Using multi-channel priority scheduling mechanism to improve the transmission efficiency of Store-Forward processing structure under RISC-V architecture

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.

The rapid development of aviation technology has made the application of unmanned aerial vehicles (UAVs) more popular in recent years. Due to the inadequate capability of a single UAV, flying ad hoc networks (FANETs), which consist of multiple different UAVs, not only break the limits of single UAV by introducing the cooperation between UAVs, but also complete more complex missions by extending the communication range at infrastructure-less areas. One of the most important design issues for FANETs is the communication and cooperation between UAVs, and the number of UAV relays has been proved to be closely related to the improvement of system performance. However, few FANET protocols or algorithms have been proposed from the perspective of scheduling the number of relays. Inspired by the idea, this paper proposes an optimal relay number selection algorithm based on a more realistic network model which includes a novel cooperative ( $n+2$ )-node system model considering the distance metrics between relays and a Nakagami- $m$ short-term static fading channel model more suitable for UAV operation environments. The system outage probability is calculated by introducing the Meijer-G function, and a three-dimensional discrete time Markov chain (DTMC) model is established and analyzed to derive the closed-form binary expressions of throughput, energy efficiency and average transmission delay. In order to balance the three performance to achieve a comprehensive system performance, a trade-off factor named EDT is further proposed and maximized to evaluate the optimal number of relays. Finally, simulation results assess the impact of network parameters on system performance, and verify the superiority of the proposed algorithm over its fixed relay number counterpart.

We proposed a technique to improve the transmission efficiency by handling the impedance inside the cavity by utilizing a monopole probe (matching probe) terminated by a variable reactance circuit. As a result of measurement, the reflection coefficient of the transmitter side reduced from 0.895 to 0.324 before and after inserting the matching probe. The reflection coefficient of the receiver side reduced from 0.967 to 0.882. The transmission efficiency improved from 0.67 % to 13.09 %. From the above, it was confirmed that transmission efficiency is improved by handling cavity impedance.

In order to reduce the difficulty of the tuning work and improve transmission efficiency at the 320-kV heavy ion multidisciplinary research facility (IMRF), an online optimization system was developed. In this paper, the difficulties of manual tuning are analyzed amply. Various optimization-based approaches were considered and a Bayesian optimization method was selected and implemented in hardware. We have applied the online optimization system at the beamline to increase transmission efficiency. The transmission efficiency obtained by this online tuning system can reach 50%, which is better than the manual tuning 30% to 35%. The general work developed in this paper can provide a reference for the design and implementation of online tuning approaches for various complex systems, not just accelerator facilities.

Integrating Messy Genetic Algorithms and Simulation to Optimize Resource Utilization

Quantum Key Distribution (QKD) is increasingly pivotal in securing communication channels against the looming threats posed by quantum computing. However, existing QKD protocols encounter challenges related to efficiency and transmission capabilities. In response, this research investigates the integration of metamaterials into QKD systems, aiming to fortify security and enhance practicality. In the current landscape of quantum communication, where the vulnerability of classical encryption methods is magnified by rapid advancements in quantum computing, finding innovative solutions is imperative. This study is motivated by the need to strengthen the security and viability of QKD protocols to meet the demands of evolving cryptographic threats. By integrating metamaterials, the authors optimise quantum state control, improve signal‐to‐noise ratio (SNR), and enable longer transmission distances. Through mathematical modelling and simulations, the authors demonstrate how metamaterials reduce errors and enhance the robustness of QKD systems. Our findings show significant improvements in transmission efficiency and security, making Metamaterial‐Based Quantum Key Distribution (MQKD) a promising approach for future quantum communication networks. The study not only advances the understanding of the theoretical foundations, but also presents simulated results illustrating the practical effectiveness of MQKD. The exploration of these innovative techniques contributes to the ongoing efforts to secure quantum communication channels.

The authors discussed a variable bit rate voice coding system in digital communications networks. A novel voice compression method which provides significant improvement in transmission efficiency and flexibility for communications systems is described. The basic scheme used for the investigations involves the use of split vector quantized (SVQ) transform coding (TC) in conjunction with pitch prediction (PP) to achieve excellent voice quality at rates of 4.8 kb/s and below. The authors describe of the algorithm and its implementation for a variable bit rate voice coding system from 4.8 kb/s to 2.4 kb/s. >

In the field of wireless power transfer technologies, the improvement of transmission efficiency is one of the critical issues. It is important to understand how much power loss is imposed in which areas of a wireless power transfer system in the development phase. Since the transmission characteristics change according to ambient conditions, a measuring instrument capable of observing electric power during power transfer is required. We, Daihen Corporation, have developed a real-time power and impedance sensor useful as a data collection tool in order to improve power transmission efficiency. We have verified, using this sensor, the feasibility of the above objective by optimizing the circuit constants based on measured values obtained during practical power transfer.

<sec>Magnetic resonance wireless power transfer (WPT) has gradually become a popular research topic of near-field regulation in recent years, with wide applications in the fields of mobile phones, implantable medical devices, electric vehicles, and many other fields. However, several challenges remain to be addressed: near-field coupling, which induces multiple frequency splits and prevents the system from maintaining a fixed operating frequency; coupled arrays, which are susceptible to structural errors and parameter perturbations; current research, which primarily focuses on single-load transmission and has resulted in undeveloped multi-load transmission systems; the direction of transmission, which is difficult to control flexibly. In recent years, photonic artificial microstructures have provided a flexible platform for studying topological physics, arousing significant research interest in their fundamental topological characteristics. The most prominent features of topological structures are their nonzero topological invariant and the robust edge states determined by the bulk-edge correspondence: these features can overcome disturbances caused by defects and disorders. Moreover, by modulating the wave function distribution of topological states, energy can be precisely localized, enabling directional WPT. Therefore, implementing topological modes in WPT systems are of significant scientific importance.</sec><sec>This review summarizes recent researches on topological models for robust WPT, which are divided into three main parts. The first part introduces one-dimensional periodic topological structures, focusing primarily on the significant improvements in transmission efficiency and robustness achieved by utilizing topological edge states in the Su-Schrieffer-Heeger (SSH) model for WPT. Moreover, a composite chain formed by two SSH chains is constructed to realize a higher-order parity-time (PT) symmetric topological model. This approach solves the frequency splitting caused by coupled edge states and exhibits lower power losses in standby mode. The second part discusses several types of aperiodic one-dimensional topological chains. By introducing topological defect states at the interface between two different dimer chains, robust multi-load WPT is achieved. Furthermore, based on the integration of artificial intelligence algorithms, the SSH-like topological model enables more efficient and robust WPT than traditional SSH model. The asymmetric edge states in quasi-periodic Harper chain provide a solution for directional transmission in WPT applications. By introducing nonlinear circuits, this model enables active control of the transfer direction. The third part presents the application of high-order topological corner states in multi-load robust WPT, demonstrating the selective excitation of both symmetric and asymmetric corner modes.</sec><sec>Finally, the application prospects of topological modes in WPT systems are discussed. With the development of new physics, the integration of non-Hermitian physics and topological physics holds great promise for achieving simultaneous energy-information transfer, and is expected to achieve compatible WPT, wireless communication, and wireless sensing within a single system. Such a fusion technology will provide breakthroughs in efficiency, robustness, and multifunctionality for next-generation wireless systems.</sec>

Effect of receptor binding domain mutations on receptor binding and transmissibility of avian influenza H5N1 viruses

This paper analyses the unicore X86 architecture and records the Translation Look Aside Buffer and Level 2 Cache statistics. These statistics include collecting hits and misses for each memory instruction for both TLB and L2 cache. To bring these statistics together Gem5 simulator and DineroIV cache simulator have been used. After assembling these statistics system's performance can be inferred. Detailed issues related to this analysis have been discussed in this paper.

The increasing demand for stringent quality of service (QoS) guarantees and low latency in mission and time‐critical 6G internet of things (IoT) applications necessitates advanced call admission control (CAC) mechanisms. Although 6G networks offer enhanced capabilities, resource limitations like bandwidth and processing power persist. Consequently, efficient resource allocation strategies are crucial to balancing the needs of diverse services, particularly for real‐time streaming applications. This paper introduces a novel Analytics & Fuzzy Call Admission Control (AF‐CAC) algorithm combined with an Adaptive Leaky Bucket (ALB) scheduling mechanism. The AF‐CAC algorithm integrates predictive analytics and fuzzy logic to make informed, intelligent admission decisions, ensuring reliable communication and optimal resource utilization. It prioritizes critical data transmission and prevents network overloading by controlling the number of admitted calls. Concurrently, the ALB mechanism dynamically adapts to changing network conditions, user mobility, and traffic patterns, efficiently allocating resources to meet diverse QoS requirements. The proactive resource allocation is enhanced with a convolutional neural network (CNN) and reinforcement learning (RL) agents, enabling dynamic and efficient resource management to ensure high‐quality multimedia streaming, support mission‐critical applications, and maintain uninterrupted service during mobility. The average throughput for the AF‐CAC technique is 1350 packets/slot, and the average delay over the range of 300 simulated devices is 840 ms. Hence, it exhibits significant enhancements in admitting connections and overall QoS compared to existing approaches for managing multimedia traffic in 6G networks.

Planetary gear transmission mechanisms (PGTMs) are widely used in mechanical transmission systems due to their compact structure and high transmission efficiency. To implement the reliability design and optimization of a PGTM, a novel multi-objective dynamic reliability optimization approach is proposed. First, a multi-objective reliability optimization model is established. Furthermore, considering the strength degradation of gears during service, a dynamic reliability analysis is conducted based on the theory of nonlinear fatigue damage accumulation. In addition, to improve computing efficiency, a random forest surrogate model based on the particle swarm optimization algorithm is proposed. Finally, an adaptive multi-objective evolutionary algorithm based on decomposition (AMOEA/D) is designed to optimize the mechanism, along with an adaptive neighborhood updating strategy and a hybrid crossover operator. The feasibility and superiority of the proposed approach are verified through an NGW planetary gear reducer. The results show that the proposed surrogate model can reduce the calculation cost and has high accuracy. The AMOEA/D algorithm can improve transmission efficiency, reduce gear volume and ensure reliability at the same time. It can provide guidance for actual gear production.

A new structure for a high-frequency isolated AC/AC converter is proposed to enhance traditional high-frequency link AC-DC-AC converters. This circuit structure eliminates the need for large-capacity intermediate DC energy storage element, achieves first-stage power conversion between input and output, which simplifies the design and enhances energy transmission efficiency. Additionally, the soft-switching control strategy for high-frequency isolated AC/AC converters enables zero-voltage conduction and turn-off of the switching devices, effectively reducing switching losses and further improving transmission efficiency. Through simulation and experimental results, the correctness of the theoretical analysis and the feasibility of the circuit structure were verified.

CC-OLIA: A Dynamic Congestion Control Algorithm for Multipath QUIC in Mobile Networks