Tata Consultancy Services Patente

The disclosure herein relates to identification of agro-phenological zones. Further the disclosed method and system also shares techniques for updating of the identified/existing agro-phenological zones. In a diverse geographical domain (like India), in order to maximize the crop production (avoid crop failures) from the available resources and prevailing diverse climatic conditions it necessary to use the resources and technology to infer the best agriculture approach on an individual location. The invention enables identification of the agro-phenological zones based on satellite image, weather data, soil data and cloud free historical satellite data using several techniques that includes machine learning, time series analysis, heuristic time series analysis technique and clustering. Further the invention also discloses techniques to update the identified/existing agro-phenological zones using historic data of agro-phenological zones of satellite image.

Gas turbines are one of leading sources for power generation with lower greenhouse gas emissions. However, due to environmental concerns, gas turbines are moving towards adopting greener fuels. The shift towards greener fuels comes with own set of challenges as performance of gas turbine at different operating points needs to be accurately predicted as experiments are very costly to perform. Existing arts perform their analysis at operating line and performance estimation at other operating points is not specified. Present application provides systems and methods for estimating performance of gas turbine accurately in wide operating region. The system first accurately estimates outlet conditions for each stage of compressor. The system then utilizes estimated outlet conditions to determine outlet conditions associated with other component of gas turbine. The outlet conditions are then utilized to estimate steady state and transient state variables that further helps in identifying optimal process settings for gas turbine.

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State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in an enclosed package. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting liquid food quality in a non-invasive manner. A near infra-red (NIR) radiation is transmitted through a semi-transparent opening configured on an enclosed package containing a liquid food item and the resulting NIR reflection spectra is collected. The quality of the liquid food item is estimated by corelating a plurality of features derived from the NIR reflection spectra with the concentration of the biomarker contained in the liquid food item, using a trained machine learning model and the remaining shelf life of the liquid food item is estimated based on the concentration of the biomarker.

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Decisions related to need to service selection in the multi-cloud environment be taken by the enterprise at the cloud adoption phase as well as post cloud adoption phase. Existing approaches addressing the cloud selection and data placement do not appear to be capable of various requirements/parameters that are to be considered for optimum selection of cloud data centers. Embodiments disclosed here in provide a method and system for carbon-aware storage service selection in the multi-cloud environment, in which the system facilitates selection of storage in the multi-cloud environment by generating a plurality of migration solutions. The migration solutions are generated by the system by selectively prioritizing a) a migration cost, b) an operational cost, and c) a carbon footprint. After migrating the non-compliant UCs by selective prioritization, a list of feasible solutions is obtained. These solutions are used as input to obtain optimal solutions with minimal migration cost, operational cost and carbon footprint lower than the new threshold.

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A system and method for determining a configuration of a plurality of tasks to meet the specified deadline of a linear workflow of a real-time heterogeneous network. Often times, while meeting expected application performance in the heterogeneous network, it may possible to have graceful degradation of quality for ensuring timing constraints at the same time. In a multi-layered architecture, where each layer is equipped with multiple computational resources, the time optimization for each of the plurality of tasks can be achieved through approximate computing and analyzing all possible configurations of each task in a workflow within a particular layer.

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A telecommunication network performs data uplink and data downlink associated with data communication between multiple transmitter-receiver pairs connected to the network. The telecommunication network uses specific scheduling schema as part of the data processing. However, communication between certain transmitter-receiver pairs may be related to critical operations, which in turn demands prioritized scheduling. Disclosed are method and system for performing a delay-aware uplink scheduling in the communication network. For an uplink request received form a transmitter, the system estimates downlink delay at the corresponding receiver side. The system further estimates a processing delay for the received uplink request. Based on the estimated downlink delay and the processing delay, the system determines an uplink delay budget for the transmitter. Further, based on the uplink delay budget and 'an achievable data rate' computed for the transmitter, the system schedules uplink for the transmitter.

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This disclosure relates generally to system and method for assessing insider influence on enterprise assets. Existing work focuses on the detection of insider threat and does not consider the influence of an insider on their peers and subordinates. The present disclosure aggregates and preprocesses the enterprise data specific to the individuals received from various sources, and further creates an enterprise graph between entities. Weights of every edge connected between any two entities in the enterprise graph is then calculated. Community of the individuals are detected wherein, relevant insider(s) are identified, and susceptibility of the individuals for probable influence by relevant insider(s) based on the analysis scenarios(s) is calculated. Paths taken by the relevant insider(s) is calculated for estimating probability of data loss. The present disclosure identifies the assets which are under possible threat from the relevant insider(s), obtains cumulative risk associated with the enterprise and generates an analysis report accordingly.

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The embodiments of present disclosure herein address unresolved problem of key capabilities that an intelligent robot must possess to perform complex and long-horizon object manipulation tasks without human intervention. The present disclosure overcomes above mentioned challenges by decoupling imitation learning task into two parts using a decouple neural network architecture comprising two modules. A high-level module that segments out sequence of primitive actions from video of human demonstration of a complex task and a low-level module that executes those primitives sequentially on robot. The high-level module, Task Sequencing Network (TSNet), is a hybrid neural network composed of Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Connectionist Temporal Classification (CTC) loss, while low-level controller is built using dynamic movement primitives (DMP). The network is trained in a supervised learning setting. The CNN, RNN with CTC combination helps in efficiently segmenting task primitives in sequence for given input video demonstrations.

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The use of mobile telephones have come into widespread use in such a way that day to day life has become very much dependent on them. This results in unintentional or intentional use of mobile phones in the prohibited areas such as examination centers. A method for detecting active radiofrequency wireless communication signal in a region has been provided. The method involves two stages, calibration stage and mobile phone detection stage. In the calibration stage, the frequency on which the mobile towers in that region are operating are identified. The identified frequency is then used to detect the active wireless RF communication using a plurality of software defined radios (SDR) and the existing client systems present in the region. The method further configured to determine the exact location of the mobile phone from the RF communication signal is generated.

In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

The disclosure generally related to a learning-based modelling of an emergent behavior of a complex system. Existing decision-making at complex systems primarily relies on qualitative approaches, which often results in inaccurate outputs. The disclosed system includes a digital twin of the complex system and a digital twin of an environment of said complex system and captures an interaction and dynamic behavior of agents of the digital twins. The agents of the digital twins are simulated and modelled using learning-based models such as RL and genetic algorithms that learns the behavior (i.e. actions and their outcomes) over a period of time. Hence, the agents (or actors) of the digital twins are dynamic in nature. The actor-based bottom up simulation approach is capable of producing sufficient insight for effective decision making prior to implementation.

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Most techniques to estimate the service life of coatings are experimental in nature. Experimental testing of coatings is expensive (destructive in nature) and time consuming. Present disclosure provides system and method that predict change in chemical composition of the coating using reaction mechanism of the coating components in presence of weathering causing environmental agents. The system partial differential equations wherein chemical model is combined with the physical model. The changes in chemical composition enable determining the change in surface roughness and thickness as the weathering of the coating happens which can be correlated with other parameters such as gloss loss, fracture toughness, and the like. Rate of initiation reaction which leads to micro-randomness in concentration profile is randomized to mimic inherently stochastic (random) nature of coating degradation. Service life of coating is further estimated based on values of physical properties of coating beyond which the coating is characterized as unserviceable.

This disclosure relates generally to the field of source code processing, and, more particularly to a method and system for identification of redundant function-level slicing calls. The method disclosed generates program dependence graphs (PDGs) based on a slicing criteria and a function corresponding to the function-level slicing call. Further the method classifies the function-level slicing call into redundant or non-redundant by traversing the PDGs and checking if a predefined condition is satisfied or not. The function-level slicing calls are classified as redundant if the check is not satisfied and are classified as non-redundant if the check is satisfied. The disclosed method can be used in identifying redundant function-level slicing calls in applications such as automated false positive elimination (AFPE), automated test case generation and so on.

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Embodiments herein provide a system for electrically heated gas mixture mediated industrial plant processes providing a fossil fuel free approach. The system utilizes a gas mixture as heating medium that directly transfers heat to a raw material required for processing the raw material into an end product. The gas mixture used as a heating medium can be a byproduct generated during processing or a combination of stable gases that are reutilized and recirculated to carry heat and dissipate the heat at various stages of processing in accordance with a heating medium design loop. The heating medium design loop disclosed is designed such that it eliminates the need of heat management equipment like a Thermal Energy Storage (TES) system used by existing approaches. Entire heating of the plant is carried out using electric gas heaters heated using renewable electric sources to provide the fossil fuel free design.

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