Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range underwater acoustic applications such as the long-term monitoring of the sea, the PA works in full consumption mode. Thus, it becomes a challenging task to minimize power consumption and unnecessary distortion. To mitigate this problem, a receiver-based nonlinearity distortion mitigation method is proposed, assuming that the transmitting side has enough computation power. We propose a novel approach to identify the nonlinear power model using a modern deep learning algorithm named frequentative decision feedback (FFB); PAPR performance is verified by the clipping method. The simulation results prove the better performance of the PA model with a BER with the shortest learning time.

Internet of Things (IoT) networks face significant cybersecurity risks primarily due to their extensive connectivity, node heterogeneity, lack of robust security measures, and the considerable amount of sensitive data they accumulate and transmit. Cyber intrusions in IoT networks can result in severe effects, including privacy violations, data breaches, and even physical harm. For traditional centralized learning (CL)-based intrusion detection (ID) and identification methods to work, the local IoT data has to be sent to a third-party central server for training. This uses a lot of bandwidth and poses privacy risks. To deal with this challenge, federated learning (FL) emerges as a promising solution for ID as it enables on-device learning without transmitting private IoT data to a central server. To the best of our knowledge, existing FL-based IDs are limited to binary classification. This paper addresses this limitation by implementing an FL-based ID with multi-class classification of intrusions on the N-BaIoT dataset. Enabling multi-class classification of intrusions allows for implementing more targeted and effective attack-specific countermeasures. We implement multi-class intrusion classification on both CL and FL-based methods (FedAvg and FedAvg+) and focus on key metrics such as accuracy and F1-score. Our results demonstrates that the FedAvg+ approach yields performance comparable to CL while offering added advantage of enhanced privacy. Additionally, the FL-based method outperforms traditional CL, particularly in identifying intrusions from Mirai and Bashlite botnet attacks.

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings.

Devices that record data generated by an aircraft and its crew during operation are known as flight data recorders (FDR) and cockpit voice recorders (CVR). Data recorded by these devices enable the analysis of aircraft state and behavior from take-off to landing. The information provided by these devices is used to conduct inspections, investigations, training, and other measures by aviation experts following hazardous events or accidents. As of today, multiple instruction manuals covering the specifications of FDRs and CVRs have been presented by various organizations, experts, and scientists within the aviation industry. However, no consolidated research document that brings all this information together for regulatory and technical purposes has been published yet. Our objective is to carry out an in-depth study of FDRs for both manned and unmanned systems. We aim to examine the requirements used in the past, current technological advancements, and future intelligent FDR approaches, intending to advance FDR technology. Starting with manned aviation systems, we provide a comprehensive overview of general FDRs and then look at new challenges and the resulting requirements for unmanned aerial vehicles (UAVs). After reviewing all relevant resources, we present a roadmap for the next generation of FDRs.