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1. Modified Arcsine Law for One-Bit Sampled Stationary Signals with Time-Varying Thresholds

   Eamaz, A; Yeganegi, F.; Soltanalian, M. (January 2021, Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing)

   null (Ed.)

   One-bit quantization has attracted considerable attention in signal processing for communications and sensing. The arcsine law is a useful relation often used to estimate the normalized covariance matrix of zero-mean stationary input signals when they are sampled by one-bit analog-to-digital converters (ADCs)---practically comparing the signals with a given threshold level. This relation, however, only considers a zero threshold which can cause a remarkable information loss. For the first time in the literature, this paper introduces an approach to extending the arcsine law to the case where one-bit ADCs apply time-varying thresholds. In particular, the proposed method is shown to accurately recover the variance and autocorrelation of the stationary signals of interest. 

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2. One-bit digital radar

   https://doi.org/10.1109/ACSSC.2017.8335529  

   Ren, Jiaying; Li, Jian (October 2017, Signals, Systems, and Computers, 2017 51st Asilomar Conference on)

   This paper introduces a one-bit digital radar involving direct one-bit sampling with unknown dithering of the received radio frequency (RF) signal. Due to avoiding the analog mixer and the down-conversion of the RF signal, the digital radar can be energy-efficient and low-priced. The use of unknown dithering allows for the one-bit samples to be processed efficiently using conventional algorithms. A computationally efficient range-Doppler estimation method based on fractional Fourier transform (FRFT) and fast Fourier transform (FFT) is used for linear frequency modulated continuous wave (LFMCW) transmissions, and the CLEAN algorithm is used for target parameter estimation. 

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3. Hesitant adaptive search with estimation and quantile adaptive search for global optimization with noise

   https://doi.org/10.1007/s10898-023-01307-7  

   Zabinsky, Zelda B; Linz, David D (September 2023, Journal of Global Optimization)

   Adaptive random search approaches have been shown to be effective for global optimization problems, where under certain conditions, the expected performance time increases only linearly with dimension. However, previous analyses assume that the objective function can be observed directly. We consider the case where the objective function must be estimated, often using a noisy function, as in simulation. We present a finite-time analysis of algorithm performance that combines estimation with a sampling distribution. We present a framework called Hesitant Adaptive Search with Estimation, and derive an upper bound on function evaluations that is cubic in dimension, under certain conditions. We extend the framework to Quantile Adaptive Search with Estimation, which focuses sampling points from a series of nested quantile level sets. The analyses suggest that computational effort is better expended on sampling improving points than refining estimates of objective function values during the progress of an adaptive search algorithm. 

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4. Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings

   https://doi.org/10.1038/s41598-020-65492-1  

   Khalifa, Yassin; Coyle, James L.; Sejdić, Ervin (December 2020, Scientific Reports)

   Abstract High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process. 

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5. Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

   https://doi.org/10.1109/TVT.2023.3274763  

   Kim, Dohyun; Castellanos, Miguel R.; Heath, Robert W. (October 2023, IEEE Transactions on Vehicular Technology)

   Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper address the question: how can the best relay be selected with minimal overhead from beam management? In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam in- dices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numeri- cal experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels. 

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