Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of use and high-resolution sensing. In this paper, we provide a review of the mmWave radar data processing frameworks, starting from mathematical foundations to applications. Specifically, we focus on the mmWave radar point cloud as a robust data structure representing compressed signatures for target recognition and classification. We first focus on the generation of the radar point clouds, and the signal processing algorithms designed for their unique characteristics. Then, we illustrate how the radar point clouds are prepared for feature extraction and classification using machine learning and deep learning approaches. Finally, we summarize the state-of-the-art applications, open datasets, developments and future research directions in this field.

Atrial fibrillation (AF) is a recurrent and life-threatening disease leading to rapid growth in the mortality rate due to cardiac abnormalities. It is challenging to manually diagnose AF using electrocardiogram (ECG) signals due to complex and varied changes in its characteristics. In this article, for the first time, an end-to-end edge-enabled machine learning-based VLSI architecture is proposed to classify ECG excerpts having AF from normal beats. Researchers have found that abnormal atrial activity is confined to the low-frequency range through the decades. Therefore, in the proposed work, this frequency band is directly analyzed for AF detection, which has not previously been discussed. The proposed architecture is implemented using 180-nm bulk CMOS technology consuming $11.098~\mu {\mathrm{ W}}$ at $25~ {\text {kHz}}$ and exhibits an accuracy of 92.37% for class-oriented classification and 81.60% for subject-oriented classification. The low-power realization of the proposed design, as compared to the state-of-the-art methods, makes it suitable to be used for wearable devices.

Synthesized-reference-Wave holographic techniques offer relatively simple and cost-effective measurements of antenna radiation characteristics, and reconstruction of complex aperture fields using near-field intensity-pattern measurements. These methods allow utilization of the advantages of the methods for probe compensation for near-field amplitude and phase measurements for planar and cylindrical scanning, including accuracy analyses. Accuracy analyses using mathematical models considering random processes with correlation intervals are presented. Numerical simulations, taking into account random as well as deterministic errors and the processing of measurement statistics, are also presented. It is demonstrated that the given analyses correspond to our measurements and/or numerical simulations.

Spiking Neural Networks (SNNs) have drawn attention due to their biological behaviour and low energy consumption compared to conventional neural networks. The efficiency of SNN depends on the neurons, and thus, proper selection of neurons becomes imperative for exhibiting appropriate biological behaviour. This paper realizes an adaptive Leaky Integrate-and-Fire (LIF) neuron, modeled using presynaptic spike inputs, adaptive threshold, and adaptive leaky rate. The proposed LIF neuron employs a low firing rate, which reduces the computational cost and floating-point operations at different presynaptic input frequencies and noise levels. Comparatively, the proposed LIF neuron significantly improves resource utilization and latency. The number of flipflops, Look-Up Table (LUT), and latency of this LIF neuron are respectively 2.5×, 5.7×, and 2.0× better than the best-performing contemporary LIF neuron. To evaluate the performance of proposed neuron on the network, we implement an SNN on an Artix-7 Field Programmable Gate Array (FPGA). This SNN achieves 95% accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset classification. The total delay in classifying an image using our proposed neuron-based SNN is 0.9 ms, 60% less than the best-performing contemporary SNN. This SNN is 68% more energy-efficient in image processing.