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The Discrete Cosine Transform and Its Impact on Visual Compression: Fifty Years From Its Invention

IEEE signal processing MAGAZINE 2023年第6期40卷 14-17页

作者： Wang, Yao Mukherjee, Debargha NYU Dept Elect & Comp Engn Tandon Sch Engn New York NY 11201 USA NYU Tandon Sch Engn Dept Biomed Engn New York NY 11201 USA Google Mountain View CA 94043 USA

Compression is essential for efficient storage and transmission of signals. One powerful method for compression is through the application of orthogonal transforms, which convert a group of [Formula Omitted] data samples into a group of [Formula Omitted] transform coefficients. In transform coding, the [Formula Omitted] samples are first transformed, and then the coefficients are individually quantized and entropy coded into binary bits. The transform serves two purposes: one is to compact the energy of the original [Formula Omitted] samples into coefficients with increasingly smaller variances so that removing smaller coefficients have negligible reconstruction errors, and another is to decorrelate the original samples so that the coefficients can be quantized and entropy coded individually without losing compression performance. The Karhunen–Loève transform (KLT) is an optimal transform for a source signal with a stationary covariance matrix in the sense that it completely decorrelates the original samples, and that it maximizes energy compaction (i.e., it requires the fewest number of coefficients to reach a target reconstruction error). However, the KLT is signal dependent and cannot be computed with a fast algorithm.

关键词： Visualization Technological innovation Transform coding signal processing algorithms Transforms Entropy Decorrelation

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Hybrid Reconfigurable Intelligent Surfaces-Assisted Near-Field Localization

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IEEE COMMUNICATIONS LETTERS 2023年第1期27卷 135-139页

作者： Zhang, Xing Zhang, Haiyang Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China

Reconfigurable intelligent metasurface (RIS) assisted user localization has been a hot topic along with the development of the sixth generation wireless communications. The employment of large-scale antennas goes far beyond the improvement of spectral efficiency, resulting in near-field communications between users and the RIS. In this letter, we consider to employ the hybrid RIS (HRIS) which is capable of simultaneous reflection and reception in near-field user localization for the first time. With such a new architecture, we derive the Cramer-Rao lower bound (CRLB) for assessing its localization performance, and subsequently propose an automatic differentiation-based gradient descent approach to optimize the HRIS configuration. The theoretical analysis is further supported by numerical simulations, indicating the superiority of the HRIS over the conventional purely reflective RIS in near-field user localization.

关键词： Human-robot interaction Location awareness Metasurfaces Antennas Synchronization Wireless communication signal processing algorithms Hybrid RIS near-field localization CRLB

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Robust Subspace Clustering by Logarithmic Hyperbolic Cosine Function

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IEEE signal processing LETTERS 2023年 30卷 508-512页

作者： Cao, Lei Shi, Long Wang, Jun Yang, Zhendong Chen, Badong Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Financial Intelligence & Financial Engn Key Lab Si Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Sch Management Sci & Engn Chengdu 611130 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

As an important category of clustering methods, subspace clustering algorithms have arisen particular attention during the last decade. Most subspace clustering algorithms are designed by first constructing a similarity matrix and then using spectral clustering algorithms to perform clustering. How to learn a suitable representation matrix to construct the similarity matrix is essential to the clustering performance. In most existing algorithms, the representation matrix is solved by norm-minimization, which commonly enforces the error matrix with nuclear norm or sparsity norm. However, these methods may fail to achieve satisfactory performance for real data contaminated by complex noise. To this end, we propose a novel robust subspace clustering method based on the Logarithmic Hyperbolic Cosine Function (LHCF). We theoretically analyze the grouping effect, as well as the convergence behavior, which illustrates that highly correlated samples can be grouped into the same cluster. Experimental results conducted on the Extended Yale B dataset show that the newly proposed algorithm yields better clustering performance compared with some advanced methods.

关键词： Clustering algorithms signal processing algorithms Sparse matrices Convergence Behavioral sciences Robustness Correlation Complex noise convergence behavior grouping effect logarithmic cosine function subspace clustering

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Distributed Consensus in Wireless Networks With Probabilistic Broadcast Scheduling

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IEEE signal processing LETTERS 2023年 30卷 41-45页

作者： Herrera, Daniel Perez Chen, Zheng Larsson, Erik G. Linkoping Univ Dept Elect Engn SE-58183 Linkoping Sweden

We consider distributed average consensus in a wireless network with partial communication to reduce the number of transmissions in every iteration/round. Considering the broadcast nature of wireless channels, we propose a probabilistic approach that schedules a subset of nodes for broadcasting information to their neighbors in every round. We compare several heuristic methods for assigning the node broadcast probabilities under a fixed number of transmissions per round. Furthermore, we introduce a pre-compensation method to correct the bias between the consensus value and the average of the initial values, and suggest possible extensions for our design. Our results are particularly relevant for developing communication-efficient consensus protocols in a wireless environment with limited frequency/time resources.

关键词： Convergence Broadcasting Probabilistic logic Wireless networks signal processing algorithms Topology Consensus algorithm Average consensus broadcast transmission scheduling wireless networks

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A New CFAR Detector Based on the EM Algorithm

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IEEE signal processing LETTERS 2025年 32卷 2035-2039页

作者： Orlando, Danilo Ricci, Giuseppe Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy

This paper proposes a simple Constant False Alarm Rate (CFAR) approach relying on the expectation-maximization algorithm to deal with clutter edges. The possible signal backscattered from a target is modeled as an incoherent pulse train and the newly-introduced CFAR technique is assessed in conjunction with an energy detector. Natural competitors are the well-known Cell-Averaging (CA), Greatest Of (GO), and Smallest Of (SO) CFAR techniques. The performance analysis shows that the proposed approach outperforms the state-of-the-art competitors in terms of false alarms control while maintaining satisfying detection performance.

关键词： Detectors Clutter Vectors signal processing algorithms Program processors Probability density function Image edge detection Covariance matrices Radar detection Noise Cell-averaging constant false alarm rate (CA CFAR) clutter edges detection expectation-maximization algorithm greatest of (GO) CFAR radar smallest of (SO) CFAR

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Sequence Detection with Dependent Observations under Parameter Uncertainty

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IEEE signal processing LETTERS 2023年 30卷 603-607页

作者： Dulek, Berkan Isk, Selin Hacettepe Univ Dept Elect & Elect Engn Beytepe Campus TR-06800 Ankara Turkiye

The problem of detecting the state sequence of a binary stochastic process with multiple sensors is considered. It is assumed that the sensors' observations are coupled both spatially and temporally. Copula theory is used to model the unknown spatial coupling across the observations of different sensors while the temporal dependency is taken into account using a first order Markov chain. A per-survivor processing-based algorithm is proposed to estimate the unknown parameter vector and determine the correct state sequence with reduced computational complexity. Numerical examples indicate that it is possible to keep track of the underlying state sequence while satisfactorily estimating the unknown parameters with the proposed method.

关键词： Sensors Measurement Markov processes Sensor fusion signal processing algorithms Random variables Probability density function Copula theory hypothesis testing parameter uncertainty per-survivor processing state sequence detection temporal and spatial dependence

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Window Function Design for Asymmetric Beampattern Synthesis and Applications

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IEEE signal processing LETTERS 2023年 30卷 284-288页

作者： Wei, Wenqiang Yu, Xianxiang Lu, Qinghui Wang, Xiangrong Cui, Guolong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

This letter proposes a novel window function design to synthesize asymmetric beampatterns while minimizing the gain loss for clutter/interference suppression as required in practical applications, such as airborne phased array radar (APAR). Compared with beampattern synthesis in terms of designing the weight vector directly, the proposed method can achieve asymmetric sidelobes for multiple steering directions resort to designing one window while circumventing repetitive computations. To tackle the resultant nonconvex optimization problem, an iterative alternating optimization (IAO) algorithm is introduced where each iteration involves multiple subproblems that can be solved via either convex approximation (CA) approach or closed-form solutions. Finally, numerical simulations are provided to verify the effectiveness of the proposed method comparing with the classical adjustable window functions (CAWFs).

关键词： Optimization Mathematical models Clutter signal processing algorithms Phased arrays Array signal processing Airborne radar Window function design asymmetric beampattern synthesis array gain constraint nonconvex optimization airborne phased array radar

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Versatile Time-Frequency Representations Realized by Convex Penalty on Magnitude Spectrogram

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IEEE signal processing LETTERS 2023年 30卷 1082-1086页

作者： Arai, Keidai Yamada, Koki Yatabe, Kohei Tokyo Univ Agr & Technol Tokyo 1848588 Japan

Sparse time-frequency ( T-F) representations have been an important research topic for more than several decades. Among them, optimization-based methods (in particular, extensions of basis pursuit) allow us to design the representations through objective functions. Since acoustic signal processing utilizes models of spectrogram, the flexibility of optimization-based T-F representations is helpful for adjusting the representation for each application. However, acoustic applications often require models of magnitude of T-F representations obtained by discrete Gabor transform (DGT). Adjusting a T-F representation to such a magnitude model (e.g., smoothness of magnitude of DGT coefficients) results in a non-convex optimization problem that is difficult to solve. In this letter, instead of tackling difficult non-convex problems, we propose a convex optimization-based framework that realizes a T-F representation whose magnitude has characteristics specified by the user. We analyzed the properties of the proposed method and provide numerical examples of sparse T-F representations having, e.g., low-rank or smooth magnitude, which have not been realized before.

关键词： Manganese Optimization Spectrogram Time-frequency analysis Convex functions Discrete cosine transforms signal processing algorithms Sparse time-frequency analysis basis pursuit perspective function convex optimization primal-dual splitting

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A Correlation Coefficient Sparsity Adaptive Matching Pursuit Algorithm

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IEEE signal processing LETTERS 2023年 30卷 190-194页

作者： Li, Yanjun Chen, Wendong Beijing Inst Remote Sensing Equipment Beijing 100039 Peoples R China

This letter presents a correlation coefficient sparsity adaptive matching pursuit (CCSAMP) algorithm for practical compressed sensing (CS). The sparsity adaptive matching pursuit (SAMP) has been enhanced using the CCSAMP algorithm. The CCSAMP's capacity to accurately reconstruct the signal with fewer repetitions is its most novel characteristic when compared to other state-of-the-art SAMP enhancement methods. This makes it a candidate for many practical applications that need fast reconstruction. The proposed algorithm constructs two correlation vectors, which represent the input signals recovered from the support set and candidate set. The step size is transformed by their Pearson correlation coefficients (PCCS). Compared to the residual energy, the correlation coefficient is more sensitive. The CCSAMP reduces the number of iterations while maintaining the SAMP's capability of signal reconstruction without prior knowledge of the sparsity. Simulation shows that the CCSAMP can significantly reduce the number of iterations compared to the SAMP algorithm. The CCSAMP can be used for radar detection, radar 3D imaging, and other fields where fast and accurate reconstruction of signals is required.

关键词： Matching pursuit algorithms signal processing algorithms Correlation coefficient Sensors Indexes Image reconstruction Backtracking Compressed sensing (CS) sparse reconstruction algorithm correlation coefficient variable step size

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Hardware Acceleration of SIKE on Low-End FPGAs

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IEEE EMBEDDED SYSTEMS LETTERS 2023年第2期15卷 73-76页

作者： Lara-Nino, Carlos Andres Diaz-Perez, Arturo Morales-Sandoval, Miguel CNRS Lab Hubert Curien F-42000 Paris France CINVESTAV Unidad Guadalajara Unidad Guadalajara Zapopan 45019 Mexico Cinvestav Un Tamaulipas Unidad Tamaulipas Victoria 87138 Mexico

In this letter, we present the design and implementation results of two hardware accelerators for the supersingular isogeny key encapsulation (SIKE) suite. These designs aim at enabling quantum-safe cryptography solutions for constrained platforms by offloading the cost of bulk arithmetic from the main processor. One of the proposed architectures has area reduction as the main implementation goal;the second design improves the former on the energy footprint. This software-hardware co-design addresses the challenges of performing bulk arithmetic in software and reducing the control complexity in hardware, thus minimizing the communication overheads found on simple arithmetic accelerators. Compared to other designs in the literature, the proposed architectures do not rely on in-fabric memory and processing units, a key point for porting such solutions to any implementation technology such as field-programmable gate arrays (FPGAs).

关键词： Field programmable gate arrays Arithmetic Registers signal processing algorithms Table lookup Public key Complexity theory Coprocessor field-programmable gate array (FPGA) hardware software co-design low area low energy supersingular isogeny key encapsulation (SIKE) VHDL

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