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Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery

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arXiv 2024年

作者： Sledge, Isaac J. Byrne, Dominic M. King, Jonathan L. Ostertag, Steven H. Woods, Denton L. Prater, James L. Kennedy, Jermaine L. Marston, Timothy M. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Naval Sea Systems Command United States The Applied Sensing and Processing Branch Naval Surface Warfare Center Panama CityFL United States The Littoral Acoustics and Target Physics Branch Naval Surface Warfare Center Panama CityFL United States The Division Head of the Sensing Sciences and Systems Division Naval Surface Warfare Center Panama CityFL United States The Applied Physics Laboratory University of Washington SeattleWA United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL United States The University of Florida United States

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant. © 2024, CC BY.

关键词： Semantic Segmentation

Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion

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arXiv 2021年

作者： Gao, Teng Zhou, Jian Wang, Huabin Tao, Liang Kwan, Hon Keung Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Whispered speech is a special way of pronunciation without using vocal cord vibration. A whispered speech does not contain a fundamental frequency, and its energy is about 20dB lower than that of a normal speech. Converting a whispered speech into a normal speech can improve speech quality and intelligibility. In this paper, a novel attention-guided generative adversarial network model incorporating an autoencoder, a Siamese neural network, and an identity mapping loss function for whisper to normal speech conversion (AGANW2SC) is proposed. The proposed method avoids the challenge of estimating the fundamental frequency of the normal voiced speech converted from a whispered speech. Specifically, the proposed model is more amendable to practical applications because it does not need to align speech features for training. Experimental results demonstrate that the proposed AGANW2SC can obtain improved speech quality and intelligibility compared with dynamic-time-warping-based methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

CycleGAN with dual adversarial loss for bone-conducted speech enhancement

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arXiv 2021年

作者： Pan, Qing Gao, Teng Zhou, Jian Wang, Huabin Tao, Liang Kwan, Hon Keung Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Compared with air-conducted speech, bone-conducted speech has the unique advantage of shielding background noise. Enhancement of bone-conducted speech helps to improve its quality and intelligibility. In this paper, a novel CycleGAN with dual adversarial loss (CycleGAN-DAL) is proposed for bone-conducted speech enhancement. The proposed method uses an adversarial loss and a cycle-consistent loss simultaneously to learn forward and cyclic mapping, in which the adversarial loss is replaced with the classification adversarial loss and the defect adversarial loss to consolidate the forward mapping. Compared with conventional baseline methods, it can learn feature mapping between bone-conducted speech and target speech without additional air-conducted speech assistance. Moreover, the proposed method also avoids the over-smooth problem which is occurred commonly in conventional statistical based models. Experimental results show that the proposed method outperforms baseline methods such as CycleGAN, GMM, and BLSTM. © 2021, CC BY.

关键词： Speech enhancement

Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

Beamforming Design and Association Scheme for multi-RIS multi-User mmWave Systems Through Graph Neural Networks

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arXiv 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Di Renzo, Marco Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research Department of Engineering LondonWC2R 2LS United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multiuser millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. Copyright © 2025, The Authors. All rights reserved.

关键词： Beamforming

Annotating motion primitives for simplifying action search in reinforcement learning

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arXiv 2021年

作者： Sledge, Isaac J. Bryner, Darshan W. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Branch Head of the Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL32407 United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States The Computational NeuroEngineering Laboratory University of Florida United States

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives;motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives' motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space. © 2021, CC BY.

关键词： Reinforcement learning

Beamforming Design and Association Scheme for multi-RIS multi-User mmWave Systems Through Graph Neural Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Renzo, Marco Di Debbah, Merouane Yuen, Chau Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS Laboratoire des Signaux et Systèmes CentraleSupélec 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research – Department of Engineering LondonWC2R 2LS United Kingdom Khalifa University KU6G. Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. © 2002-2012 IEEE.

关键词： Beamforming

Generalized multi-View Embedding for Visual Recognition and Cross-Modal Retrieval

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2542-2555页

作者： Cao, Guanqun Iosifidis, Alexandros Chen, Ke Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Aarhus Denmark

In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and nonlinear embeddings. Numerous methods including canonical correlation analysis, partial least square regression, and linear discriminant analysis are studied using specific intrinsic and penalty graphs within the same framework. Nonlinear extensions based on kernels and (deep) neural networks are derived, achieving better performance than the linear ones. Moreover, a novel multi-view modular discriminant analysis is proposed by taking the view difference into consideration. We demonstrate the effectiveness of the proposed multi-view embedding methods on visual object recognition and cross-modal image retrieval, and obtain superior results in both applications compared to related methods.

关键词： Cross-modal retrieval multi-view discriminant embedding multi-view subspace learning visual recognition

Automatic Modulation Classification of Frequency-Hopping signals Using High-dimensional Phase Diagrams

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Automatic Modulation Classification of Frequency-Hopping Sig...

IEEE International Symposium on Broadband multimedia Systems and Broadcasting (BMSB)

作者： Qibo Chen Kun Yan Hsiao-Chun Wu Shih Yu Chang Xiao Yan Yiyan Wu Haonan Chang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Department of Applied Data Science San Jose State University San Jose CA USA School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu P. R. China Communications Research Centre Canada Ottawa Canada Haonan Chang is with Xi'an University Xi'an P. R. China

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.

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