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1. SDR Pathfinder for Understanding Transient and Noise-Level Interference in the Karoo (SPUTNIK)

   https://doi.org/10.1142/S2251171722500064  

   Josaitis, Alec T.; DeBoer, David R.; de Lera Acedo, Eloy (June 2022, Journal of Astronomical Instrumentation)

   In this work, the SDR Pathfinder for Understanding Transient and Noise-level Interference in the Karoo (SPUTNIK) is presented. We describe how a low-cost radio frequency interference (RFI) monitoring system, using solely consumer-off-the-shelf (COTS) components, directly contributes to the analysis efforts of a precision 21[Formula: see text]cm cosmology instrument. A SPUTNIK system overview is provided, as well as a generalized software-defined radio (SDR) internal calibration technique to achieve wideband, [Formula: see text][Formula: see text]dBm-level accuracy and a measured dynamic range of [Formula: see text][Formula: see text]dB. 

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2. Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

   https://doi.org/10.3390/electronics12163505  

   Jenkinson, George; Abbasi, Muhammad_Ali Babar; Molaei, Amir Masoud; Yurduseven, Okan; Fusco, Vincent (August 2023, Electronics)

   This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot. 

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3. Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform

   https://doi.org/10.3390/s23031742  

   Komeylian, Somayeh; Paolini, Christopher (February 2023, Sensors)

   To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%. 

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4. Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

   https://doi.org/10.1145/3616020  

   Arefeen, Asiful; Akbari, Ali; Mirzadeh, Seyed Iman; Jafari, Roozbeh; Shirazi, Behrooz A; Ghasemzadeh, Hassan (January 2024, ACM Transactions on Computing for Healthcare)

   Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques. 

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5. Interference detection in radio astronomy: applying Shapiro–Wilks normality test, spectral entropy, and spectral relative entropy

   https://doi.org/10.1093/rasti/rzae035  

   Cao, Zhicheng; Schmid, Natalia_A; Bandura, Kevin; Lorimer, Duncan_R; Dameron, Morgan; Crockett, Katelyn; Grubick, Clayton; Schmid, Andreas; Zheng, Shaonan (August 2024, RAS Techniques and Instruments)

   Abstract Radio-frequency interference (RFI) is becoming an increasingly significant problem for most radio telescopes. Working with Green Bank Telescope data from PSR J1730+0747 in the form of complex-valued channelized voltages and their respective high-resolution power spectral densities, we evaluate a variety of statistical measures to characterize RFI. As a baseline for performance comparison, we use median absolute deviation (MAD) in complex channelized voltage data and spectral kurtosis (SK) in power spectral density data to characterize and filter out RFI. From a new perspective, we implement the Shapiro–Wilks (SW) test for normality and two information theoretical measures, spectral entropy (SE) and spectral relative entropy (SRE), and apply them to mitigate RFI. The baseline RFI mitigation algorithms are compared against our novel RFI detection algorithms to determine how effective and robust the performance is. Except for MAD, we find significant improvements in signal-to-noise ratio through the application of SE, symmetrical SRE, asymmetrical SRE, SK, and SW. These algorithms also do a good job of characterizing broad-band RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests. 

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