The paper presents a simple method to design a tri-band bandpass filter (BPF) using only two pairs of open-loop uniform impedance resonators (OLUIRs). The three passbands can be designed independently by selecting the lengths of two pairs of the OLUIRs. The design procedure is described in this paper. A tri-bandpass filter example is designed at 1.93, 2.6 and 3.9 GHz that can be applied to digital enhanced cordless telecommunications (DECT) and worldwide interoperability for microwave access (WiMAX). By means of the cross-coupling effect with different coupling paths, several transmission zeros appear near the passbands, thus enhancing the selectivity of the passbands. The measured results of the fabricated tri-BPF match the simulated results and confirm the design concept.

Ultraviolet (UV) networks are important approaches to modern wireless communications owing to their unique characteristics, such as non-line-of-sight communication, strong anti-interference ability, and all-weather working mode. Given the lack of media access control (MAC) protocols suitable for UV networks at present, a novel UV lossless contention MAC (UVLLC-MAC) protocol is proposed herein based on UV physical properties. A fully connected finite-population multinode UV model is adopted, and its network performance is systematically derived and mathematically analyzed under the UVLLC-MAC protocol. Compared with the time-slot ALOHA protocol, the UVLLC-MAC protocol achieves 36.7% better throughputs, with 13.2% drop in packet loss rates, and the excellent performance of the protocol is obtained and fully verified. The UVLLC-MAC protocol can provide guidance for multinode and multiservice UV networking.

The timely and reliable handling of post-disaster emergency monitoring tasks is crucial for effective rescue operations. UAV-assisted edge computing plays a pivotal role in the rapid deployment of such systems. However, challenges persist due to communication and computation resource bottlenecks when dealing with delay-sensitive monitoring tasks. In dynamic post-disaster environments, effective task scheduling and resource allocation decisions directly impact the system’s ability to process tasks. Therefore, this paper proposes an adaptive task migration decision-making system for emergency monitoring tasks in UAV-assisted edge computing. Firstly, We decomposed the optimization objectives based on the task processing workflow, then devised a stepwise delay risk control and resource recovery mechanism based on early discarding. Secondly, by integrating multi-agent reinforcement learning (MARL), optimal strategies for task offloading, UAV queue scheduling, and communication resource allocation are learned to enhance the decision system’s environmental awareness and maximize the successful completion of emergency monitoring tasks. Simulation experiments demonstrate that the algorithm significantly improves the success rate of migration tasks and data processing capacity, thereby validating its convergence and effectiveness.

More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.

Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.

This paper investigates the problem of coordinated resource allocation for multiple unmanned aerial vehicles (UAVs) to address the scarcity of communication resources in disaster-affected areas. UAVs carrying modules of free space optical (FSO) and radio frequency (RF) serve as relay nodes and edge offloading nodes, presenting an FSO/RF dual-hop framework. Considering the varying urgency levels of tasks, we assign task priorities and transform the proposed problem into distributed collaborative optimization problem. Based on the K-means algorithm and the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we propose a UAV-coordinated K-means MADDPG (KMADDPG) to maximize the number of completed tasks while prioritizing high-priority tasks. Simulation results show that KMADDPG is 5% to 10% better than the benchmark DRL methods in convergence performance.