Tata Consultancy Services Patente

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.

Existing multistatic configurations of Radar systems requires a direct LoS signal and/or time synchronization among the Radar transmitter and the multistatic distributed Radar receivers. The present disclosure provides a phaseless frequency-modulated continuous-wave multistatic Radar (PFMR) imaging that relaxes requirement of the direct LoS signal and only requires a plurality of parameters of a FMCW signal comprising a chirp signal rate, a carrier frequency and, a period of chirp to be known. Further, it also removes condition of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers. However, because of absence of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers, an unknown random phase offset appears after deramping. The present disclosure eliminates the unknown random phase offset, by performing autocorrelation function on a mixed signal, resulting in a phaseless measurement data corresponding to a plurality of FMCW Radar imaging signals.

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Load disaggregation is useful for both the consumers and producers of energy. The present-day supervised learning models for load disaggregation necessitate the learning of models for every appliance load of interest, which incurs high computational costs. Embodiments of the present disclosure implement a Restricted Boltzmann Machine (RBM) based source-separation model with application to load disaggregation of appliances of interest. Representations of appliance of interest are learnt, between the power aggregate data and the appliance signatures, to output the mapping of data representations on the appliance signatures, for load disaggregation. Discriminative ability for each load/appliance of interest is achieved by adding the free energies of softmax layers of the RBM on other loads/appliance, as a discriminating gradient to the approximate gradients obtained on the load under consideration.

While performing heart rate estimation of a user, if the user is in motion, measured signal is prone to have noise data, which in turn affects accuracy of estimated heart rate value. Disclosed herein is a method and system for heart rate estimation when the user is in motion. The system estimates value of a noise signal present in a measured PPG signal by performing a Principle Component Analysis (PCA) of an accelerometer signal collected along with the PPG signal. The system further estimates value of a true cardiac signal for a time window, based on value of the true cardiac signal in a predefined number of previous time windows. The system then estimates frequency spectrum of a clean PPG signal based on the estimated noise signal and the true cardiac signal. The system further performs heart rate estimation based on the clean PPG signal.

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Non-invasive methods for accurately classifying Coronary Artery Disease (CAD) is a challenging task. In the present disclosure, a two stage classification is performed. In the first stage of classification, a metadata based rule engine is utilized to classify a subject into one of a confirmed CAD subject, a CAD subject and a non-CAD subject. Here, a set of optimal parameters are selected from a set of metadata associated with the subject based on a difference in frequency of occurrence of the CAD among a disease population and a non-disease population. Further, an optimal threshold associated with each optimal parameter is calculated based on an inflexion based correlation analysis. Further, the CAD subject, classified by the metadata based rule engine is further reclassified in a second stage by utilizing a set of cardiovascular signal into one of the CAD subject and the non-CAD subject.

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There is a need for design and implementation of interfaces for providing user-friendly feedback while creating and updating compliant passwords. This disclosure relates to a method of creating compliant password by in-place feedback to a password composition policy (PCP). A policy-enabled-virtual keyboard (PKBD) receives input from a user, which is processed based on parameters associated with the PCP to identify accessible and inaccessible keys on the PKBD with in-place feedback. The in-place feedback is provided to highlight accessible keys of the PKBD for the user if a class associated with the characters received or a number of characters mandated by the PCP is already covered. On the other hand, keys of the PKBD are disabled for access to the user if it is determined that a class is not covered by the characters received and there is a deviation of the parameters. A compliant password is created based on the PCP by providing in-place feedback for a resultant validation.

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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 an 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 a training data. The one or more descriptive copies associated with grocery products are generated by mapping the validated vocabulary model with the training data.

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In industrial inspection scenarios, early detection of machine malfunction is extremely essential as it helps in preventing any significant damage and the associated economic losses. Embodiments herein provide a method and system for an acoustic based anomaly detection in industrial machines using a beamforming and a sequential transform learning. Herein, the system employs two-stage multichannel source separation technique that uses the well-known delay and sum beamforming followed by a recent data-driven sequential transform learning (STL) approach to obtain clean sources. The STL is a solution to linear state-space model where operators/matrices are learnt from data and is used here to model the dynamics of time-varying source signals for source separation. Subsequently, a reference template matching is employed on each separated source to detect an anomaly. The numerical results obtained with the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset demonstrate superior performance for source separation and anomaly detection.

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Continuous monitoring of subject's cardiac system using biological signal(s) (BS) during day-to-day activities is essential for managing personal cardiac health/disorders, etc. Conventional systems/methods lack in improvising overall classification results and configured for specific device/signal say ECG or PPG and so on. Present disclosure provides systems and methods for classifying BS obtained from users, wherein BS are preprocessed to obtain filtered signals (FS). Corresponding feature extraction module is utilized for feature set extraction based on features in FS. The feature set is reduced and segmented into test and training data. Biological signal classification model(s) are generated using training data and a BCM is applied on test data to classify biological signals (BS) as one of Atrial Fibrillation (AF), a non-AF, a cardiac arrythmia disorder, or ischemia. Accelerometer features of connected device associated with the users can be obtained to detect activities which in conjunction with the BCM's output improvises above classification.

This disclosure relates generally to system and method for identifying suitable equipment for pelletizations of particles. Conventional approaches for identifying pelletization equipment for a process are cumbersome and unmethodical. Even though, methods of identifying optimal operating conditions in a particular equipment for a desired product specification are widely discussed in literature, these research works lack in considering practical criteria like equipment cost, equipment capacity, equipment maintenance, etc., in addition to product specifications while deciding a suitable pelletization equipment. Present disclosure provides system and method that involve optimization to identify the best range of operating parameters in a pelletization equipment to achieve desired product size distribution for a given feed size distribution. The method is then used in a system for comparing different pelletization equipment based on different criteria and identifying a suitable pelletization equipment amongst them for use in obtaining pellets with desired product attributes.

This disclosure relates to a method and system for hybrid data augmentation for estimating performance degradation in industrial plant. Performance degradation in industrial plants cannot be measured by sensors or laboratory measurements and there are no methods to annotate performance degradation state. The embodiments of the present disclosure provide a knowledge-based data augmentation that use physics based information to model performance degradation. The disclosed method augments high fidelity data with knowledge-based methods into high and low confidence data which are used to calculate performance score of high confidence data. A physics-informed machine learning model is trained on high confidence data. The resulting model is then used to predict performance score for low confidence data. The model is further used for training prognostics and diagnostics models to predict and identify root causes responsible for performance degradation. The disclosed method is used for predicting ring formation inside the sponge iron kiln.