In this work we present an experimental demonstration of the comparative study of imaging quality of one of the non-uniform sized (NUS) optically-sparse aperture (OSA) mirror configurations - the Taylor-ln design as proposed in [1] against that of a conventional segmented mirror having sub-apertures of equal size, in laboratory. The perfomance of the proposed NUS OSA mirrors are noted to be equivalent to those of uniform-sized segmented mirrors, using simulations in the ideal scenario of noiseless imaging systems. Therefore, it is of utmost importance to demonstrate the efficiency of such NUS OSA mirror configurations, in the laboratory frame at least, so that real-life applications of these designs can be surmised. Therefore, in this paper we design masks that mimic an uniform sized segmented mirror with each sub-aperture as large as 2 cm and a NUS OSA mirror whose aperture sizes vary as the coefficients of the Taylor expansion series for ln. We consider only the average intensity information of the images captured by these two types of segmented mirrors. Thereafter, simple deconvolution with respective Point Spread Functions (PSFs) results in nearly similar reconstructed images, at least by visual inspection. In case of the uniform sized segmented mirror the Peak Signal-to-Noise-Ratio (PSNR) is 7.8 dB alongwith the Structural Similarity Index Measure (SSIM) being 0.1. The respective values for the NUS system are similar (8.8 dB and 0.24, respectively). This explicitly demonstrates very little degradation of performance even though the fill factor for the NUS design is reduced to 31% of the uniform OSA mirror. However, we emphasize on the fact that this is a very basic experimental setup, demonstrating the efficiency of imaging for such NUS systems and suggest further improvement of the reconstructed image quality with better quality mirrors used for the purpose as well as incorporating advanced reconstruction algorithms coupled with a suitable denoising algorithm. The success of this kind of an experiment is a promising breakthrough to encourage implementation in real-life ultra-lightweight space-based telescopic systems, encompassing a wide plethora of scientific objectives viz., astronomy, natural resources as well as infrastructure monitoring, security and surveillance and even disaster prediction like earthquakes.

Any high spatial resolution space-borne electro-optical sensing system operating in long wavelengths, like Earth-observation facilities operating in the Longwave Infrared are subjected to an inherent design and implementation challenge of deploying large monolithic primary aperture mirrors. To outflank this issue, many present-date missions design and commission lightweight segmented mirrors, mostly with equal sized sub-apertures. To go one step ahead, these sub-apertures could be of non-uniform sizes, in a given proportion, thereby ensuring an even smaller and lighter primary, with a marginal compromise in imaging quality. However, a fragmented mirror is very sensitive to failure of one or more of the sub-apertures, in some random fashion (most likely being hit by space debris or surge of high energy particles) or in a cluster (possibly due to failure of the actuators or control system). In such scenarios, the system suffers loss of the high-frequency components to various extents, so that the edge detection in the reconstruction process (deconvolution) becomes questionable. This is particularly true for the central sub-aperture, rendering it as the most critical component in the partially-filled primary aperture design. In such cases, the imaging system malfunctions as a whole, thereby defeating the purpose of designing of such ultra-lightweight optically-sparse primary aperture mirror with non-uniform sized sub-apertures for satellite-based sensing systems.

With the advent of industry 4.0, data-driven techniques have gained a lot of popularity for machine condition monitoring, ensuring reliable and safe operation of the machines. In most practical application scenarios, domain discrepancy may arise between the training (source domain) and test (target domain) data due to various factors like changes in the operating conditions, different sensor locations, etc. Traditional data-driven techniques fail to address this domain shift, and hence domain adaptation techniques are required to ensure reliable performance. This work presents an unsupervised domain adaptation method where labeled data is available only in the source domain via subspace interpolation using deep dictionary learning. Deep dictionaries learn rich representations from the data and hence are used for subspace interpolation to capture the domain shift and form a shared feature space for cross-domain analysis. The proposed method is evaluated for the challenging scenario of adaptation between different but related machines. Experimental results obtained with two publicly available bearing fault datasets are promising; the proposed method significantly outperforms all the state-of-the-art methods.

Scientific objectives from earth observation to astronomy require high-resolution observations from space-based platforms. However, designing space telescopes with large primary apertures to achieve high-resolution and high Signal-to-Noise Ratio observations, especially for those operating in longer wavelengths (like Thermal Infrared, TIR), is not feasible due to difficulties in manufacturing, launching, and post-deployment stabilizing. This work proposes three novel lightweight, optically-sparse (also known as partially-filled) mirrors with non-uniform sub-aperture sizes. These designs reduce the mass of the primary mirror and its supporting framework. The crux of these designs is, however, significant suppression of sidelobes in the resulting Point Spread Functions (PSFs). The study includes restored images and image quality indices, demonstrating the effectiveness of such lightweight unequal sub-apertures as replacements for large monolithic mirrors with only a marginal loss in performance.

In this paper, we propose StereoGest-SNN which is a robust low power edge compatible ultrasound based gesture detection system. StereoGest-SNN uses easily available off-the-shelf stereo speaker and microphone setup inbuilt in most commercial devices. The stereo setup mimics a$2\times 2$Multiple Input Multiple Output (MIMO) system which provides the requisite diversity to effectively address fading. It also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A 5-layer CNN has been trained on these distinctive CIRs and the trained model is converted into an equivalent Spiking Neural Network (SNN) via an ANN-to-SNN conversion mechanism. The StereoGest-SNN with converted SNN is found to classify gestures with an accuracy of around 92% (an improvement of over 6% compared to state-of-the-art) and shows$3\times$reduction in number of operations compared to CNN resulting in further energy benefit when run on neuromorphic computation platforms.

In this paper, we propose EchoWrite-SNN, a robust edge compatible air-writing recognition system (used in applications such as AR/VR, HRI etc.) based on principles of SONAR and neuromorphic computing. The bare finger movements in air are captured by a pair of commonly available speaker-microphone pair. A new tracking algorithm based on windowed difference cross-correlation and ESPRIT is employed which shows better tracking accuracy compared to state-of-the-art methods with a median tracking error of only 3.31mm. To classify these air-written shapes, a 5-layer CNN is trained and then converted to a Spiking Neural Network (SNN) using ANN-to-SNN conversion technique to reap the benefits of low power neuromorphic computing on edge. Experimental results show that the converted SNN achieves 92% accuracy (a mere 3% less than the CNN) while showing 4.4 × reduction in number of operations compared to CNN resulting in further energy benefit when run on actual neuromorphic computation platforms.

Fault diagnosis plays a vital role in ensuring the normal operation of the machine and safe production. In recent years, data-driven techniques have gained a lot of popularity for machine fault diagnosis. But most of these techniques assume the training and test data have the same distribution. However, in most practical application scenarios, domain discrepancy can be observed between the training (source) and test (target) data due to different factors like changes in the operating conditions, different sensor locations, etc. Classical approaches fail to address such domain discrepancy, which leads to poor performance. The problem becomes more challenging when the target is completely unlabeled. To address this scenario, domain adaptation techniques are used to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, adversarial network based domain adaptation has been extensively explored for fault diagnosis. But the adversarial loss alone does not guarantee the translation of the source to the desired target domain (class consistent). Here, we propose to use cycle-consistency loss employing 1D-CycleGAN for learning the source to target mapping for unsupervised adaptation for bearing fault diagnosis. The proposed method is evaluated for two different scenarios, with the source and target from (i) same machine but different working conditions and (ii) different but related machines. Experimental results show that while the proposed method performs comparable to the best-performing benchmark for the first case, it significantly outperforms all the state-of-the-art methods for the challenging second case.

Insights from multiple imaging modalities have recently been applied in solving many computer vision related applications. In this paper, we model the cross-modal dependencies between different modalities for Multimodal Image Super-Resolution (MISR), i.e., enhance the Low Resolution (LR) image of target modality with the guidance of a High Resolution (HR) image from another modality. We introduce a joint optimization based transform learning frame-work referred to as Joint Coupled Transform Learning (JCTL) to combine the information from multiple modalities to generate the HR image of the target modality. All the necessary intermediate steps and the corresponding closed form solution updates are pro-vided. The performance of the proposed JCTL is benchmarked against the state-of-the-art MISR approaches on different multi-modal datasets with different upscaling factors. The results show better performance with the proposed JCTL approach compared to other state-of-the-art techniques both in terms of PSNR and SSIM.

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

In this paper, we present a kernelized dictionary learning framework for carrying out regression to model signals having a complex nonlinear nature. A joint optimization is carried out where the regression weights are learnt together with the dictionary and coefficients. Relevant formulation and dictionary building steps are provided. To demonstrate the effectiveness of the proposed technique, elaborate experimental results using different real-life datasets are presented. The results show that non-linear dictionary is more accurate for data modeling and provides significant improvement in estimation accuracy over the other popular traditional techniques especially when the data is highly non-linear.

Micro aerial vehicles (MAVs), an emerging class of aerial drones, are fast turning into high value mobile sensing assets. While MAVs have a large sensory gamut at their disposal; vision continues to dominate the external sensing scene, with limited usability in scenarios that offer acoustic clues. Therefore, we endeavor to provision a MAV auditory system (i.e., ears); and as part of this goal, our preliminary aim is to develop a robust acoustic localization system for detecting sound sources in the physical space-of-interest. However, devising this capability is extremely challenging due to strong ego-noise from the MAV propeller units, which is both wideband and non-stationary. It is well known that beamformers with large sensor arrays can overcome high noise levels; but in an attempt to cater to the platform (i.e., space, payload and computation) constraints of a MAV, we propose DroneEARS: a binaural sensing system for geo-locating sound sources. It combines the benefits of sparse (two elements) sensor array design (for meeting the platform constraints), and our proposed mobility-aided beamforming (for overcoming the severe ego-noise and its other complex characteristics) to significantly enhance the received signal-to-noise ratio (SNR). We demonstrate the efficacy of DroneEARS by empirical evaluations, and show that it provides a SNR improvement of 15-18 dB compared to many conventional and widely used techniques. This SNR gain translates to a source localization accuracy of approximately 40 cm within a scan region of 6m × 3m , that is, one order of magnitude better than competing methodologies.

In this paper, we address the important problem of direction of arrival (DOA) estimation using a single directional antenna under the multipath environment. We address this practically important problem by following a three step approach; i) Restoration, ii) Separation and iii) Estimation. By assuming constant modulus signal similar to earlier works, we employ the constant modulus algorithm (CMA) to restore the constant modularity which gets destroyed due to multipath, in the restoration step. In the subsequent separation step, using the restored signal as the reference, we separate the power profile corresponding to each of the direct and reflected paths. Further, to estimate DOA from these separated power profiles, a compressed sensing based algorithm is also proposed. Both the simulation and experimental results show significantly better multipath mitigation with the proposed approach than those by the existing approaches.

The ubiquitous location sensing trend has increased the demand for low-cost GPS receivers; but their energy needs are still too high. For delay-tolerant applications, investigations show that significant energy saving can be obtained by offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS+. Based on the sparse decomposition model, it overcomes many limitations of SparseGPS [1] to yield better signal-to-noise ratio and detection accuracy; which translates to 30% more energy savings compared to the state-of-the-art.

We propose a novel real-time system design for multiband signal blind reconstruction using multi-coset sampling theory. Multi-channel signals are acquired under sub-Nyquist sampling frequency to perfectly reconstruct the original signal spectrum. A novel system design with Field-Programmable Gate Array (FPGA) implementation is presented in this paper. There are two main contributions in this paper. Firstly, the FPGA system uses 32-bit single precision floating point dataflow rather than conventional 16-bit fixed point to recover signals with much lower Signal-Noise Ratio (SNR). Secondly, we introduce a novel Jacobi CORDIC eigenvalue decomposition (EVD) core using parallel pivot-seeking circuit and parallel 3-CORDIC design to improve speed significantly. Hermitian matrices of dimensions from 2 to 10 are tested to compare conventional 2-CORDIC EVD and proposed EVD. The proposed EVD effectively reduces on average 36% of processing time for mesh connection system and over 50% for parallel system.

Spectrum blind reconstruction and direction-of-arrival (DOA) estimation of multiple narrow-band signals spread over wide spectrum sampled at sub-Nyquist sampling rates are considered in this paper. A new sub-Nyquist sampling receiver architecture which requires minimal hardware along with an efficient algorithm for estimation of the parameters and spectrum reconstruction is presented. We further show with the proposed approach, that a minimum average sampling rate of 2(N + 1)B would be sufficient in order to reconstruct the spectrum as well as estimate their corresponding DOA of N narrow-band signals of maximum bandwidth B. Simulation results are also provided which shows the performance very close to the Cramer Rao bound.

This paper considers spectrum-blind reconstruction (SBR) of multi-band signals (MBS) which are sampled with multi-coset sampling (MCS) architecture. A new SBR algorithm which we refer to as Khatri-Rao SBR (KR-SBR) is presented. With this new KR-SBR algorithm, the average MCS rate can be reduced by 50% whilst attaining the same performance as that of the existing state-of-art SBR algorithms. Under certain conditions we will also show that the proposed KR-SBR algorithm has the capacity to achieve SBR when the average MCS rate approaches the Landau sampling rate. Simulation results are also presented to demonstrate the advantages of the proposed KR-SBR algorithm.