专利检索 ap:("Ayan Banerjee" OR "Sandeep Gupta" OR "Prajwal Paudyal") AND inv:"Ayan Banerjee" 第 3 页

21.

发明申请
SYSTEMS AND METHODS FOR CROWDSOURCING REAL-TIME MOBILE CROWD SENSING APPLICATIONS 有权

公开(公告)号：US20220369082A1

公开(公告)日：2022-11-17

申请号：US17814445

申请日：2022-07-22

申请人： Sandeep Gupta , Ayan Banerjee , Madhurima Pore , Vinaya Chakati

发明人： Sandeep Gupta , Ayan Banerjee , Madhurima Pore , Vinaya Chakati

IPC分类号： H04W4/38 , H04W4/20 , H04L67/10 , H04W76/10 , H04W4/029 , G16H50/80 , G06N20/00 , G06N7/00 , H04L67/5683

摘要： Various embodiments of systems and methods for mobile crowd sensing applications are disclosed herein.

22.

发明申请
SYSTEMS AND METHODS FOR A HAND HYGIENE COMPLIANCE CHECKING SYSTEM WITH EXPLAINABLE FEEDBACK 有权

公开(公告)号：US20210398416A1

公开(公告)日：2021-12-23

申请号：US17353241

申请日：2021-06-21

申请人： Sandeep Gupta , Ayan Banerjee , Venkata Naga Sai Apurupa Amperayani

发明人： Sandeep Gupta , Ayan Banerjee , Venkata Naga Sai Apurupa Amperayani

IPC分类号： G08B21/24 , H04W4/02 , H04W4/80 , G06F1/16 , G06N20/10

摘要： Various embodiments of a system and associated method for evaluating handwashing protocol compliance are disclosed herein. The system considers temporal combinations of segments of a test handwashing routine to recognize components of a compliant handwashing routine within the test handwashing routine. The system determines whether the components of the handwashing routine have been adequately performed and provides actionable feedback to a user to improve adherence to handwashing routine guidelines.

23.

发明授权
Brain-mobile interface optimization using internet-of-things 有权

公开(公告)号：US10796246B2

公开(公告)日：2020-10-06

申请号：US15857794

申请日：2017-12-29

申请人： Sandeep Gupta , Ayan Banerjee , Mohammad Javad Sohankar Esfahani , Seyed Koosha Sadeghi Oskooyee

发明人： Sandeep Gupta , Ayan Banerjee , Mohammad Javad Sohankar Esfahani , Seyed Koosha Sadeghi Oskooyee

IPC分类号： G06F16/00 , G06N20/00 , G05D1/00 , H04L29/08 , H04W40/24

摘要： A Brain-Mobile Interface (BMoI) system is provided. A control circuit is configured to execute a predictive model to generate a defined number of predicted signal features in future time based on a number of signal features extracted from a first type sensory data (e.g., electroencephalogram (EEG) data). A predicted future mental state(s) can thus be generated based on the number of predicted signal features and used to trigger a corresponding action(s) in a BMoI application(s). In a non-limiting example, a second type sensory data (e.g., electrocardiogram (ECG) data) can be used to improve accuracy of the predictive model. By using the predicted signal features to generate the predicted future mental state(s) to control the BMoI application(s), it is possible to duty-cycle the BMoI system to help reduce power consumption and processing latency, thus allowing the BMoI application(s) to operate in real-time with improved accuracy and power consumption.

24.

发明授权
Compressive sensing of quasi-periodic signals using generative models 有权

公开(公告)号：US10575788B2

公开(公告)日：2020-03-03

申请号：US15787534

申请日：2017-10-18

申请人： Sandeep Gupta , Ayan Banerjee

发明人： Sandeep Gupta , Ayan Banerjee

IPC分类号： A61B5/00 , A61B5/0404 , A61B5/0456 , A61B5/0432 , A61B5/0452 , A61B5/04 , G16H50/20 , G16H40/63

摘要： Methods and systems are described for sensing and recovery of a biological signal using generative-model-based compressive sensing. A transformation is applied to sparsify the quasi-periodic signal removing morphology parameters and leaving temporal parameters. The sparsified signal is sampled and the sampled signal data is transmitted to a base station. A homotopy recovery algorithm is applied to the received sampled signal data by the base station to recover the temporal parameters of the biological signal. Generative modelling is applied using previously captured morphology parameters to generate a reconstructed signal. Finally, the reconstructed signal is adjusted and scaled based on the recovered temporal parameters to provide a reconstructed signal that is diagnostically equivalent to the original biological signal.

25.

发明授权
Real-time diet assessment and food identification using thermal imaging 有权

公开(公告)号：US10074028B2

公开(公告)日：2018-09-11

申请号：US15298581

申请日：2016-10-20

申请人： Sandeep Gupta , Ayan Banerjee

发明人： Sandeep Gupta , Ayan Banerjee

IPC分类号： G06K9/00 , G06K9/46 , G06T7/90 , G09B19/00

CPC分类号： G06K9/4652 , G01N25/72 , G01N33/02 , G06K9/00 , G06K9/6289 , G06K9/629 , G06K2209/17 , G06T7/11 , G06T7/12 , G06T7/136 , G06T7/162 , G06T7/174 , G06T7/90 , G06T2207/10024 , G06T2207/10048 , G06T2207/20061 , G06T2207/20152

摘要： Systems and methods are described for automatically identifying a food item. A color image and a thermal image are received by an electronic processor with a first food item in the field of view of both the color image and the thermal image. The electronic processor identifies a region of pixels in the color image that corresponds to the first food item based at least in part on a temperature intensity of the pixels in the identified region of pixels relative to other pixels in the thermal image. At least one feature is extracted from the identified region of pixels in the color image corresponding to the first food item and the electronic processor automatically identifies a type of food corresponding to the first food item based at least in part on the at least extracted feature.

26.

发明申请
BRAIN-MOBILE INTERFACE OPTIMIZATION USING INTERNET-OF-THINGS 审中-公开

公开(公告)号：US20180189678A1

公开(公告)日：2018-07-05

申请号：US15857794

申请日：2017-12-29

申请人： Sandeep Gupta , Ayan Banerjee , Mohammad Javad Sohankar Esfahani , Seyed Koosha Sadeghi Oskooyee

发明人： Sandeep Gupta , Ayan Banerjee , Mohammad Javad Sohankar Esfahani , Seyed Koosha Sadeghi Oskooyee

IPC分类号： G06N99/00 , H04L29/08 , G05D1/00

CPC分类号： G06N20/00 , G05D1/0088 , H04L67/12 , H04W40/24 , Y02D30/40

摘要： A Brain-Mobile Interface (BMoI) system is provided. A control circuit is configured to execute a predictive model to generate a defined number of predicted signal features in future time based on a number of signal features extracted from a first type sensory data (e.g., electroencephalogram (EEG) data). A predicted future mental state(s) can thus be generated based on the number of predicted signal features and used to trigger a corresponding action(s) in a BMoI application(s). In a non-limiting example, a second type sensory data (e.g., electrocardiogram (ECG) data) can be used to improve accuracy of the predictive model. By using the predicted signal features to generate the predicted future mental state(s) to control the BMoI application(s), it is possible to duty-cycle the BMoI system to help reduce power consumption and processing latency, thus allowing the BMoI application(s) to operate in real-time with improved accuracy and power consumption.

27.

发明申请
COMPRESSIVE SENSING OF QUASI-PERIODIC SIGNALS USING GENERATIVE MODELS 审中-公开

公开(公告)号：US20180103911A1

公开(公告)日：2018-04-19

申请号：US15787534

申请日：2017-10-18

申请人： Sandeep Gupta , Ayan Banerjee

发明人： Sandeep Gupta , Ayan Banerjee

IPC分类号： A61B5/00 , A61B5/0404 , A61B5/0456 , A61B5/0432

CPC分类号： A61B5/7232 , A61B5/0006 , A61B5/04011 , A61B5/04014 , A61B5/0404 , A61B5/0432 , A61B5/04525 , A61B5/0456 , A61B5/681 , A61B5/6823 , A61B5/7264 , A61B5/7275 , A61B5/7278 , A61B2560/0209 , A61B2562/04 , G16H40/63 , G16H50/20

摘要： Methods and systems are described for sensing and recovery of a biological signal using generative-model-based compressive sensing. A transformation is applied to sparsify the quasi-periodic signal removing morphology parameters and leaving temporal parameters. The sparsified signal is sampled and the sampled signal data is transmitted to a base station. A homotopy recovery algorithm is applied to the received sampled signal data by the base station to recover the temporal parameters of the biological signal. Generative modelling is applied using previously captured morphology parameters to generate a reconstructed signal. Finally, the reconstructed signal is adjusted and scaled based on the recovered temporal parameters to provide a reconstructed signal that is diagnostically equivalent to the original biological signal.