Tata Consultancy Services Patente

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This disclosure relates generally to a method and a system for profiling of metagenome samples. Most state of-art techniques for metagenomic profiling use homology-based, curated database of identified marker sequences generated after complex and costly pre-processing. The disclosed method and system for profiling of metagenome samples are a non-homology based, a non-marker based and an alignment free strain level profiling tools for microbe profiling. The disclosure works with a several k-mer based indexing techniques for constructing a compact and comprehensive multi-level indexing, wherein the multi-level indexing includes a LI-Index and a L2-Index. The multi-level indexing is used for profiling metagenomics by abundance estimation, wherein the abundance estimation includes a relative abundance and an absolute abundance.

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This disclosure relates generally to field agnostic source localization. Conventional state-of-the-art methods perform source localization for near-field scenario by estimating carrier frequency and direction of arrival (DOA) at or above Nyquist sampling rate. Embodiments of the present disclosure provide a method for source localization at sub Nyquist sampling rate. The method estimates parameters such as range, carrier frequency and DOA of source signals from data sources in a mixed field scenario, i.e., the data sources may reside in far-field as well as near-field. The method considers a delay channel to a sensor receiver architecture for estimating the parameters. The disclosed method can be used in applications like cognitive radio to determine the carrier frequency, DOA and range of various source signals from data sources in mixed field.

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Pulverizers are very critical equipment in overall functioning of a plant. They need to be controlled and monitored properly for the optimized operation of the pulverizers. A system and method for performance and health monitoring to optimize operation of a pulverizer is provided. The system comprises a digital twin that can mimic the performance of the pulverizer in real-time and assist the operators in decision making related to operation, maintenance and scheduling. The digital twin is configured to receives real-time sensor data from a plurality of data sources and provides real-time soft sensing of key health and performance parameters of the pulverizer. One more key aspect of the solution is the advisory system that alerts and recommends corrective actions in terms of parameters controlled through other equipment or changes in operation or design or changes in cleaning schedule.

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Storage and retrieval systems demand time and space efficient approaches. Embodiments herein provide a storage system (100) with guided plate (400A-N) based lifting apparatus (200A-B) for payload (600A) loading-unloading on storage rack (500A-N). The system deploys an Automated Guided Vehicle/Autonomous Mobile Robot (AGV/AMR) (300) mounted with multiple lifting apparatuses (200A-B) in between two adjacent storage racks (500A-N). The racks are modified with guide plates (400A-N) having entry cuts (402A-N) for horizontal movements and entry guides (404A-N) for vertical movement of lifting apparatus (200A-B). Ramp (406A-N) provided in the guide plates (400A-N) at the bottom of racks (500A-N) ensures there is enough clearance for lifting apparatus (200A-B) to go inside an aisle and have smooth vertical lifting. Wheel retention mechanism (222A-1) guides the vertical lifting maintaining required clearance. A conveyor belt mechanism (732) mounted to base plate (206) of lifting apparatus (200A-B) consists of geared rotary motor (230) for conveyor belt (228) that has a forward and backward movement, enabling the lifting apparatus (200A-B) to load/unload the payload (600A) inside/outside the racks (500A-B).

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In state of the art methods for object goal navigation, scene understanding is implicit in their goal oriented exploration policies. Implicit scene understanding coupled with navigation is shown to be specific to tasks for which training is done and not generalizable to new tasks. Thus, embodiments of present disclosure propose a method of goal-conditioned exploration wherein scene understanding is decoupled from the exploration policies. Here, the scene understanding required for navigation is provided by a region classification network that is trained using semantic graphs representing the scene and agent can be navigated towards the goal either by using any state of the art pure exploration policies or by traversing through potential sub-goals identified based on a Co-occurrence Likelihood score calculated by using predictions from the region classification network. Hence, the method of present disclosure can be easily generalized to new tasks and new environments.

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State of the art track width adjustment mechanism fail to support track width adjustment while vehicle is in motion. They also require manual intervention, which causes inconvenience. The disclosure herein generally relates to wheel track adjustment, and, more particularly, to a changeable wheel track mechanism for track width adjustment (referred to as TWA mechanism). The TWA mechanism includes a steer and drive unit, a plurality of links, and a sliding unit. The TWA mechanism allows movement of a wheel in inward and outward directions, causing the track width adjustment. The track width is achieved by changing the wheel position to be at a wider position, or a narrower position, or any intermediate position. The TWA mechanism may be associated with any vehicle for which the track width adjustment is to be achieved.

Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.

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The disclosure relates generally to methods and systems for generating an optimized planting schedule of a crop to overcome storage capabilities. Conventional techniques in the art are limited in dealing with actual planting schedule of the crops in accordance with the storage capacities, thus leading to suboptimal harvest cycles that fail to meet the optimal storage requirements. In accordance with the present disclosure, the optimization model makes use of the cumulative maturity value (CMV) data for each day of the planting period and the harvest period of the crop, and optimizes the planning associated with planting of crops for a set of farms so that interval between harvest period is minimized and the end-of-harvest produce volumes meet certain thresholds to facilitate storage of all procurement without wastage and as per the market demand.

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The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.