Dynamic Channel Research Articles

Lightweight Ship Object Detection Algorithm for Remote Sensing Images Based on Multi-scale Perception and Feature Enhancement

As global trade and maritime traffic develop, exploring ship detection in remote sensing images has become a research hotspot. However, ships in remote sensing images are so small that it leads to a high detection leakage rate and excessive model parameters, making them difficult to apply on remote sensing equipment with limited resources. To address the challenge, we propose a light-weight ship object detection algorithm, adaptive layered multi-scale You Only Look Once version 8 (ALM-YOLOv8), based on multi-scale perception and feature enhancement. To enhance the model's perception of contextual information in complex backgrounds, a multi-scale channel fusion module is constructed to extract features of various scales. To enhance the extracted features, a small-object detection layer and a dynamic channel attention convolution that assigns dynamic weights are proposed. Additionally, this study embeds the large separable kernel attention mechanism into the original network, which lightens the model. Experiments on the HRSC2016 dataset demonstrate the effectiveness of ALM-YOLOv8.

DCS-RISR: Dynamic channel splitting for efficient real-world image super-resolution.

Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light degradation prediction network to regress the degradation vector to simulate the real-world degradations, upon which the channel splitting vector is generated as the input for an efficient SR model. Then, a learnable octave convolution block is proposed to adaptively decide the channel splitting scale for low- and high-frequency features at each block, reducing computation overhead and memory cost by offering the large scale to low-frequency features and the small scale to the high ones. To further improve the RISR performance, non-local regularization is employed to supplement the knowledge of patches from LR and HR subspace with free-computation inference. Extensive experiments demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our DCS-RISR not only achieves the best trade-off between computation/parameter and PSNR/SSIM metric, but also effectively handles real-world images with different degradation levels.

Performance of Ad Hoc Wireless Networks under Coexistence Scenario: A Case Study

Owing to the possibility of interference, signal contention, and performance degradation, coexisting IEEE 802.11 (WiFi) and IEEE 802.15.4 (Zigbee, 6LoWPAN) technologies in the 2.4 GHz ISM band presents considerable issues. More packet loss, delay, and decreased throughput for low-power networks can arise from IEEE 802.11's larger bandwidth, higher transmission power, and higher data rates overpowering IEEE 802.15.4's lower power and narrower bandwidth transmissions. This work explores the main facets of their coexistence, emphasising the effects of interference on performance measures including throughput, latency, and energy efficiency and frequency overlap. Dynamic frequency selection and channel allocation algorithms are only a few of the interference reduction techniques that are examined in the analysis. This research offers valuable insights into the more harmonious coexistence of these two wireless technologies in shared surroundings, enabling dependable operation for low-power IoT networks and high-speed Wi-Fi applications. These insights are gained through simulation and experimental validation.

FusionMamba: dynamic feature enhancement for multimodal image fusion with Mamba

Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing methods based on local convolutional neural networks (CNNs) struggle to capture global features efficiently, while Transformer-based models are computationally expensive, although they excel at global modeling. Mamba addresses these limitations by leveraging selective structured state space models (S4) to effectively handle long-range dependencies while maintaining linear complexity. In this paper, we propose FusionMamba, a novel dynamic feature enhancement framework that aims to overcome the challenges faced by CNNs and Vision Transformers (ViTs) in computer vision tasks. The framework improves the visual state-space model Mamba by integrating dynamic convolution and channel attention mechanisms, which not only retains its powerful global feature modeling capability, but also greatly reduces redundancy and enhances the expressiveness of local features. In addition, we have developed a new module called the dynamic feature fusion module (DFFM). It combines the dynamic feature enhancement module (DFEM) for texture enhancement and disparity perception with the cross-modal fusion Mamba module (CMFM), which focuses on enhancing the inter-modal correlation while suppressing redundant information. Experiments show that FusionMamba achieves state-of-the-art performance in a variety of multimodal image fusion tasks as well as downstream experiments, demonstrating its broad applicability and superiority.

Design and Optimization of Reconfigurable Intelligent Surfaces Structures

This research investigates the design and optimization of reconfigurable intelligent surfaces (RIS) structures to enhance simultaneous wireless information and power transfer (SWIPT) in cooperative relay networks. The integration of RIS technology with SWIPT offers promising opportunities to improve the energy efficiency and spectral efficiency of wireless communication systems. In this study, we propose novel RIS deployment strategies and optimization algorithms to maximize the performance of SWIPT-enabled cooperative relay networks. First, we analyze the fundamental principles of RIS-assisted SWIPT and cooperative relay networks, considering the unique characteristics and challenges of each component. We then formulate optimization problems to jointly optimize the placement of RIS elements, relay selection, transmit power allocation, and RIS phase configuration to achieve the desired trade-off between communication reliability, energy efficiency, and spectral efficiency. Furthermore, we develop efficient algorithms for channel estimation, feedback, and coordination among RIS, relays, and destination nodes to adapt to dynamic channel conditions and user requirements. We also investigate the impact of RIS deployment on the energy harvesting performance of SWIPT-enabled relay nodes, considering factors such as RIS reflection coefficients, relay-RIS distance, and incident signal power distribution. Moreover, we address security and privacy considerations in RIS-assisted SWIPT, including potential vulnerabilities, authentication mechanisms, and privacy-preserving communication techniques. Finally, we validate the proposed design and optimization techniques through comprehensive simulations and real-world experiments, considering practical constraints such as hardware implementation, energy harvesting efficiency, and deployment scenarios. Overall, this research contributes to advancing the understanding and practical implementation of RIS technology for SWIPT in cooperative relay networks, offering insights into the design, optimization, and performance evaluation of next-generation wireless communication systems.

Enhancement in Reliability of IEEE 802.15.4 WBAN Using Greedy Spider Monkey Algorithm

Wireless Body Area Networks (WBANs) hold immense potential in healthcare monitoring, but ensuring reliable data transmission is crucial. While the IEEE 802.15.4 standard offers low-power operation and basic security, its Contention-Based Access (CBA) mechanism can lead to packet collisions and reduced reliability, especially in congested scenarios. This paper proposes a novel approach to enhance reliability in 802.15.4 WBANs by incorporating a Greedy Spider Monkey Optimization Algorithm (GSMA). The GSMA mimics the intelligent foraging behaviour of spider monkeys, enabling dynamic channel selection and optimal transmission scheduling. Our approach aims to minimize packet collisions by selecting channels with lower traffic based on historical data and real-time network conditions; the GSMA reduces the likelihood of collisions and data loss. Secondly, the algorithm prioritizes critical data packets based on their urgency and channel availability, ensuring the timely delivery of essential medical information. Finally, by reducing collisions and optimizing scheduling, the GSMA helps maintain efficient data flow within the WBAN. This paper presents the design and implementation of the GSMA-based approach within the 802.15.4 framework. Simulation results are presented to evaluate the effectiveness of the proposed method in improving reliability, reducing packet loss, and enhancing overall network performance in WBANs. The findings demonstrate the potential of the GSMA to address the limitations of 802.15.4 and contribute to developing more reliable and efficient WBAN solutions for healthcare applications.

RADD-CycleGAN: unsupervised reconstruction of high-quality ultrasound image based on CycleGAN with residual attention and dual-domain discrimination

Plane wave (PW) imaging is fast, but limited by poor imaging quality. Coherent PW compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio and a 22.2% increase in the structural similarity index measure. The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Exploring Field-evolution and Dynamical-capture Coalescing Binary Black Holes in GWTC-3

We investigate formation channels for merging binary black holes (BBHs) in GWTC-3, with a dedicated semiparametric population model. The model first describes or excludes a high-spin (with magnitudes of ∼0.7) and high-mass (ranging in ∼20–80M ⊙) subpopulation, which was identified by previous works and can be interpreted as hierarchical mergers. We find that the rest of BBH population can be categorized into two subpopulations with different mass and mass-ratio distributions, as indicated by a Bayes factor of lnB=1.8 . One subpopulation, characterized by nearly aligned spins and consistent with isolated field formation, likely dominates the 10-solar-mass peak in the primary-mass function. The other subpopulation, with isotropic spins and consistent with the dynamical channels, shows a stronger preference for symmetric pairing, and mainly contributes to the 35-solar-mass peak in the primary-mass function. Note that the Bayes factor is not high enough with the currently available data, so that the case of a single population is still acceptable. Additionally, we compare the mass distributions between merging BBHs and black holes (BHs) in high-mass X-ray binaries (HMXBs). We find that the primary mass of the aligned subpopulation is slightly lighter than those of the HMXB BHs, while the isotropic subpopulation is consistent with the HMXB BHs, if the power-law index of its mass function is shifted by 2, as indicated by the dynamical formation channels. However, the spin magnitudes of both subpopulations are significantly smaller than those of the HMXB BHs.

Detection Rate of Galaxy Cluster-lensed Stellar Binary Black Hole Mergers by the Third-generation Gravitational-wave Detectors

Gravitational waves (GWs) from stellar binary black hole (sBBH) mergers can be strongly gravitational lensed by intervening galaxies/galaxy clusters. Only a few works have investigated cluster-lensed sBBH mergers by adopting oversimplified models, while galaxy-lensed ones have been intensively studied. In this paper, we estimate the detection rate of cluster-lensed sBBH mergers with third-generation GW detectors and its dependence on the lens models. We adopt detailed modeling of galaxy cluster lenses by using the mock clusters in the Synthetic Sky Catalog for Dark Energy Science with LSST (cosmoDC2) and/or approximations of the pseudo-Jaffe profile or an eccentric Navarro–Frenk–White dark matter halo plus a bright central galaxy with singular isothermal sphere profile. Considering that the formation of sBBH mergers is dominated by the evolution of the massive binary star (EMBS) channel, we find that the detection rate of cluster-lensed sBBHs is ∼5–84 yr−1, depending on the adopted lens model and uncertainty in the merger rate density, which is about ∼13−2.0+28 yr−1 if adopting relatively more realistic galaxy clusters with central main and member galaxies in the cosmoDC2 catalog, close to the estimated detection rate of sBBH mergers lensed by galaxies. In addition, we also consider the case in which the production of sBBH mergers is dominated by the dynamical interactions in dense stellar systems. We find that the detection rate of cluster-lensed sBBHs from the dynamical channel is about 1.5 times larger than that from the EMBS channel, and the redshift distribution of the former peaks at a higher redshift (∼3) compared with that from the latter (∼2).

Dynamic LSTM-Based Channel Quality Indicators Prediction for LEO Satellite Communication

Dynamic LSTM-Based Channel Quality Indicators Prediction for LEO Satellite Communication

Energy conversions at shock wave interaction with pulse discharge in profiled channel

The objective of this paper is to conduct a comprehensive investigation of the thermal and gas dynamic flow fields generated during the interaction of pulsed volume discharge plasma with high-speed channel flow. A comparative visualization was carried out using high-speed infrared thermography and shadowgraphy techniques. We examined phenomena related to both plasma and gas dynamic interactions within a special test section of the gas dynamic channel. The spatial–temporal characteristics of the thermal fields associated with these plasma and gas dynamic interactions were analyzed, together with infrared radiation intensity diagrams. The dynamics of discontinuities and inhomogeneities resulting from the interaction of shock waves with the pulsed volume discharge plasma—referred to as discontinuity breakdown—were also investigated. We compared two physical mechanisms of energy conversion into infrared radiation recorded by the thermal imager in the range of 1.5–5.1 μm. These mechanisms include low-temperature plasma emission from a sub-microsecond localized volume discharge and the sub-millisecond radiation from the inner surfaces of glass walls heated due to thermal conductivity at the interface with the gas flow boundary layer.

Primary channel selection in dynamic channel bonding using laser chaos decision maker

Primary channel selection in dynamic channel bonding using laser chaos decision maker

Efficient Pruned-DFT FBMC-OQAM for Performance Enhancement of MIMO Channel Equalization Techniques

A The next generation of wireless systems, like 5G/6G, ought to accommodate a wide range of potential use cases like massive real-time and machine-type communications. Accordingly, such systems need to provide high throughput, low latency, and a low peak to average power ratio (PAPR). It is not feasible to fulfil each of these needs with a single 5G/6G technique. On one hand, MIMO technique can potentially improve spectral efficiency and hence support more date rates. On the other hand, the utilization of multi-carriers’ techniques like FBMC/OQAM, or offset quadrature amplitude modulation, offers a compelling substitute for the traditional cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM) because of its excellent spectral efficiency and extremely low out-of-band radiation. However, the FBMC/OQAM orthogonality constraint is loosened, confined to the real field alone, resulting in intrinsic interference. This interference causes incompatibilities between FBMC/OQAM and certain MIMO schemes. Also, equalizing the time-varying MIMO channel is even more difficult when the complex valued symbol—which includes both real and imaginary FBMC/OQAM task, primarily due to the general non-flatness of the subchannels. From this perspective, we first suggest in this paper an efficient pruned-DFT-based implementation of MIMO to restoring the complex orthogonality and also reduce the complexity of the whole system. Then, we optimize MIMO channel equalization by efficiently separating and equalizing multiple parallel channels using various equalization techniques. The numerical results, presented as MSE versus subcarrier index, show that Pruned DFT technique consistently achieves lower MSE values compared to the standard MIMO FBMC-OQAM across various configurations, which indicates better error performance and its robustness in dynamic channel conditions.

A Cluster-Based Dynamic Narrow-Beam Channel Model for Vehicle-to-Infrastructure Communications

A Cluster-Based Dynamic Narrow-Beam Channel Model for Vehicle-to-Infrastructure Communications

Moving Target Detection for Array Antennas in Varying Multipath Environments

Detecting moving targets remains a significant challenge in radar applications, particularly in dense multipath environments where signal complexities present formidable obstacles. However, the application of time reversal (TR) technology offers promising prospects for enhancing target detection capabilities. Utilizing this technology, we propose a detection algorithm tailored specifically for a moving target, designed exclusively for array antennas (AA) operating in dense multipath environments with dynamic channel variations. We establish an AA-TR signal model within this framework and derive the AA-TR likelihood ratio test (AA-TR-LRT) detector. To assess the effectiveness of our proposed approach, we also develop the AA conventional LRT (AA-C-LRT) detector as a comparative benchmark. Comprehensive numerical tests consistently verify the outstanding effectiveness of our detection algorithm, underscoring its precision in detecting moving targets, particularly in highly complex multipath scenarios with varying channels.

A Deep Reinforcement Learning‐Based Physical Layer Security Enhancement Design for RIS‐Aided mmWave Communications With Practical Constraints

ABSTRACTReconfigurable intelligent surfaces (RIS) offer new opportunities for enhancing security in millimeter‐wave (mmWave) communications. However, some significant practical challenges still need to be addressed before their extensive implementation in future wireless networks. This article considers the practical constraints in a secure mmWave system aided by distributed RISs, including imperfect channel state information (CSI) in dynamic channel conditions and high complexity of non‐convex optimization in complex environments. To address these challenges, we propose a robust and efficient physical layer security (PLS) enhancement algorithm based on the deep reinforcement learning (DRL) framework to effectively tackle the issues of limited dynamic adaptation and high computational complexity encountered with conventional optimization methods. This algorithm, utilizing an actor‐critic architecture, can dynamically track channel variations and optimize strategies for improved system secrecy rate. Numerical simulations demonstrate that the proposed DRL‐based PLS enhancement algorithm outperforms non‐convex optimization benchmarks in robustness and efficiency for secure mmWave communication systems aided by distributed RISs and affected by imperfect CSI.

Concurrent Direct Inter-ONU and Upstream Communications in IMDD PONs Incorporating P2MP Flexible Optical Transceivers and Advanced Passive Remote Nodes

Driven by a large number of emerging diversified services, in the 5G and beyond era, concurrent direct inter-ONU and upstream communications inside a PON-based mobile access network are highly desirable to provide dynamic, ultra-dense, and fast ONU-to-ONU (without involving an OLT) and ONU-to-OLT connections. To cost-effectively deliver highly dynamic and low latency direct inter-ONU communications, this paper proposes and experimentally demonstrates novel concurrent direct inter-ONU and upstream communications in an upstream 27 km, >62.47 Gbit/s IMDD PON. For supporting inter-ONU communications between a large number of ONUs, an advanced passive remote node is also proposed. Based on different passive optical components, this remote node can be implemented using two approaches, which can, respectively, reduce the inter-ONU signal power losses by >12.2 dB and >16.6 dB (for 128 ONUs) in comparison with existing inter-ONU communication techniques’ remote nodes. In each ONU and OLT, a single pair of cascaded IFFT/FFT-based point-to-multipoint (P2MP) flexible optical transceivers are employed to simultaneously and dynamically establish multiple ONU-to-ONU and ONU-to-OLT communications according to actual users’ requirements. Experimental results show that the proposed network has excellent robustness against various transmission system impairments, including chromatic dispersion, the Rayleigh and Brillouin backscattering effects, and the channel interference effects. For each ONU, dynamic channel allocation can be made without compromising its overall performance.

A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks

The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the performance of the YOLOv10 model in concrete crack detection, and the effectiveness of this module is visually demonstrated through the application of interpretable analysis algorithms. In this paper, a concrete dataset with a complex background is used. Experimental results indicate that the DECA module significantly improves the model’s accuracy in crack localization and the detection of discontinuous cracks, outperforming the existing Efficient Channel Attention (ECA). When compared to the similarly sized YOLOv10n model, the proposed YOLOv10-DECA model demonstrates improvements of 4.40%, 3.06%, 4.48%, and 5.56% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Moreover, even when compared with the larger YOLOv10s model, these performance indicators are increased by 2.00%, 0.04%, 2.27%, and 1.12%, respectively. In terms of speed evaluation, owing to the lightweight design of the DECA module, the YOLOv10-DECA model achieves an inference speed of 78 frames per second, which is 2.5 times faster than YOLOv10s, thereby fully meeting the requirements for real-time detection. These results demonstrate that an optimized balance between accuracy and speed in concrete crack detection tasks has been achieved by the YOLOv10-DECA model. Consequently, this study provides valuable insights for future research and applications in this field.

Tuning Nanochannel Microenvironments of a Thermoresponsive MXene Membrane for Mixed Molecule Gradient Separation.

Drawing inspiration from dynamic biological ion channels, researchers have developed various artificial membranes featuring responsive nanochannels. Typically, these membranes modify mass transport behaviors by manipulating the responsive layer on the inner surfaces of the intrinsic layer. In this study, we build two-dimensional lamellar membranes composed of titanium carbide MXene and poly(N-isopropylacrylamide), endowed with dual-level regulatable nanochannels, achieved through adjustments of nanochannel microenvironments. The size of these two-dimensional nanochannels can be altered by both the thermoresponsive polymer layer and the intrinsic MXene layer that could undergo spontaneous oxidation. The multilevel regulation strategy substantially enhances the molecular selectivity of MXene separation membranes, which is further applied for precise gradient separation toward multiple molecules. This advancement showcases the versatility and transformative capabilities of responsive nanochannel technology, setting the stage for innovative developments in diverse fields.

Multiport event-triggered finite-time PID control for underactuated unmanned surface vessels under cyber attacks

This paper studies the network security control problem of underactuated unmanned surface vessels (USVs) affected by injection and deception attacks and proposes a novel finite-time PID (FT-PID) control method. To solve the problem of lateral drive mismatch of underactuated vessels, the heading harmonized dynamic control method (HHDCM) is used to utilize the characteristics of the arctangent function to closely combine the motion control with the geometric direction. Then, all uncertain dynamics in the system are converted into the form of neural single-parameter learning, and the kinematic channel and dynamics channel control mechanisms are given the function of high-speed self-adjusting control gain by constructing a dynamic error integrated variable (DEIV). To reduce the network communication load and improve data security, a novel event-triggered control (ETC) approach is constructed by introducing a variable that describes the internal characteristics of the measurement error. Stability analysis based on Lyapunov theory shows that the proposed FT-PID control mechanism can ensure that all signals in the underactuated USV closed-loop system are bounded. Finally, the effectiveness of the control scheme is verified through simulation.