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公开(公告)号：US20150031965A1
公开(公告)日：2015-01-29
申请号：US14444745
申请日：2014-07-28
Applicant: TATA CONSULTANCY SERVICES LIMITED
Inventor： Aishwarya Visvanathan , Arpan Pal , Anirban Dutta Choudhury , Tanushyam Chattopadhyay , Anurag Kumar , Rohan Banerjee , Aniruddha Sinha
IPC: A61B5/00 , A61B5/1455 , A61B5/0402 , A61B5/0205
CPC classification number: A61B5/0059 , A61B5/0205 , A61B5/021 , A61B5/02416 , A61B5/02438 , A61B5/04028 , A61B5/08 , A61B5/14551 , A61B5/6898 , A61B5/7267 , A61B2505/07 , G06F19/00
Abstract: A method for monitoring physiological parameters associated with a subject using a hand held device is described herein. In an implementation, the method includes obtaining a plurality of sample photoplethysmographic (PPG) features associated with a sample subject, from a video of a body part of the sample subject. From among the plurality of sample PPG features, at least one relevant sample PPG feature associated with the physiological parameter, is selected based on a ground truth value of the physiological parameter for the subject. Further, based on the at least one relevant sample PPG feature and the ground truth value of the physiological parameter, a mathematical model indicative of a correlation between the relevant sample PPG feature and the physiological parameter, is determined. The mathematical model can be deployed for monitoring the physiological parameter in real time.
Abstract translation: 本文描述了使用手持式装置监测与受试者相关的生理参数的方法。 在一个实现中，该方法包括从样本对象的身体部分的视频获得与样本对象相关联的多个样本光体积描记图（PPG）特征。 从多个样本PPG特征中，基于对象的生理参数的基本真值，选择与生理参数相关联的至少一个相关样本PPG特征。 此外，基于至少一个相关样本PPG特征和生理参数的基本真值，确定指示相关样本PPG特征和生理参数之间的相关性的数学模型。 可以部署数学模型，实时监测生理参数。
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52.发明申请Computer Platform for Development and Deployment of Sensor-Driven Vehicle Telemetry Applications and Services 有权用于开发和部署传感器驱动的车辆遥测应用和服务的计算机平台公开(公告)号：US20140380264A1
公开(公告)日：2014-12-25
申请号：US14345545
申请日：2012-09-18
Applicant: Tata Consultancy Services Limited
Inventor： Prateep Misra , Arpan Pal , Balamuralidhar Purushothaman , Chirabrata Bhaumik , Deepak Swamy , Venkatramanan Siva Subrahmanian , Avik Ghose , Aniruddha Sinha
CPC classification number: G06F8/00 , G06F8/36 , G06F8/60 , G06F11/3668 , H04L41/00 , H04L41/04 , H04L41/5096 , H04L43/00 , H04L67/10 , H04L67/12
Abstract: A computing platform for intelligent development, deployment and management of vehicle telemetry applications is disclosed herein. Further, the present disclosure provides a method and system that enables provision of Intelligent Transportation Service on the Cloud-based Platform that facilitates creation and deployment of vehicle telemetry applications configured for enabling traffic measurements, traffic shaping, vehicle surveillance and other vehicle related services.
Abstract translation: 本文公开了用于车辆遥测应用的智能开发，部署和管理的计算平台。 此外，本公开提供了一种方法和系统，其能够在基于云的平台上提供智能交通服务，其有助于构建和部署车辆遥测应用，所述车辆遥测应用被配置为用于实现交通量测量，流量整形，车辆监控和其他车辆相关服务。
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53.发明授权公开(公告)号：US12038949B2
公开(公告)日：2024-07-16
申请号：US18383930
申请日：2023-10-26
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Arpan Pal , Soumadeep Saha , Utpal Garain
IPC: G06F16/28
CPC classification number: G06F16/285
Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model. The disclosed method is used for electrocardiogram classification, shape classification and so on.
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54.发明授权公开(公告)号：US11990685B2
公开(公告)日：2024-05-21
申请号：US17663241
申请日：2022-05-13
Applicant: Tata Consultancy Services Limited
Inventor： Tapas Chakravarty , Aman Kumar , Arpan Pal , Achanna Anil Kumar , Roshan Khobragade , Poornima Surojia , Pranay Sahay , Manish Jain
CPC classification number: H01Q3/08 , H01Q3/22 , H01Q9/0414
Abstract: This disclosure relates generally to Millimeter Wave (MMW) frequency antenna scanning system. Conventional approaches available for scanning an antenna beam over a large angular swath with high directivity are unable to address concerns of size and cost involved. The technical problem of providing an MMW frequency antenna scanning system using a single small size antenna capable of scanning as desired at a desired precision is addressed in the present disclosure. The antenna scanning system provided is an electromechanical system that makes the system cost effective. Computer control provides precision control in beam steering from remote. Use of a metasurface and configuration of a radiating patch and a shorting pin in a microstrip antenna addresses the concern with regards to the size of the antenna scanning system.
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55.发明授权公开(公告)号：US11638527B2
公开(公告)日：2023-05-02
申请号：US17156395
申请日：2021-01-22
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Chowdhury , Smriti Rani , Anwesha Khasnobish , Taniya Das , Tapas Chakravarty , Arpan Pal
IPC: A61B5/0205 , A61B5/05 , A61B5/00 , A61B5/107 , A61B5/0245 , A61B5/08
Abstract: The disclosure herein generally relates to the field of determination of cardiopulmonary signals for multi-persons, and, more particularly, to determination of cardiopulmonary signals for multi-persons using in-body signals obtained by ultra-wide band (UWB) radar. The disclosed method determines of cardiopulmonary signals for multi-persons using in-body signals, wherein a UWB radar signals/waves reflected from inside a human body is utilized for efficient determination of cardiopulmonary signals. The disclosed method and system utilize the UWB radar signals to identify a number of persons along with several details about the persons that include a girth of the each identified person and the orientation of the identified person towards the one or more UWB radar. Further a chest wall distance, a breathing rate, a heart wall distance and a heart rate are determined for all the identified persons based on the identified girth and the identified orientation along with the UWB radar signals.
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Patent Agency Ranking
公开(公告)号：US11593469B2
公开(公告)日：2023-02-28
申请号：US17212659
申请日：2021-03-25
Applicant: Tata Consultancy Services Limited
Inventor： Tanushree Bannerjee , Venkata Subramanian Viraraghavan , Kartik Muralidharan , Dibyanshu Jaiswal , Mithun Basaralu Sheshachala , Ramesh Kumar Ramakrishnan , Arpan Pal , Balamuralidhar Purushothaman
Abstract: Embodiments herein provide a method and system for continuously validating a user during an established authenticated session using Photoplethysmogram (PPG) and accelerometer data. State of the art approaches are mostly based on feature extraction and ML modelling for PPG based continuous session validation, while a template based approach in the art follows a complicated approach. The method disclosed herein utilizes less computation intensive template based approach to continuously validate the user across the session. The method comprises preprocessing a PPG data or PPG signal acquired from a wearable device worn by the user to identify segments of negligible motion. A first segment, after authentication using conventional authentication mechanism, serves as the initial reference. The chosen segments are then tested one by one with respect to the reference. If the templates in a segment match those of the reference, it is updated as the new reference, else a re-authentication is triggered.
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公开(公告)号：US11531830B2
公开(公告)日：2022-12-20
申请号：US16102069
申请日：2018-08-13
Applicant: Tata Consultancy Services Limited
Inventor： Arijit Ukil , Soma Bandyopadhyay , Chetanya Puri , Rituraj Singh , Arpan Pal
IPC: G06K9/62 , A61B5/1455 , A61B5/0205 , G06N7/08 , G06N20/00 , G16H50/70 , G16C20/70
Abstract: In many real-life applications, ample amount of examples from one class are present while examples from other classes are rare for training and learning purposes leading to class imbalance problem and misclassification. Methods and systems of the present disclosure facilitate generation of an extended synthetic rare class super dataset that is further pruned to obtain a synthetic rare class dataset by maximizing similarity and diversity in the synthetic rare class dataset while preserving morphological identity with labeled rare class training dataset. Oversampling methods used in the art result in cloning of datasets and do not provide the needed diversity. The methods of the present disclosure can be applied to classification of noisy phonocardiogram (PCG) signals among other applications.
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58.发明授权公开(公告)号：US11475341B2
公开(公告)日：2022-10-18
申请号：US16179771
申请日：2018-11-02
Applicant: Tata Consultancy Services Limited
Inventor： Ishan Sahu , Snehasis Banerjee , Tanushyam Chattopadhyay , Arpan Pal , Utpal Garain
Abstract: Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.
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公开(公告)号：US11375021B2
公开(公告)日：2022-06-28
申请号：US17136436
申请日：2020-12-29
Applicant: Tata Consultancy Services Limited
Inventor： Chirabrata Bhaumik , Rajat Das , Arpan Pal , Tapas Chakravarty , Raj Rakshit , Avik Ghose , Amit Swain , Arijit Sinharay
IPC: H04L12/00 , H04L67/125 , G06F16/25 , G16Y30/00 , G16Y40/10
Abstract: Any sensing system is faced with triangle of dilemma between accuracy, latency and energy. High energy and high latency sensing systems are often very accurate but less useful. Embodiments herein provide a method and system for edge based sensor controlling in the IoT network for event monitoring. The system disclosed herein applies a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor.
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公开(公告)号：US11373757B2
公开(公告)日：2022-06-28
申请号：US15913682
申请日：2018-03-06
Applicant: Tata Consultancy Services Limited
Inventor： Deepan Das , Rohan Banerjee , Anirban Dutta Choudhury , Parijat Dilip Deshpande , Nital Shah , Vijay Anil Date , Arpan Pal , Kayapanda Muthana Mandana
Abstract: A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.
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