Tata Consultancy Services Limited

Indian Institute of Technology Kharagpur

Kriti Kumar

Achanna Anil Kumar

Mariswamy Girish Chandra

Angshul Majumdar

Tapas Chakravarty

Andrew Gigie

Naveen Kumar Thokala

Saurabh Sahu

Arpan Pal

Balamuralidhar Purushothaman

Srinivasan Jayaraman

Rishab Khawad

Riddhi Panse

Ravikumar Karumanchi

Kalyan Prathap Kamakolanu Guru

Suresh Upparapalli

Madhusudhan Kamma Chavala Chowdary

Prasanna Madhavrao Kulkarni

Pareshkumar Bhawanishankar Sharda

Debasish Mishra

Surjya Kanta Pal

Aditi Kuchibhotla

Krishna Kanth Rokkam

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

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

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

This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.

Method and system for low sampling rate electrical load disaggregation

The present subject matter relates to a computer implemented method for real time determination of stress levels of an individual. The method includes receiving at least one stream of physiological data from at least one primary sensor for a predetermined duration, and preprocessing the at least one stream of physiological data to extract physiological parameters, where the preprocessing includes performing a preliminary analysis on the at least one stream of physiological data. The method further includes determining a stress level of the individual based on at least the physiological parameters, wherein the determining comprises performing a statistical analysis on the physiological parameters.

Real-time stress determination of an individual

System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data. System predicts enterprise system response time by using trained predictive model, input time series data or lag between input time series data and historical output time series data.

System and method for predicting response time of an enterprise system

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Bengaluru, India

Kriti Kumar