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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior

Intelligent Reflecting Surface OFDM Communication with Deep ...

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IEEE International Conference on Communications (ICC)

作者： Tomer Fireaizen Gal Metzer Dan Ben-David Yair Moshe Israel Cohen Emil Bjö rnson Signal and Image Processing Laboratory (SIPL) Faculty of Electrical &amp Computer Engineering Technion &#x2013 Israel Institute of Technology Haifa Israel Faculty of Engineering Tel Aviv University Tel Aviv Israel Dept. of Electrical Engineering (ISY) Link&#x00F6 ping University and KTH Royal Insitute of Technology Sweden

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683484

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

关键词： Wireless communication Training Deep learning OFDM image processing Neural networks Generators

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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Remote Sensing 2024年第1期16卷

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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U-Net Based Estimation of Functional Connectivity from Time Series Multi-Channel EEG from Schizophrenia Patients

U-Net Based Estimation of Functional Connectivity from Time ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Alireza Khodabakhsh Hossein Arabi Habib Zaidi Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran Division of Nuclear Medicine & Molecular Imaging Geneva University Hospital Geneva Switzerland

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Human brain, as a complex network, is affected by many mental disorders and neurodegenerative diseases. Brain functional and structural alteration could be captured by imaging modalities such as CT and MR imaging. However, these modalities have low sensitivity to properly capture the brain connectivity map as a biomarker for the early diagnosis of neurodegenerative diseases. In this light, complimentary examination tools (such as EEG) are employed to estimate the brain functional connectivity (FC) map. To decode the brain FC map from EEG signals, conventional approaches rely on hand-craft feature extraction, leading to suboptimal performance/effectiveness. In this light, this work set out to implement a novel deep neural network based on U-Net to extract the brain FC maps and identify (based on the obtained FC map) the type of neurodegenerative disease from the patient’s EEG signals. Due to the absence of the ground truth brain FC maps, the proposed approach extracts the patient-specific brain FC maps in an unsupervised approach. To evaluate the performance of the proposed deep learning model, a publicly available dataset of EEG signals from healthy control and schizophrenia patients was employed. The proposed model exhibited an accuracy of 94.11% to classify schizophrenia patients. Moreover, the estimated brain FC maps for both healthy control and schizophrenia patients exhibited highly discriminative patterns to differentiate abnormalities from the healthy controls. The proposed unsupervised model, which is applicable to EEG and functional MR data, exhibited promising performance to extract brain FC maps and classify neurodegenerative diseases.

关键词： Deep learning Sensitivity Mental disorders Time series analysis Brain modeling Feature extraction Electroencephalography

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On a dualband rectangular dielectric resonator antenna for WLAN applications 5

On a dualband rectangular dielectric resonator antenna for W...

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5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Xiaosheng, Fang Shuangming, Chen Lingpeng, Weng Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou University Shantou China

ISBN: (纸本)9781538671115

A slot-fed dualband rectangular dielectric resonator antenna (DRA) for WLAN applications is investigated. ANSYS HFSS was used to study the reflection coefficient, radiation patterns and antenna gain of the dualband DRA. The results show that it can provide two passbands in the ranges of 2.4-2.82 GHz and 5.07-5.54 GHz, which completely cover the 2.4-GHz/5.2-GHz WLAN bands. The proposed dualband DRA can provide a relatively larger high-to-low frequency ratio of ~2.1. © 2019 IEEE.

关键词： Microwave antennas

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Robust control of hydraulic system with delay input 3

Robust control of hydraulic system with delay input

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3rd International Conference on Control, Automation and Diagnosis, ICCAD 2019

作者： Anouar, Benamor Chakib, Ben Njima Hassani, Messaoud University of Monastir Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir 5019 Tunisia

ISBN: (纸本)9781728122922

In this paper we propose a new robust sliding mode control for the uncertain systems with delay on the input.. The Control law is function of the estimated upper bound and is based on a choice of Lyapunov function. This new approach is carried out in two steps;the first consists on the determination of a state predictor in discrete time for the uncertain systems. The second is the synthesis of a first order sliding mode control. Simulation results prove the efficiency of the proposed resulting methodology. © 2019 IEEE.

关键词： Robust control

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Robust adaptive control for uncertain systems with unknown time varying state delay 3

Robust adaptive control for uncertain systems with unknown t...

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3rd International Conference on Control, Automation and Diagnosis, ICCAD 2019

作者： Anouar, Benamor Chakib, Ben Njima Hassani, Messaoud University of Monastir Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir Tunisia5019 Tunisia

ISBN: (数字)9781728122922

ISBN: (纸本)9781728122922

In this paper, we solve the problem of variable delayed in the presence of uncertainties and perturbations for discrete system by proposing a new robust adaptive sliding control mode law. In this work, disruption and uncertainty are assumed to be unknown. Hence, an upper limit has been determined. Accordingly, new control law for this kind of systems has been defined based on a Lyapunov function choice. This new approach consists of two steps;the first one is that the conditions for the existence of linear sliding surfaces are derived within the linear matrix inequalities (LMIs) framework by employing the free weighting matrices. The second step is the determination of the control law based on a sliding surface that will converge to zero and estimate of the upper limit of the disturbance and uncertainties. This new control law has been validated in simulation on an uncertain numerical system and comparative study proved the efficiency of the proposed resulting methodology. © 2019 IEEE.

关键词： Sliding mode control

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Can We Distinguish Genuine Minutiae from Imposter Minutiae

Can We Distinguish Genuine Minutiae from Imposter Minutiae

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Krisada Phromsuthirak Vutipong Areekul Kasetsart Signal & Image Processing Laboratory Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

we propose a novel method to differentiate genuine minutiae from impostor minutiae of an enhanced latent fingerprint. Because poor-quality latent fingerprints are deposited on uncontrolled backgrounds with noises, the imposter minutiae are incorrectly detected by a conventional fingerprint enhancement process. As a result, these imposter minutiae decrease identification performance. We introduce a histogram of the spectrum difference between the original latent fingerprint and its enhanced image. This new feature is suitable for distinguishing between genuineness and imposter. The minutiae set from the NIST SD27 latent fingerprint database is used as ground truth to evaluate the performance of the proposed method and the existing quality assessment methods. The experimental results show that the proposed method outperforms the current methods in terms of precision and F-measure.

关键词： Fingerprint recognition NIST Histograms Databases image matching Frequency-domain analysis image processing

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Expansion of the switched FIR model parameters on Laguerre bases

Expansion of the switched FIR model parameters on Laguerre b...

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2019 International Conference on signal, Control and Communication, SCC 2019

作者： Bengharat, Ibtissem Mbarek, Abdelkader Dhouibi, Hedi National School of Engineers of Monastir University of Monastir Research Laboratory of Automatic Signal and Image Processing 5019 Tunisia

ISBN: (纸本)9781728135960

This paper proposes a new representation of the S-FIR model by developing its coefficients using Laguerre orthonormal functions. The resulting model, entitled S-Laguerre model, ensures a significant parameter number reduction as well as simple recursive representation. However, this reduction is strongly linked to an optimal choice of the S-Laguerre poles defining the Laguerre bases. Therefore, we propose to extended the works of Tanguy et al. (2000) by proposing an optimisation algorithm to provide the optimal values for S-Laguerre poles. The Fourier coefficients of the proposed model are identified using the classical Recursive Least Square algorithm. The performances of the proposed S-Laguerre model and the proposed identification approach are illustrated on a numerical simulation. © 2019 IEEE.

关键词： Poles

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Adaptive Sequential Interpolator Using Active Learning for Efficient Emulation of Complex Systems

Adaptive Sequential Interpolator Using Active Learning for E...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Luca Martino Daniel Heestermans Svendsen Jorge Vicent Gustau Camps-Valls Dep. of Signal Processing Universidad Rey Juan Carlos Madrid Spain Image Processing Laboratory (IPL) Universität de València Spain Magellium Company in Geoinformation and Image Processing Toulouse France

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Many fields of science and engineering require the use of complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, due to the high cost involved, the required study becomes a cumbersome process. This paper introduces an interpolation procedure which belongs to the family of active learning algorithms, in order to construct cheap surrogate models of such costly complex systems. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. We illustrate its efficiency in a toy example and for the construction of an emulator of an atmosphere modeling system.

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