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共 6145 条记录，以下是101-110 订阅

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Music Classification Model based on Multi-Feature image Fusion and Machine Learning Algorithms

Music Classification Model based on Multi-Feature Image Fusi...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Chen Wang School of Children’s Welfare Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

How to effectively classify massive music data has become a key research topic in the field of music information retrieval. This study proposes a novel music classification model that combines multi-feature image fusion (MFIF) and machine learning (ML) algorithms to improve the accuracy and efficiency of music classification. The specific method is to first convert music signals into various visual representations such as spectrograms, Mel spectrograms, and chromaticity maps, and then combine these different visual features into a comprehensive feature map through image fusion technology. In addition, the study also introduced a deep learning framework, using Convolutional Neural Network (CNN) to learn these fused image features. In the absence of noise, the model exhibits high performance with an accuracy of 0.87 and an F1 score of 0.855, indicating that the model can perform music classification tasks well under ideal input conditions. This article provides technical support for related music recommendation systems and music copyright management.

关键词： Deep learning Visualization Accuracy Machine learning algorithms image recognition Computational modeling Multiple signal classification Convolutional neural networks image fusion Spectrogram

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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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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) 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 Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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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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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Shanchen Pang Xue Zhao Yulin Zhang Yawu Zhao Hengtao Ding Zhiyuan Zhao Sibo Qiao College of Computer Science and Technology Qingdao Institute of Software China University of Petroleum (East China) Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China The College of Software Tiangong University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： image segmentation Adaptation models Codes Attention mechanisms Fuses Aneurysm signal processing Acoustics Speech processing Biomedical imaging

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Secure and High-Fidelity Medical image Generation: A GAN-based Approach for Steganography

Secure and High-Fidelity Medical Image Generation: A GAN-Bas...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： B. Sumitra S. Selvin Ebenezer G. Vallathan B. Sundaramurthy Venkata Rami Reddy Dorsela Department of ECE Geethanjali College of Engineering & Technology Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Vinayaka Missions Research Foundation Vinayaka Missions Kirupananda Variyar Engineering College (Deemed to be University) Salem India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Traditional image steganography methods embed secret data into cover images but often face detection by steganalysis tools due to detectable pixel modifications, which increase the risk of data leakage. These techniques also typically degrade image quality and offer limited capacity. To overcome these challenges, a novel approach for medical imaging is proposed, integrating Generative Adversarial Networks (GANs), PatchGAN, Adaptive LSB steganography, and a dual attention mechanism. PatchGAN improves fine-grained texture details, while Adaptive LSB securely embeds sensitive data within the images. The dual attention mechanism highlights critical regions, enhancing both image clarity and diagnostic accuracy. This method improves image quality and security, benefiting clinical decision-making and patient outcomes. The system is evaluated against traditional methods like GAN and IDGAN using metrics such as embedding capacity, classification accuracy (stego, cover, global), and discriminator loss. Results demonstrate that the proposed system outperforms existing techniques, offering significant improvements in both performance and security for medical imaging applications.

关键词： image quality Steganography Attention mechanisms Adaptive systems Accuracy image synthesis Generative adversarial networks Security Data communication Medical diagnostic imaging

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Dynamic ROI Adaptation for Accurate Non-Contact Heart Rate Estimation Using VGG-13 based Encoder-Decoder Model and Facial Landmarks

Dynamic ROI Adaptation for Accurate Non-Contact Heart Rate E...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Aravind A Anil Srinivasa Karthik Mohanasankar Sivaprakasam Jayaraj Joseph Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Heart rate monitoring using cameras is gaining attention for unobtrusive health tracking. This paper proposes an improved method for HR calculation using ROI adaptation tailored to individual facial features. A VGG-13 based encoder-decoder model was used for skin segmentation, classifying image pixels into left cheek, right cheek, and forehead regions, achieving a Dice coefficient of 0.90. ROIs were defined by the exterior coordinates of these skin pixels, adapting naturally to features like forehead size, cheek size, hairstyles, and spectacles. To address the challenge of ROIs with insufficient skin pixel coverage, we integrated Google MediaPipe landmarks to refine ROI selection, ensuring that at least 30 percent of the pixels in each region were skin pixels. The adaptive multi-ROI method was compared with traditional single-forehead and non-adaptive multi-ROI methods using the POS and CHROM rPPG algorithms. The adaptive approach showed better performance, achieving MAE values of 1.51 and 2.15 for POS and CHROM.

关键词： Heart rate Adaptation models Forehead signal processing algorithms signal processing Cameras Skin internet Speech processing Facial features

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High-Frequency Vision-based Tactile Sensor with Embedded Transparent Piezoelectric Module for Humanoid Robotic Perception

High-Frequency Vision-Based Tactile Sensor with Embedded Tra...

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IEEE international conference on Micro Electro Mechanical systems

作者： Zhengyi Xie Chunpeng Jiang Haoxiang Jiang Yimeng Sun Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Zhangjiang Laboratory China Shanghai University of Electric Power China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper reports a novel composite high-frequency vision-based tactile sensor with an embedded transparent MEMS-based piezoelectric module. Taking advantage of the transparency of the piezoelectric module, the sensor can simultaneously capture high-quality image signals and piezoelectric signals. By combining high-frequency piezoelectric signals with the interpolation algorithm, the sensor enhances the frame rate of image signals, enabling the acquisition of high-frequency, high-quality image signals. Additionally, this integrated approach offers advantages in terms of miniaturization and low cost. This research provides a new direction for the development of humanoid robotic perception and has broad application potential.

关键词： Micromechanical devices Human computer interaction Interpolation Visualization image resolution Heuristic algorithms Multimodal sensors Tactile sensors Humanoid robots signal resolution

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IoT-based Real-Time Data Monitoring of a Green House Farm with Offline Monitoring Capability

IoT-Based Real-Time Data Monitoring of a Green House Farm wi...

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international conference on Robotics,Electrical and signal Processing Techniques (ICREST)

作者： Tushar Bhadra Mujakkir-Ul-Islam Nowshin Alam Department of EEE Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Greenhouses have transformed modern agriculture by precisely managing the environmental conditions of delicate plant species to promote crop growth. A conceptual and practical framework for a low-cost greenhouse farm monitoring system has been proposed in this paper to track real-time data on greenhouse farms with online and offline both monitoring capabilities. The internet of Things (IoT) technology has been used to develop a system prototype, which uses two sensors to collect data on environmental factors, including temperature, humidity, heat index, and soil moisture. An ESP32 microcontroller module collects and sends this sensor data to a Raspberry Pi 4 microcomputer, which displays it on a web application. The application is hosted on a local Raspberry Pi server and built with ***, a JavaScript runtime environment. The communication between ESP32 and Raspberry Pi is established with REST API (Representational State Transfer Application Programming Interface), a way to connect applications and components in microservices architectures. A highly robust PostgreSQL-based database is used to store the data in Raspberry Pi, which allows the collection of farm data locally without having to connect to any web storage. With an internet connection, data can be monitored from anywhere worldwide using a global server made of Vercel, a cloud-based platform that can host websites and web applications. The local Raspberry pi server would be accessible to the internet through SSH (secure shell) tunneling with Ngrok, a cross-platform application.

关键词： Temperature sensors Temperature measurement Databases Soil moisture Prototypes Greenhouses Real-time systems Sensors Servers Monitoring

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Processing Continuous-Valued signals for Multimodal Spike-based Pose Regression

Processing Continuous-Valued Signals for Multimodal Spike-Ba...

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2025 IEEE international conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Sudevan, Vidya Zayer, Fakhreddine Kausar, Rizwana Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates Sorbonne University Dept. Science and Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331516857

In underwater missions, energy-efficient operation is critical to maximize the operability of an autonomous robots and mission duration. At the same time, accurate pose estimation at reduced computational cost is essential for various underwater robotics and exploration applications. This paper presents an energy-efficient model for pose regression tasks based on direct Spike-Coding-Decoding (SCD) with adaptive threshold mechanism. The proposed approach enables efficient processing of multimodal continuous-valued images and IMU data. The effectiveness of the proposed module is tested using an open-sourced underwater simulator. In underwater robotics domain, real-world testing remains critical due to limited access to actual underwater environment, making simulators invaluable for rapid algorithm development and refinement. Accordingly, HoloOcean simulator is utilized to collect images and IMU sensor data from various underwater scenarios, which are then fed into a pose estimation framework. The SCD module uses 2D and 1D convolutional layers followed by LIF neurons with adaptive threshold to convert continuous-valued data into sparse spike-based signals. During spike decoding, the spike-based signals are converted back to continuous-values by utilizing the accumulated membrane potential of the output LIF neurons. The feasibility of SCD module is evaluated by its ability to introduce sparsity in spike-based representations with minimal information loss that enables efficient signal reconstruction. Custom multimodal data is collected using the HoloOcean simulator and is used to train the network. We compared the performance of the adaptive learnable threshold method with fixed threshold methods. The experimental results show that spike coding with an adaptive threshold mechanism is highly effective compared to a fixed threshold mechanism, as it can encode the continuous-valued input to its spike-based representation with ≈ 2× higher sparsity and a lesser spike rate. ©

关键词： image reconstruction

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Representation learning Correlation Time series analysis Closed box signal processing Knowledge discovery Usability Speech processing Alzheimer's disease Biomedical imaging

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Machine Learning to Enhance the V2V Communication-based IoD Using Companding Dataset

Machine Learning to Enhance the V2V Communication-based IoD ...

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IEEE SSD international Multi-conference on systems, signals and Devices

作者： O. Daoud Q. Hamarsheh A. Damati S. Al Jaafrah O. Qasem M. Al-Khawaldah Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Electrical Engineering Department Faculty of Engineering and Technology Philadelphia University Amman Jordan Researcher

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper investigates the improvement of Vehicle-to-Vehicle (V2V) communications in the internet of Drones (IoD) scenario for the reduction of the Peak-to-Average Power Ratio (PAPR) problem in Orthogonal Frequency Division Multiplexing (OFDM) systems. Motivated by the recent research on machine learning and deep neural network, this study will attempt to optimize Quality of Service (QoS) by means of a multiprocessing approach based on Daubechies wavelets. The proposed scheme integrates high-performance PAPR reduction schemes, such as µ-law companding, hybrid precoding-companding, and neural network-based adaptive algorithms. Deep reinforcement learning-driven approach in clipping and filtering is further proposed, which can offer real-time adaptation for the high-demanding communications. Results show a considerable gain in PAPR, over 60% PAPR reduction under high SNR conditions. These findings show that IoD can sustain stable V2V connections in urban use cases, thereby providing a scalable solution for 5G and beyond networks.

关键词： 5G mobile communication Vehicular ad hoc networks Peak to average power ratio Machine learning Quality of service Real-time systems internet Reliability Drones signal to noise ratio

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