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59th Annual ieee international conference on Communications (ieee ICC)

作者： Huang, Wei Huang, Xueqing Zhang, Haiyang Sun, Kunyang Kai, Caihong He, Shiwen Hefei Univ Technol Sch Comp Sci & Informat Hefei 230601 Peoples R China NJUPT Sch Commun & Informat Engn Nanjing 210096 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

ISBN: (纸本)9798350304060;9798350304053

In this paper, we develop a novel beam training scheme for extremely large-scale multiple-input-multiple-output (XL-MIMO) system by exploiting the visual image information. Different from the conventional beam training schemes that consumes a large number of in-band (time/frequency) resources, the proposed scheme only leverages the out-of-band (vision image) information, which can efficiently reduce the training overhead. Specifically, we proposed a vision image-aided beam training cascaded framework integrating YOLOv5 and ResNet18 networks, where the YOLOv5 uses the object detection technique to extract the size and location information of the mobile vehicles (MVs) and the ResNet18 based the extracted information infers the optimal beam index without occupying in-band overhead. The simulation results demonstrate that the proposed vision image-aided beam training scheme outperforms the benchmark scheme.

关键词： Vision image Near-field Beam Training

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Learning CRF potentials through fully convolutional networks for satellite image semantic segmentation 17

Learning CRF potentials through fully convolutional networks...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Pastorino, Martina Moser, Gabriele Serpico, Sebastiano B. Zerubia, Josiane University of Genoa Diten Dept. Italy Inria Université Côte d'Azur Sophia-Antipolis France

ISBN: (纸本)9798350370911

This paper introduces a method to automatically learn the unary and pairwise potentials of a conditional random field (CRF) from the input data in a non-parametric fashion, within the framework of the semantic segmentation of remote sensing images. The proposed model is based on fully convolutional networks (FCNs) and fully connected neural networks (FCNNs) to extensively exploit the semantic and spatial information contained in the input data and in the intermediate layers of an FCN. The idea of the model is twofold: first to learn the statistics of a CRF via a convolutional layer, whose kernel defines the clique of interest, and, second, to favor the interpretability of the intermediate layers as posterior probabilities through the FCNNs. The method was tested with the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, after modifying the ground truths to approximate the ones found in realistic remote sensing applications, characterized by scarce and spatially non-exhaustive annotations. The results confirm the effectiveness of the proposed technique for the semantic segmentation of satellite images. © 2023 ieee.

关键词： Semantic Segmentation

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Lightweight Machine Sound Anomaly Detector based on Parallel Discrete Wavelet Transform

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ieee SENSORS JOURNAL 2025年第10期25卷 18529-18542页

作者： Choi, Eunhye Park, Hyunggon Agcy Def Dev Daejeon 34186 South Korea Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

The development of Industrial internet of Things (IIoT) technology and network infrastructures has enabled the acquisition of substantial data, enabling data-driven condition monitoring and analysis. Detecting anomalies in machinery equipment is crucial in IIoT environments for safety enhancement, productivity, and reliability. To provide effective anomaly detection at IIoT edge nodes without delay, it is necessary to efficiently collect and process vast amounts of data from various sensors. While this demands a significant amount of computing resources, edge nodes only have limited data storage and processing capabilities. Therefore, our focus is on developing a lightweight anomaly detection algorithm for acoustic signal processing, considering the computational resources of the IIoT edge node. In this article, we propose the parallel discrete wavelet transform (PDWT) as an efficient method for compressing and processing acoustic signals received at edge nodes. This approach significantly alleviates memory consumption and reduces the computational time at the edge. In addition, by harnessing preprocessed features through PDWT, we can develop lightweight anomaly detection models suitable for deployment at the edge, making them highly practical for real-world implementation. The experimental results using real-world data collected from industrial machines confirm the effectiveness of the proposed solution.

关键词： Anomaly detection Feature extraction image edge detection Acoustics Industrial internet of Things Computational modeling Training Autoencoders Transforms Discrete wavelet transforms Acoustic signal anomaly detection autoencoder discrete wavelet transform (DWT) edge computing Industrial internet of Things (IIoT)

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Kuschel, Maurice Alkhatib, Amr Hasija, Tanuj Boström, Henrik Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data. © 2025 ieee.

关键词： CEGA Deep Canonical Correlation Analysis Explainable AI Multiview Representation Learning SHAP

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Comparative Evaluation of Fixed Windowing Strategies on CT Brain images Using Multiple Deep Learning Models 17

Comparative Evaluation of Fixed Windowing Strategies on CT B...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Viriyavisuthisakul, Supatta Kaothanthong, Natsuda Sanguansat, Parinya Yamasaki, Toshihiko Songsaeng, Dittapong Sirinhorn International Institute of Technology School of Management Technology Thammasat University Pathumthani Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand The University of Tokyo Department of Electrical Engineering Tokyo Japan Mahidol University Siriraj Hospital Faculty of Medicine Department of Radiology Bangkok Thailand

ISBN: (纸本)9798350370911

Window setting in CT brain images is the crucial pre-processing step to examine the abnormalities for diagnosing disease. Recently, many methods have been proposed to determine the suitable window automatically instead of fixing the window. However, fixed windowing methods may still be used in clinical practice due to their simplicity and ease of use. Here, we propose to evaluate the 45 different fixed windowing in noncontrast cranial computer tomography (NCCT) images without computer tomography perfusion (CTp). The 15 latest deep learning models are performed on all interested windows to classify between the hyperacute or acute phases of ischemic stroke and normal brain. The experiments can provide the reference fixed windowing value to optimize the deep learning model and help clinicians to choose the appropriate fixed windowing values. © 2023 ieee.

关键词： image classification

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Design of Artificial Intelligence image Detection System based on internet of Things 24

Design of Artificial Intelligence Image Detection System Bas...

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5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Wu, Zhengnan Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9798400718106

In the rapid development of the internet of Things technology, image recognition and detection technology is used in all walks of life. In order to solve the limitations of traditional image detection methods in practical application, such as slow efficiency, low precision and lack of in-depth analysis, this study will design an artificial intelligence image detection system based on the internet of Things, starting from the requirements of synthetic signal images, image features acquisition and cloud image processing functions. Complete the design of image analysis system, image feature acquisition system and image integration system. Finally, the artificial intelligence image detection system is simulated and debugging. The test results prove that the artificial intelligence image detection system based on the internet of Things has the advantages of fast detection speed, high recognition accuracy and big data integration analysis, and can meet the stable operation of any configuration computer platform, providing new ideas for the development and research of image detection system. © 2024 ACM.

关键词： internet of things

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 ieee.

关键词： denoising Medical image fusion text-guided fusion

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Zero Reference based Low-light Enhancement with Wavelet Optimization 20

Zero Reference based Low-light Enhancement with Wavelet Opti...

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20th ieee international conference on Advanced Video and signal-based Surveillance (AVSS)

作者： Deshmukh, Vivek Dukre, Adinath Kulkarni, Ashutosh Patil, Prashant W. Vipparthi, Santosh Kumar Murala, Subrahmanyam Gonde, Anil Balaji Shri Guru Gobind Singhji Inst Engn & Technol Nand Nanded India Indian Inst Technol Ropar CVPR Lab Rupnagar India Indian Inst Technol Guwahati Gauhati India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland

ISBN: (纸本)9798350374292;9798350374285

images captured in low light conditions usually suffer from poor visibility, a high amount of noise, and little information stored in the dark image, which has a negative impact on subsequent processing for outdoor computer vision applications. Presently, numerous deep learning based methods achieved superior performance with multi-exposure paired training data or additional information. However, obtaining multi-exposure data samples is a tedious task in real-time scenarios. To mitigate this challenge, we propose a zero reference based learnable wavelet approach without multi-exposure paired training data requirement for low-light image enhancement. Our proposed approach generates the low light image and learns to project an image into noise free similar looking image, then we enhance the image using retinex theory. Further, we have proposed learnable wavelet block to remove the hidden noise amplified while enhancement. We introduce Gaussian-based supervision to improve the smoothness of the image. Extensive experimental analysis on synthetic as well as real-world images, along with thorough ablation study demonstrate the effectiveness of our proposed method over the existing state-of-the-art methods for low-light image enhancement. The code is provided at https://***/vision-lab-sggsiet/Zero-Reference-based-Low-light-Enhancement-with-Wavelet-Optimization.

关键词： image enhancement

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TFTI: A Transformer Framework Text Information-aided for Person Re-Identification in internet of Things 8

TFTI: A Transformer Framework Text Information-aided for Per...

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8th international conference on Smart internet of Things

作者： Huang, Haihong Xiong, Xuanrui Zhang, Junlin Zhang, Hanchi Gao, Yunan Xiao, Lei Guo, Xingyou Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Shenzhen MSU BIT Univ Guangdong Engn Ctr Social Comp & Mental Hlth Artificial Intelligence Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350366457;9798350366440

Person re-identification aims to recognize a target pedestrian across non-overlapping camera views based on source information. The internet of Things (IoT) provides a wide range of application scenarios for pedestrian re-identification technology-smart city management, resource optimization, and multi-source data fusion. It is crucial for IoT applications like intelligent video surveillance but remains challenging due to factors like low image resolution, varying angles, lighting changes, and occlusion. In this paper, we propose a multi-task learning approach that integrates text information to enhance recognition accuracy. Using a dual-stream Transformer encoder, we extract both image and text features. To improve feature interaction and learning, we perform multimodal interaction for fine-grained alignment and share feature for modality-invariant feature representation and learning. Our method, TFTI, outperforms state-of-the-art techniques in person re-identification, as validated on the CUHK-PEDES dataset.

关键词： Person Re-identification internet of Things(IoT) Text Information-aided Multi-task Learning

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Research on Vulnerability Mining Method based on Artificial Intelligence in internet of Things 2

Research on Vulnerability Mining Method Based on Artificial ...

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2nd ieee international conference on image Processing and Computer Applications, ICIPCA 2024

作者： Ma, Peiyue Zhu, Liehuang Zhang, Chuan School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350360240

In recent years, with the continuous development of computer technology, internet of Things (IoT) technology has been widely used in various fields and has played an important role in various industries. The internet of Things is responsible for information transmission and storage, and once there are network security issues, it will bring huge disasters to various industries. Detecting vulnerabilities in the internet of Things is an urgent problem that needs to be addressed. In order to improve the efficiency of IoT vulnerability detection, this paper designs an IoT vulnerability mining solution based on the LSTM algorithm. The designed artificial intelligence IoT vulnerability mining algorithm uses deep learning for identification, and this framework includes steps such as data collection, data learning, and data detection. The results show that after comparing three models: LSTM, SeqGAN, and WGAN, the LSTM algorithm exhibits the highest accuracy. © 2024 ieee.

关键词： Data accuracy

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