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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms 8

Brain tumor classification model of ResNet-50 network based ...

引用

8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Zou, Menghan Ma, Mingze Wang, Anan Li, Yujie Toe, Teoh Teik Harbin Institute of Technology Weihai School of Computer Science and Technology WeiHai China Harbin Institute of Technology Weihai Welding Technology and Engineering WeiHai China Harbin Institute of Technology Weihai Marine Information Engineering WeiHai China Harbin Institute of Technology Weihai Information Management and Information System WeiHai China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350302455

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement. © 2023 ieee.

关键词： image classification

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A Comparative Analysis of image Steganography Techniques in Spatial and Transform Domains

A Comparative Analysis of Image Steganography Techniques in ...

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2023 ieee international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Shehada, Dina Bouridane, Ahmed University of Sharjah Department of Electrical Sharjah United Arab Emirates College of Engineering and It University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350304602

image steganography is the study and practice of concealing information within images with the purpose of deceiving the viewer as if there is no information hidden within the images. Transferring the embedded information to the destination securely without being detected is considered the biggest challenge. In this paper, a comparative analysis of image steganography in both the spatial and a transform domains, particularly the Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) was conducted. Two experiments were carried out to evaluate the performance of LSB and DCT based steganography techniques with respect to the quality of the generated stego images and their robustness against some known attacks. ALASKA2 and BOSSBase datasets were utilized in this study. The results show that the DCT based stenography technique outperforms the LSB counterpart as it generates stego images with higher quality and higher (Peak to signal Ratio) PSNR values. It was also shown that DCT steganography is more robust against steganalysis. © 2023 ieee.

关键词： Discrete cosine transforms

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Hyperspectral image anomaly detection based on minimum noise fraction and tensor low-rank theory 4

Hyperspectral image anomaly detection based on minimum noise...

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4th international conference on Green Communication, Network, and internet of Things, CNIoT 2024

作者： Zhang, Pengyuan Lin, Qing Du, Binhua Wu, Yaochen Sun, Lei School of Systems Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781510685710

Hyperspectral imaging (HSI) provides a powerful guarantee for the accurate identification of various targets due to its rich spectral and spatial information. Anomaly detection, a significant application of HSI, is inevitably affected by noise during the imaging process. To achieve effective HSI anomaly detection in complex environments, this paper propose minimum noise fraction (MNF) and tensor low-rank anomaly detection by replacing principal component analysis (PCA) in PCA-tensor low-rank and sparse representation (TLRSR) with MNF as a preprocessing step. Experimental tests using real hyperspectral data demonstrate that the proposed method can effectively reduce noise in HSIs and significantly enhance the anomaly detection accuracy. © 2024 SPIE.

关键词： signal to noise ratio Detection and tracking algorithms Principal component analysis Covariance matrices Hyperspectral imaging Target detection Interference (communication) Data modeling Denoising Sensors

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Real-time Imitation of Autonomous MCG Node using Dual ECG Probing IoT Node Suitable for Delivery by UAV 19

Real-time Imitation of Autonomous MCG Node using Dual ECG Pr...

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19th ieee international Wireless Communications and Mobile Computing (ieee IWCMC)

作者： Tariq, Hasan Abualsaud, Khalid Yaacoub, Elias Abualsaud, Rana Khattab, Tamer Gehani, Abdurrazzak Qatar Univ Dept Elect Engn Coll Engn Doha Qatar Qatar Univ Dept Comp Engn Coll Engn Doha Qatar Al Ahli Hosp Ahmed Bin Ali StPOB 6401 Doha Qatar NYP NewYork Presbyterian Hosp New York NY USA

ISBN: (纸本)9798350333398

The gigantic increase in population, social isolation, and mobility constraint lifestyle since the COVID-19 era has resulted in challenges like remote availability of critical bio-instrumentation like magnetocardiography (MCG) and electrocardiography (ECG). This availability can only be made possible through portable hand-held bio-instrumentation systems. Unmanned aerial vehicles (UAVs) can be used to deliver these systems to remote areas. In cardiological bio-instrumentation, MCG and ECG are two major innovations based field effect techno-scientific approaches. The MCG systems face several major challenges that have hampered their applications and utility for cardio patients and their inspection labs. In this work, the main challenges were addressed by using a novel ML-based probabilistic interpolation algorithm over a dual ECG probing system-on-chip (SoC) with IoT capabilities to generate the identical MCG signal from two ECG signals with a segmented translation of PR, QRS, ST, and QT characteristic patches at real-time. The implementation findings provided a rich resource for approximating wave-shaping filters, frequencies, mean, and variance whilst addressing redundancy.

关键词： MCG ECG signal processing instrumentation IoT UAV

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Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation

Vision-based Distributed Multi-UAV Collision Avoidance via D...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Huang, Huaxing Zhu, Guijie Fan, Zhun Zhai, Hao Cai, Yuwei Shi, Ze Dong, Zhaohui Hao, Zhifeng Shantou Univ Coll Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy learning method for multi-UAV systems. The policy takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands, and it is trained together with the latent representation of depth images using a policy gradient-based reinforcement learning algorithm and autoencoder in the multi-UAV three-dimensional workspaces. Each UAV follows the same trained policy and acts independently to reach the goal without colliding or communicating with other UAVs. We validate our method in various simulated scenarios. The experimental results show that our learned policy can guarantee fully autonomous collision-free navigation for multi-UAV in three-dimensional workspaces, and its navigation performance will not be greatly affected by the increase in the number of UAVs.

关键词： Learning systems Training Navigation Reinforcement learning Autonomous aerial vehicles Path planning Robustness

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Comparison of Noise Removal Methods for Noisy Lenke Type 1 Idiopathic Scoliosis images 13

Comparison of Noise Removal Methods for Noisy Lenke Type 1 I...

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13th ieee international conference on Communication systems and Network Technologies, CSNT 2024

作者： Lubis, Julnila Husna Ali, Hasimah Yusof, Mohd Imran Jusman, Yessi Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Malaysia School of Medical Sciences Universiti Sains Malaysia Faculty Department of Orthopaedic Penang Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350305463

Improving the quality of medical images is essential for accurate clinical diagnosis. However, medical images are often plagued by various types of noise, posing challenges to the reliability and accuracy of image diagnosis. This study aims to compare Anisotropic, Wiener, Median, Average, and Gaussian filters on noisy images to improve the quality and accuracy of diagnostic results. To better adapt to the complex features in Lenke images, the contrast optimization algorithm was enhanced using Contrast-Limited Adaptive Histogram Equalization (CLAHE). In this study, artificial noise modeling (Gaussian and Quantum noise) was applied to Lenke X-ray images, and a comparative analysis was performed to discover the most effective filtering scheme for a particular type of noise based on various parameters such as MSE, PSNR, SNR, and SSIM. The results unveiled that the Wiener filter was relatively effective for both types of noise. The Gaussian filter also performed satisfactorily. Nonetheless, no filter could be declared the most effective for both types of noise. However, their performance varied according to the type of noise present and the evaluation parameters. © 2024 ieee.

关键词： signal to noise ratio

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Research on Mine Personnel Helmet Detection based on Improved YOLOv8n 5

Research on Mine Personnel Helmet Detection Based on Improve...

引用

5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Cao, Ying Li, Mingliang Zhang, Liya School of Information Engineering Hebei University of Geosciences Shijiazhuang China Hebei University of Geosciences Hebei Engineering Research Center for Intelligent Sensing Internet of Things Technology Shijiazhuang China Hebei University of Geology Information Center Shijiazhuang China Ccteg China Coal Research Institute Beijing China

ISBN: (纸本)9798331528911

Aiming at the problem that the complex environment of mines affects the image quality, the image enhancement method of image brightness enhancement and illumination unevenness treatment is proposed. In order to improve the accuracy of small target detection of mine operator helmet and reduce the computational volume, the YOLOv8n algorithm is improved, the Backbone structure is optimized, the FasterNet module is introduced to reduce the number of parameters, and the CBAM attention mechanism is added to improve the recognition ability. Tested on the coal mine personnel helmet dataset, the average accuracy of the improved algorithm reaches 95.0 % and 95.3 %, which is 14.7 % and 13.9 % higher than that of the original YOLOv8n, and effectively reduces the leakage and misdetection, which verifies the practical value of the improved algorithm and the application scenarios. © 2024 ieee.

关键词： Coal mines

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Novel Test Bench for End-to-End Validation of Monocular Depth Estimation Under the Influence of Glaring Situations 27

Novel Test Bench for End-to-End Validation of Monocular Dept...

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27th ieee international conference on Intelligent Transportation systems, ITSC 2024

作者： Ulreich, Fabian Kaup, Andre Ebert, Martin Friedrich-Alexander-Universität Erlangen-Nürnberg Chair of Multimedia Communications and Signal Processing Erlangen91058 Germany Technische Hochschule Ingolstadt Ingolstadt85049 Germany Technische Hochschule Ingolstadt Faculty of Electrical Engineering and Information Technology Ingolstadt85049 Germany

ISBN: (纸本)9798331505929

The black-box nature of deep learning models employed in automated driving functions requires suitable evaluation tools. Efforts are being made to increase the validity of testing environments for real-world operations. Understanding the impact of the sensor characteristics and degradation on the downstream task of perception is another field of research. We propose a test environment for vision-based autonomous driving functions in which a real camera and a deep learning model can be evaluated jointly. Our approach enables the validation under real-world brightness conditions through projector technology. To demonstrate its applicability, we employed a Vision Transformer to perform monocular depth estimation. Our experimental setup included a challenging scenario involving glare to assess the differences in performance between the testing environments: camera test bench and simulation. We quantified the gap by contrasting image quality metrics of partly-synthetic and pure synthetic data with real-world data contained in the KITTI depth dataset. With our approach, we were able to produce images that are 37 % closer to real-world than synthetic image data. Also, the gap in data variability is 18 % less than with synthetic data. In addition, we found that clipping in glare situations does not necessarily lead to large errors in depth prediction. © 2024 ieee.

关键词： Depth perception

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No Reference Quality Assessment for Screen Content images based on Entire and High-Influence Regions 48

No Reference Quality Assessment for Screen Content Images Ba...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Xu, Zhuoran Yang, Yang Zhang, Zhixiang Zhang, Weiming Anhui University School of Electronics and Information Engineering Hefei China Hefei High-Dimensional Data Technology Co. Ltd China University of Science and Technology of China Hefei China

ISBN: (纸本)9781728163277

Screen content images (SCIs) are composed of text, graphics, natural scene images and other contents, for which the human eye will have different visual perception for different regions. Considering the impact of local distortions on the visual quality of the entire SCIs, this paper proposes a no-reference quality assessment method based on entire and high-influence regions. Firstly, we select the region with higher influence on the overall quality of the image by information entropy. Then the structural features based on phase congruency and color features based on opponent color space are extracted from the entire SCIs and high-influence region. The AdaBoosting neural network (ABNN) is adopted to predict scores of the entire image and the high-influence region. Finally, the quality score of the high-influence is used to slightly adjust the score of the entire SCIs to obtain the final score through the weighting strategy. Extensive experiments demonstrate the efficiency and robustness of the proposed method. © 2023 ieee.

关键词： no-reference opponent color space phase congruency score integration screen content image

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Continuous Wavelet Transform Spectral image based Epileptic Episode Classification 25

Continuous Wavelet Transform Spectral Image Based Epileptic ...

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25th international Arab conference on Information technology, ACIT 2024

作者： Nuhi-Alamin, Md. Sarkar, Ajay Krishno Talukder, Md. Simul Hasan Aljaidi, Mohammad Swapnil, Safkat Shahrier Rajshahi University of Engineering & Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh Dhaka University of Engineering & Technology Department of Electrical and Electronic Engineering Gazipur Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331540012

Numerous individuals globally, regardless of age, have the neurological condition epilepsy. Recurrent seizures compromised motor and sensory abilities, and a hindered normal lifestyle are all signs of epilepsy. By seeing or manually examining electroencephalogram (EEG) data, neurologists can make the diagnosis of epileptic seizures. In this paper, a continuous wavelet transform spectral image-based epileptic episode classification technique is proposed. The EEG signal was first converted into 227x227 RGB images using CWT. Then these images were used to create an image dataset, and classification was performed using the proposed deep ensemble epileptic seizure classifier (DE-ESC). DE-ESC ensembled Dense-net-169, Dense-net-201, and Inception-V3. This proposed method obtained 100% accuracy (Ac), precision (Pr), recall, and F1 Score, respectively. © 2024 ieee.

关键词： Wavelet transforms

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