Tata Consultancy Services Patente

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Accurate onset detection helps in fine-tuning training regimens. However, the chaotic nature of raw EMG signals, contaminated with noise and interference from various sources, often complicates the task of accurate onset/offset detection. For the same reason, existing signal processing systems struggle to perform the onset and offset detection effectively, which in turn affects end applications. Embodiments disclosed herein provide a method and system for signal elevation based muscle activity detection. The system performs the signal elevation to highlight and detect onset and offset regions in a signal being processed. Further, based on the determined onset and offset regions, a muscle potential activity of the subject is determined.

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Technical challenge in unusual human activity detection task is to rightly identify only unexpected or unusual movements from constant regular movements present in a scene, with most techniques built on understanding that camera is static. However, ego view camera of mobile surveillance robot is in motion as robot navigates. Embodiments herein provide a method and system for anomalous activity detection for mobile surveillance robots by mimicking 'Konio-Parvocellular-Magno' cells of the human brain into a NN model, which are responsible for detecting slow, normal, and swift changes in perceived scenes. To detect anomalous activity, the static or normal movements of scene captured by ego view camera are identified as redundant information and only RoI is forwarded for further processing using the Optical flow and SSIM techniques. The NN model mimicking KPM is trained only on the RoI to detect normal or anomalous activity.

Existing approaches provide limitations to scale Hybrid DFT calculations to large systems and there is no such scalable technique to correct errors during Hybrid DFT computation with tensors. This disclosure relates generally to implementation of Hybrid Density Functional Theory on Quantum Processors with Moller-Plesset Perturbation (MP2) corrected simulation. The system receives a plurality of inputs from a classical computer component to transmit to a quantum computer component. Initially, a density matrix is computed from a core Hamilton. Further, the quantum processor computes a direct (J) matrix, a Hybrid matrix, and a correlation exchange potentials to compute a Fock matrix. Further, the density matrix is iteratively performed for a criteria where difference between the density matrix and an updated density matrix converges to a value below a threshold convergence error. MP2 energy corrections are performed for extracting the plurality of properties associated with the chemical compound.

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Precise estimation of duration of cover crop is a challenge considering multiple factors contributing to the same and complexity involved in capturing them in the estimation process. A method and system for estimation of cover crop duration and generating Integrated Cover Crop Index (ICCI) is disclosed. Firstly, the maincrop is identified and associated time series data is eliminated to avoid false positives. Detection of type of cover crop and its exact duration is derived by integrated use of satellite remote sensing data, sensor data, field observations and phenology based indicators. Duration of cover crop is estimated considering the impact of snow cover, dormant period etc., by integrated use of remote sensing and sensor data along with local domain crop knowledge of the region. The ICCI provides quantitative measure for cover crop effort and can be used for incentivizing farmers following sustainable cropping practices.

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This disclosure relates generally to apparatus and process of modular true moving bed for gas separation using heat exchange reactor. Conventional moving bed adsorption reactor, in which adsorbent particles flow under the effect of gravity from top to bottom, lack in addressing particle attrition of adsorbent particles, and this attrition results in lower particle life and increased process cost. The modular true moving bed apparatus is a heat exchange reactor arrangement directs a gaseous mixture via a tube inlet port of an adsorption zone containing sorbent material. Further, a target gas adsorbed from the gaseous mixture passed through the tube inlet port by the sorbent material present inside each cartridge. Then, the target gas is evacuated from the plurality of cartridges while passing through the cooling zone. The counter-flow between the target gas and the sorbent material containing carriages results in shorter reaction enhancing heat and mass transfer characteristics.

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Nowadays, platforms are advocating the use of passphrases with the aim to provide a more secure yet memorable form of authentication as passphrases offer ease of remembering and improved adaptability to password policies without compromising usability. Existing password detection methods fail to detect passphrases due to the distinct nature of passphrases, as they involve use of multiple words, symbols, numbers, and special characters. The present disclosure provides a method and a system for detecting passphrases in plain text. The system first receives a plurality of files. Then, the system filters files based on file attributes to obtain potential files. Thereafter, a sensitivity analysis of the potential file is performed based on sensitivity indicators to obtain a sensitivity score for each potential file. Further, the system generates a set of context from text present in each potential file. Finally, the system utilizes a set of context and a sensitivity score of each potential file to identify a set of potential passphrases present in text using a pre-trained machine learning based language model.

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State of the art automated bin packing systems fail to handle dynamic scenarios in which information on dimensions of objects to be loaded is not available in advance. These systems also fail to consider capabilities of robots used for the automated packing of objects/bins. The disclosure herein generally relates to automated bin packing, and, more particularly, to a system and method for autonomous multi-bin parcel loading system. The system handles an online object packing in which information on dimensions of objects to be loaded is not available in advance. The system is also configured to consider capabilities of one or more robots used for loading objects to containers, while generating recommendations for object packing.

The disclosure relates generally to methods and systems for implementation of density functional theory on quantum computing through meta generalized gradient approximation (meta GGA). Conventional methods implement DFT simulations on classical processors which is time consuming. The present disclosure provides a quantum circuit for computing the direct matrix efficiently on a quantum processor. Initially, a plurality of atomic coordinates of each atom of a chemical compound whose one or more properties are to be extracted, are obtained. Electron integrals, a core Hamiltonian, and a collocation matrix are computed from the plurality of atomic coordinates. The core Hamiltonian is diagonalized to obtain an initial density matrix of the chemical compound. The initial density matrix is further updated iteratively until a convergence criteria is satisfied using the meta GGA to obtain a final density matrix. The final density matrix is used to extract the one or more properties of the chemical compound.

Detection of textual phrases in a non-horizontal orientation at a scene is a target problem. This disclosure relates to a processor implemented method to detect a text from multimedia content captured at a scene. An input original image is processed by a trained model to obtain individual character with bounding box on the original image. The original image is positioned by a gradient to obtain a rotated image if number of detected characters is not equal to number of expected characters on the original image. At least one missing character bounding box on the original image and on the rotated image are estimated to construct a horizontal text image if number of detected characters is not equal to number of expected characters on the rotated image. At least one missing character in the estimated bounding box is detected by at least one text returned from an optical character reader.

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This disclosure relates generally to a system and method for product demand transfer estimation in retail. Demand transfer happens across one or more products within a category of substitutable products. Product level demand transfer indicates the amount of demand that is going to be transferred from the unavailable product to another product within the category. An inventory availability is considered herein rather than assortment to ensure that the product is present or not for a particular point in time. The system is configured to estimate product level demand transfer value based on the attributes values present in the one or more products considered. Moreover, the embodiments herein further provide product level demand transfer using the attribute value and demand transfer of values of those attributes that are different between two products.

Even though serverless computing has advantages like lower cost, simplified scalability, simplified backend code and quicker turnaround, they have also given rise to a new form of cyber-attacks. Most of the conventional approaches are reactive based where steps are taken to control the aftereffects of an attack and preventive mechanism is of less focus. The present honeypot based approach for serverless cloud architecture, honeypots capture unauthorized users in order to provide security of the overall system. The honeypots redirect the attackers to a set of dummy nodes that form a random loop. Using a controlled time based loop, the attacker could be trapped for a maximum amount of time, thus penalizing the attacker by a negative award. It would result in exhaustion of resources and bandwidth of attacker. The present disclosure is also applicable to multi-cloud or hybrid cloud setup.

Reactive systems commonly feature an outer infinite loop that captures environmental input and according to the inputs decides the system's response. The outer infinite loop implies that almost every reactive system contains nested loops. Existing verification techniques, such as model checking and loop abstraction methods, often struggle in terms of accuracy and efficiency in the presence of nested loops. Present disclosure provides a method and a system for performing outer loop abstraction for verification of reactive systems. The system first checks whether code in outer loop can be executed before the outer loop. Then, the system performs optimization of the outer loop. Thereafter, the system abstracts outer loop which infinitely read and process environmental input. Further, the system transforms the input code to obtain outer loop abstracted code which is then passed on to an industrial verifier for verification of the reactive system.

The present disclosure provides an abstraction refinement for analyzing timing properties of real-time applications in an efficient, scalable and accurate manner. Conventional methods focus on closed-form analytical tests because of their efficiency and lacks scalability. The present disclosure receives a plurality of task specifications. A time horizon is computed based on the plurality of task specifications. A plurality of time spawned jobs is obtained based on the computed time horizon. Further, a Directed Acyclic Graph (DAG) is constructed for the plurality of time spawned jobs. A plurality of symbolic variables are obtained from the DAG and a plurality of timing constraints are computed. The plurality of constraints and a plurality of timing properties are verified by a verification engine to obtain a set of timing property violated jobs. Here, spurious traces are identified from the execution trace and removed using a plurality of refinement properties.