Abstract: The present disclosure provides a method for surface wear inspection using millimeter wave radar. The system initially receives a plurality of uncompressed raw Synthetic Aperture Radar (SAR) images. Further, a plurality of reconstructed SAR images are generated based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm (RDA). Further, a master image and a slave image are selected from the reconstructed SAR images and corresponding anchor points are assigned. Further a plurality of fine level and coarse level shift coordinates are computed based on the corresponding anchor points. Further, a net shift value is computed based on the plurality of fine level and coarse level shift coordinates. The master and the slave images are aligned based on the net shift value and the interferogram is generated. The interferogram is further analyzed to profile the corresponding deformation pertaining to the surface under test.

Abstract: This disclosure relates generally to system and method for ambient intelligence based user interaction. Prior methods for touchless user interaction are sensitive to ambient temperature in a lab environment, susceptible to noise from metallic surfaces and ambient radio waves and are dependent on ambient lighting. Embodiments of the present disclosure provides a multi-modal sensor fusion method which captures touchless gestures from a user or a group of users with their physical context information fused and tagged to these gestures for user interaction. Further pose graphs are generated for user interaction systems using a data association technique and Gaussian mixture model technique. The disclosed method provides a hands-free interface to operate instruments in a smart space, using principles of ambient intelligence.

Abstract: Lighting conditions affect quality of images being captured. In traditional systems, illuminators (light sources) are used to illuminate the objects without considering illumination levels at different zones/sides of an object being photographed. As a result, all illuminators may run at maximum capacity, resulting in wastage of power and compromising efficiency of the system. The disclosure herein generally relates to illumination of objects, and, more particularly, to a method and system for adaptive illumination of objects. The system determines illumination at different zones of the object, and further identifies zones that are not illuminated properly in comparison with a threshold of illumination.

Abstract: Ultra Wide Band (UWB) based Real Time Location Systems (RTLS) that are being used for location tracking suffer from due to environment specific errors that are introduced due to factors such as difference in reflection and propagation. The disclosure herein generally relates to object localization, and, more particularly, to a method and system of error modelling for object localization using Ultra Wide Band (UWB) sensors. The error modelling allows the system to correct a determined location of an object being tracked, to determine a corrected location.

Abstract: Radar based HR and BR measurements by simultaneous decoding is a technical problem due to presence of intermodulation of BR and HR harmonics, which degrades simultaneous decoding. Embodiments herein provide a method and system for unobtrusive liveliness detection and monitoring of a subject using a Dual Frequency Radar (DFR) in an IOT network. The system has the capability to completely process the captured raw signals onboard to by applying required signal conditioning and extraction of relevant information using unique signal processing techniques for determining the HR and the BR of the subject accurately. The intermodulation of BR and HR harmonics is eliminated by the system by performing frequency spectrum averaging of both radars signals, which improves the accuracy. Further, the system is configured with a light MQTT protocol and encoding modules for any data to be shared for off board processing, ensuring data security and privacy compliance.

Abstract: Small and compact Deep Learning models are required for embedded Al in several domains. In many industrial use-cases, there are requirements to transform already trained models to ensemble embedded systems or re-train those for a given deployment scenario, with limited data for transfer learning. Moreover, the hardware platforms used in embedded application include FPGAs, AI hardware accelerators, System-on-Chips and on-premises computing elements (Fog/Network Edge). These are interconnected through heterogenous bus/network with different capacities. Method of the present disclosure finds how to automatically partition a given DNN into ensemble devices, considering the effect of accuracy—latency power—tradeoff, due to intermediate compression and effect of quantization due to conversion to AI accelerator SDKs.

Abstract: Any sensing system is faced with triangle of dilemma between accuracy, latency and energy. High energy and high latency sensing systems are often very accurate but less useful. Embodiments herein provide a method and system for edge based sensor controlling in the IoT network for event monitoring. The system disclosed herein applies a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor.

Abstract: Any sensing system is faced with triangle of dilemma between accuracy, latency and energy. High energy and high latency sensing systems are often very accurate but less useful. Embodiments herein provide a method and system for edge based sensor controlling in the IoT network for event monitoring. The system disclosed herein applies a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor.

Abstract: Radar based HR and BR measurements by simultaneous decoding is a technical problem due to presence of intermodulation of BR and HR harmonics, which degrades simultaneous decoding. Embodiments herein provide a method and system for unobtrusive liveliness detection and monitoring of a subject using a Dual Frequency Radar (DFR) in an IOT network. The system has the capability to completely process the captured raw signals onboard to by applying required signal conditioning and extraction of relevant information using unique signal processing techniques for determining the HR and the BR of the subject accurately. The intermodulation of BR and HR harmonics is eliminated by the system by performing frequency spectrum averaging of both radars signals, which improves the accuracy. Further, the system is configured with a light MQTT protocol and encoding modules for any data to be shared for off board processing, ensuring data security and privacy compliance.