Abstract: 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.

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: 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: 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 ? ambiguity. The embodiments of the proposed disclosure provide for resolving ? 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.

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 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.

Abstract: This disclosure relates to attention shifting of a robot in a group conversation with two or more attendees, wherein at least one of them is a speaker. State of the art has dealt with several aspects of Human-Robot Interaction (HRI) including responding to a source of sound at a time, addressing a fixed viewing area or determining who is the speaker based on eye gaze direction. However, attention shifting to make the conversation human-like is a challenge. The present disclosure uses audio-visual perception for speaker localization. Only qualified direction of arrivals (DOAs) are used for the audio perception. Further the audio perception is complimented by visual perception employing real time face detection and lip movement detection. Use of HRI rules, clustering of the DOAs, dynamic adjustment of rotation of the robot and a dynamically updated knowledge repository enriches the robot with intelligence to shift attention with minimum human intervention.

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: This disclosure relates generally to field agnostic source localization. Conventional state-of-the-art methods perform source localization for near-field scenario by estimating carrier frequency and direction of arrival (DOA) at or above Nyquist sampling rate. Embodiments of the present disclosure provide a method for source localization at sub Nyquist sampling rate. The method estimates parameters such as range, carrier frequency and DOA of source signals from data sources in a mixed field scenario. i.e., the data sources may reside in far-field as well as near-field. The method considers a delay channel to a sensor receiver architecture for estimating the parameters. The disclosed method can be used in applications like cognitive radio to determine the carrier frequency, DOA and range of various source signals from data sources in mixed field.

Abstract: This disclosure relates generally to field agnostic source localization. Conventional state-of-the-art methods perform source localization for near-field scenario by estimating carrier frequency and direction of arrival (DOA) at or above Nyquist sampling rate. Embodiments of the present disclosure provide a method for source localization at sub Nyquist sampling rate. The method estimates parameters such as range, carrier frequency and DOA of source signals from data sources in a mixed field scenario. i.e., the data sources may reside in far-field as well as near-field. The method considers a delay channel to a sensor receiver architecture for estimating the parameters. The disclosed method can be used in applications like cognitive radio to determine the carrier frequency, DOA and range of various source signals from data sources in mixed field.

Abstract: This disclosure relates to attention shifting of a robot in a group conversation with two or more attendees, wherein at least one of them is a speaker. State of the art has dealt with several aspects of Human-Robot Interaction (HRI) including responding to a source of sound at a time, addressing a fixed viewing area or determining who is the speaker based on eye gaze direction. However, attention shifting to make the conversation human-like is a challenge. The present disclosure uses audio-visual perception for speaker localization. Only qualified direction of arrivals (DOAs) are used for the audio perception. Further the audio perception is complimented by visual perception employing real time face detection and lip movement detection. Use of HRI rules, clustering of the DOAs, dynamic adjustment of rotation of the robot and a dynamically updated knowledge repository enriches the robot with intelligence to shift attention with minimum human intervention.

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: Systems and methods for reconstructing super-resolution images under total aliasing based upon translation values. Traditional systems and methods provide for extracting super-resolution (SR) image/s from low-resolution (LR) image/s but include aliasing as a background noise which degrades the performance or include aliasing into an image model which leads to an enormously high computational complexity. Embodiments of the present disclosure provide for reconstructing super-resolution images based upon translation values by taking aliasing in consideration by capturing a set of LR images, estimating, using a Fast Fourier Transformation, a set of translation values based upon the set of LR images, obtaining, using a multi-signal classification technique, one or more optimized frequency spectrums based upon the set of translation values and reconstructing one or more SR images based upon the set of translation values.

Abstract: Detecting sound sources in a physical space-of-interest is challenging due to strong ego-noise from micro aerial vehicles (MAVs)' propeller units, which is both wideband and non-stationary. The present subject matter discloses a system and method for acoustic source localization with aerial drones. In an embodiment, a wideband acoustic signal is received from an aerial drone. Further, the wideband acoustic signal is splitted into multiple narrow sub-bands having cells. Moreover, from a measurement position corresponding to each of the multiple narrow sub-bands, power in each of the cells is measured by forming a beam to each of the cells. In addition, intra-band and inter measurement fusion of the measured power at each of the cells is performed. Also, geo-location of an acoustic source corresponding to the wideband acoustic signal is identified upon performing intra-band and inter measurement fusion of the measured power.

Abstract: This disclosure relates generally to image processing, and more particularly to generate a high resolution image from multiple low resolution images. In an embodiment, the system collects a plurality of low resolution images as input, processes the collected images, and generates a high resolution image as output. During this process, the system pre-processes the collected low resolution images, and during this process, at least one characteristic of each of the low resolution image is changed with respect to a reference image. After the pre-processing stage, the images are then enhanced based on a plurality of final enhancement factors that are dynamically determined. Further, the enhanced images are fused to generate the high resolution image.

Abstract: 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 ? ambiguity. The embodiments of the proposed disclosure provide for resolving ? 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.

Abstract: Systems and methods for reconstructing super-resolution images under total aliasing based upon translation values. Traditional systems and methods provide for extracting super-resolution (SR) image/s from low-resolution (LR) image/s but include aliasing as a background noise which degrades the performance or include aliasing into an image model which leads to an enormously high computational complexity. Embodiments of the present disclosure provide for reconstructing super-resolution images based upon translation values by taking aliasing in consideration by capturing a set of LR images, estimating, using a Fast Fourier Transformation, a set of translation values based upon the set of LR images, obtaining, using a multi-signal classification technique, one or more optimized frequency spectrums based upon the set of translation values and reconstructing one or more SR images based upon the set of translation values.

Abstract: Detecting sound sources in a physical space-of-interest is challenging due to strong ego-noise from micro aerial vehicles (MAVs)? propeller units, which is both wideband and non-stationary. The present subject matter discloses a system and method for acoustic source localization with aerial drones. In an embodiment, a wideband acoustic signal is received from an aerial drone. Further, the wideband acoustic signal is splitted into multiple narrow sub-bands having cells. Moreover, from a measurement position corresponding to each of the multiple narrow sub-bands, power in each of the cells is measured by forming a beam to each of the cells. In addition, intra-band and inter measurement fusion of the measured power at each of the cells is performed. Also, geo-location of an acoustic source corresponding to the wideband acoustic signal is identified upon performing intra-band and inter measurement fusion of the measured power.