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Generic Reversible Visible Watermarking via Regularized Graph Fourier Transform Coding

IEEE TRANSACTIONS ON IMAGE processing 2022年 31卷 691-705页

作者： Qi, Wenfa Guo, Sirui Hu, Wei Peking Univ Wangxuan Inst Comp Technol Beijing 100871 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images or video frames to declare the copyright ownership information, but also completely erases the visible watermark image and thus enables restoring the original host image without any distortion. However, existing RVW algorithms mostly construct the reversible mapping mechanism for a specific visible watermarking scheme, which is not versatile. Hence, we propose a generic RVW framework to accommodate various visible watermarking schemes. In particular, we obtain a reconstruction data packet-the compressed difference image between the watermarked image and the original host image, which is embedded into the watermarked image via any conventional reversible data hiding method to facilitate the blind recovery of the host image. The key is to achieve compact compression of the difference image for efficient embedding of the reconstruction data packet. To this end, we propose regularized Graph Fourier Transform (GFT) coding, where the difference image is smoothed via the graph Laplacian regularizer for more efficient compression and then encoded by multi-resolution GFTs in an approximately optimal manner. Experimental results show that the proposed framework has much better versatility than state-of-the-art methods. Due to the small amount of auxiliary information to be embedded, the visual quality of the watermarked image is also higher.

关键词： Watermarking Image coding Visualization Image restoration Image reconstruction signal processing algorithms Approximation algorithms Reversible visible watermarking graph Fourier transform graph Laplacian regularizer versatility

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Accelerating LMS-Based Equalization With Correlated Training Sequence in Bandlimited IM/DD Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第13期40卷 4268-4275页

作者： Yao, Shuang Tang, Xizi Zhang, Rui Zhou, Qi Su, Shang-Jen Hsu, Chin-Wei Alfadhli, Yahya Ma, Xiaoli Chang, Gee-Kung Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30308 USA

As higher symbol rates are utilized in the intensity modulation and direct detection (IM/DD) scheme to meet the unrelenting growth of data traffic, overcoming the inter-symbol interference (ISI) induced by the limited bandwidth has become increasingly crucial. Channel equalization based on digital signal processing (DSP) is an effective solution, where least-mean squares (LMS) algorithm is adopted to adjust tap coefficients. However, the LMS algorithm usually has slow rate of convergence and requires lots of training symbols. This work proposes a novel training sequence to accelerate the LMS-based equalization. A first-order Markov chain (MC) is employed for sequence generation, which introduces correlation between samples and shapes signal spectrum. Compared with the conventional training sequence that consists of independent and identically distributed (i.i.d.) samples and has a white spectrum, the MC sequence enables faster convergence of tap coefficients and mean-squared error (MSE). Moreover, an experimental demonstration of a 43 Gbaud PAM-4 signal shows that the proposed sequence can achieve a lower pre-forward-error-correction (pre-FEC) bit error rate (BER) than that of the i.i.d. sequence with the same length. When the PAM-4 signal is transmitted over a 5-km standard single mode fiber (SSMF) with 6-dB system bandwidth of 10 GHz, more than 70% training sequence length reduction can be attained. When the fiber length is increased to 10 km and the signal suffers from severe power fading, more than 48% reduction can be achieved.

关键词： Training Bandwidth Convergence Equalizers signal processing algorithms Eigenvalues and eigenfunctions Symmetric matrices Channel equalization digital signal processing intensity modulation and direct detection least-mean squares

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Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3921-3934页

作者： Tuncer, Barkin Orguner, Umut Ozkan, Emre Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in terms of accuracy with lower computational complexity.

关键词： Kinematics Covariance matrices Shape Target tracking Partitioning algorithms signal processing algorithms Computational modeling Extended target tracking random matrix variational Bayes

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Fast and Robust Sparsity Learning Over Networks: A Decentralized Surrogate Median Regression Approach

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 797-809页

作者： Liu, Weidong Mao, Xiaojun Zhang, Xin Shanghai Jiao Tong Univ Sch Math Sci Sch Life Sci & Biotechnol MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Iowa State Univ Dept Stat Ames IA 50011 USA

Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. To obtain the robust and sparse estimators, a natural idea is to adopt the non-smooth median loss combined with a l(1) sparsity regularizer. However, most of the existing methods suffer from slow convergence performance caused by the double non-smooth objective. To accelerate the computation, in this paper, we proposed a decentralized surrogate median regression (deSMR) method for efficiently solving the decentralized sparsity learning problem. We show that our proposed algorithm enjoys a linear convergence rate with a simple implementation. We also investigate the statistical guarantee, and it shows that our proposed estimator achieves a near-oracle convergence rate without any restriction on the number of network nodes. Moreover, we establish the theoretical results for sparse support recovery. Thorough numerical experiments and real data study are provided to demonstrate the effectiveness of our method.

关键词： Convergence Peer-to-peer computing Computational modeling signal processing algorithms Data models Training Network systems Decentralized sparsity learning robust regression median loss ADMM

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Escaping Saddle Points for Successive Convex Approximation

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 307-321页

作者： Bedi, Amrit Singh Rajawat, Ketan Aggarwal, Vaneet Koppel, Alec US Army Res Lab Adelphi MD 20783 USA IIT Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Amazon Supply Chain Optimizat Technol Bellevue WA 98004 USA

Optimizing non-convex functions is of primary importance in modern pattern recognition because it underlies the training of deep networks and nonlinear dimensionality reduction. First-order algorithms under suitable randomized perturbations or step-size rules have been shown to be effective for such settings as their limit points can be guaranteed to be local extrema rather than saddle points. However, it is well-known that the practical convergence of first-order methods is slower than those which exploit additional structure. In particular, empirically, successive convex approximation (SCA) converges faster than first-order methods. However, to date, SCA in general non-convex settings converges to first-order stationary points, which could either be local extrema or saddle points whose performance is typically inferior. To mitigate this issue, we propose calibrated randomized perturbations of SCA, which exhibit the improved convergence rate as compared to the gradient descent counter part. In particular, our main technical contributions are to establish the non-asymptotic performance of SCA algorithm and its perturbed variant converges to an approximate second-order stationary point. Experiments on multi-dimensional scaling, a machine learning problem whose training objective is non-convex, substantiate the performance gains associated with employing random perturbations.

关键词： Convergence Optimization Perturbation methods Gradient methods Approximation algorithms Training signal processing algorithms Stochastic optimization non-convex optimization first order methods

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Resource Allocation for IRS Assisted SGF NOMA Transmission: A MADRL Approach

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第4期40卷 1302-1316页

作者： Chen, Jian Guo, Liang Jia, Jie Shang, Jianhui Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Minist Educ Engn Res Ctr Secur Technol Complex Network Syst Shenyang 110819 Peoples R China

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has been viewed as one of the promising technologies to meet massive connectivity requirements of the next-generation networks. A novel intelligent reconfigurable surface (IRS) assisted SGF NOMA transmission system is proposed, where the IRS is employed to satisfy the channel gain requirements for grant-based users (GBUs) and grant-free users (GFUs). The dynamic optimization on the sub-carrier assignment and power allocation for roaming GFUs, and the amplitude control and phase shift design for reflecting elements of the IRS, is formulated. Aiming at maximizing the long-term data rate of all GFUs, the optimization problem is first modeled as a multi-agent Markov decision problem. Then, three multi-agent deep reinforcement learning based frameworks are proposed to solve the problem under three different IRS cases, including the ideal IRS, non-ideal IRS with continuous phase shifts, and non-ideal IRS with discrete phase shifts. Specifically, for each GFU agent, a sub-carrier assignment deep Q-network (DQN) and a power allocation deep deterministic policy gradient (DDPG) are integrated to dynamically assign network resources for each GFU. For the only IRS agent, two DDPGs are integrated to dynamically assign phase shift and amplitude for each reflecting element of ideal IRS. The single DDPG for dynamically assigning continuous phase shifts, and parallel DQNs for dynamically assigning discrete phase shifts for non-ideal IRS with fixed amplitude are also proposed. Simulation results demonstrate that: 1) The network sum rates of all GFUs can achieve a significant improvement with the aid of IRS, comparing with the system without IRS. 2) The network sum rates of the NOMA assisted SGF transmissions are superior to that of OMA assisted GF transmissions.

关键词： NOMA Resource management Reinforcement learning Quality of service Silicon carbide signal processing algorithms Uplink DDPG DQN IRS NOMA resource allocation SGF

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Fast Sampling and Reconstruction for Linear Inverse Problems: From Vectors to Tensors

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 6376-6391页

作者： Wang, Fen Cheung, Gene Li, Taihao Du, Ying Ruan, Yu-Ping Zhejiang Lab Hangzhou 311121 Peoples R China York Univ Dept EECS Toronto ON M3J 1P3 Canada

signals typical in the real world have different modes, expressed as vectors, matrices, or higher-order tensors. In practice, a target signal is commonly assumed to be linear in the residing factor mode(s) with low-dimensional parameters, and thus can be recovered from partial samples by solving a linear inverse problem (LIP). Sampling for LIPs is to partially query signals for better recovery. There exist many fast sampling methods for vector signals, but they are not applicable for higher-order tensors due to high computation and storage costs. In this paper, we propose a fast sampling algorithm for vector signals first and then extend it to sample tensors with low complexity. Specifically, a tensor signal with R modes is modelled as multilinear on R factor matrices {U-i}(i=1)R, whose vectorized version is linear on a matrix Phi-the Kronecker product of U-i's. Thus, it can be estimated from partial noisy samples via the least-squares (LS) method, where the mean square error (MSE) depends on chosen samples and matrix Phi. Instead of MSE, we minimize a modified MSE problem and prove it has the same optimal and greedy solutions to a problem with a sample-dependent sub-matrix of Phi Phi(T). For one-order vector signals, where Phi = U-1 is given, we propose a fast algorithm to solve the sampling problem via simple vector-vector multiplications based on a matrix inversion formula and greedy solution reuse. For higher-order tensors, we extend our sampling algorithm to select entries by addressing matrices U-i's directly, thus removing the burden of obtaining Phi explicitly. Accompanying our sampling strategy, we propose an iterative reconstruction scheme to estimate the LS solution. Experiments on synthetic and real-world signals from vectors to tensors validated the performance and efficacy of our strategy compared to previous methods.

关键词： Tensors Inverse problems Matrix decomposition Image reconstruction Complexity theory signal processing algorithms Mathematical models Sampling linear inverse problem tensors matrix inversion formula Kronecker product

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Generalized Memory Approximate Message Passing for Generalized Linear Model

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 6404-6418页

作者： Tian, Feiyan Liu, Lei Chen, Xiaoming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Networking Hangzhou 310007 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Hangzhou 314400 Peoples R China Japan Adv Inst Sci & Technol Sch Informat Sci Kanazawa Ishikawa 9208580 Japan

For signal reconstruction in a generalized linear model (GLM), generalized approximate message passing (GAMP) is a low-complexity algorithm with many appealing features such as an exact performance characterization in the high-dimensional limit. However, it is viable only when the transformation matrix has independent and identically distributed (IID) entries. Generalized vector AMP (GVAMP) has a wider applicability but with high computational complexity. To overcome the shortcomings of GAMP and GVAMP, we propose a low-complexity and widely applicable generalized memory AMP (GMAMP) framework, including an orthogonal memory linear estimator (MLE) and two orthogonal memory nonlinear estimators (MNLE), which guarantee the asymptotic IID Gaussianity of estimation errors and state evolution (SE) in GMAMP. The proposed GMAMP is universal since the existing AMP, convolutional AMP, orthogonal/vector AMP, GVAMP, and memory AMP (MAMP) are its special instances. More importantly, we provide a principle toward building new advanced AMP-type algorithms based on the proposed GMAMP framework. As an example, we construct a Bayes-optimal GMAMP (BO-GMAMP) algorithm, which adopts a memory match filter estimator to suppress the linear interference, and thus its complexity is comparable to GAMP. Furthermore, we prove that the SE of BO-GMAMP with optimized parameters converges to the same fixed point as that of the high-complexity GVAMP. In other words, BO-GMAMP achieves the replica minimum (i.e., potential Bayes-optimal) mean square error (MSE) if its SE has a unique fixed point. Finally, simulation results are provided to validate the accuracy of the theoretical analysis.

关键词： Maximum likelihood estimation signal processing algorithms Matched filters Iterative methods Rail to rail inputs Message passing Mathematical models Approximate message passing (AMP) generalized vector AMP generalized memory AMP right rotationally invariant Bayes optimality low complexity

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Wide and Deep Graph Neural Network With Distributed Online Learning

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3862-3877页

作者： Gao, Zhan Gama, Fernando Ribeiro, Alejandro Univ Cambridge Dept Comp Sci Technol Cambridge CB3 0FD England Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94709 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks. In these scenarios, the underlying graph often changes with time due to link failures or topology variations, creating a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be leveraged to retrain GNNs at testing time to overcome this issue. However, most online algorithms are centralized and usually offer guarantees only on convex problems, which GNNs rarely lead to. This paper develops the Wide and Deep GNN (WD-GNN), a novel architecture that can be updated with distributed online learning mechanisms. The WD-GNN consists of two components: the wide part is a linear graph filter and the deep part is a nonlinear GNN. At training time, the joint wide and deep architecture learns nonlinear representations from data. At testing time, the wide, linear part is retrained, while the deep, nonlinear one remains fixed. This often leads to a convex formulation. We further propose a distributed online learning algorithm that can be implemented in a decentralized setting. We also show the stability of the WD-GNN to changes of the underlying graph and analyze the convergence of the proposed online learning procedure. Experiments on movie recommendation, source localization and robot swarm control corroborate theoretical findings and show the potential of the WD-GNN for distributed online learning.

关键词： Robot kinematics signal processing algorithms Convergence Heuristic algorithms Graph neural networks Testing Training Graph neural networks distributed learning online learning stability analysis convergence analysis

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Real-Time Rail Fault Diagnosis Based on Vibration signal Analysis and Second-Order Sinusoidal Model

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IEEE SENSORS JOURNAL 2022年第4期22卷 3382-3396页

作者： Song, Yan Huang, Lidong Xu, Panfeng Wan, Min Liaoning Univ Phys Coll Shenyang 110036 Peoples R China Liaoning Univ Informat Coll Shenyang 110036 Peoples R China

To ensure the reliability of rail transit, it is necessary to diagnose and monitor rail faults. Although the first-order sinusoidal signal model can be used for rail diagnosis, its accuracy is too low. This paper proposes a second-order sinusoidal model to solve this problem. First, the parameters of the second-order sinusoidal model are optimized to approximate the average signal via the least-squares batch learning. Next, with the rail vibration signal model based on the second-order sinusoidal signal model, information related to the rail average signals, which includes the amplitude modulations and the phase modulations, is extracted and analyzed, and the process of rail crack generation is determined. The second-order sinusoidal model extracts the rail characteristics of the amplitude modulation and the phase modulation, reflects the rail fault information and monitors the rail breaking process. Finally, with the experiment (Fig. 0: on the right) and actual rail data, rail fault diagnosis are demonstrated, which are beneficial for the safety of rail transit.

关键词： Rails Vibrations Fault diagnosis signal processing algorithms Phase modulation Feature extraction Fault detection Second-order sinusoidal model regularization principle feature extraction rail fault diagnosis least-squares batch learning

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