Tata Consultancy Services Patente

Existing electricity consumption prediction approaches depend largely on data-based models, which may be statistical techniques or more precisely time-series predictions that predict based on the auto-regressive nature of the load curve with a few external variables at best, such as calendar events and ambient temperature. While these models are effective to a certain degree for the overall grid level requirements, they may not be able to predict disruptive changes that may happen over longer periods of time, such as the demographic shifts, etc. Method and system disclosed herein predict the electricity consumption by taking into consideration various parameters associated with such disruptive changes, and then predict the electricity consumption in a target area by aggregating the electricity consumption predicted at the agent level.

Retailers need information about their competitor's pricing and promotions and marketing collaterals are one of the most vital sources of this information. Conventional approaches for extracting product names from marketing collaterals depends on large volume of data repositories and complex machine learning based approaches. The present disclosure extracts product name blocks from marketing collaterals using image processing techniques. The inputs to the present disclosure are seed words and the marketing collateral. A plurality of word level text regions from the image and text value are obtained. Further, a plurality of text characteristics corresponding to each of the plurality of word level text regions are extracted and matching seed word regions are obtained. Further a plurality of meaningful text blocks and a plurality of seed blocks are obtained. Finally, a plurality of product names are extracted using a matrix based product name detection technique and updated in the product dictionary.

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The present disclosure discloses device and method for detecting objects placed at multiple ranges from vehicle. Images of the objects may be captured by an image capturing unit housed in the vehicle. The image may be splitted into plurality of sub-images. Further, one or more features may be extracted from the plurality of sub-images. Further, each of the plurality of sub-images may be simultaneously processed for computing gradients associated with the plurality of sub-images. Further, a cell histogram may be created by casting weighted vote for an orientation based histogram channel based on values associated with the gradient. The gradients computed may be normalized by grouping the cells in spatial blocks. Further, a Support vector Machine (SVM) linear classifier may be applied on the plurality of sub-images in order to classify the near object and the far object in a category of a pedestrian or a vehicle.

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Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem. Embodiments of the present disclosure provide systems and methods that are robust and more efficient for classifying rhythms for example, normal, AF, other abnormal rhythms and noisy ECG recordings by implementing a spectrogram based noise removal that obtains clean ECG signal from an acquired single-lead ECG signal, an optimum feature selection at each layer of classification that selects optimum features from a pool of extracted features, and a multi-layer cascaded binary classifier that identifies rhythms in the clean ECG signal at each layer of the classifier.

Rotary knifes/cutters play an important role in manufacturing of finished products. The rotary cutters tend to lose their cutting material over time. Hence to compensate, pressure applied by cylinder over rotary cutter needs to be changed. But this change in pressure needs to be optimum as too high pressure can lead to loss of material and too low pressure can stop cutting operation. Present application provides methods and systems for real time estimation of pressure change requirements for rotary cutters. The system first determines minimum and maximum usage limit for rotary cutter based on historical rotary cutter usage data and real-time pressure value using first trained model. The system, upon determining that minimum usage limit is reached, determines time for next pressure change based on physical parameters using second trained model. Thereafter, system compares estimated time with estimated maximum usage limit and displays notification to change pressure based on comparison.

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Predictive regression models are widely used in different domains such as life sciences, healthcare, pharma etc. and variable selection, is employed as one of the key steps. Variable selection can be performed using random or exhaustive search techniques. Unlike a random approach, the exhaustive search approach, evaluates each possible combination and consequently, is a computationally hard problem, thus limiting its applications. The embodiments of the present disclosure perform i) parallelization and optimization of critical time consuming steps of the technique, Variable Selection and Modeling based on the Prediction (VSMP) ii) its applications for the generation of the best possible predictive models using input dataset (e.g., Blood Brain Barrier Permeation data) and iii) business impact of predictive models that are requires the selection of larger number of variables.

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.

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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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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.

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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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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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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.

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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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