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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Compressed Image Super Resolution using Convolutional Neural Network

Compressed Image Super Resolution using Convolutional Neural...

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IEEE Region 10 International Conference TENCON

作者： Ei Ei Tun Aktanin Konkitkriengkrai Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Image compression is a topic of significant interest as it reduces file sizes in stored data. In this paper, we propose a model that achieves multiple levels of compression, thereby minimizing the storage space required for images, which typically consume substantial amounts of data due to their size and resolution. We combine an image downscaling and upscaling model with an image compression model. By leveraging convolutional techniques to identify image features, we can effectively reduce the size of the image through downscaling and subsequently upscaling it. Additionally, we employ entropy image compression and arithmetic encoding to compress and reconstruct the image while preserving its lossless data. Through experimentation with the Kodak dataset, we observed that our proposed model achieved a compression rate of 96.92%, significantly reducing the data needed for file storage. Moreover, our reconstructed images attained a standardized measure with a signal-to-noise ratio of 33.10 dB and a structural similarity of 0.9219. Notably, the perceptual quality of the images, including intricate details, remained intact to the human eye.

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave Systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

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24th IEEE International Workshop on Signal processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

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A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks

A Distributed Adaptive Algorithm for Node-Specific Signal Fu...

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European Signal processing Conference (EUSIPCO)

作者： Cem Ates Musluoglu Alexander Bertrand KU Leuven Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve spatial filtering and signal fusion problems in a distributed fashion over a wireless sensor network, reducing the communication and energy costs compared to a centralized approach. The DASF framework assumes that there is a common goal across the nodes, i.e., all nodes collaborate to optimize the same network-wide objective function. However, some applications require node-specific objectives, which are linked via a common latent target signal subspace. In this work, we propose the distributed adaptive node-specific signal fusion (DANSF) algorithm which builds upon the DASF framework, and extends it to allow for such node-specific spatial filtering problems.

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Probabilistic Gain Control in a Multi-Speaker Setting Using EEG-Based Auditory Attention Decoding

Probabilistic Gain Control in a Multi-Speaker Setting Using ...

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European Signal processing Conference (EUSIPCO)

作者： Nicolas Heintz Simon Geirnaert Iris Van De Ryck Tom Francart Alexander Bertrand Department of Electrical Engineering (ESAT) KU Leuven STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium Department of Neurosciences KU Leuven ExpORL Belgium

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Current hearing aids have difficulties focusing on the correct speaker in complex environments where multiple people are talking simultaneously. This is because hearing aids do not know to which speaker the user aims to listen. A promising solution is to use so-called auditory attention decoding (AAD) algorithms, which infer the at-tended speaker based on brain activity recorded with, e.g., elec-troencephalography (EEG). AAD models decode on a window-to-window basis to which person a subject wishes to listen. However, only limited research has been done on how these AAD decisions can be converted into a gain control system that controls the volume of each competing speaker in a hearing aid. Existing gain control systems are either difficult to tune, unstable and/or not designed for use in situations with more than two competing speakers. We therefore propose a novel general purpose gain control system that can be easily used on any AAD model and in scenarios with an arbitrary number of speakers. We demonstrate that the gain control system is stable, even for AAD algorithms with very low accuracies, and even in scenarios with more than 2 speakers.

关键词： Accuracy Signal processing algorithms Oral communication Switches Signal processing Hearing aids Brain modeling Stability analysis Decoding Gain control

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches 8

An External Denoising Framework for Magnetic Resonance Imagi...

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8th International Conference on Signal and Image processing, ICSIP 2023

作者： Mei, Lifeng Liu, Sixing Tang, Chenhui Cai, Jiaqia Wang, Jingli Liu, Yilong Wu, Ed X. Lyu, Mengye Shenzhen Technology University College of Health Science and Environmental Engineering Shenzhen China Shenzhen Technology University College of Big Data and Internet Shenzhen China Guangzhou China The University of Hong Kong Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397932

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Learning Structured Compressed Sensing with Automatic Resource Allocation

Learning Structured Compressed Sensing with Automatic Resour...

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

作者： Han Wang Eduardo Pérez Iris A. M. Huijben Hans van Gorp Ruud Van Sloun Florian Römer Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Electronic Measurements and Signal Processing Ilmenau University of Technology Germany Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis.

关键词： Location awareness Ultrasonic imaging Supervised learning Signal processing algorithms Software Resource management Sparse matrices Speech processing Compressed sensing Unsupervised learning

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution

arXiv

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arXiv 2023年

作者： Hayat, Mansoor Armvith, Supavadee Achakulvisut, Titipat Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Biomedical Engineering Mahidol University Bangkok Thailand

Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometrical optics

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Combined Channel and Spatial Attention-Based Stereo Endoscopic Image Super-Resolution

Combined Channel and Spatial Attention-Based Stereo Endoscop...

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IEEE Region 10 International Conference TENCON

作者： Mansoor Hayat Supavadee Armvith Titipat Achakulvisut Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand Department of Biomedical Engineering Mahidol University Bangkok Thailand

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