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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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proceedings of 2019 2nd international conference on signal processing and machine learning, SPML 2019

Proceedings of 2019 2nd International Conference on Signal P...

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2nd international conference on signal processing and machine learning, SPML 2019

ISBN: (纸本)9781450372213

The proceedings contain 19 papers. The topics discussed include: the development and trend of ECG diagnosis assisted by artificial intelligence;learning how to avoiding obstacles for end-to-end driving with conditional imitation learning;multi-scale fusion and channel weighted CNN for acoustic scene classification;multi-source radar data fusion via support vector regression;multi-scale deep convolutional nets with attention model and conditional random fields for semantic image segmentation;discrete sidelobe clutter determination method based on filtering response loss;deep neural network-based scale feature model for BVI detection and principal component extraction;and an attention-enhanced recurrent graph convolutional network for skeleton-based action recognition.

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2023 2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

2023 2nd International Conference on Paradigm Shifts in Comm...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

ISBN: (纸本)9798350310719

The proceedings contain 97 papers. The topics discussed include: affective computing for emotion identification using dual-stage filtered multi-channel EEG signals;investigation of the direct source to drain tunneling in 5 nm nanotube junctionless field effect transistor;analysis and implementation of approximate 4-2 compressors for approximate multipliers;commercial clustering of Indian bamboo species using machine learning techniques;optimizing MRC tasks: understanding and resolving ambiguities;analyzing word stress and its effects on Assamese and Mizo using machine learning;anomaly detection by predicting future frames using convolutional LSTM in video surveillance;and suppression of false-terms in Wigner-Ville distribution using time and frequency windowing.

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CONF-SPML 2022 - proceedings of the 2nd international conference on signal processing and machine learning

CONF-SPML 2022 - Proceedings of the 2nd International Confer...

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2nd international conference on signal processing and machine learning, CONF-SPML 2022

The proceedings contain 16 papers. The topics discussed include: melanoma classification using feature extraction methods and machine learning approaches;strategies to solve the problem of information cocoon â€" research progress of cross-domain recommendation algorithm based on mining the potential interests of users;the development of ray tracing and its future;convolutional neural network and its application in handwritten digit and traffic sign recognition;a two-step rumor detection and classification method using machine learning;lip reading using multi-dilation temporal convolutional network;comprehensive survey on video denoise methods;optimization of CNN and LSTM based application on RC frame and long-span structural health monitoring;comparison of underlying algorithms in recommendation systems;and can PID make the electronic stability program more effective? research on co-simulation of electronic stability program.

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd international conference on Communication Networks and machine learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Reinforcement learning-Driven Adaptive signal processing for Low-Power Edge Devices in Dynamic Environments 2

Reinforcement Learning-Driven Adaptive Signal Processing for...

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2nd international conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Kashikar, Raj Virginia Polytechnic Institute and State University Department of Computer Science Blacksburg United States

ISBN: (纸本)9798331505745

Reinforcement learning (RL) offers a transformative approach to adaptive signal processing on low-power edge devices operating in dynamic environments. This work introduces an RL-driven framework utilizing Proximal Policy Optimization (PPO) in conjunction with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to autonomously enhance signal quality and reduce energy consumption. The model dynamically adjusts to fluctuating noise and interference by fine-tuning filtering techniques, optimizing compression, and adapting hardware configurations. Tests on platforms like NVIDIA Jetson Nano and Raspberry Pi show a 12% improvement in signal-to-noise ratio (SNR) and a 25% reduction in power consumption compared to baseline methods, showcasing RL's potential for real-time, energy-efficient edge processing. Future applications of this framework could extend to larger IoT networks, employing federated learning for distributed optimization across interconnected low-power devices. © 2025 IEEE.

关键词： Reinforcement learning

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Fall Detection Using Sound with FPGA signal Analysis and machine learning Integration 2

Fall Detection Using Sound with FPGA Signal Analysis and Mac...

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2nd international conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Andre, Mariano Viegas Ozdemir, Ahmet Turan Kavuncuoglu, Erhan Erciyes University Department of Electric and Electronic Engineering Kayseri Turkey Erciyes University Department of Electric and Electronic Engineering KayseriTR-38039 Turkey Gemerek Vocational School Cumhuriyet University Department of Computer Tecnology SivasTR-58840 Turkey

ISBN: (纸本)9798331505745

Falls represent a considerable risk to the well-being of geriatric populations, frequently resulting in injuries or mortality. In response to this pressing concern, we introduce an innovative fall detection apparatus employing acoustic sensors and FPGA (Field Programmable Gate Array) technology to effectively alleviate these hazards. Our system capitalizes on acoustic phenomena, particularly those associated with footstep sounds and falls, which are gathered through routine activities and video documentation to develop a comprehensive model adept at differentiating between fall incidents and non-fall occurrences. The primary attributes encompass the derivation of Mel-frequency cepstral coefficients (MFCC) from the processed auditory signals. To enhance operational efficacy, an FPGA is utilized to execute all data processing functions, inclusive of Fourier transformations and a threshold-based predictive algorithm. This methodology diminishes dependence on conventional computational systems while preserving elevated processing efficiency. The derived features are subsequently employed to formulate a data classification model utilizing a Convolutional Neural Network (CNN) and Random Forest Tree (RFT), which proficiently discriminates between sounds indicative of falls and those not related to falls. © 2025 IEEE.

关键词： Data integration

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3DIT-MSPandDL 2020

2nd International Conference on 3D Imaging Technologies-Mult...

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3DIT-MSPandDL 2020

ISBN: (纸本)9789811633904

The proceedings contain 39 papers. The special focus in this conference is on 3D Imaging Technologies-Multidimensional signal processing and Deep learning. The topics include: Appearance Defect Detection of Injection Parts Based on Deep learning;thresholding Method for the Sonar Image of a Seabed Target Based on Two-Dimensional Renyi's Entropy;noise Reduction in Sonar Images of Seabed Targets Based on a Variational Method;sonar Image Segmentation of Seabed Targets Using a Variational Approach;balance the Robustness and Invisibility of Digital Watermarking by Image Entropy in Multiple Domains;research Progress of Electrical Resistance Tomography;research on Low-Resolution Image Matching and Recognition Algorithm Based on Unified Feature Space;research on Realistic Representation Technology in Virtual Environment;intelligent Customer Service Operation Management System Solution Based on Intelligent Voice Analysis;Research Status of Motor Imagery EEG signal Based on Deep learning;research on Car Seat Comfort Evaluation System Based on Chinese Population;research on Building Extraction from High-Resolution Remote Sensing Image Based on Improved U-Net;a Survey of Image Classification Algorithms Based on Graph Neural Networks;an Improved Method of Non-local Means Denoising Based on Histogram;short-Term Traffic Volume Prediction Based on Gray Wolf Optimization Algorithm;Biological Particle Recognition Based on BP Neural Network Algorithm;imageCube: A Low-Cost 6D Controller with Smooth Tracking for Mobile Augmented Reality;A Clinical Decision-Making System for COVID-19;License Plate Recognition System Based on OpenCV;LUCC and Landscape Pattern Variation in the Lower Tarim River by Remote Sensing;image Recognition Based on Super-Resolution Wasserstein Generative Adversarial Nets with Gradient Penalty.

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3D IT-MSPandDL 2020

2nd International Conference on 3D Imaging Technologies-Mult...

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3D IT-MSPandDL 2020

ISBN: (纸本)9789811631795

The proceedings contain 40 papers. The special focus in this conference is on 3D Imaging Technologies-Multidimensional signal processing and Deep learning. The topics include: Sensitivity Analysis of NVH Structure of Car Body Based on Modal Analysis;research on the Design of Nutrition and Sports Health Management System Based on Artificial Intelligence;research on English Word Intelligent learning System Based on Mobile Education Concept;research on the Application of Big Data Analysis in English Language Education;CFD Analysis of Droplet Diffusion in Negative Pressure Ward;research on Cognitive Analysis Method of Command Mechanism Based on Operational Data Flow Motifs;confirmatory Factor Analysis of Place Attachment in Agritourism;application of Computer Sensor and Detection Technology in Mechatronics;establishment of Smart Customer Service System Based on Data Mining;a Drawing of Digital Printing Gamut Map;application and Development of Natural Language processing Service in Intelligent Customer Service System;research on University Network Ecological Governance Based on Block Chain Technology;research on Informatization Construction of University Laboratory Under the Background of New Infrastructure;run-Time Prediction Practices of Multimedia Web Design in Technology Management;Design and Simulation of Orthogonal Frequency-Division Multiplexing (OFDM) signaling;research on Intelligent Financial Framework of Colleges and Universities in Information Age;VHF Band Dual-Channel High-Power Transmitter and Receiver Module;Research and Implementation of Point Multiplication Over Elliptic Curve GF(2m) Based on VLSI;non-contact Hand Sanitizer Auxiliary Device for Public Places;Simulation of Young’s Double Slit Interference Experiment Based on MATLAB;an Improved Contour Feature Extraction Method for the Image Butterfly Specimen.

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Classifying Music-Induced Emotion Using Multi-Modal Ensembles of EEG and Audio Feature Models

Classifying Music-Induced Emotion Using Multi-Modal Ensemble...

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

作者： Paukner, Philipp Ripoll, Marisa Sabir, Dilvan Erdoğan, Deniz Onat Sacchetto, Luca Diepold, Klaus TUM School of Computation Information and Technology Technical University of Munich Arcisstr. 21 Munich80333 Germany

ISBN: (纸本)9798350368741

In this paper, we present our submission to the EEG-Music Emotion Recognition Challenge at ICASSP 2025. Our work focused on Task 2, where the objective was to classify the emotional state of subjects in a discrete valence-arousal space while they listen to music. Our proposed solution adopts an ensemble approach, integrating electroencephalography (EEG) signals, raw audio signals, and song features. We incorporated a diverse set of models, including Audio Spectrogram Transformer (AST) [1] and a dedicated EEG model. By combining insights from these modalities, we aimed to capture the interplay between music and emotional responses. We achieved a balanced accuracy of 41.34% on held-out data, improving the baseline by more than 11% and thus 2nd place in the competition. © 2025 IEEE.

关键词： Audio EEG Ensemble machine learning Multi Modality Music Induced Emotion

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Optimizing DDoS Attack Detection Using machine learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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