Tata Consultancy Services Patente

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Retail stores have limited visibility of on shelf inventory. Conventional approaches for targeted replenishment are reactive in nature and are also infrastructure and labor heavy. Present disclosure provides systems and methods for identification and replenishment of targeted items on shelves of stores wherein input data pertaining to sales of items is pre-processed and stock keeping unit (SKU) wise optimal bucket size is determined for predicting sales events for individual SKU based on historical events. Top-up requests are generated for each SKU for the planning bucket sizes and further a pick-up list using smart batching of the top-up requests is created based on SKU priorities. The pick-up list and top-up requests are executed to ensure items are topped up at the right time. Further, rate of sales or forecast the rate of sales are continually monitored throughout the day to ensure items are identified for targeted replenishment in retail stores.

Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance. Information on fine-tuning parameters of the subsystem and reward function are used for training RL agents.

Conventionally, mechanical methods of measuring linear position over a scale required physical coupling with system(s), such as potentiometer-based feedback etc. Such kinds of mechanical feedback mechanisms need geared coupling. And along with a mechanical system comes problems of wear and tear of parts which can lead to increased inaccuracy of the setup over time. With increased capabilities of computer vision-based techniques, a non-contact image-based measurement of linear position has become of prime importance. Embodiments of the present disclosure provide system and method that implement direct visual measurement of liquid level inside a linear measurement setup (syringe here) by employing various techniques such as computer vision, machine learning techniques, and the like, wherein various features are extracted that in turn allow to have a measurement of a liquid level identified or indicator (plug/meniscus) position in a syringe/container.

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Developability of a drug candidate is decided based on the Pharmacokinetic (PK) and Pharmacodynamic (PD) parameters of the drug candidate under investigation. Present disclosure provides systems and methods that are implemented using universal PK parameters' bounds and optimization technique(s) to produce robust and optimized set of PK parameters. More specifically, the system and method for estimating optimized set of PK parameters by a) creating universal parameter bounds, b) performing logical operations on universal PK parameters' bounds to create multiple bound combinations c) computing a performance threshold for residual sum of squares (RSS) d) performing global optimization to estimate globally optimized set of PK parameters act as initial PK parameters and e) performing local optimization of initial PK parameters to estimate locally optimized set of PK parameters, the best PK parameters that can used for assessing the developability of drug candidates within Pharmaceutical industry.

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Tracking motion using inertial sensors embedded in commercial grade wearables like smartwatches has proved to be a challenging task, especially if real-time tracking is a requirement. Present disclosure provides system and method wherein data from sensors are obtained and scaled. Further, Euler Rodrigues Matrix (ERM) is generated based delta value obtained using sensor data. The scaled sensor data and ERM are used for generating feature vectors. Windowing technique is applied for subsets of feature vectors to obtain label for each window and machine learning model is trained with the label and window. Further, during real-time, sensor data is obtained, and steps of ERM, feature vectors generation, and application of windowing technique are repeated, and coordinates are estimated for each window in real-time based on which trajectories are tracked in real-time for each window.

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Current solutions in literature use Zero Knowledge Proofs (ZKP) to ensure cryptographic guarantees for account balances. These are highly complex and require large memory. Hence there is a need for a computationally efficient mechanism that can preserve the privacy of customer financial information and provides verifiable cryptographic guarantees for debits and credits of a transaction without revealing customer information. Embodiments of the present disclosure provide a method and system for an inter-bank transaction with privacy enabled auditing and a privacy enabled inter-bank settlements in blockchain network. The method disclosed leverages cryptographic primitives such as Boneh-Lynn-Shacham (BLS) to preserve the privacy of the customer's financial information and to enable faster auditable settlement between the banks in the presence of a governing body.

This disclosure relates generally to bio-signal detection, and more particularly to method and system for non-contact bio-signal detection using ultrasound signals. In an embodiment, the method includes acquiring an in-phase I(t) baseband signal and a quadrature Q(t) baseband signal associated with an ultrasound signal directed from the sensor assembly towards the target. Magnitude and phase signals are calculated from the in-phase and quadrature baseband signals, and are filtered by passing through a band pass filter associated with a predefined frequency range to obtain filtered magnitude and phase signals. Fast Fourier Transformation (FFT) of the filtered magnitude and phase signals is performed to identify frequency of dominant peaks of spectrum of the magnitude and phase signals in the ultrasound signal. Value of the bio-signal associated with the target is determined based on weighted values of the frequency of the dominant peaks of the magnitude and phase signals.

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A composition(s) and method(s) for preparation of hydrophobic coating composition are described. The hydrophobic coating composition includes a formulation of alumina - silica based nano composite and a resin onto which the formulation is dispersed to form the hydrophobic coating composition. The formulation includes silica nano-particles derived from TetraEthoxySilane and HexaDecylTriMethoxySilane, ammonia as catalyst and aluminum iso propoxide as a precursor for synthesis of alumina and a resin onto which the formulation is dispersed to form the hydrophobic coating composition.

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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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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.

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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.

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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.

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.