Abstract: 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.

Abstract: Multi-sensor fusion is a technology which effectively utilizes the data from multiple sensors so as to portray a unified picture with improved information and offers significant advantages over existing single sensor-based techniques. This disclosure relates to a method and system for a multi-label classification using a two-stage autoencoder. Herein, the system employs autoencoder based architectures, where either raw sensor data or hand-crafted features extracted from each sensor are used to learn sensor-specific autoencoders. The corresponding latent representations from a plurality of sensors are combined to learn a fusing autoencoder. The latent representation of the fusing autoencoder is used to learn a label consistent classifier for multi-class classification. Further, a joint optimization technique is presented for learning the autoencoders and classifier weights together.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.