Tata Consultancy Services Patente

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Traditional infrastructure monitoring systems have the disadvantage that they either fail to consider dependency between metrices or consider the dependency only at an abstract level, which adversely affects efficiency with which a performance assessment of the infrastructure being monitored can be carried out. Another disadvantage of the existing systems is that manual intervention is required at different stages of the infrastructure monitoring. The disclosure herein generally relates to infrastructure monitoring, and, more particularly, to a method and system for identifying correlation between metrices in the infrastructure. The system uses a hybrid correlation approach which considers determining a dominant correlation from among a direct correlation and a chained correlation that may exist between each pair of metrices being considered at a time for determining the correlation.

The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.

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This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.

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This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNN model having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features

This disclosure relates to portable speckle imaging system and method for automated speckle activity map based dynamic speckle analysis. The embodiments of present disclosure herein address unresolved problem of capturing variations in speckle patterns where noise is completely removed and dependency on intensity of variations in speckle patterns is eliminated. The method of the present disclosure provides a correlation methodology for analyzing laser speckle images for applications such as seed viability, fungus detection, surface roughness analysis, and/or the like by capturing temporal variation from frame to frame and ignoring the intensity of speckle data after denoising, thereby providing an effective mechanism to study speckle time series data. The system and method of the present disclosure performs well in terms of time efficiency and visual cues and requires minimal human intervention.

This disclosure relates generally to methods and systems for real time unobtrusive monitoring of physiological signals of a subject confined to a bed. Output of single channel continuous wave (CW) radars are dependent upon distance of the subject from the radar. Dual channel IQ radars are more accurate but are costly and availability in the market is a constraint. The present disclosure provides a cheap and easily replicable pseudo IQ radar based system. The pseudo IQ radar comprises two CW radars placed at a calibrated distance from each other such that optimum points of one CW radar spatially overlaps null points of the other CW radar and phase imbalance is suppressed. Three such pseudo IQ radars are positioned in a predetermined configuration around the subject being monitored. A Supervised Complex Signal Demodulation (SCSD) method configured to suppress amplitude and DC imbalance is also provided for evaluating the physiological signals.

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This disclosure relates generally to assessing soil carbon sequestration. One of the driving factors for climate change and global warming is emission of greenhouse gas emissions. Of the possible ways to reduce climate change and global warming adoption, sustainable agricultural practices will enable efficient soil carbon sequestration thereby reducing the greenhouse gases as well as increasing the crop yield. The disclosure is a method and a system for real time assessing soil carbon sequestration of farm based on remote sensing. The soil carbon sequestration of farm is assessed by continuously monitoring the farm at real time based on remote sensing using a plurality of satellite data and a plurality of farming data using several techniques. The several techniques utilized for assessing soil carbon sequestration includes machine learning, a carbon sequestration estimation technique, estimating a crop health index and an adoption index and computing a set of carbon sequestration parameters.

This disclosure relates generally to relates to the field of estimation of remaining useful life (RUL) in lithium based batteries, and, more particularly, to estimation of remaining useful life in lithium based batteries based on coupled estimation of a state of charge (SOC) and a state of health (SOH) during charging/discharging in constant current (CC) and constant voltage (CV) modes. The disclosed RUL estimation technique considers the inter-dependency of SOC-SOH and influence of internal-external parameters/factors during the coupled estimation of SOC-SOH. The coupled estimation of SOC and SOH is based on a reduced order physics based modelling technique and considers the influence real time environment obtained using real-time dynamic data obtained by several sensors during the coupled estimation of SOC-SOH.

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This disclosure relates generally to method and system for enhancing Quality of experience (QoE) for rendering live interactive videos in real-time. The conventional methods utilize an additional field in packet header to determine which packet sequences were lost, so that the receiver could request a retransmission. Due to unpredictable delay and loss, the receiver may not get the end-of-frame packet which has been sent using best-effort. The disclosed method and system introduced an additional indicator to identify an expected maximum number of packets transmitted for each frame, and further determine the distribution of loss so that the rendering unit can intelligently decide on whether to allow the frame to display, or to drop it without using any additional retransmission or error-concealment strategy, thereby enhancing the QoE of reception at the receiver.

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Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

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Applications such as interworking between mission critical push-to-talk (MCPTT) and terrestrial trunked radio (TETRA) networks is not feasible due to absence of vendor-independent protocol for location information exchange between the MCPTT and TETRA networks. The disclosure herein generally relates to telecommunication networks, and, more particularly, to a method and a network for location information exchange between the MCPTT and TETRA networks. Protocols are defined for facilitating location information exchange between a MCPTT server and a TETRA location server, for various triggers including Periodic, CellChange, Immediate Request, PowerOn, and EnterService. In the event of any of these triggers, an Interworking Module (IM) situated between the MCPTT server and the TETRA location server performs mapping between Session Initiation Protocol (SIP) messages from the MCPTT and Location Information Protocol (LIP) messages from the TETRA location server to facilitate the location information exchange between the MCPTT server and the TETRA location server.