This paper proposes an amplitude-phase decoupling scheme for reconfigurable intelligent surface (RIS)-enabled backscatter systems, which aims to achieve the standard quadrature amplitude modulation (QAM) constellation on the harmonics by considering the amplitude-phase coupling effects in the non-linear modulation procedure. To begin with, we derive closed-form expressions of the symbol error rate (SER) and throughput of the underlying system with and without decoupling. Moreover, we formulate the maximum amplitude expression of the lthorder harmonic for the coupling amplitude-phase curve. Finally, numerical results show that the proposed amplitude-phase decoupling scheme significantly improves the system performance in terms of SER and throughput, very close to the performance of the ideal one with perfect fitting function.

Multi-modal image fusion, particularly between infrared (IR) and visible (VIS) images, integrates complementary information from diverse imaging sources to enhance perception in applications like autonomous driving and surveillance. While IR images capture thermal radiation and VIS images provide rich texture details, existing fusion methods face challenges in preserving infrared thermal signatures under high illumination, where overexposed VIS regions dominate fusion outputs. To address the above problems, we propose XOR-Fuse, a novel logical operation-driven fusion framework that explicitly captures complementary pixel-level discrepancies between IR and VIS modalities. The XOR-Fuse defines the pixel-wise analogy for the logical XOR operation, which rectifies the suppression of IR features caused by conventional maximum-intensity fusion rules under high illumination. To further reinforce IR feature preservation, we integrate multi-scale Gabor wavelet filtering and wavelet decomposition for illumination-invariant texture extraction and VGG-based semantic constraints, ensuring structural congruence between IR and VIS details. Experiments on MSRS, RoadScene, and TNO datasets demonstrate significant improvements over four baseline models (DeepFuse, SDNet, U2Fusion, and DATFuse). For instance, the enhanced DeepFuse achieves SD=48.27 and VIF=0.62 on MSRS, outperforming the original model (SD=33.79, VIF=0.42). Qualitative results under variable illumination confirm the recovery of suppressed IR details while retaining VIS textures.

This paper proposes a reconfigurable intelligent surface (RIS) enabled symbiotic backscatter non-orthogonal multiple access (NOMA) system based on uniquely factorable constellation pair (UFCP) rule, where the RIS elements are used to passively modulate the backscatter signal over the primary NOMA signal. According to the coding design of UFCP, the backscatter signal modulated at the RIS and the primary NOMA signal emitted by the source, are jointly constructed to form a UFCP for each primary NOMA signal, thereby facilitating the decoding of the primary NOMA signal and backscatter signal at the receiver. To recover the backscatter signal modulated at the RIS as well as the primary NOMA signal, a successive interference cancellation (SIC) based detector is proposed and a closed-form expression of the symbol error rate (SER) is derived. Finally, extensive numerical results show that the proposed system possesses a signal-to-noise-ratio (SNR) gain of more than 10 dB at the SER level of 10−2in comparison with its counterpart without the UFCP design.

With the rapid advancement of autonomous driving technology, there is an increasingly urgent demand for faster and more accurate object detection frameworks. Recently, numerous deep learning-based object detectors have shown impressive performance in real-time driving applications. However, the detection of small objects such as various traffic signs remains challenging due to the complex nature of these objects. This paper proposes a transformer-based detector TS-DETR to improve the accuracy of small object detection in autonomous driving systems. We introduce a constrained decoder structure to focus the model's attention on the predicted boxes. Additionally, a content-aware deformable cross-attention mechanism is proposed to obtain more comprehensive attention weights. Experimental results on challenging public datasets such as TT100K and CCTSDB2021 demonstrate that our method demonstrates substantial performance enhancements while introducing only a slight increment in parameter count compared to current algorithms.

This paper investigates the multi-valued fault-tolerant distributed consensus problem that pursues exact output. To this end, the voting validity, which requires the consensus output of non-faulty nodes to be the exact plurality of the input of non-faulty nodes, is investigated. Considering a specific distribution of non-faulty votes, we first give the impossibility results and a tight lower bound of system tolerance achieving agreement, termination and voting validity. A practical consensus algorithm that satisfies voting validity in the Byzantine fault model is proposed subsequently. To ensure the exactness of outputs in any non-faulty vote distribution, we further propose safety-critical tolerance and a corresponding protocol that prioritizes voting validity over termination property. To refine the proposed protocols, we propose an incremental threshold algorithm that accelerates protocol operation speed. We also optimize consensus algorithms with the local broadcast model to enhance the protocol’s fault tolerance ability.

Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.