Abstract: Existing works perform subsurface imaging without paying attention to the presence of surface undulation, which can cause irregular travel paths for waves emitted by a Radar and may produce defocusing effects in the reconstructed image. The present disclosure estimates noise level of a LiDAR in a stationary mode by considering the standard deviation of a LiDAR sensor data. Successive LiDAR values at every position are tracked for a pre-defined time intervals and if it satisfies a LiDAR stabilization criterion, the LiDAR is considered stable and the LiDAR value obtained is logged as a LiDAR elevation data. A two-layer velocity model is estimated from the LiDAR elevation data and a phase shift operator of a Phase shift migration (PSM) is modified by incorporating the two-layer velocity model. Surface normal information is extracted from the LiDAR elevation data and an amplitude correction factor is formulated using the extracted surface normal information.

Abstract: Volume solids (VS) is considered to be a very important and critical property of paint in paint related industries. Conventional methods for VS measurement are usually cumbersome, time-consuming and laborious. The present disclosure resolves the problems of the conventional approaches by providing a system and method for predicting volume solids measurement of paints based on non-invasive photoacoustic sensing. The present disclosure provides a non-invasive and compact photoacoustic (PA) sensing method with artificial intelligence (AI) model to predict volume solid measurements of paint samples. In the method of the present disclosure, a time domain photoacoustic (PA) signal obtained from the paint is used to predict the volume solids measurements. A temporal shift of the time domain PA signal is used as a feature and a regression based prediction model is trained to predict volume solids measurements of the paint samples.

Abstract: State of art techniques for measurement of blood viscosity, RBC concentration and the like are in vitro methods. Embodiments of the present disclosure provide a method and system for in vivo non-invasive measurement of blood parameters using Laser Enabled Swept Frequency Acoustic Interferometry (LE-SFAI) principle. A Continuous Wave Laser diode based Photo Acoustic (CWPA) signal in frequency domain generated from laser excitation incident on a biological tissue of a subject carrying blood vessel is acquired by the ultrasound sensor and converted to a windowed time domain CWPA signal limited to two peaks. The signal characteristics such as risetime, amplitude, and time difference between first peak and the second peak are obtained to derive blood viscosity, Red Blood Corpuscles (RBC) concentration, and blood vessel diameter.

Abstract: Phase gradient metasurface surface plays a crucial role in wireless and satellite communication, radars and remote sensing applications. However, conventional approaches for obtaining high gain of an incident wave suffer a low phase variation. The present disclosure provides a ring loaded alford-loop based phase gradient metasurface lens for x-band applications to achieve 0-360° transmission phase variation. The phase gradient metasurface lens of present disclosure includes a two-dimensional periodic array of a plurality of unit cells arranged as a M*N matrix along x-axis and y-axis. Each of the plurality of unit cells is a four layered slot typed structure with a periodicity. Each of a plurality of unit cell layers in the four layered slot typed structure comprises a modified alford-loop structure with four L-shaped arcs and it is enclosed by an outer square ring. The four layered slot typed structures are identical and separated by an air gap.

Abstract: Existing telescope designs require increasing number of sub-apertures for optimum performance. However, with the increasing number of sub-apertures, number and amplitudes of sidelobes increase. Along with that, increase in the primary maxima, resulting trade-off of imaging quality. An optically sparse primary aperture for high spatial resolution imaging is disclosed, comprising central sub-aperture and plurality of peripheral sub-apertures, the peripheral sub-apertures form concentric patterns around 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: The embodiments of the present disclosure herein address unresolved problems of unavailability of a scalable framework to enable large spectrum of optical response-design combination with reduced computing time. Further, there are limitations in the choice of features to obtain the desired sensitivity. Embodiments herein provide a method and system for a generative artificial intelligence (GenAI) based sensor design synthesis of metamaterial. Herein, the sensor design synthesis framework is based on deep generative models. The GenAI based design synthesis model would provide that unknown information that would immensely be useful for optimizing the design towards the highest sensitivity, addressing the limitations in the choice of features using numerical simulator and easiest fabrication-feasibility following sensitivity response (SenR). The SenR is the sensitivity relationship between the physical properties of the ambient medium and the property of the sensor.

Abstract: Monitoring and detection of oedema severity is crucial for effective treatment of the underlying ailments. Conventional methods involve setups that require harmful radiation, are expensive, tedious, and bulky. The present disclosure provides a system and method for unobtrusive oedema severity classification and quantification using microwave sensing. A vector network analyzer, a microstrip RF patch antenna customized for operation at 4 GHz under dielectric loading of a numerical human phantom and associated RF components are used for oedema severity monitoring of a sample under test. For oedema severity classification, a machine learning (ML) based approach is implemented making use of reflection parameters, and accordingly physics-based electromagnetic features are extracted. The physics-based electromagnetic features are used to classify the sample under test into a specific oedema severity category.

Abstract: Polarization is a parameter that plays a pivotal role in communication during transmission and reception of signals. Presence of lumped elements along a signal path or around a region of interaction of incoming waves with surface may trigger distortion in the signals, leading to loss or damage of information. Generally, electronic lumped components are used but their functional abilities are limited during practical operations. The present disclosure addresses the unresolved problems of the conventional system by providing a system and method for real time multi-frequency operation using metasurface-based mechanically controlled tunable polarization conversion.

Abstract: This disclosure relates generally to a method and system for local interpolation function (LIF) based synthetic aperture radar (SAR) imaging for irregular scanning geometries. Current available methods are computationally expensive, sensitive to initialization and lacks generalization, particularly when applied across sampling schemes in continuous sampling scale scenarios which is a key characteristic of irregular scanning geometries. The present disclosure achieves enhanced SAR image across various spatial coordinates. Noisy SAR images are obtained using Non-uniform Fast Fourier Transform (NUFFT) based Range Migration Algorithm (RMA). The noisy SAR image is mapped into a LIF feature map using an encoder of a trained LIF neural network. An enhanced SAR image is further predicted based on the LIF feature map using a decoder of the LIF neural network.

Abstract: This disclosure relates generally to metasurface beam steering antenna and method of setting antenna beam angle. Conventional approaches perform electronically beam steering using phase array which requires bandwidth with higher data rates. The present disclosure enables metasurface antennas tilt antenna beam in a given direction, where the varactor diodes are operated in reverse bias so that different values of capacitors combination lead to electronic beam scanning. The processor of the metasurface beam steering antenna receives a command having an input angle to tilt the angle beam position. The processor processes the command by mapping the input angle with the set of c-shaped copper patch combination having the capacitor values using a predefined lookup table for setting the antenna beam angle based on a reference voltage generated by the varactor diode. The lookup table is iteratively updated with the capacitor values of the c-shaped copper patches.

Abstract: This disclosure relates generally to a composite reconfigurable intelligent surface system for real time beam steering and method thereof. Conventional method for real time beam steering includes bulky systems with several antenna elements and amplifiers to shape and control the beam. The present disclosure provides a composite reconfigurable intelligent surface (CRIS) for real time beam steering. The CRIS is formed from a set of constituent reflecting surfaces which in turn are generated from unit cells. These unit cells are responsible for providing individual phase reflection performance utilizing the installed varactor diode in each unit cell. The CRIS can provide a) multi-beam reflection performance, b) polarization sensitive operation, c) 2D beam steering abilities, d) real-time beam control, e) null point reduction for beam shaping requirements and f) dependable pattern stability over a suitable range of frequencies.

Abstract: In sensor data analytics, physics-based models generate high quality data. However, these models consume lot of time as they rely on physical simulations. On the other hand, generative learning takes much less time to generate data, and may be prone to error. Present disclosure provides system and method for generation of synthetic machine data for healthy and abnormal condition using hybrid of physics based and generative model-based approach. Finite Element Analysis (FEA) is used for simulating healthy and faulty parts in machinery with set of parameters and pre-condition(s). Small output data from FEA is fed into a generative model for generating synthesized data by learning data distribution knowledge and representing into latent space. Rule engine is built using statistical features wherein realistic bounds serve as faulty data indicators. Synthesized data which does not satisfies features bounds are discarded. Further, AI-based validation framework is used to analyze quality of synthesized data.

Abstract: This disclosure generally relates to the field of structural health monitoring, and, more particularly, to a method and system for evaluating residual life of components made of composite materials. Existing methods require performing computational methods such as Finite Element Analysis (FEA) on the results of Non-Destructive Testing (NDT) every time a component is inspected. This makes the process expensive and time-consuming. Thus, embodiments of present disclosure provide a method wherein NDT is performed using different sensing methods such as ultrasound, ultrasound pulse echo, thermography to determine type of defect, location of defect and depth of defect in a test component which are then fed into a pre-trained machine learning model to predict residual life of the component. Testing time is greatly reduced since the pre-trained machine learning model is trained offline using results of the computational methods.

Abstract: The usage of microwave imaging for biomedical (BMWI) applications is still challenging due to the imprecise reconstruction of the relative permittivity of tissues and the ill-posed inverse scattering problem. Anomaly detection in biological tissues demand in-vivo, non-invasive, and non-contact measurements. Considering and proving the anomaly as a point object in the microwave imaging is erroneous and results in false implications about the anomaly's presence, location, and characteristics. Present disclosure provides systems and methods for anomaly detection in biological tissues. An intensity map of target region is generated to detect tumor. Area around the tumor is processed to obtain a refined image. As the tumor is embedded in tissues of higher dielectric constant, image thus formed is larger in size than actual tumor. An iterative numerical computation method is implemented to estimate relative permittivity.

Abstract: The present invention relates to a method and system for Phaseless Passive Synthetic Aperture Radar (PPSAR) imaging. Existing method for image reconstruction requires large number of measurements for satisfactory PPSAR image reconstruction. However, this leads to provisioning of more on-board storage and/or a high-speed data link between a mobile platform and a ground station. These requirements are undesirable in practice as PPSAR image reconstruction systems are deployed on resource constrained platforms. The present disclosure uses a regularized Wirtinger Flow (rWF) based approach that uses appropriate regularizers to facilitate the PPSAR image reconstruction with fewer measurements. Further the PPSAR image reconstruction is achieved using Alternating Direction Method of Multipliers (ADMM) by employing standard denoisers such as Total Variation (TV), Block-matching and 3D filtering (BM3D) and, Deep Image Prior (DIP).

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: 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: In fluids, viscosity changes with shear rate, thus measuring viscosity during flowing conditions is essential. Conventionally, rheometers are used for measurement of fluid viscosity but high cost limits its usage. The present disclosure provides systems and methods for real-time measurement of fluid viscosity in flowing conditions using photoacoustic sensing. In the present disclosure, a set of viscosity features are extracted from a plurality of frequency domain photoacoustic (FDPA) signals. The set of viscosity features determine viscosity measurements with high accuracy. A viscosity model is trained from the plurality of FDPA signals when a fluid sample is under static condition. However, same viscosity model is used to measure the viscosity of the fluid sample in flowing conditions by adding an error correction factor. The error correction factor enables training of the viscosity model in static conditions and measuring the viscosity in the flowing conditions in real time.

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: 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.