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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning Image quality Convolution Surveillance Redundancy signal processing algorithms Feature extraction Real-time systems Speech processing Remote sensing

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signal processing Algorithm and System Realization of Broadband Parametric Array Sub-Bottom Profiler

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IEEE SENSORS JOURNAL 2022年第17期22卷 17090-17102页

作者： Zhou, Hanyun Li, Wei Huang, S. H. Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Huawei Technol Co Ltd Intelligent Vehicle Solut Business Unit Hangzhou 310051 Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316000 Peoples R China

Parametric acoustic array can generate a low frequency narrow sound beam with sufficient penetrating ability with a small size transducer. Besides, the generated beam has almost no side lobe, and it is easy to form a wide band signal at low frequencies, thus effectively improving the signal processing gain. Therefore, broadband parametric array shows advantages in shallow water sub-bottom profiling. In this paper, the signal processing algorithm of transmitting end and receiving end of parametric array sonar was studied, and a parametric array sub-bottom profiler was designed and realized for experimental verification. Based on Berktay's far-field solution, the performance of different modulation methods was analyzed and compared, and suppressed carrier double-sideband modulation was chosen as the optimal modulation method. Furthermore, corresponding echo signal processing algorithm was designed to improve the detection resolution. According to lake trials of the broadband parametric array sub-bottom profile measurement system as designed, a clear sub-bottom profile was obtained, which verified the system's effectiveness.

关键词： Frequency modulation Array signal processing Acoustic beams Broadband communication Acoustic arrays signal processing algorithms Transducers Parametric acoustic array sub-bottom profiler signal processing system realization

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Optimal signal processing in Ground-Based Forward Scatter Micro Radars

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2012年第4期48卷 3006-3026页

作者： Hu, Cheng Sizov, V. Antoniou, M. Gashinova, M. Cherniakov, M. Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China Univ Birmingham Birmingham B15 2TT W Midlands England Moscow Inst Elect Technol Moscow Russia

The received signal in forward scatter radar (FSR) depends on the target's electrical size and trajectory, which are unknown a priori. As a result, in practical situations, it is impossible to obtain the accurate reference function at the reception side, and adaptation of optimal filtering is therefore proposed for this case. This paper presents a signal processing algorithm for ground target detection using FSR, which includes the construction of the adaptive reference functions and the identification of target velocity and its observation time. Furthermore, the algorithm performance is analytically determined under practical motion trajectories such as different motion directions and baseline crossing points, which indicate the effectiveness of the proposed algorithm in a practical case for FSR. The effectiveness of the algorithm is shown using both simulated and experimental data. Finally, the resolution in convoy targets in ground-based FSR is analytically obtained for the first time;the resolution is totally different from the resolution in conventional radar theory because of the target signature characteristics in ground-based FSR. The majority of the analytical results are verified experimentally.

关键词： optimal filters Resolving power trajectory radar temporal resolution signal processing algorithms algorithms Object detection signal processing received signal Radar theory Flexed Sidebent Rotated

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On Noise-Sensitivity of Unlimited Sampling in Line Spectral Estimation

On Noise-Sensitivity of Unlimited Sampling in Line Spectral ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wenlong Wang Zai Yang School of Mathematics & Statistics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The unlimited sampling framework, utilizing modulo analog-to-digital converters (ADCs), has recently been introduced to mitigate the information loss caused by the dynamic range limitations of traditional ADCs. In this paper, we evaluate the noise sensitivity of unlimited sampling in line spectral estimation by deriving the Cramér–Rao bound (CRB) and show that estimation based solely the modulo measurements can be sensitive to noise. To address this issue, we propose integrating the modulo ADC-based unlimited sampling with the sign information of the original signal. We compare the CRBs of different unlimited sampling frameworks and show the benefit of enhanced robustness to noise by the sign-aided approach.

关键词： Sensitivity Parameter estimation Noise Estimation signal processing algorithms Dynamic range Robustness Noise measurement Speech processing Analog-digital conversion

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So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis signal processing algorithms Reinforcement learning Data augmentation Prediction algorithms Path planning Data models Kernel Speech processing

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Hierarchical Nash Equilibrium over Variational Equilibria via Fixed-point Set Expression of Quasi-nonexpansive Operator

Hierarchical Nash Equilibrium over Variational Equilibria vi...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shota Matsuo Keita Kume Isao Yamada Dept. of Information and Communications Engineering Institute of Science Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The equilibrium selection problem in the generalized Nash equilibrium problem (GNEP) has recently been studied as an optimization problem, defined over the set of all variational equilibria achievable through a lower-level non-cooperative game among players. However, to make such a selection fair for every player, we have to rely on an unrealistic assumption, that is, the availability of a trusted center that does not induce any bias for every player. In this paper, we study a new equilibrium selection problem, named the hierarchical Nash equilibrium problem (HNEP), and propose an iterative algorithm for solving the HNEP. The HNEP is designed to ensure a fair selection without assuming any trusted center. More precisely, the HNEP is the GNEP for an upper-level non-cooperative game defined over the set of all variational equilibria of the lower-level non-cooperative game. The proposed algorithm for the HNEP is established by applying the hybrid steepest descent method to a variational inequality defined over the fixed point set of a quasi-nonexpansive operator. Numerical experiments show the effectiveness of the proposed equilibrium selection problem and its algorithmic solution.

关键词： signal processing algorithms Games signal processing Nash equilibrium Acoustics Iterative methods Speech processing Optimization

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Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution

Multiple Sclerosis Detection with Reinforcement Learning and...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jing Yang Jin Yang Chenwei Wu Gaozhe Jiang Yaning Lv Bingyan Liu Feng Xu Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis USA Evident Battery Inc. Massachusetts USA Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS.

关键词： Adaptation models Multiple sclerosis Heuristic algorithms Magnetic resonance imaging signal processing algorithms Clustering algorithms Reinforcement learning Feature extraction Hyperparameter optimization Classification algorithms

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Spatial-Frequency Cross-Domain Mutual Guidance for Visible-Infrared Image Fusion

Spatial-Frequency Cross-Domain Mutual Guidance for Visible-I...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhili Lin Biao Leng School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary objective of Visible-Infrared Image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transformations, which results in fused images lacking sufficient detail and failing to effectively preserve crucial details from the source images. In this paper, we propose Spatial-Frequency Mutual Guidance for VIF. The framework comprises two branches. Each branch reconstructs input features in the frequency and spatial domains. We introduce a novel cross-domain mutual guidance mechanism. It fully integrates information between the frequency and spatial domains to enhance the detail quality of fused images. Furthermore, a weight allocation network is utilized to adaptively assign importance to visible and infrared images based on scene characteristics. Experiments on three VIF datasets show that our method outperforms recent advanced algorithms.

关键词： Image color analysis Frequency-domain analysis Heuristic algorithms signal processing algorithms signal processing Resource management Speech processing Image fusion Image reconstruction Visual perception

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Robust Kernel Sparse Subspace Clustering

Robust Kernel Sparse Subspace Clustering

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ivica Kopriva Division of Computing and Data Science Rudjer Boskovich Institute Zagreb Croatia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Kernel methods are widely used in pattern recognition, including subspace clustering (SC). They transform nonlinear problems in the input data space into linear ones in a high-dimensional feature space. This transformation, achieved through the kernel trick, makes computationally tractable nonlinear algorithms possible. However, kernelizing linear algorithms through the kernel trick is infeasible in case of gross sparse corruptions that are modeled by the ℓ 1 -norm of the error term. To address this, we propose, for the first time, a robust kernel sparse SC (RKSSC) algorithm for data with gross sparse corruptions. We validated the proposed approach on two well-known datasets, using the linear robust SSC algorithm and nonlinear (kernel-based) SSC algorithm as baseline models. According to the Wilcoxon test, the RKSSC's algorithm clustering performance is statistically significantly better than baselines.

关键词： Computational modeling Clustering algorithms signal processing algorithms Transforms signal processing Pattern recognition Sparse matrices Kernel Speech processing Optimization

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Positional Differential Encoding for Distributed Learning

Positional Differential Encoding for Distributed Learning

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Leah Woldemariam Anna Scaglione Department of Electrical and Computer Engineering Cornell University New York NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A growing amount of available data and computational power makes training neural networks over a network of devices, and distribution optimization in general, more realizable. As a consequence, efficient communication becomes more important and can often be the bottleneck of such algorithms. Traditional data compression techniques for these scenarios consider the correlation of the data values over time: although the transmitted data itself may not be sparse, the changes within the data are increasingly sparse as the algorithm converges. In this work, we propose an encoding scheme for discrete symbols that adapts a source encoder to consider the positional correlations through time. We provide a bound on the bit rate yielded by our scheme and verify our results through experiments.

关键词： Training Computer aided instruction Correlation Distance learning Bit rate Neural networks signal processing algorithms Symbols Encoding Speech processing

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