Tata Consultancy Services Patente

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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.

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.

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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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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.

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 method and system for sensing best-connected future path for a mobile telerobot based on radio signal strength (RSS) prediction algorithm through in-situ radio-sensing. State-of-the-art methods predict the future path from the plurality of possible paths based on a radio-source in the environment. However, prediction of the suitable future path in the absence of the radio-source or in no signal zone is not yet achieved. The proposed in-situ algorithm is based on Log-Normal Shadowing Model (LNSM) and found efficient for prediction error minimization. The method enables the mobile telerobot to predict the future path on a trajectory of the telerobot even without prior knowledge of a radio-source location. The mobile telerobot can predict the most suitable path from a plurality of possible paths for a move based on virtual location estimation.

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.

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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.

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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.

The disclosure relates generally to methods and systems for predicting a remaining shelf-life of a millet flour using data-driven approach. Conventional techniques for estimating the remaining shelf life of the millet flour are very limited and not effective. The methods and systems of the present disclosure employs a Long short-term memory (LSTM) network architecture-based model to predict the remaining shelf-life of the millet flour based on both the nutritional quality and the rancidity using the information of millet variant and days after milling of the grain. The LSTM network based model is trained on the training data obtained using the chemical reaction kinetic model from the input data comprising a peroxide content, and a fat acidity. The present disclosure predicts the remaining shelf-life of the millet flour in terms of both the remaining nutritional shelf-life and the remaining rancid shelf-life.

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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.

Existing approaches in this domain are targeted to identifying specific anomalies, and there is no system available that determines implant health comprehensively by considering various aspects that may represent health of the implants. The method and system disclosed herein provide an implant health monitoring approach in which by processing an X-ray image received as input, a system identifies location of an implant in body of a subject, and identifies manufacturer and subtype details of the implant. Further, an implant size and implant fixation details are determined. Further, anomaly detection is performed to determine presence of one or more anomalies in the implant, based on the determined manufacturer and subtype details, the determined location of the implant, a determined region around the implant, the implant fixation details, and the implant size information, and accordingly the implant is classified as one of Faulty, Healthy, and Probable faulty.

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The disclosure generally relates to methods and systems for graph assisted unsupervised domain adaptation for machine fault diagnosis. The present disclosure solves the technical problems in the art using a Graph Assisted Unsupervised Domain Adaptation (GA-UDA) technique for the machine fault diagnosis. The GA-UDA technique carries out the domain adaptation in two stages. In the first stage, a Class-wise maximum mean discrepancy (CMMD) loss is minimized to transform the data from both source and target domains to a shared feature space. In the second stage, the augmented transformed (projected) data from both the source and the target domains are utilized to construct a joint graph. Subsequently, the labels of target domain data are estimated through label propagation over the joint graph. The GA-UDA technique of the present disclosure helps in addressing significant distribution shift between the two domains.

Realistic data is an important aspect for training vision based systems. Conventional approaches need complex prerequisites to generate such data and are quite expensive as well. The present disclosure provides one stop solution for different artificial intelligence rendering, scenario generation and future video prediction. The present disclosure provides a provision for fusion of Generative Artificial Intelligence (GenAI), deep learning and image processing techniques and explores the possibilities of generating data from simulated, real, or fused environments. Further, the present disclosure utilizes a panoptic Segmentation approach to create semantic labels and a flow map-based methodology taking cues from a sequence of frames helps to address long time temporal coherence which is a key issue in generated data. This goes as an input to the Generative AI network which generates synthetic dataset based on the aspects of dynamic objects, scene changing and environment variations.

Multi-object tracking (MOT) in video sequences plays a critical role in various computer vision applications. The primary objective of MOT is to accurately localize and track objects across consecutive frames. However, existing MOT approaches often suffer from computational limitations and low frame rates in commodity machines, which hinders real-time performance. Present disclosure provides method and system for performing content aware multi-object tracking. The system first classifies video into slow and fast moving object content videos depending on features of objects to be tracked in frames. Then, system applies a computationally intensive deep sort algorithm to perform tracking of objects by selectively skipping frames. Thereafter, the system applies linear approximate Kalman prediction for slow object content videos and quadratic interpolation for fast object content videos as low computation tracking techniques for tracking objects present in skipped frames, thus significantly improving execution speed while reducing computational load on the system.

To maintain a healthy and safe battery, the Battery Management System (BMS) require numerous sensors which is a costly process. Immersion cooling applications where the battery stack is cooled with a dielectric coolant fluid has complex flow patterns that are not modeled easily without high fidelity modeling. The present disclosure provides a solution by combining physics-based and data-driven techniques. Initially, a first reduced subspace and a second first reduced subspace are extracted from the high-fidelity or full-order model (FOM) data with the help of proper orthogonal decomposition (POD) in offline. Further, the dynamics of the reduced spaces (first reduced subspace and the second reduced subspace) are captured by computing the reduced coefficients. The reduced coefficients are computed by solving the reduced order equations. The reduced model is solved in the same manner as the high-fidelity or FOM model. The reduced-order solution is constructed once the reduced coefficients are obtained.

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A system and method for navigation of a robot from a first area to a second area in a facility is provided. The present disclosure is providing robot navigation using the 'Areagoal' Navigation technique. 'Areagoal' class of problem is divided into two subtasks: identifying the area; and navigation from one area to another. The robot starts in first location and goes out of the current area if it is not in the target area. If there are multiple openings from the first area, it needs to select the most statistically close one to the target area and go there. If the target area is not reached, it backtracks to an earlier viable branch position to continue the target area search. The system takes input from RGB-D camera and odometer, while the output is action space (left, right, forward) with goal of moving to target area.

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