Reconfigurable intelligent surfaces (RISs) can not only enhance communication performance but also convey additional information through tunable reflection coefficients. However, due to the multiplicative fading effect, the potential of conventional passive RISs is limited. In this paper, an active-passive hybrid RIS-based reflection modulation (HRIS-RM) scheme is proposed for multiple-input single-output (MISO) communications. In the proposed HRIS-RM scheme, all reflecting elements (REs) on the RIS are partitioned into groups, each of which operates either in an active or a passive state. The hybrid RIS assists the multi-antenna transmitter (Tx) in beamforming while conveying additional information through the number of active RE groups. Signal models and detection methods are developed for both continuous and discrete RIS phase-shift conditions. Moreover, upper bounds on the theoretical bit error probabilities (BEPs) for both Tx information and RIS information are derived under these two conditions. The HRIS-RM scheme is further extended to the case of imperfect channel state information, and its theoretical BEP performance is analyzed. Simulation results validate the theoretical analysis and demonstrate that the proposed HRIS-RM scheme consistently outperforms existing benchmark schemes in terms of error performance under both continuous and discrete RIS phase-shift conditions.

Accurate detection of small objects in unmanned aerial vehicle (UAV) imagery remains a significant challenge in computer vision, primarily due to limited pixel coverage and weak feature representation for targets occupying fewer than 50 pixels. Current state-of-the-art methods face critical limitations: conventional detectors like YOLO rely on static feature concatenation failing to adaptively integrate multi-scale information; popular attention mechanisms (CBAM, SENet) depend on global pooling operations that lose fine-grained spatial details essential for precise localization; and there exists a notable trade-off between accuracy and computational efficiency for resource-constrained UAV platforms. To address these issues, this paper introduces BDMA-YOLO, a parameter-efficient framework for edge deployment that incorporates two new components. The Bidirectional Lightweight Fusion Network (BLFNet) replaces standard connections with dynamic weighting using swish activation, achieving adaptive feature fusion across scales. The Partial Attention Cross-Stage Fusion Block (PACSFB) combines the Multi-Scale Residual Mobile Block (MRMB) with directional spatial attention to preserve key local features. Experiments on the SIMD dataset demonstrate that BDMA-YOLO achieves notable improvements over YOLOv8-S baseline: [email protected] increases by 2.1–81.9%, [email protected]:0.95 improves by 2.1–67.0%, and recall rises by 3.9–81.5%. These gains are attained with a 7.2% reduction in parameters and only 1.4% increase in computational load, demonstrating competitive performance compared to recent models such as YOLOv10-S and RT-DETR-L in efficiency–accuracy balance. The source code is available at: https://github.com/fhxf-pro/BDMA-YOLO

Reconfigurable intelligent surface (RIS) is a promising technology that can modify wireless propagation environment, enabling cost-effective spectral and energy efficient communications. Moreover, due to its controllable reflection coefficients, an RIS can function as a backscatter device while enhancing the communication link. However, the application potential of conventional passive RIS is limited by the multiplicative fading effect. In this paper, an active-passive hybrid RIS-assisted reflective spatial modulation (HRIS-RSM) scheme is proposed for backscatter communication, where the backscatter information is embedded into the modulated signal from the transmitter and jointly conveyed by the number of active reflecting element groups on the RIS and the receive antenna indices at the receiver. Subsequently, maximum likelihood (ML) and greedy detections are formulated for the HRIS-RSM scheme. The theoretical bit error probabilities for the transmitter information, backscatter information, and overall information in the proposed HRIS-RSM scheme are derived under both ML and greedy detections. Simulation results validate the theoretical derivations and evaluate the performance of the proposed HRIS-RSM scheme, demonstrating its superior bit error rate performance compared to reference schemes.

Reconfigurable intelligent surface (RIS) is a promising technology that can modify the wireless propagation environment to improve system performance. Similar to index modulation technique, RIS can exploit reflection coefficients and reflecting element indices to convey information, further improving spectral efficiency. However, the application potential of conventional passive RIS is limited by themultiplicative fadingeffect. To address this limitation, we present a hybrid RIS-based reflective index modulation (HRIS-RIM) scheme where active and passive reflecting elements coexist on the RIS. In the scheme, information is transmitted through the number of active reflecting element groups of the RIS and the antenna indices at the receiver. Then, the transmission model of the HRIS-RIM under imperfect channel state information (CSI) is proposed. Subsequently, maximum likelihood detection and greedy detection for the HRIS-RIM scheme with imperfect CSI are formulated, and their theoretical bit error probabilities are derived. Finally, the achievable rate of the system is derived based on an information theoretic approach. Simulation results validate the theoretical derivations and show the impact of imperfect CSI on the system performance. Additionally, the results demonstrate that the proposed HRIS-RIM scheme outperforms reference schemes in terms of bit error performance, achievable rate, and energy efficiency.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBAN). However, current solutions still cannot satisfy high performance requirements of low latency and energy efficiency for emergency data reporting. In this article, we propose a Multi- Factor Emergency Scheduling Scheme (MESS) for meeting such a strict demand. First, we design a Data Classification method including periodic data and emergency data. Unlike consistent data characteristics in other schemes, data heterogeneity is considered in our solution, which is more practical for different nodes. Second, we carefully devise a Multi-Factor Priority Division Scheme according to the Disease-Related Factor, Critical Degree Factor, Health Severity Factor and Age of Information Factor. This is more comprehensive consideration of the key characteristics of the node. In addition, we design a Dynamic Slot Allocation and Sequencing Approach, in which time slots of nodes are allocated based on the Data Classification and Multi-Factor Priority-Based Ordering. This enhances low latency and guarantees energy efficiency of nodes, and short waiting time of emergency data. Extensive simulations exhibit the advantages of MESS in terms of delay and energy efficiency.

In this paper, deep learning is combined to learn the bit posterior probability (soft information) of iterative decoding for a bit-interleaved coded modulation with iterative decoding (BICM-ID) system. The deep neural network (DNN) is adopted to learn and replace multiple modules of the receiver, which can jointly deal with multiple problems and improve the efficiency of the whole system. The output of direct learning iteration reduces the computational cost of iteration to a certain extent. Simulation is carried out under Rayleigh channel and multiple modulation modes and results show that the proposed scheme without iteration is better than traditional BICM systems, and very close to traditional BICM-ID systems which needs iteration between demodulation and decoding.

In this paper, based on the characteristics of inertial data collected by smart terminals carried by pedestrians, a dynamic estimation algorithm of stride length using motion mode recognition is proposed. The motion mode includes pedestrian motion status and a smart terminal attitude. As pedestrians move forward, different motion modes will have a corresponding impact on the stride length estimation model, resulting in the stride length estimation model directly affecting the accuracy of the pedestrian dead reckoning system (PDR). Therefore, different motion modes need to make corresponding adjustments to the stride length estimation model to give a specific stride-length adjustment gain. In order to avoid errors caused by the change of the motion mode, a dynamic time warping (DTW) algorithm is proposed to identify the motion mode of the smart terminal, so as to select an appropriate gain to adjust the stride length estimation model, thereby improving the positioning accuracy of the pedestrian dead reckoning system. Experimental results show that the average positioning error of the stride-length estimation model based on DTW motion mode recognition is 1.78 meters in five motion modes. In performance, this algorithm is superior to other traditional stride length estimation models.

Abstract In laparoscopic surgery, the smoke generated by operations including electrocautery and laser ablation seriously degrades the quality of endoscopic images. It not only reduces the visibility of the surgery, leading to increased risk of surgery, but also affects the performance of image processing in computer‐assisted surgery such as segmentation, 3D reconstruction and tracking. Therefore, a desmoking algorithm is required to eliminate smoke in endoscopic images. In this article, we study a U‐Net model that can eliminate smoke of the laparoscopic image in real‐time and preserve the natural appearance of the organ surface. Our method is based on the improved U‐Net in which convolutional block attention module is used as an embedded guide mask of the decoder part. The laparoscopic image dataset is provided by the Hamlyn Center, and the Blender software is used to simulate various situations of smoke added to the laparoscopic image for training and testing. For the proposed method, the peak signal‐to‐noise ratio value is up to 29.27 and the structural similarity index is up to 0.945 over the test images. The experimental results demonstrate that the proposed method achieves a better performance than other six existing methods, which is applicable for real‐time endoscopic image processing.

Cross-modal hashing has attracted considerable attention as it can implement rapid cross-modal retrieval through mapping data of different modalities into a common Hamming space. With the development of deep learning, more and more cross-modal hashing methods based on deep learning are proposed. However, most of these methods use a small batch to train a model. The large batch training can get better gradients and can improve training efficiency. In this paper, we propose the DHLBT method, which uses the large batch training and introduces orthogonal regularization to improve the generalization ability of the DHLBT model. Moreover, we consider the discreteness of hash codes and add the distance between hash codes and features to the objective function. Extensive experiments on three benchmarks show that our method achieves better performance than several existing hashing methods.

In recent years, data security becomes a critical issue restricting the wider acceptance of Internet of Things (IoT) devices and Cyber-physical systems since they have limited hardware resources and power supply while high data security protection requires relatively large hardware resources and power supply utilization. This contradiction is particularly prominent in the field of Wireless Body Area Networks (WBANs) which is a segment of the IoT field. WBANs are dedicated to transmit and process biomedical data collected from human beings, any kind of tampering or hacking may cause severe consequences to users. However, the limited computing ability and battery supply of biomedical sensors attached or implanted in the users restrict the security protection strength of the data in WBANs. In this paper, a quantized Logistic mapping-based stream encryption scheme for WBANs is proposed. Meanwhile, Power spectral entropy (PSD) and Peak-to-average Power Ratio (PAPR) analysis of the quantized chaotic sequences have been performed to evaluate the chaotic characteristic among different quantization precision to resolve the ineffectiveness of Lyapunov factor in quantized systems. This encryption scheme utilizes chaotic systems with different quantization precision based on the security requirement of every individual communication, which leads to higher hardware and power efficiency. Finally, the proposed encryption scheme is implemented with VHDL and synthesized using SMIC 60 CMOS technology. The evaluation results illustrate that the proposed encryption scheme has the advantages of high-security performance and high-efficiency hardware resources utilization.

Body Area Networks (BAN) have caused wide interest in academic and industrial areas in recent years due to the strong demands of health condition monitoring by people. Consequently, security becomes a critical consideration, which restricts the development of BAN while the data transferring in BAN is increasingly significant and their privacy is of the utmost importance. Therefore, an ASIC implementation of security scheme for BAN is proposed by the authors, based on the IEEE standard 802.15.6, which is synthesized using the SIMC 65nm CMOS technology.

Body area networks (BANs) are systems of wearable computing devices for long‐term monitoring of personal health care. BAN is an emerging technology for the worldwide ageing population. In the BAN system, the transceiver is the most energy‐consuming part of a sensor node and radio transmission in the vicinity of the human body is highly lossy and inefficient. Therefore, the energy of the sensor node constraints the life cycle and quality of service of the network; consequently, low‐cost protocol shaves attracted wide interest. This study proposes a frame structure model of a self‐adaptive guard band protocol, which introduces a GB in each time slot according to the allowed maximum time drift of the crystal, adaptively adjusts the value of the GB based on the actual time drift, and then ensures that the node simultaneously maintains the sleeping state and synchronisation with the coordinator during beacon transmission, thus reducing the energy consumption.

Selection cooperation is an attractive cooperative strategy for its simplicity and automatic repeat request (ARQ) mechanism can bring additional diversity benefit for wireless networks. In this paper, we combine the distributed selection cooperation protocols with ARQ mechanism to develop more powerful cooperative schemes for delay-tolerant wireless networks and analyze their performance from the perspective of diversity-multiplexing-delay (D-M-D) tradeoff. For small networks where any two nodes have direct links, we investigate the general ARQ scheme which directly extends the selection cooperation protocol with single round of feedback to multiple rounds. We show that the D-M-D tradeoff is determined by the ability of relays in signal combining and demonstrate that allowing relays to perform combining reception can achieve optimal D-M-D tradeoff. Then we propose a simplified scheme which greatly reduces the number of feedbacks and almost achieves the optimal performance. For large networks where direct links are limited in the neighbors of each node, we present a diffusion ARQ protocol which can effectively exploit the channels of nodes that have no direct links with the source. The D-M-D tradeoff analysis and simulation results demonstrate the significant performance improvement of all the proposed schemes.

Reversible logic is a key technique for quantum computing so leading to low-power designs. However, current synthesis algorithms for reversible circuits are low efficiency and do not obtain optimized reversible circuits, so they are only applied to small logic functions. In this paper, we propose a new method based on positive Davio expansion to synthesize reversible circuits, which generates a positive Davio decision diagram for a logic function and transfers diagram nodes to reversible circuits. The algorithm has advantages of optimizing area and fast synthesis speed compared to BDD (Binary decision diagram) based and RM (Reed-Muller) based synthesis method, so it can be adapted for large functions.

As a new technique for low-power design and quantum computing, reversible logic has been paid much attention. A significant part of research lies in synthesizing a reversible network from non-reversible specification. However, current synthesis algorithms for reversible circuits suffer low efficiency and do not reach area optimization, so they are only applicable to small logic functions. In this paper, we propose a new method based on positive Davio expansion to synthesize reversible circuits, which generates a positive Davio decision diagram for a logic function and transfers diagram nodes to reversible circuits. Compared to BDD-based and Reed-Muller (RM) based synthesis methods, our algorithm can optimize area and have fast synthesis speed, so it is suitable for large functions.

Single relay selection has been shown to be an attractive strategy for cooperative communications. In this work, we extend the distributed selection cooperation protocol with feedback to multisource multidestination cooperative networks and investigate the best relay confliction problem existing in the scenario. In the high signal-to-noise ratio (SNR) regime, due to the fact that sharing the same best relay by multiple source nodes has no influence on the diversity-multiplexing tradeoff (DMT) performance for each communication pair, the single relay sharing method is a simple and effective solution. For some practical systems with low or medium SNR, we propose two power allocation algorithms for the shared best relay and show that the method based on maximizing the number of successful relay flows is efficient in distributed scenarios with low complexity.