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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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internet of Things Enabled Smart Hand Gesture Virtual Mouse System 14

Internet of Things Enabled Smart Hand Gesture Virtual Mouse ...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Chelekkodan, Danish Muneer Jayapandian, N. Abhinav, S. Shyjan, Aaron Department of Computer Science and Engineering Christ University Kengeri Campus Bangalore India

ISBN: (纸本)9798331531935

This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%. © 2025 IEEE.

关键词： Comparators (optical)

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A Grouping Strategy-based Progressive Fusion Network for Hyperspectral image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 IEEE.

关键词： group convolution Hyperspectral image image fusion super-resolution

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Dehazing images Using Vision Transformer Algorithm

Dehazing Images Using Vision Transformer Algorithm

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, Anyapu Reddy, B.Siva Chanikya Ranjani, M. Sandhia, G.K. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331509828

image dehazing is an important task to obtain clear images from blurry vision in low vision individuals. Although traditional methods and deep learning have made progress in this field, there are still challenges, especially in solving the real-life haze problem. This paper studies the application of Vision Transformer architecture, especially the Dehaze Former model, to improve the image dehazing performance. We present a new dataset that provides a comprehensive evaluation of the capabilities of Dehaze Former for difficult haze conditions (and perform function adjustments), and it outperforms existing models using state- of-the-art techniques when measured with new data. We propose several metrics to validate our findings, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Also a good comparison shows the effectiveness of this model in preserving image details and reducing artifacts. This research contributes to the advancement of image dehazing technology by demonstrating the ability of Vision Transformers to process grassy lawns. The insights gained from this study not only highlight the strengths of the Dehaze Former model, but also pave the way for future explorations into dehazing performance using Transformer-based architectures © 2025 IEEE.

关键词： Dataset Deep Learning image Dehazing Non-Homogeneous Haze Single image Restoration Vision Transformer

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Research on high accuracy classification of temperature signals based on OFDR technology and EfficientNet 4

Research on high accuracy classification of temperature sign...

引用

4th international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2024

作者： Shang, Chuanyin Li, Peng Tong, Zhengrong Wang, Hao Li, Sibo Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology the Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with Optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems. © 2025 SPIE.

关键词： Education and training Data modeling Performance modeling Fiber optics sensors Machine learning Matrices Fiber optics image processing Reliability signal processing

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Multi-Domain image Translation Adversarial Network based on Artificial Intelligence 3

Multi-Domain Image Translation Adversarial Network Based on ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Zhang, Lili College of Digital Art and Media Shandong University of Art & Design Jinan China

ISBN: (纸本)9798331508456

Traditional image translation methods often face difficulties such as differences in cross domain data distribution and difficulty in maintaining semantic consistency. Therefore, this article cites a generative adversarial network (GAN) based approach to capture mapping relationships between different domains. Firstly, this article adopts the Conditional Generative Adversarial Network (cGAN) framework to generate multi domain images. Next, this article introduces a shared encoder and multiple decoder structure. Finally, this article improved the performance of image translation results through optimization. In the experimental results, our method performed well on multiple standard datasets. The SSIM (Structural Similarity Index) value for the ink painting to oil painting task remains stable at around 0.88, and the PSNR (Peak signal to Noise Ratio) reaches 32.5 dB. In terms of diversity generation, this method also significantly improves the diversity and visual naturalness of image translation. Finally, the method proposed in this article significantly improves the quality and diversity of style transfer. © 2025 IEEE.

关键词： Generative adversarial networks

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Power internet of Things Access System based on Low Power Communication technology 14

Power Internet of Things Access System Based on Low Power Co...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Jiang, Zhigang Ren, Liming Zhu, Bing Jiang, Jiafeng School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China School of Department of Electronic Information and Electrical Engineering Hefei University Anhui Hefei230000 China School of Computer and Information Hefei University of Technology Anhui Hefei230000 China School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014017 China

ISBN: (纸本)9798331531935

In response to the issues of data transmission energy efficiency and network coverage in the power internet of Things system, this study introduces an access scheme based on low-power communication technology. By optimizing the network architecture and communication protocol, the aim is to improve the energy efficiency of sensor nodes in the power internet of Things and reduce communication latency. Firstly, an access scheme based on low power wide area network (LPWAN) technology is designed. LoRa (Long Range Radio) or NB-IoT (Narrow Band internet of Thing) is selected as the communication protocol. By optimizing the working mode of sensor nodes, periodic transmission and event-driven mechanism are adopted. Secondly, an adaptive data transmission mechanism is designed to dynamically adjust the transmission frequency and rate according to environmental changes to ensure reliability under different network conditions. Furthermore, deep sleep mode and power consumption scheduling algorithm are adopted, and the network architecture adopts star topology to ensure the efficiency of signal coverage and data forwarding, and the secure transmission of data information is guaranteed by encryption and authentication mechanism. Finally, through simulation and actual deployment, the performance of the scheme in the actual environment is verified, and its stability and data transmission efficiency under different network scales are evaluated. The battery life of periodic wake-up mode is significantly lower than that of deep sleep mode. Under low-frequency transmission (once an hour), the battery life is 320 days. In periodic wake-up mode, the average power consumption of sensor nodes is significantly higher than that of deep sleep mode. At low frequency transmission, average power consumption is 3.5 mW and battery life drops to 75 days. The power internet of Things access system based on low-power communication technology proposed in this work effectively solves the bottleneck pr

关键词： internet of things

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ETIS international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ETIS International Conference on Emerging Technologies for I...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ISBN: (纸本)9798331507541

The proceedings contain 104 papers. The topics discussed include: intelligent traffic signal management system;an automated pothole detection and repair system for effective road maintenance and safety;on-board data acquisition system for spacecraft;cyber-physical modeling and simulation of four-axis articulated robot;an intelligent system for prediction of depression based on facial emotions and textual data;evaluating the impact of dataset variability on Malayalam emotion detection system;an automated smart incubator system for poultry farms and hatcheries;image-driven face presentation attack detection in biometric systems;and soft computing approaches in enhancing healthcare data security using deep neural networks and reversible data hiding.

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-scale Fusion Network for Aneurysm image Segmentation

FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-sca...

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

作者： Pang, Shanchen Zhao, Xue Zhang, Yulin Zhao, Yawu Ding, Hengtao Zhao, Zhiyuan Qiao, Sibo Qingdao266580 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao266590 China The College of Software Tiangong University Tianjin300387 China

ISBN: (纸本)9798350368741

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov-Arnold-based (FKAN) Group Multi-scale Fusion Network, termed FKAN-GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced-FKAN block. Furthermore, we develop the Attention Group Multi-scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high- and low-scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN-GMFNet significantly outperforms seven state-of-the-art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS-L dataset are 88.82% and 80.09%, respectively. Code is available at https://***/zx123868/FKAN-GMFNet. © 2025 IEEE.

关键词： Fourier Kolmogorov-Arnold Intracranial aneurysms Medical image Segmentation

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