Tata Consultancy Services Patente

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Millimeter(mm) waves, in comparison to microwaves, have short wavelengths and can penetrate to few centimeters inside the body. The embodiments herein provide a method and system for millimeter (mm) wave synthetic aperture radar (SAR) imaging for superficial implant monitoring. The mmWave SAR and consecutive an autofocusing SAR imaging are suitable for a superficial tissue and subsequent continuous implant monitoring due to their smaller form-factor and faster processing coupled with focused dielectric lens. Additionally, a limb topography is approximated for localization of implant region on interest (ROI) in the SAR amplitude image. Further, the method and system provide a bone implant monitoring in order to assess any unwanted mobility or dislocation of the implant, and thus bone health is a critical issue.

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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.

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This disclosure relates generally to automatic conversion of text-based privacy policy (PP) to video format. Typically, the PP are cognitively loaded with the content and hence difficult to read and understand. The disclosed method and system facilitate in automatic conversion of text-based PP to video format. Said video formats may incorporated in animated forms for ease of understanding of the user. The disclosed system identifies segments from target PP and maps them to template PP segments stored in a template repository. The template PP segments selected from the template repository are mapped to short video templates or snippets stored in a video repository to identify video templates representative of the selected template segments. The selected video templates are sequenced in an optimal manner considering preferences and dependencies to obtain the portions of or complete target PP.

This disclosure relates generally to method and system to generate pricing for charging electric vehicles. With Electric vehicles becoming more mainstream, public chargers may not be able to match demand supply without extensive deployments. The present disclosure dynamically generates pricing policies to maximize aggregator revenue based on a stochastic model constraints and user behavioral models. The system involves three primary stake holders comprising a demand side having EV users requesting efficient charging at lowest possible price, a supply side which includes a public or private EVSE operators, and an EV charging aggregator which acts as intermediator between the demand side and the supply side. The EV charging aggregator having an RL agent receives user requests to generate pricing based on a tentative demand pool, an actual demand pool, and a service pool. The reward to the RL agent is the total revenue obtained in that timestep of the control action.

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This disclosure relates more particularly to risk assessment of preterm delivery (PTD) in the subject and designing a personalized recommendation for the same. Conventional techniques for PTD risk assessment are either invasive or minimally invasive and leaves little time for subjects to take precautionary or corrective medical advice or procedures to reduce or obviate the risk. The present disclosure provides the risk assessment of the PTD, by quantifying a microbial abundance in oral or gut microbiome for a pregnant woman, identifying a certain combination of microbial biomarkers using an ensemble of models for accurate risk assessment of the PTD and subsequently suggesting a personalized recommendation for at risk subject. The present assessment technique is completely non-invasive and further helps in characterizing the risk of the PTD.

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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.

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The present disclosure provides a federated learning based identification of non-malicious classification models where the conventional model fails to perform. Initially, the system receives a local classification model from each of a plurality of clients. Further, a set of one-dimensional arrays are obtained based on a plurality of local classification models associated with the plurality of clients using a flattening technique. Further, a major cluster and a minor cluster are obtained by clustering the set of one-dimensional arrays using a clustering technique. After clustering, a plurality of active classification models are selected based on the major cluster and the minor cluster using an epsilon cluster selection technique. Further, a global classification model is selected from the plurality of active models using a random selection technique. Finally, the selected global classification model is transmitted to each of the plurality of clients.

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Direct usage of endosomatic EDA has multiple challenges for practical cognitive load assessment. Embodiments of the method and system disclosed provide a solution to the technical challenges in the art by directly using the bio-potential signals to implement endosomatic approach for assessment of cognitive load. The method utilizes a multichannel wearable endosomatic device capable of acquiring and combining multiple bio-potentials, which are biomarkers of cognitive load experienced by a subject performing a cognitive task. Further, extracts information for classification of the cognitive load, from the acquired bio-signals using a set of statistical and a set of spectral features. Furthermore , utilizes a feature selection approach to identify a set of optimum features to train a Machine Learning (ML) based task classifier to classify the cognitive load experienced by a subject into high load task and low load task.

This disclosure relates generally to material quality inspection. Conventional approaches available for material quality inspection are unable to address concerns of complexity and cost involved. The technical problem of occluded object detection and material quality inspection for intrinsic defects identification is addressed in the present disclosure. The present disclosure provides a system and method for non-intrusive material quality inspection using three-dimensional monostatic radar based imaging, where the object under inspection undergoes a circular translation motion on a rotating platform. A modified delay-and-sum (m-DAS) algorithm is built by incorporating virtual antenna array to obtain a 3D image reconstruction of the object. From 3D reconstructed images, radial displacement as well as the angular locations of the object is identified which are further used for quality inspection of the material comprised in the object.

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Currently teleconferencing robotic systems available are not smart and unable to navigate based on specific path and fail to focus on presenter based on overall environment. This disclosure relates to method of dynamic localization of a telepresence robot based on plurality of live markers. A plurality of images is received from an image capturing device connected to the telepresence robot. The plurality of images is processed to identify the plurality of live markers in a path of the telepresence robot. A binary matrix is decoded to identify at least one identifier (ID) associated with the at least one live marker from the plurality of live markers. A plurality of parameters is identified based on the at least one ID associated with the at least one live marker. A further path is dynamically localized to navigate the telepresence robot based on the plurality of parameters and the plurality of live markers.

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