Tata Consultancy Services Patente

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Electrocardiography (ECG) signals contain important markers for Coronary Heart Disease (CHD). State of the art systems and methods rely on clinically available multi-lead ECG for CHD classification which is not cost effective. Moreover the state of the art methods are applied on digital ECG time series data only. Also, discriminative HRV markers are not often present in short ECG recordings necessitating long hours of ECG data to analyze. In accordance with the present disclosure, systems and methods described hereinafter extract ECG time series from ECG images obtained from commercially available low-cost single lead ECG devices through a combination of image and signal processing steps including Histogram analysis, Morphological operation-thinning, Extraction of lines, Extraction of Reference Pulse, Extraction of ECG and interpolating missing data. Further, domain independent statistical features such as self-similarity of raw ECG time series and average Maharaj's distance along with domain specific features are used for classifying CHD.

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The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.

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Unaddressed technical problem is how to make the window manager select and update the appropriate visual design given the Just-In-Time constraint to optimize Profit/Economic Goal for a Basic Emergent User and not have a locking/permanent binding on a particular Socio Economic Cohort (SEC) or Contract Management System Companies (CMSCs). A method and system is disclosed that provides interface layout to discover and optimize economics of information exchange of digitally virtualized physical space for a Basic Emergent User (BEU). A correlation score is determined based on correlation between i) the expected profit range provided by the BEU indicating the tolerance around the SP(selling price) and ii) the buying price(BP) and the tolerance specified by the SEC or the CMSC to identify a best match for the BEU based on goal provided by the BEU in terms of expected profit range and corresponding time range within which it is to be achieved.

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Traditional machine learning (ML) based systems used for scene recognition and object recognition have the disadvantage that they require huge quantity of labeled data to generate data models for the purpose of aiding the scene and object recognition. The disclosure herein generally relates to image processing, and, more particularly, to method and system for generating 3D mesh generation using planar and non-planar data. The system extracts planar point cloud and non-planar point cloud from each RGBD image in a sequence of RGBD images fetched as input, and then generates a planar mesh and a non-planar mesh for planar and non-planar objects in the image. A mesh representation is generated by merging the planar mesh and the non-planar mesh. Further, an incremental merging of the mesh representation is performed on the sequence of RGBD images, based on an estimated camera pose information, to generate representation of the scene.

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A computer implement format preservation based masking system and method is provided. The system obtains a first set of letters and a private key, and encrypts the first set of letters to obtain an encrypted letters list using the first set and private key. The encrypted letters list comprises a set of encrypted letters. A dynamic map is generated based on the encrypted letters, which includes one or more keys, each key being specific to a letter in the first set letters. A position of each of maskable letters in a second set of letters is calculated using the dynamic map, and performs masking of the maskable letters based on the position of each of the maskable letters to obtain masked data using the dynamic map.

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as 'process parameters') and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

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This disclosure relates generally to methods and systems for predicting wait time of queues at service area such as market places including retail stores and super markets. The present methods and systems accurately predicts the wait times of the plurality of queues by utilizing various visual cues of the customers, along with the number of service items and the efficiency of the service operator. The visual cues including a demographic factor such as age, gender, ethnicity of the customer and a senti-motional factor such as sentiments including positive attitude or negative attitude and emotions of the customers including happy state, sad state, and irritation state of the customer. The customers may choose the queue having least predicted wait time and may take informed decision hoping for faster check-out, based on the predicted wait times of the queues. Hence, customer experience and customer satisfaction may be achieved.

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This disclosure relates more particularly to risk assessment of autism spectrum disorder (ASD) present in the subject and designing a personalized recommendation for the same. Current diagnostic tools and procedures, though abundant in numbers, are all based on psychiatric or behavioral evaluations, checklists and associated statistical inferences, which highlight the inherent limitation in making a reliable and early diagnosis. The present disclosure makes use of oral microbial samples of both saliva and dental plaque. The present disclosure involves a paired extraction and quantification of site-specific unique microbial sequences pertaining to the oral microbial samples of an ASD subject and subsequent classification of the subject under the ASD risk category using a metric based on a predefined ensemble of mathematical formulas. Further, a guided development of personalized microbial cocktail(s) is then designed based on the most relevant formula-set for the subject.

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Enterprise software relies on numerous types and instances of mathematical and logical calculations for proper functioning, which are formally specified in spreadsheets, known as Calculation Specification (CS) sheets, that provide illustrations of the inputs and outputs of the calculation, and are expressed in inputs and formulae applied on them to get values for outputs. The values for such inputs are retrieved by executing ad hoc queries on respective application's database. Present disclosure provides systems/methods to synthesize code using CS sheets by formulating a Programming By Example (PBE) problem for each input involved in the calculation. A query synthesis technique is then implemented that uses natural language processing and enumerative program synthesis techniques to infer queries corresponding to the PBE problem. These queries are filtered based on whether their output is consistent with expected output in PBE. Finally, the synthesized queries and calculation formulae are translated into a desired target programming language.

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The present disclosure provides a holistic model for feature specification and dependency representation for requirement specification documents where the conventional models fail to provide. The present disclosure receives a plurality of requirement specification documents and a related data. A product feature model is generated based on the plurality of requirement specification documents and the related data using a feature model generation technique. The product feature model includes a plurality of product feature elements. The plurality of product feature elements includes a feature area, a major feature and a plurality of features. A specification model is generated further for each of the plurality of features using a specification extraction technique. Post generating the specification model, a plurality of dependency associations are generated for each of a plurality of specification elements of the specification model using a dependency extraction technique. Finally, the plurality of dependency associations are updated in the specification model.

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Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore setpoints. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

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The disclosure relates generally to methods and systems for real time video driven human 3-dimensional (3-D) posture estimation during physical activities. Conventional techniques do not exploit temporal information, they do not give smooth transition of postures over time. Furthermore, the techniques that exploit the temporal information suffer from higher time requirements due to two state computations. The present disclosure solves the technical problems in the art with the methods and systems for real time video driven human 3-D posture estimation during physical activities. The present invention discloses a smart-phone camera based automatic posture monitoring system designed with an auto-encoder based architecture. The disclosed auto-encoder based cross-modal method uses monocular video (2-D image sequences) from a single low-end mobile device (for example, smart-phone camera) for estimating human 3-D posture in real time (~ 5 fps) with high accuracy (less than 1 cm error per joint location).