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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是641-650 订阅

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Optical Performance monitoring using Q-learning optimized least square support vector machine in optical network

Optical Performance monitoring using Q-learning optimized le...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Qiang Sun Zhuojia Yang Xiaodong Chen Chengqing Yu School of Electronic Information Engineering Beijing Jiaotong University Beijing China School of Traffic and Transportation Engineering Central South University Changsha China

ISBN: (纸本)9781665446006

Optical performance monitoring (OPM) is one of the important technologies to ensure the normal transmission of optical network data. Although there are many techniques have been applied to OPM, it is still necessary to improve its monitoring performance and implement multidimensional monitoring. A novel method is proposed for signal-to-noise (OSNR) and chromatic dispersion (CD) estimation by using least square support vector machine (LSSVM) optimized based on Q-learning in optical communication system with the signal modulation format of QPSK, DP-16PSK and DP-16QAM. the signal features extracted from the asynchronous amplitude histogram (AAH) is used as the input to the model. Experiment simulation have been carried out in the value of OSNR range from 21dB to 32dB and the CD coefficient range from 8-17ps/km/nm. the results show that the proposed method achieves high estimation accuracy with the estimation error of OSNR and CD coefficient are 0.2064dB and 0.1597ps/km/nm, respectively. Compared with the traditional support vector machine (SVM), Q-LSSVM improved the performance of OSNR and CD coefficients by 16.3% and 27.9%, respectively. the proposed method is helpful to the multi-dimensional and accurate optical performance monitoring in the future optical networks.

关键词： Support vector machines Integrated optics Q-learning Optical fiber networks Optical imaging Feature extraction Adaptive optics

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Event-based Neural Network for ECG Classification with Delta Encoding and Early Stopping 6

Event-based Neural Network for ECG Classification with Delta...

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6th international conference on Event-Based Control, Communication, and signal processing (EBCCSP)

作者： Jobst, Matthias Liu, Chen Partzsch, Johannes Yan, Yexin Kappel, David Gonzalez, Hector A. Ji, Yue Vogginger, Bernhard Mayr, Christian Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuromicroelect Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human In The Loop CeTI Dresden Germany Georg August Univ Gottingen Bernstein Ctr Computat Neurosci Phys Biophys 3 Gottingen Germany

ISBN: (纸本)9781728195810

We present a scalable architecture based on a trained filter bank for input pre-processing and a recurrent neural network (RNN) for the detection of atrial fibrillation in electrocardiogram (ECG) signals, with the focus on enabling a very efficient hardware implementation as application-specific integrated circuit (ASIC). Our already very efficient base architecture is further improved by replacing the RNN with a delta-encoded gated recurrent unit (GRU) and adding a confidence measure (CM) for terminating the computation as early as possible. With these optimizations, we demonstrate a reduction of the processing load of 58 % on an internal dataset while still achieving near state-of-the-art classification results on the Physionet ECG dataset with only 1202 parameters.

关键词： ECG classification machine learning recurrent neural networks event-based delta encoding early stopping

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Distributed Analysis Dictionary learning Based on Consensus Constraints

Distributed Analysis Dictionary Learning Based on Consensus ...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Liu Yang Jing Dong Jie Zhang Xiaoqing Luo Jian Guan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9781665446006

Distributed dictionary learning is to learn a global dictionary so that all data distributed in a network has sparse representation in the domain of the dictionary. Existing works are based on sparse synthesis model. We consider this problem based on the sparse analysis model, and propose a distributed analysis dictionary learning (ADL) algorithm using consensus constraints. In particular, local dictionaries corresponding to local data at each node are introduced, and the distributed ADL problem is formulated as a minimization problem with consensus constraints on local dictionaries and the global dictionary. An optimization method consisting of a sparse coding stage and a dictionary update stage is then developed. Experimental results have shown that the performance of the proposed algorithm is comparable to centralized ADL algorithms.

关键词： Analytical models Dictionaries Image coding conferences Optimization methods Distributed databases machine learning

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A Network Attack Blocking Scheme based on threat Intelligence

A Network Attack Blocking Scheme based on Threat Intelligenc...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Kun Li Rui Wang Haiwei Li Yan Hao First Research Institute of the Ministry of Public Security of PRC Beijing China Beijing China

In the current network security situation, the types of network threats are complex and changeable. With the development of the Internet and the application of information technology, the general trend is opener. Important data and important business applications will face more serious security threats. However, with the development of cloud computing technology, the trend of large-scale deployment of important business applications in cloud centers has greatly increased. the development and use of software-defined networks in cloud data centers have greatly reduced the effect of traditional network security boundary protection. How to find an effective way to protect important applications in open multi-step large-scale cloud data centers is a problem we need to solve. threat intelligence has become an important means to solve complex network attacks, realize real-time threat early warning and attack tracking because of its ability to analyze the threat intelligence data of various network attacks. Based on the research of threat intelligence, machine learning, cloud central network, SDN and other technologies, this paper proposes an active defense method of network security based on threat intelligence for super-large cloud data centers.

关键词： Cloud computing Data centers signal processing Market research Real-time systems Security Communication networks

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Enhancement Of Covid-19 Segmentation Using machine learning Analyses Of Lung Imaging 21

Enhancement Of Covid-19 Segmentation Using Machine Learning ...

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2021 6th international conference on Biomedical Imaging, signal processing

作者： Bhuvan Mittal Junghwan Oh Department of Computer Science and Engineering University of North Texas USA

ISBN: (纸本)9781450385817

the Covid-19 pandemic has caused more then 193 million cases and 4.1 million deaths worldwide as of July 2021. the Fleischner Society reported that Computerized Tomography (CT) is a useful tool for the early identification of Covid-19. Covid-19 disease induces lung changes which can be observed in lung CT predominantly as ground-glass opacification (GGO) and occasional consolidation in the peripheries. Moreover, it was reported that the percentage of lung showing disease correlates with the severity of the disease. therefore, segmentation of the disease areas in CT images is a logical first step to quantify disease severity. In this paper, we propose ‘CoviSegNet Enhanced’ based on a U-Net with an 813-layer EfficientNetB7 encoder having an attention mechanism to segment the Covid-19 disease area observed in CT images of Covid-19 patients. CoviSegNet Enhanced is an improvement of our previous work ‘CoviSegNet’. the experiments performed on three public CT datasets and a detailed comparison with recently published work confirms that the proposed CoviNet Enhanced using deep learning approaches is highly effective for Covid-19 segmentation.

关键词： lung computerized tomography (CT) scan deep learning Covid-19 segmentation

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Intelligent Traffic Management System Using machine learning and Traffic signal Control Algorithms for Optimizing Vehicular Flow in Lima 6th

Intelligent Traffic Management System Using Machine Learning...

引用

6th international conference on international conference on Applied Technologies, ICAT 2024

作者： Toribio, Julio Huarcaya, Frank Huarcaya, Nelly Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9783031897597

A 2023 TomTom study identifies Lima as the most congested city in Latin America and the fifth worldwide, causing significant impacts such as increased stress, long travel times, and lower productivity. Despite various approaches, none fully address the unique challenges of developing cities like Lima. To address this, the proposed system uses the Max Pressure (MP) algorithm for traffic signal control and a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for traffic prediction. the MP algorithm dynamically adjusts signal timing to optimize traffic flow, while the Bi-LSTM predicts future traffic patterns. Validated through simulations on Javier Prado Avenue, the system achieved a 13.8% increase in vehicle throughput and an 18% reduction in travel times. these findings highlight its potential to enhance urban traffic management. the developed web app offers a practical tool, bridging research and real-world application to address Lima's traffic congestion effectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Travel time

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Hardware Implementation of Automated Seizure Detection System using EEG signals and Edge Computing 6

Hardware Implementation of Automated Seizure Detection Syste...

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6th international conference on Trends in Electronics and Informatics (ICOEI)

作者： Torse, Dattaprasad A. Khanai, Rajashri Pai, Krishna Iyer, Sridhar KLE Dr MSSCET Belagavi India

ISBN: (纸本)9781665483285;9781665483292

Epilepsy is an unrelenting, constantly recurring neurological disorder characterized by abnormal electrical activity in the brain. Epilepsy acts as a condition with inconsistent symptoms involving spasmodic abnormal electrical activities in the brain. Clinical data pertinent to such abnormalities is nonlinear, non-stationary, and multi-dimensional. the processing and analysis of such data generates a combination of computing research challenges. Electroencephalogram (EEG) is one of the most clinically and scientifically utilized signals recorded from human brain. It is a powerful source for providing valuable insight of the brain dynamics. Accurate and careful analyses of EEG signals play a prominent role in diagnosis of brain disorders such as epilepsy. EEG signal has exceptional temporal resolution, usability, and non-invasive recording set-up with low costs which makes it popular in the field of biomedical research. Currently, trained neurologists take diagnostic decision by observing EEG recordings visually to decide probable treatment plan. the visual inspection of non-stationary and high-dimensional EEG recordings is a time-consuming task and results in errors. An automated seizure detector is an essential system not only for the better understanding of seizure generation but also a boon for medical professionals who are subjected to fatigue in visualizing a continuous and enormous long-term recording. the multichannel automated seizure detection is a bulky system as it involves recording from at least 16 channels. the use of such system on subjects who are remotely located is a challenge. In this paper, a unique embedded system based on envelop generation technique while sifting of EEG signals by Ensemble Empirical Mode Decomposition (EEMD) has been proposed to classify seizure signals. the modifications in the algorithm have resulted in an equivalent performance on multivariate signals when compared to simulation results. the use of 5-channel EMOTIV (R) head

关键词： Electrocochleogram seizure classification machine learning edge computing

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Computer Based Automatic Detection and Classification of Osteoporosis in Bone Radiographs 6

Computer Based Automatic Detection and Classification of Ost...

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6th international conference on signal processing and Integrated Networks (SPIN)

作者： Bhattacharya, Sayantan Nair, Devesh Bhan, Anupama Goyal, Ayush Amity Univ Dept Elect & Commun Engn Noida India Texas A&M Univ Kingsville Frank H Dotterweich Coll Engn Kingsville TX USA

ISBN: (纸本)9781728113807

Osteoporosis is a skeletal disorder which is mostly observed in the elderly and women. the radiographs are still in a standard practice to diagnose the hone health especially osteoporosis. In this paper, an automatic methodology is developed as an algorithm that could classify a radiographic image of the bone and predict whether the patient is osteoporotic or healthy which generally is difficult to visualize as radiographs are dark and do not exhibit any information unless exposed to some brightness. A novel approach has been developed which is a fully automatic method tbr feature selection, classification and detection by the application of machine learning classifiers all Support Vector machines (SVMs) and Nearest -Neighbours(NNs). Further a promising result has been obtained using machine learning accuracy of 95% which is clinically significant for osteoporosis detection.

关键词： Radiographs machine learning feature selection

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On a new ATR tool for coastal surveillance pulse Doppler radars 6

On a new ATR tool for coastal surveillance pulse Doppler rad...

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6th international conference on signal processing and Integrated Networks (SPIN)

作者： Nguyen Van Loi Tran Trung Kien Tran Vu Hop Vu Viet Tien Viettel R&D Inst Radar Inst Hanoi Vietnam

ISBN: (纸本)9781728113807

We propose a new automatic target recognition (ATR) tool for coastal surveillance pulse-Doppler radars. the novelty of this tool consists of the new features for classification and a new classification algorithm which is performed by combining the predict models with M/N logic rule. Moreover, the detail design of ATR tool is also described. the proposed ATR tool is tested with real data for the purpose of fishing vessel recognition. the independent tests show that the proposed ATR tool has a better classification performance than that one given in some recent publications.

关键词： Automatic target recognition machine learning Feature extraction Pulse-Doppler radar

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Empirical Analysis of Shallow and Deep Architecture Classifiers on Emotion Recognition from Speech 6

Empirical Analysis of Shallow and Deep Architecture Classifi...

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6th IEEE international conference on Cyber Security and Cloud Computing (IEEE CSCloud) / 5th IEEE international conference on Edge Computing and Scalable Cloud (IEEE EdgeCom)

作者： Singh, Vaibhav Sharma, Kapil Delhi Technol Univ Dept Comp Sci Discipline Software Engn New Delhi India Delhi Technol Univ Dept Informat Technol New Delhi India

ISBN: (纸本)9781728116617

Emotion recognition or detection from speech is currently a very crucial area of research with a plethora of applications in day to day life. Human communication depends heavily on mood, emotions and feelings. Availability of advanced signal processing techniques and artificial intelligence techniques like machine learning architecture (shallow classifiers) and neural network architecture (deep classifiers) have made this domain a booming area of research with increased efficiency and accuracy. this paper aims to empirically analyze various statistical machine learning algorithms like Naive Bayes, Support Vector machine, Random Forest and deep learning algorithms like Convolutional Neural Network, Long Short Term Memory over emodh dataset which is publicly available for emotion classification into angry, sad, happy, neutral, other classes. A comparison of shallow classifiers on the basis of accuracy will help future researchers in providing hindsight into the field of emotion detection. Same goes for the comparison between the deep learning techniques.

关键词： machine learning emotion detection deep learning mfcc

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