Tata Consultancy Services Limited

Indian Institute of Technology Kharagpur

Achanna Anil Kumar

Tapas Chakravarty

Arpan Pal

Kriti Kumar

Mariswamy Girish Chandra

Balamuralidhar Purushothaman

Girish Mariswamy Chandra

Andrew Gigie

Angshul Majumdar

Jayavardhana Rama Gubbi Lakshminarasimha

Sounak Dey

Arun George

Roshan Khobragade

Narendra N

Poornima Surojia

Rokkam Krishna Kanth

Saurabh Sahu

Arijit Chowdhury

Arijit Sinharay

Debatri Chatterjee

Chayan Sarkar

Rahul Dasharath Gavas

Smriti Rani

Anwesha Khasnobish

Raj Kumar Rakshit

Ramesh Kumar Ramakrishnan

Hrishav Bakul Barua

Akshaya Ramaswamy

Venkata Subramanian Viraraghavan

Naveen Kumar Thokala

Mithun Basaralu Sheshachala

Prasant Kumar Misra

Manish Jain

Soumya Chakravarty

Gopi Kandaswamy

Sandeep Nelliamkunnath

Krishna Kanth Rokkam

Kuchibhotla Aditi

Rishab Khawad

Arunkumar Kalimuthu Rameshwaran

Lakshmi Narayana Meda

Aditi Kuchibhotla

Surjya Kanta Pal

Debasish Mishra

Pranay Sahay

Riddhi Panse

Subhadeep Basu

Supriya Gain

Subhasri Chatterjee

Aman Kumar

Balamura Lidhar Purush Othaman

M Girish Chandra

Pavan Kumar Reddy

Balamuralidhar P

Avyarthana Ghosh

In industrial inspection scenarios, early detection of machine malfunction is extremely essential as it helps in preventing any significant damage and the associated economic losses. Embodiments herein provide a method and system for an acoustic based anomaly detection in industrial machines using a beamforming and a sequential transform learning. Herein, the system employs two-stage multi-channel source separation technique that uses the well-known delay and sum beamforming followed by a recent data-driven sequential transform learning (STL) approach to obtain clean sources. The STL is a solution to linear state-space model where operators/matrices are learnt from data and is used here to model the dynamics of time-varying source signals for source separation. Subsequently, a reference template matching is employed on each separated source to detect an anomaly. The numerical results obtained with the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset demonstrate superior performance for source separation and anomaly detection.

Method and system for an acoustic based anomaly detection in industrial machines

This disclosure relates to multi-sensor fusion using Transform Learning (TL) that provides a compact representation of data in many scenarios as compared to Dictionary Learning (DL) and Deep network models that may be computationally intensive and complex. A two-stage approach for better modeling of sensor data is provided, wherein in the first stage, representation of the individual sensor time series is learnt using dedicated transforms and their associated coefficients and in the second stage, all the representations are fused together using a fusing (common) transform and its associated coefficients to effectively capture correlation between the different sensor representations for deriving an inference. The method and system of the present disclosure can find application in areas employing multiple sensors that are mostly heterogeneous in nature.

Method and system for multi-sensor fusion using transform learning

Existing multistatic configurations of Radar systems requires a direct LoS signal and/or time synchronization among the Radar transmitter and the multistatic distributed Radar receivers. The present disclosure provides a phaseless frequency-modulated continuous-wave multistatic Radar (PFMR) imaging that relaxes requirement of the direct LoS signal and only requires a plurality of parameters of a FMCW signal comprising a chirp signal rate, a carrier frequency and, a period of chirp to be known. Further, it also removes condition of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers. However, because of absence of the time synchronization among a plurality of FMCW multistatic distributed Radar receivers, an unknown random phase offset appears after deramping. The present disclosure eliminates the unknown random phase offset, by performing autocorrelation function on a mixed signal, resulting in a phaseless measurement data corresponding to a plurality of FMCW Radar imaging signals.

Method and systems for phaseless frequency-modulated continuous-wave multistatic radar imaging

Use of Swept Frequency Acoustic Interferometry (SFAI) is becoming ubiquitous in taking non-invasive measurements of fluid parameters like sound speed, sound attenuation and density of fluid. But measurement using SFAI is relatively slow as one needs to sweep a wide range of frequencies and for each probing frequency one needs to wait for settling time. Further, SFAI works well only on steady flow as sudden change in fluid flow destroys resonance condition, thereby making it unsuitable for flowing fluid. Present application provides method and system for faster assessment of sound speed in fluids using compressive sensing technique. The system first uses random samples in defined frequency scanning range of frequency sweep signal for generating pseudo analytic signal vector. The system then estimates pulse-echo view by applying compressive sensing technique over pseudo analytic signal vector. Thereafter, system calculates sound speed in fluid using pulse-echo view and pre-defined sound speed calculation formula.

Method and system for faster assessment of sound speed in fluids using compressive sensing technique

Existing Mueller Matrix polarization techniques that rely only on polarization properties are insufficient for accurate characterization of transparent objects. Embodiments of the present disclosure provide a method and system for Mueller Matrix polarimetric characterization of transparent object using optical properties along with the polarization properties to accurately characterize the transparent object. The polarization properties of are derived from a decomposed Mueller matrix element. Additionally, the method derives the optical properties by further subjecting the decomposed Mueller matrix element to Fresnel's law-based analysis and a reverse Monte Carlo analysis to extract optical properties such as a material refractive index and a material attenuation index. Optical properties vary with changes in the material property caused due to several factors such as manufacturing defect, aberration, inclusion of an impurity such as bubble or dust etc. Thus, considering the optical properties along with the polarization properties enables enhanced, accurate characterization of the transparent object.

Polarimetric characterization of transparent objects

Beamforming is a vital component in space division multiple access (SDMA) and currently employed sectorial beamforming that radiates throughout sector(s) is not sufficient for next generation communication systems. Hence, massive Multiple-Input Multiple-Output (MIMO) having large number of antenna elements is employed in 5G and beyond communication systems that has capability to provide practically realizable SDMA. However, such setups are not economical as large number of antenna elements are employed. Present disclosure provides systems and methods that implement electro-mechanical beam tilting based approach, that uses a single antenna element coupled with a meta-surface mounted on top which provides a directional beam leading to an improvement in signal to noise ratio (SNR). The meta-surface-based beamforming architecture of the system provides an improved bit error rate (BER) and spectral efficiency (SE) with huge reduction in number of antenna elements without using phase-shifter.

Systems and methods for meta-surface-based beamforming

This disclosure relates generally to a method and system for multi-modal image super-resolution. Conventional methods for multi-modal image super-resolution are performed using joint image based filtering, deep learning and dictionary based approaches which require large datasets for training. Embodiments of the present disclosure provide a joint optimization based transform learning framework wherein a high-resolution (HR) image of target modality is reconstructed from a HR image of guidance modality and a low-resolution (LR) image of target modality. A set of parameters, transforms, coefficients and weight matrices are learnt jointly from a training data which includes a HR image of guidance modality, a LR image of target modality and a HR image of target modality. The learnt set of parameters are used for reconstructing a HR image of target modality. The disclosed joint optimization transform learning framework is used in remote sensing, environment monitoring and so on.

Method and system for multi-modal image super-resolution

Gesture recognition is a key requirement for Human Computer Interaction (HCl) and multiple modalities are explored in literature. Conventionally, channel taps are estimated using least square based estimation and tap corresponding to finger motion is tracked. These assume that noise component is negligible and can reduce the tracking accuracy for low SNR. Thus, to mitigate the above-mentioned limitation, the system and method of the present disclosure explore the feasibility of using speaker and microphone setup available in most of smart devices and transmit inaudible frequencies (acoustic) for detecting the human finger level gestures accurately. More specifically, System implements the method for millimeter level finger tracking and low power gesture detection on this tracked gesture. The system uses a subspace based high resolution technique for delay estimation and use microphone pairs to jointly estimate the multi-coordinates of finger movement.

System and method for acoustic based gesture tracking and recognition using spiking neural network

This disclosure relates generally to material quality inspection. Conventional approaches available for material quality inspection are unable to address concerns of complexity and cost involved. The technical problem of occluded object detection and material quality inspection for intrinsic defects identification is addressed in the present disclosure. The present disclosure provides a system and method for non-intrusive material quality inspection using three-dimensional monostatic radar based imaging, where the object under inspection undergoes a circular translation motion on a rotating platform. A modified delay-and-sum (m-DAS) algorithm is built by incorporating virtual antenna array to obtain a 3D image reconstruction of the object. From 3D reconstructed images, radial displacement as well as the angular locations of the object is identified which are further used for quality inspection of the material comprised in the object.

Systems and methods for non-intrusive material quality inspection using three- dimensional monostatic radar based imaging

This relates generally to a method and a system for spatio-temporal polarization video analysis. The spatio-temporal polarization data is analyzed for a computer vision application such as object detection, image classification, image captioning, image reconstruction or image inpainting, face recognition and action recognition. Numerous classical and deep learning methods have been applied on polarimetric data for polarimetric imaging analysis, however, the available pre-trained models may not be directly suitable on polarization data, as polarimetric data is more complex. Further compared to analysis of the polarimetric images, a significant number of actions can be detected by polarimetric videos, hence analyzing polarimetric videos is more efficient. The disclosure is a spatio-temporal analysis of polarization video. The disclosed techniques include configuring a set of parameters from the polarization video to train a spatio-temporal deep network architecture for analyzing polarimetric videos for computer vision applications.

Method and a system for spatio-temporal polarization video analysis

This disclosure relates generally to a method and system for assessment of sustained visual attention of a target. The conventional methods utilize various markers for assessment of attention, however, blink rate variability (BRV) series signal is not explored yet. In an embodiment, the disclosed method utilizes BRV series signal for assessing sustained visual attention of a target. A gaze data of the target is recorded using an eye tracker and a set of uniformly sampled BRV series signal is reconstructed from each of the BRV series. One or more frequency domain features, including pareto frequency, are extracted from the set of uniformly sampled BRV series signal. The values of frequency domain features extracted from the set of BRV series signals are compared with corresponding threshold values to determine visual sustained attention of the target.

System and method for eye-blink based assessment of sustained visual attention of a target

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.

Computer controlled electromechanical MMW frequency antenna scanning system and beam steering thereof

Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).

Acoustic system and method based gesture detection using spiking neural networks

Systems and methods for a dense surface reconstruction of an object using graph signal processing is provided. None of the traditional systems and methods provide for a dense or three-dimensional surface reconstruction of objects by resolving &#x3c0; ambiguity. The embodiments of the proposed disclosure provide for resolving &#x3c0; ambiguity by identifying, from one or more sparse three-dimensional shapes extracted, a first set of azimuth values corresponding to a first region of the object; constructing, using a phase angle, a graph capturing a relational structure between the first set of azimuth values and a second set of azimuth values to be estimated; obtaining, a Graph Fourier Transform (GFT) matrix corresponding to the constructed graph; and estimating, from the GFT matrix and the first set of azimuth values, the second set of azimuth values corresponding to a second region of the object by the graph signal processing technique.

Systems and methods for dense surface reconstruction of an object using graph signal processing

The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.

Method and system for impurity detection using multi-modal imaging

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Bengaluru, India

Achanna Anil Kumar