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.

Abstract: This disclosure relates to non-destructive estimation of coating layer thickness based on sweep frequency photo acoustic guided wave technique. Coating of a substrate/surface protects it from wear, corrosion and serves the cosmetic aspect, hence making coating technology is an essential part industrial process. The existing techniques for coating thickness determination are either destructive or requires a prior knowledge of the refractive index of the surface under investigation or use of sophisticated instrumentation, complicated procedure and harmful radiation during industrial deployment. The disclosure utilizes an intensity modulated Continuous Wave (CW) laser diode to excite a sample thus, making the technique a partially contact based method. Further a calibration curve is plotted by determining a frequency spectrum and resonance frequency. The calibration curve is used for estimation of a coating layer thickness.

Abstract: 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.

Abstract: Conventional transport mode detection relies either on GPS data or uses supervised learning for transport mode detection, requiring labelled data with hand crafted features. Embodiments of the present disclosure provide a method and system for identification of transport modes of commuters via unsupervised learning implemented using a multistage learner. Unlabeled time series data received from accelerometer of commuters mobiles from a diversified population is processed using a unique journey segment detection technique to eliminate redundant data corresponding to stationary segments of commuter or user. The non-stationary journey segments are represented using domain generalizable Invariant Auto-Encoded Compact Sequence (I-AECS), which is a learned compact representation encompassing the encoded best diversity and commonality of latent feature representation across diverse users and cities.

Abstract: Synthetic data is an annotated information that computer simulations or algorithms generate as an alternative to real-world data. synthetic data is created in digital worlds rather than collected from or measured in the real world. Embodiments herein provide a method and system for generating synthetic data with domain adaptable features using a neural network. The system is configured to receive seed data from a source domain as an input data. The seed data is considered as a normal state of a machine. The normal state, which is an initial stage of the source domain, consists of a set of features with a certain range of values. Further, a neural network based model is used to generate high quality data with adaptation of the domain specific features. To obtain large amount data for training robust deep learning models to adapt domains emphasizing set of features/providing higher importance selectively.

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: Embodiments of the present disclosure provide a method and system for co-operative and cascaded inference on the edge device using an integrated Deep Learning (DL) model for object detection and localization, which comprises a strong classifier trained on largely available datasets and a weak localizer trained on scarcely available datasets, and work in coordination to first detect object (fire) in every input frame using the classifier, and then trigger a localizer only for the frames that are classified as fire frames. The classifier and the localizer of the integrated DL model are jointly trained using Multitask Learning approach. Works in literature hardly address the technical challenge of embedding such integrated DL model to be deployed on edge devices. The method provides an optimal hardware software partitioning approach for components or segments of the integrated DL model which achieves a tradeoff between latency and accuracy in object classification and localization.

Abstract: 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).

Abstract: This disclosure relates generally to the field of Electroencephalogram (EEG) classification, and, more particularly, to method and system for EEG motor imagery classification. Existing deep learning works employ the sensor-space for EEG graph representations wherein the channels of the EEG are considered as nodes and connection between the nodes are either predefined or are based on certain heuristics. However, these representations are ineffective and fail to accurately capture the underlying brain's functional networks. Embodiments of present disclosure provide a method of training a weighted adjacency matrix and a Graph Neural Network (GNN) to accurately represent the EEG signals. The method also trains a graph, a node, and an edge classifier to perform graph classification (i.e. motor imagery classification), node and edge classification. Thus, representations generated by the GNN can be additionally used for node and edge classification unlike state of the art methods.

Abstract: Model-based image reconstruction (MBIR) methods using convolutional neural networks (CNNs) as priors have demonstrated superior image quality and robustness compared to conventional methods. Studies have explored MBIR combined with supervised and unsupervised denoising techniques for image reconstruction in magnetic resonance imaging (MRI) and positron emission tomography (PET). Unsupervised methods like the deep image prior (DIP) have shown promising results and are less prone to hallucinations. However, since the noisy image is used as a reference, strategies to prevent overfitting are unclear. Recently, Bayesian DIP (BDIP) networks that model uncertainty tend to prevent overfitting without requiring early stopping. However, BDIP has not been studied with data-fidelity term for image reconstruction. Present disclosure provides systems and method that implement a MBIR framework with a modified BDIP.

Abstract: The present invention relates to the field of evaluating clinical diagnostic models. Conventional metrics does not consider context dependent clinical principles and is unable to capture critically important features that ought to be present in a diagnostic model. Thus, present disclosure provides a method and system for evaluating clinical efficacy of multi-label multi-class computational diagnostic models. Diagnosis for a given dataset of diagnostic samples is obtained from the diagnostic model which is then classified as wrong, missed, over or right diagnosis, based on which a first penalty is calculated. A second penalty is calculated for each diagnostic sample using a contradiction matrix. The first and second penalties are summed up to compute a pre-score for each diagnostic sample. Finally, the diagnostic model is evaluated using a metric that is based on sum of pre-scores, and scores from a perfect and a null multi-label multi-class computational diagnostic model.

Abstract: Portable ECG monitors available in market have the disadvantage that the ECG data they provide as input aren't directly interpretable and requires medical knowledge for the users. The disclosure herein generally relates to Electrocardiogram (ECG), and, more particularly, to a method and system for generating 2d representation of electrocardiogram (ECG) signals. The system provides a mechanism for determining variability between a plurality of segments of an ECG data measured, and uses the information on the determined variability to generate the 2D representation corresponding to the ECG signal. The system further provides means to generate a data model that can be further used for processing real-time ECG data for generating corresponding interpretations. This allows a user to obtain the interpretations as output.

Abstract: The disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.

Abstract: This disclosure relates to systems and methods for shapelet decomposition based recognition using radar. State-of-the-art solutions involve use of standard machine learning classification techniques for gesture recognition which suffer with problem of dependency on collected data. The present disclosure overcome the limitations faced by the state-of-the-art solutions by obtaining a plurality of time domain signal using a radar system comprising three vertically arranged radars and one or more sensors, identifying one or more gesture windows to obtain one or more shapelets corresponding to one or gestures which are further decomposed into a plurality of sub shapelets. Further, at least one of (i) a positive or (i) a negative time delay is applied to each of the plurality of sub shapelets to obtain a plurality of composite shapelets which are further mapped with a plurality of trained shapelets to recognize gestures comprised in one or more activities performed by a subject.

Abstract: One of the biggest challenges faced by oil and gas companies is to monitor such long pipelines for leak events and generate false leak event alarms during routine pipe maintenance. A data associated with a first sensing unit is processed to obtain an instant timing information (T0) of a leak event in a conduit at a test environment. A data associated with a second sensing unit is processed to obtain a transient signal associated with the leak event at a specific band. An accelerometer data is filtered to obtain a band passed filtered accelerometer signal (Accelbpf). The Accelbpf is truncated in a time domain from the T0 to a duration Td of the leak event to obtain a temporal template signal (Acceltemplate). A leak event of a real-time conduit is dynamically detected at a physical environment based on Acceltemplate when a cross-correlation value is greater than a threshold value (?).

Abstract: This disclosure relates generally to method and system for jointly pruning and hardware acceleration of pre-trained deep learning models. The present disclosure enables pruning a plurality of DNN models layers using an optimal pruning ratio. The method processes a pruning request to transform the plurality of DNN models and the plurality of hardware accelerators into a plurality of pruned hardware accelerated DNN models based on at least one user option. The first pruning search option executes a hardware pruning search technique to perform search on each DNN model and each processor based on at least one of a performance indicator and an optimal pruning ratio. The second pruning search option executes an optimal pruning search technique, to perform search on each layer with corresponding pruning ratio.