dynamic disbursement schedule is based on the personalized or customized parameters associated with the current status of farm and farm assets linked to the credit collateral.

Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph readout layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.

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This disclosure relates generally to method and system for structure-based drug design using a multi-modal deep learning model. The method processes a target protein for designing at least one optimized molecule by using a multi-modal deep learning model. The GAT-VAE module obtains a latent vector of at least one active site graph comprising of key amino acid residues from the target protein. The SMILES-VAE module obtains at least one latent vector from the target protein. Further, the conditional molecular generator concatenates the active site graph with the latent vector to generate a set of molecules. The RL framework is iteratively performed on the concatenated latent vector to optimize at least one molecule by using the drug-target affinity (DTA) predictor module to predict an affinity value for the set of molecules towards the target protein. Further, at least one optimized molecule is designed with an affinity of the target protein.

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Embodiments herein provide automated bin packing of heterogeneous objects to fill maximum number of objects in an available bin space in an efficient manner. Conventional methods are less efficient in packing heterogeneous objects in a space and time efficient manner. The apparatus receives a plurality of 3D objects through a primary conveyor and diverts each of the plurality of 3D objects to an identified cuboidal bin through a corresponding secondary conveyor. Each of the plurality of 3D objects are diverted based on a tracking code associated with each of the plurality of 3D objects and a computed available bin space of the identified cuboidal bin corresponding to each of the plurality of 3D objects. Here the available bin space is computed based on a bin space information received from a corresponding bin vision sensor and a corresponding state space of the identified cuboidal bin.

State of the art telescope designs require increasing number of sub-apertures for optimum performance, however, with the increasing number of sub-apertures, number, and amplitudes of the sidelobes increase along with that of the primary maxima, resulting in a trade-off of the imaging quality. Disclosed herein are three configurations using a central sub-aperture and a plurality of peripheral sub-apertures, encompassing the central sub-aperture. Size of the central sub-aperture and the plurality of peripheral sub-apertures is in a proportionate relationship. Further, the plurality of the peripheral sub-apertures forms at least two concentric zones, wherein each concentric zone has equal number peripheral sub-apertures from among the plurality of peripheral sub-apertures, and the sizes of the peripheral sub-apertures in each two adjacent concentric zones have a proportionate relationship. This way there is significant side lobe suppression compensating for the imaging performance loss due to reduced aperture area.

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This disclosure relates generally to system and method for molecular property prediction. The disclosed method includes mapping node embeddings of a molecular graph to a graph-level embedding characterizing the molecular graph. The graph level representation is acquired by pooling characteristics of hidden states of the nodes in the molecular graph by performing an iterative content based attention in a plurality of iterations. The content based attention is performed by considering an edge information fused transformed hidden state vector of the nodes of the molecular graph. The graph level embedding is fed through the linear projection to predict the molecular properties of the molecular graphs.