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Quaternion-Based Arithmetic in Quantum Information processing: A promising approach for efficient color quantum imaging

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IEEE signal processing MAGAZINE 2024年第2期41卷 64-74页

作者： Grigoryan, Artyom M. Agaian, Sos S. Univ Texas San Antonio Dept Elect Engn San Antonio TX 78249 USA CUNY Coll Staten Isl Dept Comp Sci Staten Isl NY USA

Classical color image processing, image recognition, and machine learning introduce nonlinearity, causing the collapse of the quantum state into classical probability perceptrons after measurements, due to the inherent linearity of quantum computing. To address this challenge, quaternion-based arithmetic offers a promising approach. By treating the primary color components as a single unit using quaternion algebra, nonlinear relationships can be implemented, effectively manipulating higher-dimensional color data. This article aims to achieve efficient and accurate color quantum image processing (QIP) by introducing new quaternion quantum-based color imaging tools based on multiplicative arithmetic on two-qubits and quantum superpositions. The approach includes the concept of a quaternion Fourier transform (QFT) in two-qubit-based color image representation. To end, we discuss possible applications of the proposed methods in color quantum imaging.

关键词： Quantum computing Image color analysis Algebra Visual communication Quaternions signal processing algorithms Color

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Urban SAR Tomography With Z-Structure Constraint of Buildings

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 9945-9960页

作者： Guo, Rui Gao, Yuxin Ren, Zishuai Zhang, Zhao Xie, Jinwei Yang, Bei Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Jiangsu Shuguang Electroopt Co Ltd Yangzhou 225009 Peoples R China ZEEKR Automobile Co Ltd Shanghai 200232 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China

Synthetic aperture radar tomography (TomoSAR) technology effectively mitigates issues such as severe layover in high-resolution synthetic aperture radar (SAR) urban imagery, presenting numerous applications in reconstructing complex 3-D urban scenes. However, prevailing TomoSAR methodologies usually overlook the geometrical feature of the targets, which are particularly pronounced in buildings in urban scenes. To improve the quality of 3-D reconstruction of building targets for large-scale urban scenes, we propose geometric-guided TomoSAR processing in this article. First, the geometrical feature referred to a Z-structure of buildings in TomoSAR point cloud is studied. Then, a Z-structure information extraction approach is proposed to provide prior information for subsequent geometric constraints. Finally, the Z-structure-constraint-based tomographic algorithm is proposed, optimizing the solution space of the original algorithms. Experiments are conducted on real SAR data, and 3-D point clouds of the entire urban scenes are obtained. The proposed algorithm demonstrates commendable performance in point cloud entropy and concentration, especially in the inversion of different floor structures.

关键词： Buildings Point cloud compression Three-dimensional displays Imaging Tomography Image reconstruction signal processing algorithms Compressed sensing (CS) geometrical feature synthetic aperture radar tomography (TomoSAR) 3-D reconstruction of buildings

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Ridge Detection for Nonstationary Multicomponent signals With Time-Varying Wave-Shape Functions and its Applications

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4843-4854页

作者： Su, Yan-Wei Liu, Gi-Ren Sheu, Yuan-Chung Wu, Hau-Tieng Natl Yang Ming Chiao Tung Univ Dept Appl Math Hsinchu 300093 Taiwan Natl Cheng Kung Univ Dept Math Tainan 70101 Taiwan Natl Taiwan Univ Natl Ctr Theoret Sci Taipei 10617 Taiwan NYU Courant Inst Math Sci New York NY 10012 USA

We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method shape-adaptive mode decomposition-based multiple harmonic ridge detection (SAMD-MHRD). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of SAMD-MHRD through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject.

关键词： Harmonic analysis signal processing algorithms Mathematical models Time-frequency analysis Accelerometers Time series analysis Spectrogram Feature extraction Oscillators Noise reduction ridge detection curve extraction wave-shape function synchrosqueezing transform

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DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第5期21卷 5733-5755页

作者： Ye, Miao Zhao, Chenwei Wen, Peng Wang, Yong Wang, Xiaoli Qiu, Hongbing Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

The multicast routing problem in software-defined networking (SDN) is an NP-hard problem. The existing solution methods based on deep strength learning suffer from the problems of branch redundancy, an excessively large action space and slow convergence of the intelligent models. In this paper, an intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the optimal multicast tree problem is decomposed into two subproblems: fork node selection and the construction of an optimal path from a fork node to a destination node. Second, a multichannel matrix is designed as the state space for the internal and external controllers of hierarchical reinforcement learning based on the global network-aware information characteristics of SDN. Then, different action spaces are designed for the upper and lower subproblems, four action selection policies are designed for constructing multicast paths, and different reward policies are designed at different levels. Finally, a series of experiments and their results show that the designed algorithm not only searches the multicast tree efficiently but also converges faster and without redundant branches, with better performance in terms of bandwidth, delay and packet loss rate than the current mainstream solution algorithms. The codes for DHRL-FNMR are open and available at https://***/GuetYe/DHRL-FNMR.

关键词： Multicast algorithms Routing Aerospace electronics Heuristic algorithms signal processing algorithms Bandwidth Reinforcement learning Deep hierarchical reinforcement learning multicast tree deep reinforcement learning software-defined networking

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Hypernetwork Based Model-Driven Channel Neural Decoding

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IEEE ACCESS 2024年 12卷 73228-73237页

作者： Liang, Yuanhui Lam, Chan-Tong Wu, Qingle Ng, Benjamin K. Im, Sio-Kei Macao Polytech Univ Fac Appl Sci Macau Peoples R China Sichuan Univ Sci & Engn Sch Automat & Informat Engn Yibin 644000 Peoples R China Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Macao Polytech Univ Macau Peoples R China

Channel decoding algorithms based on model-driven deep learning, also known as channel neural decoding algorithms, have received a lot of attention in recent years. However, the internal parameters and number of layers of the current channel neural decoding algorithm cannot be changed after training. Once changed, retraining of the channel neural decoding network is required. Hypernetwork is a neural network that can generate internal parameters for the main neural network to reduce the training cost of the main neural network and improve the flexibility of the main neural network. In this study, a novel hypernetwork based channel neural decoder is proposed for neural belief propagation algorithms (NBP), including the neural normalized min-sum (NNMS) and neural offset min-sum (NOMS) algorithms. According to the type of information interaction between the hypernetwork and the main decoding network, hypernetwork-based channel neural decoders can be divided into two types: static and dynamic. The internal parameters of the static hypernetwork-based channel neural decoder can be updated as needed without retraining of the main network. In addition to this benefit, the number of layers of the dynamic hypernetwork-based channel neural decoder can also be adjusted. Experimental results show that, compared with the existing NNMS decoding algorithms, the proposed hypernetwork-based NNMS decoding algorithms can achieve better performance on both low-density parity-check (LDPC) and Bose-Chaudhuri-Hocquenghem (BCH) codes.

关键词： Decoding Artificial neural networks Neural networks Heuristic algorithms Training signal processing algorithms Deep learning Modeling Parity check codes Model-driven hypernetwork channel neural decoding LDPC codes BCH codes

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Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-Supervised Learning

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5517-5532页

作者： Mou, Zhiyu Gao, Feifei Liu, Jun Yun, Xiang Wu, Qihui Tsinghua Univ THUAI Inst Artificial Intelligence Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligence Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Beijing 100084 Peoples R China Tsinghua Univ Sch Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Baicells Technol Co Ltd Beijing 100044 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

In this paper, we study the cluster head detection problem of a hierarchical unmanned aerial vehicle (UAV) swarm network (USNET), consisting of multiple double-level clusters, from the perspective of the observers who are assumed not to know any internal information on the USNET. Specifically, we propose a multi-cluster graph attention self-supervised learning (MC-GASSL) framework for the observers. The MC-GASSL comprises two main algorithms, including the sequential metric learning (SML) algorithm for detecting the clusters in the USNET, and the graph attention self-supervised learning (GASSL) algorithm for detecting the cluster head in each cluster. The basic idea of the SML is to learn an embedding space where the UAVs can be easily clustered based on Euclidean distances, and the GASSL works by calculating the attention weights between UAVs and finding the attention center of the cluster. Extensive numerical results validate the effectiveness of the MC-GASSL. Moreover, the ablation study shows that the GASSL can detect the cluster head in single clusters with over 98% detection rate on average. The ablation study also shows that the clustering purity of the USNET with the SML exceeds that with traditional clustering algorithms by at least 10% on average.

关键词： Autonomous aerial vehicles Observers Clustering algorithms Self-supervised learning signal processing algorithms Measurement Indexes Cluster head detection graph attention network self-supervised learning hierarchical UAV swarm

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On the Impact of Random Node Sampling on Adaptive Diffusion Networks

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3973-3989页

作者： Tiglea, Daniel G. Candido, Renato Silva, Magno T. M. Univ Sao Paulo Elect Syst Engn Dept Escola Politecn BR-05508010 Sao Paulo Brazil

In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can communicate with each other to pursue a common goal of estimating an unknown system. In particular, we consider in our theoretical analysis the diffusion least-mean-squares algorithm in a scenario in which the nodes are randomly sampled. Hence, each node may or may not adapt its local estimate at a certain iteration. Our model shows that a reduction in the sampling probability leads to a noticeable deterioration in the convergence rate, and, if the nodes cooperate, to a slight decrease in the steady-state Network Mean-Square Deviation (NMSD), assuming that the environment is stationary and that all other parameters of the algorithm are kept fixed. Furthermore, we also investigate the effects of the random node sampling on the network stability.

关键词： signal processing algorithms Adaptive systems Vectors Convergence Steady-state Simulation Transient analysis Adaptive diffusion networks distributed signal processing sampling stability asynchronous networks

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Switching Games for Image Compression

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IEEE signal processing LETTERS 2025年 32卷 1016-1020页

作者： Huhtanen, Marko Univ Oulu Fac Informat Technol & Elect Engn Oulu 090570 Finland

To compress an image, a technique based on optimal scalings with diagonal matrices is described. To start the process, an initial image of high compression ratio is required. Such an image can be produced, for example, with the 2D FFT or 2D DCT of the original image. Principal component analysis is a special case of this compression technique where the initial image is extremely rough, consisting of the first 2D Fourier basis function only. This initial image is then optimally orthogonalized and expanded by iteratively applying diagonal matrices from the left and right to attain double orthogonality. The process can be viewed as a continuous version of Berlekamp's switching game.

关键词： Image coding Switches Principal component analysis Discrete cosine transforms Games Eigenvalues and eigenfunctions Approximation algorithms Solids signal processing algorithms Matrix decomposition Lossy compression double orthogonality principal component analysis 2D FFT 2D DCT

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Stochastic Analysis of FxLMS Algorithm for Feedback Active Noise Control

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IEEE signal processing LETTERS 2025年 32卷 416-420页

作者： Wang, Cong Wu, Ming Zhou, Shuang Yang, Jun Chinese Acad Sci Inst Acoust Key Lab Noise & Vibrat Res Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Feedback active noise control (ANC) systems are effective in reducing predictable noise, e.g. periodic, narrowband and colored noise. There are still few studies on the theoretical analysis of feedback ANC systems, and are limited to idealized signals such as sinusoidal or Gaussian signals. This paper presents the stochastic analysis of a feedback ANC system based on the filtered-x least mean square (FxLMS) algorithm, which is not relying on a specific noise model and perfect secondary path. The equations for the mean and mean-square convergence behavior are derived. Extensive simulations of sinusoidal, band-limited white noise, and hybrid signals illustrate the accuracy of the analysis.

关键词： Noise Vectors Mathematical models Convergence Delays Adaptive filters Stochastic processes signal processing algorithms Feedforward systems White noise Feedback active noise control (ANC) FxLMS steady-state and transient behavior stochastic analysis

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Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第7期18卷 1235-1248页

作者： Ji, Zelin Qin, Zhijin Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China State Key Lab Space Network & Commun Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

The trend of massive connectivity pushes forward the explosive growth of end devices. The emergence of various applications has prompted a demand for pervasive connectivity and more efficient computing paradigms. On the other hand, the lack of computational capacity of the end devices restricts the implementation of the intelligent applications, and becomes a bottleneck of the multiple access for supporting massive connectivity. Mobile cloud computing (MCC) and mobile edge computing (MEC) techniques enable end devices to offload local computation-intensive tasks to servers by networks. In this paper, we consider the cloud-edge-end collaborative networks to utilize distributed computing resources. Furthermore, we apply task-oriented semantic communications to tackle the fast-varying channel between the end devices and MEC servers and reduce the communication cost. To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer non-linear programming (MINLP) problem. The long-term energy consumption minimization problem is transformed into the deterministic problem in each time frame. The DRLH framework integrates a model-free deep reinforcement learning algorithm with a model-based mathematical optimization algorithm to mitigate computational complexity and leverage the scenario information, so that improving the convergence performance. Numerical results demonstrate that the proposed DRLH framework achieves near-optimal performance on energy consumption while stabilizing all queues.

关键词： Semantics Servers Task analysis Optimization signal processing algorithms Computational modeling Cloud computing Deep reinforcement learning edge computing resource management semantic communications

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