Abstract: In Microwave radar imaging, obtaining high resolution microwave image from materials remains a challenge due to comparatively longer wavelength. Embodiments of the present disclosure provide a system for microwave imaging by Multiple-Input and Multiple-Output along with Synthetic Aperture Radar. A back-scattered signal is received from object at a target as an input. The back-scattered signal is rearranged to generate sub-array elements. A Fourier transform of the sub-array elements is computed by deploying two dimensional Fast Fourier transform to obtain two dimensional Fast Fourier transform of the sub-array elements. The 2D FFT of the sub-array elements is vectorized to obtain vectorized stacked sub-array matrix. The stacked sub band of the 2D FFT of the entire aperture array is reordered to obtain a two dimensional Fast Fourier transform of the entire aperture array. Three-dimensional reflectivity function of the target is estimated from the 2D FFT of the entire aperture array.

Abstract: Building such optimized DNN models model for resource constrained devices require huge amount of workflow setup, engineering skills and research skills. There is a need to automate the process for generation of optimized DNN models for Green analytics. A method and system providing integrated platform for rapid automated generation of Tiny ML models for edge devices by integrating multiple optimization techniques and recommending the mist appropriate technique based on available input parameters is provided. Further one of the optimization technique the Fast-NAS can be generalized across multiple applications and consume 95% less computational power (GPU hours). The enhancement is achieved using a performance evaluation technique for generated models using new metric, a new reward function with adaptive parameters, an early-exit strategy to further expedite the optimization process, and a new NAS flow enhanced with AutoML (Hyper-Parameter Optimization) to minimize human intervention.

Abstract: The embodiments of the present disclosure herein address unresolved problems of quality of signals in real time for wearables to provide optimal signals which can be used for brain signal based applications. Further, conventional techniques fail to provide real-time calibration of wearable devices, to understand the quality of the signals from the wearable device. Embodiments herein provide a method and system for a real-time calibration of one or more Electroencephalography (EEG) signals received from a wearable Ear-EEG device. The system is leveraging quality of signals in real time for wearables to provide optimal signals which can be used for early detection of neurodegenerative disease and brain-computer interface (BCI) applications. Further, the system is able to detect electrodes in the wearable device where the EEG signals have not been collected because the contact was not established.

Abstract: 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.

Abstract: This disclosure relates generally to a method and system for damage localization on surfaces made of composites and metals. State-of-the-art methods for ultrasonic guided wave-based damage localization provide a reasonable accuracy. However, accuracy of prediction based on minimum number of observations is not yet achieved. The disclosed method provides damage localization by capturing response to the ultrasonic tone burst transmitted by a plurality of active piezoelectric sensors. The disclosed method provides a modified RAPID algorithm that considers an attenuation of the ultrasonic guided waves and factors energy of transmitted and received signals while predicting damage location. The method provides iterative grid search reduction mechanism to predict damage on the surfaces made of composites and metals.

Abstract: This disclosure relates generally to method and system for time series classification. Conventional methods for time-series classification requires substantial amount of annotated data for classification and label generation. The disclosed method and system are capable of generating accurate labels for time-series data by utilizing a small amount of representative data for each class. In an embodiment, the disclosed method generates a time-series data synthetically and associated labels by using a portion of the representative time-series data in each iteration, and self-correcting the generated labels based on a determination of quality of the generated labels using label quality checker models.

Abstract: State of the art telescope designs require increasing number of sub-apertures for optimum performance, however, with the increasing number of sub-apertures, number, and amplitudes of the sidelobes increase along with that of the primary maxima, resulting in a trade-off of the imaging quality. Disclosed herein are three configurations using a central sub-aperture and a plurality of peripheral sub-apertures, encompassing the central sub-aperture. Size of the central sub-aperture and the plurality of peripheral sub-apertures is in a proportionate relationship. Further, the plurality of the peripheral sub-apertures forms at least two concentric zones, wherein each concentric zone has equal number peripheral sub-apertures from among the plurality of peripheral sub-apertures, and the sizes of the peripheral sub-apertures in each two adjacent concentric zones have a proportionate relationship.

Abstract: Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.

Abstract: This disclosure relates generally to reducing earth-bound image volume with an efficient lossless compression technique. The embodiment thus provides a method and system for reducing earth-bound image volume based on a Spiking Neural Network (SNN) model. Moreover, the embodiments herein further provide a complete lossless compression framework comprises of a SNN-based Density Estimator (DE) followed by a classical Arithmetic Encoder (AE). The SNN model is used to obtain residual errors which are compressed by AE and thereafter transmitted to the receiving station. While reducing the power consumption during transmission by similar percentages, the system also saves in-situ computation power as it uses SNN based DE compared to its Deep Neural Network (DNN) counterpart. The SNN model has a lower memory footprint compared to a corresponding Arithmetic Neural Network (ANN) model and lower latency, which exactly fit the requirement for on-board computation in small satellite.

Abstract: Detecting cancer early can significantly reduce mortality rate, but this still remains a challenge owing to shortcomings in early screening and detection with existing modalities. Cancer detection is done using known screening methods such as X-ray mammography, Magnetic Resonance Imaging (MRI) and Ultrasound imaging (US). But these conventional methods have their own limitations such as compression discomfort, inherent health risks, expensive, and consume more time and effort. Present disclosure provides system and method for enhanced microwave imaging (MWI) for efficient breast tumor detection by scanning subject's specific body portion to optimize the scan duration. The MWI is framed as an inverse problem by building forward model using a Point Spread Function (PSF) and is solved by imposing sparsity prior since tumor is concentrated to limited regions. The entire scanning duration is optimized by viewing the problem as a sequential decision making process for a Deep Reinforcement Learning (DRL) agent.

Abstract: State of the art techniques have challenges for recoloring a product, which includes non-realistic images, incorrect color mapping, structural distortion, color spilling into background, and in handling multi-color, multi-apparel and multi-product scenario images. Embodiments of the present disclosure provide a method and system for recoloring a product using a dual attention (DA) U-Net based on a generative adversarial network (GAN) framework to generate a recolored product with a target color from an input image. The disclosed DAU-Net enables recoloring (i) a single-color in a single-product scenario, (ii) a plurality of colors in a single-product scenario, and (iii) multi-product scenario with a human model.

Abstract: An e-commerce business model has witnessed several cases where packages with faulty goods, returned by buyers, without procured object, rather replaced by different device. This disclosure relates a method to detect whether object under test a desired object. A plurality of back-scattered signals is received from the object under test occluded by packaging with continuous motion on conveyer based on first antenna-radar combination. The plurality of back-scattered signals is processed by applying four-tap difference filter to obtain motion-filtered data matrix. A low pass filter is applied on the motion-filtered data matrix to obtain enveloped motion-filtered data matrix. A sliding constant false alarm rate is applied on the enveloped motion-filtered data matrix to determine detection threshold value. A check is performed to detect whether the object under test is the desired object based on whether intensity of the plurality back-scattered signals exceeds the detection threshold value.

Abstract: This disclosure relates generally to method and system for predicting distance of gazed objects using IR camera. Eye tracking technology is widely used to study human behavior and patterns in eye movements. Existing gaze trackers focus on predicting gaze point and hardly analyzes distance of the gazed object from the gazer or directly classify region of focus. The method of the present disclosure predicts gazed objects distance using a pair of IR cameras placed on either side of a smart glass. The gaze predictor ML model predicts distance at least one gazed object positioned from eye of each subject during systematic execution of a set of tasks. From each pupillary information of each pupil a set of features are extracted which are utilized to classify the gazed object of the subject based on the distance into at least one of a near class, an intermediate class, and a far class.

Abstract: In recent years, researchers have been focusing on the capabilities of radar-based microwave imaging in detection of concealed objects in sealed packages, through-the-wall imaging approach which faces challenge in suppress unwanted return signals. This disclosure relates a method to detect objects in sealed packages. One or more parameters associated with a conveyor are received to obtain a scan time of a radar. A sequence of scanning is determined between one or more antenna pairs based on corresponding position. A sealed package is scanned in the predetermined sequence to obtain range-time datasets with identifiers. The range-time datasets are processed by virtual antenna-pattern-weighted delay-multiply-and-sum technique based on one or more positions of each virtual antenna to determine one or more object signature images. A trained classification model based on extracted features associated with one or more object signature images.

Abstract: Conventional ESPRIT (Estimation of Signal Parameters via Rational Invariance Techniques) cannot be directly applied to SFCW MIMO radar for localization of targets as the performance would be restricted by geometry of spatial MIMO. Thus, the present disclosure provides a method and system for localization of targets using SFCW MIMO radar. In this method, the channel response of the virtual uniform rectangular array (vURA) obtained by scanning at uniformly spaced frequency points is combined to form a larger array referred as Space-Frequency (SF) array. The 3D localization of targets is done by estimating azimuth angle, elevation angle and range using this SF array. The localization capability of the disclosed method largely depends upon the number of frequency scanning points and enables localizing far more targets than the dimension of the vURA. In addition, the inter-element spacing requirement of vURA is also greatly relaxed.

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.

Abstract: Temperature measurement is an important part of many potential applications in the fields of metallurgy. Conventional temperature measurement methods do not provide accurate and precise average temperature of fluid inside an enclosed chamber. The present disclosure provides multi-sensory techniques for measuring average temperature of mixed fluid inside a chamber. The average temperature is measured based on acoustic interferometry technique on standing wave and inputs received from one or more sensors and radar. The present disclosure utilizes radar to compensate the effect of fumes, noise based on Doppler effect. Further, the inputs received from the one or more sensors are used to determine the concentration of one or more fluids present in the chamber. The method of proposed disclosure depends on the principle of dependence of temperature on sound speed in fluid. So, measurement of sound speed can be mapped to report average temperature of mixed fluid inside the chamber.

Abstract: The present disclosure provides methods and systems for task adaptation using fuzzy deep learning architecture. In the present disclosure, a low-shot approach for knee injury classification is proposed along with a deep learning architecture utilizing a fuzzy layer. For the low-shot approach, a stage of knowledge transfer takes place from a first classification task (source task) to a second classification task (target task) through a task adaptation approach. The first classification task and the second classification task are two related diagnoses of the knee, where sufficient labeled samples are available for first classification task but very few labeled samples are available for and the second classification task. Further, the trained fuzzy deep learning architecture is used to generate pseudo-labels for a collection of unlabeled samples available for and the second classification task.

Abstract: This disclosure relates generally to method and system for spiking neural network based ECG classifier for wearable edge devices. Employing deep neural networks to extract the features from ECG signal have high computational intensity and large power consumption. The spiking neural network of the present disclosure obtains a training dataset comprising a plurality of ECG time-series data. The spiking neural network comprise a reservoir-based spiking neural network and a feed forward based spiking neural network. Each of the spiking neural network having a logistic regression-based ECG classifier are trained to classify one or more class labels. The peak-based spike encoder of each spiking neural network obtains a plurality of encoded spike trains from the plurality of ECG time-series. The peak-based spike encoder provides high performance for classifying one or more labels. Efficacy of the peak-based spike encoder for classification is experimentally evaluated with different datasets.

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.