In the near future, Uncrewed Aerial Vehicles (UAVs) are expected to evolve from simple data collectors to intelligent data gatekeepers capable of processing and analyzing information directly on board. In this paper, a decentralized UAV framework is proposed to address potential challenges in communication and coordination among fast-moving vehicles due to difficulties in maintaining seamless information exchange and synchronized operation across the network. While Integrated Sensing and Communications (ISAC) provides significant advantages, it still faces considerable challenges when dealing with such scenarios. This paper proposes DynaMo, a decentralized UAV framework addressing the challenges of tracking fast-moving vehicles in dynamic environments. DynaMo prioritizes spatiotemporal relevance and adapts to diverse vehicle behaviors with a novel freshness metric. Integrated with a Partially Observable Markov Decision Process (POMDP), the framework enables UAVs to make informed, real-time decisions under uncertainty. Simulation results highlight the superior tracking precision and adaptability of our proposed approach, offering a robust solution for ISAC systems in high-mobility scenarios.

The paper gives a brief overview of the methods used to determine soil moisture using sentinel products, emphasizing the integration of different approaches. First, the importance of sentinel products for observing microwave backscatter is emphasized, highlighting their unique characteristics. It then discusses the current state of the art in soil moisture retrieval, which includes short-term change detection, physically based scattering models and AI-based methods. The challenges and benefits of each method are systematically explored, providing a comprehensive perspective. Finally, potential future research directions are outlined, highlighting the complex role of prevailing vegetation conditions and the promising prospects for integrating machine learning into multi-SAR missions. Overall, the paper provides a thorough foundation for understanding, refining, and advancing soil moisture detection methods.

As the field of quantum computing rapidly advances, there is a growing demand for skilled professionals adept in quantum computing and programming. Recognizing this need, in this paper, we share our experiences teaching an introductory-level quantum computing course to students at Cleveland State University (CSU). The course integrates dedicated hands-on programming labs, allowing students to verify their experimental results with corresponding examples from the textbook. These labs cover a diverse range of topics, including fundamental elements such as quantum gates and circuits, quantum key distribution protocols, and quantum algorithms. As educators, our goal is to share teaching insights and resources with fellow instructors in the field. This article elucidates the rationale behind the design of each experiment, providing a deeper understanding of quantum computing.

Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.

Achieving reliable acoustic wireless video transmissionsin the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user’s experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO)-based Software-Defined Acoustic Modems (SDAMs). To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT) high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP) are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC) H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS) and Quality of Experience (QoE) given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.

Traditionally, underwater acoustic communications is half duplex, i.e., the hydrophones and transducers operate in non-overlapping time-slots/frequency-bands in one direction at a time or frequency. To double the spectral efficiency and allow simultaneous transmission and reception in Full-Duplex mode (FD), a Self-Interference Cancellation (SIC) technique in space is introduced and deployed. Specifically, a novel underwater acoustic system is proposed to perform FD-SIC efficiently via an integrated design combining underwater Acoustic Vector Sensor (AVS) and Phased Array Transducer (PAT) to realize spatial SIC. The energy focusing function of the Beamformer (BF) helps PAT avoid self and mutual spatial interference. The AVS keeps updating the direction of arrival information to let BF adjust the steering angle via an adaptive protocol. The proposal is evaluated and verified via simulations in realistic underwater acoustic channels and is able to achieve 59 dB SIC at 80 kHz steering angle at -5 ° and at least 37 dB within the steering angle region before the input of digital SIC. This indicates that the design is a promising solution for the chosen angle region to perform spatial SIC as well as to prevent the grating lobe interference. The design is being experimentally validated using the data collected from an underwater testbed and implemented on an Field Programmable Gate Array (FPGA) board that provides energy efficiency and real-time processing capabilities.

Deploying Autonomous Underwater Vehicles (AUVs) is a necessity to enable a range of civilian/military underwater applications; yet, achieving a reliable coordination among the vehicles is a challenging issue due to the time- and space-varying characteristics of the acoustic communication channel. The design of a Medium Access Control (MAC) based on a probabilistic Space Division Multiple Access (SDMA) method for short/medium distances (less than 2 km) is presented. This method considers the inherent vehicle position uncertainty due to the inaccuracies in models and the drift of the vehicles. It minimizes the acoustic interference statistically by considering the angular position of neighboring vehicles via a two-step estimation and by keeping the transmitter antenna's beamwidth of each vehicle at an optimal value. Such value is chosen considering three contrasting goals, i.e.: (i) spreading the signal beam towards the vehicle to combat position uncertainty using a coarse estimation; (ii) focusing the beam to reduce acoustic energy dispersion through a fine estimation; and (iii) minimizing interference to other vehicles. Simulation results in a sparse underwater network show that this approach mitigates interference, reduces the probability of retransmission, and achieves higher data rates over conventional underwater MAC techniques.

Underwater imagery has enabled numerous civilian applications in various domains, ranging from academia to industry, and from industrial surveillance and maintenance to environmental protection and behavior of marine creatures studies. The accumulation of litter and plastic debris at the seafloor and the bottom of rivers are extremely harmful for the aquatic life. We propose a solution for monitoring this problem using a team of Autonomous Underwater Vehicles (AUVs) to exchange the recorded video in order to reconstruct the map of regions of interest. However, underwater video transmission is a challenge in the harsh environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves-while long range-provide a very low bandwidth and unreliable channel for communication. In our solution, the scalable coded video of each vehicle is shared in-network with a selected group of receiving vehicles through the underwater acoustic channel. Presented evaluations, including both simulations and experiments, confirm the efficiency and flexibility of the proposed solution using acoustic software-defined modems.

Achieving reliable and persistent environmental field estimation in Underwater Internet of Things (UW IoT) is a challenging problem. Given the need for high-resolution spatio-temporal sensing in such environment, traditional digital sensors are not suitable due to their high cost, high power consumption, and non-biodegradable nature. Further, reliable communication techniques that avoid retransmissions are crucial for reconstructing the phenomenon in a timely manner at the fusion center such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that continuously transmit data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing Analog Joint Source Channel Coding (AJSCC), a low-complexity compression-communication technique, using biodegradable Field Effect Transistors (FETs). We then propose a correlation-aware Hybrid Automatic Repeat Request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus saving energy and time. The performance of the proposed analog sensor design and of the correlation-aware HARQ communication technique has been evaluated via simulations and shown to achieve the desired behavior.

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, require sensors that consume ultra-low power devices (so as to be able to collect data continuously and not lose temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOS-FETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. The approach is verified via Spice simulations and breadboard implementation.

Transmitting large amounts of data such as videos underwater is an important yet challenging problem in the harsh underwater environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves-while being able to propagate up to several tens of kilometers-lead to a communication channel that is very dynamic, prone to fading, spectrum limited with passband bandwidths of only a few tens of kHz, and affected by non-Gaussian noise. Notwithstanding these challenges, a hybrid solution that is capable of transmitting at high data rates underwater via acoustic waves at short/medium distances is proposed. The solution introduces a novel signaling method, called Signal-Space-Frequency Beamforming (SSFB), for a multiple antenna where each antenna consists of Uniform Circular Array (UCA) hydrophones mounted on an underwater vehicle to steer the beam in both azimuth and elevation planes; then, an array of Acoustic Vector Sensors (AVS)-hydrophones that are able to capture the acoustic particle velocity/direction of arrival in addition to measuring regular scalar pressure-are mounted on the surface buoy. Detection is performed based on the beam spatial separation and direction of arrival angles' estimation. Simulation results confirm that this solution outperforms stateof-the-art underwater acoustic transmission techniques, whose data rates are limited only to few tens of kbps.

Combating interference is an important yet challenging issue for multiuser communications especially in the harsh underwater acoustic environment. In this paper, a novel Angle-Of-Departure (AOD)-based technique is proposed, which accounts for the inherent position uncertainty of underwater propeller-driven Autonomous Underwater Vehicles (AUVs) or buoyancy-driven gliders. A new probabilistic Space Division Multiple Access (SDMA) technique is studied using confidence interval estimation, and an effective approach to manage interference statistically is discussed. Also, an optimization problem is proposed to mitigate multiuser interference while keeping the transmitter antennae beam width at a desirable value so to find a trade off among (i) spreading the beam towards the receiver to combat position uncertainty, (ii) focusing such beam to minimize dispersion, and (iii) minimizing interference to other vehicles in the surrounding. Solutions and algorithms are proposed to overcome the multiuser interference via a hybrid SDMA-Time Division Multiple Access (TDMA) method. Simulation results show that the solution mitigates statistical interference, lessens packet retransmission rate, and obtains Signal-to-Interference-plus-Noise Ratio (SINR) gain and rate efficiency over conventional TDMA and SDMA methods.