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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2953 条记录，以下是481-490 订阅

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Study of Different Gate Materials on Performance of Si Based MOSFET 3

Study of Different Gate Materials on Performance of Si Based...

引用

3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Mizan, Al Mamun Nazreen, Syeda Fahima Ashraf, Saqlain Kabir, A. Z. M. Tahmidul Gomes, John Maxwell Karmoker, Sristy Nabil, Nazmus Sakib Kabiruzzaman, Md. American International University-Bangladesh Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798350346435

In this paper, Silicon-based MOSFET has been investigated with different gate oxides for knowing which oxide shows the best result. Silicon-di oxide (SiO2), gallium oxide (Ga2O3), and aluminum oxide (Al2O3) have been used as gate materials for observing the sensitivity, threshold voltage, and drain current of the MOSFET in this study. Al2O3 gate material gives the highest terminal current and faster turn-on voltage (0.7 V) of the MOSFET compared to the other two. Al2O3 delivers the best performance;and hence, it could be a promising material for the gate layer. In addition, electron and hole concentrations of those gate oxides and electric potential have been compared. In this paper, COMSOL Multiphysics® software has been used to simulate the obtained results. © 2023 IEEE.

关键词： Aluminum oxide

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Application of mobile laser measurement system in railway inspection 3

Application of mobile laser measurement system in railway in...

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2023 3rd international conference on Detection Technology and Intelligence System, DTIS 2023

作者： Liu, Hao Yao, Lianbi Wu, Hangbin College of Surveying and Geo-information Tongji University Shanghai200092 China

Given the time constraints and demanding precision requirements of railway measurements, traditional measurement methods struggle to meet the inspection needs for the future expansion and renovation of extensive rail networks. This paper presents a railway data collection and processing system developed by using mobile laser scanning technology. To address the issue of error accumulation exceeding predefined limits in the positioning algorithm, we conducted a comprehensive study and utilized smoothing algorithms to correct acceleration and attitude angles. This optimization process not only enhances trajectory data but also improves the accuracy of the point cloud. The results indicate that this system holds significant importance in the realms of railway digital survey and design, as well as digital renovation and related areas. © Published under licence by IOP Publishing Ltd.

关键词： data handling

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A Stable Cloud Storage Algorithm for Online Interaction Effect data based on HarmonyOS 3

A Stable Cloud Storage Algorithm for Online Interaction Effe...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Liu, Yijia Shantou Polytechnic Department of Economic Management Shantou515078 China

ISBN: (纸本)9781665462167

At present, the skyrocketing amount of effective and redundant information makes the demand for storage more and more high, which provides an opportunity for the recent development of cloud storage and makes it one of the fastest accepted forms of cloud services. Under this background, this paper proposes a stable cloud storage algorithm for online interaction effect data based on the HarmonyOS. The designed model firstly optimizes the traditional Chord algorithm by introducing the genetic algorithm into the processing of data. The designed strategy allows the system to choose a relatively good path in each path selection, the system can choose a relatively good path. Then, the HarmonyOS is selected as the multi-core scenario and the online interaction effect data is selected as the sample application. Through the test, this study compares the performance of the original Scalable Chord routing protocol and the improved routing protocol in terms of resource location by using PeerSim. It can be seen that the designed model is robust. © 2023 IEEE.

关键词： Cloud storage

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic signal Analysis 3

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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

作者： Sasikala, P. Sharmini, K Subha Ponnappan, Venkatesan Sorakka Kavitha, K.R. Gunasekaran, K. Srinivasan, C. Department of Mathematics Salem India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Mechanical Engineering Chennai India Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health;nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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Design of Sports Online Multimedia Resource Sharing System Based on data Clustering 3

Design of Sports Online Multimedia Resource Sharing System B...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Xu, Jian Zhu, Lingguang China Academy of Art Department of General Physical Education Hangzhou310002 China

ISBN: (纸本)9781665472784

During the post-epidemic period, the construction of online resources for physical education courses in colleges and universities provides an important guarantee for realizing real-time conversion during the epidemic prevention and control period. Through the research of major online courses at home and abroad, the teaching reform of college physical education courses with "results as the center"as the core is put forward. The shared teaching system is researched on the success of students and designed according to the actual situation. Using data clustering technology, the information management of college physical education teaching is studied. Based on the overall functional requirements, the cloud computing architecture of the platform is analyzed in detail. Based on the data processing requirements, a data-based management system is established and data management is carried out. According to the APP architecture of student users, the requirements of integrated data collection and distribution of training tasks are carried out. Then, the specific realization of APP architecture is elaborated. At the same time, using the theory of output gain stability analysis, a linear analysis model for data analysis in sports industry is established. Using the combination of quadratic programming and linear integration, the integration of internal information and the regulation of parameters are carried out in the sports industry. Through the analysis of the sports industry, it is proved that the algorithm proposed in this paper has high accuracy and reduces the interference error in the sports industry. © 2023 IEEE.

关键词： Information management

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data Mining Technique for Construction of Electric Power Marketing Inspection System 3

Data Mining Technique for Construction of Electric Power Mar...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Jie, Sun Degui, Qiu Rui, Zhang Xiuxiu, Wang State Grid Anhui Electric Power Co. Ltd Bengbu Power Supply Company Hefei China Anhui Jiyuan Software Co. Ltd Hefei China

ISBN: (纸本)9798350317022

Quality of inspection in electric power marketing is significant for calculating power level in marketing management of power supply enterprises and complete reflection of processing outcomes and quality of service in power supply enterprises. The existing techniques causes less implementation impact of relative inspection process because of high scale power marketing information. In this research, proposed a development of electric power marketing inspection method depended on data mining technique. data mining technique is utilized for analyzing power marketing information and less support and confidence are considered as measures to determine association relationship. Proposed method attained high inspection accuracy with minimum loss. The performance of proposed method is evaluated with performance measure of loss and accuracy. The proposed method attained the high inspection accuracy of 97.57% with minimum loss of 215.27 for 4 marketing information in power system which is superior than other existing methods like Robot Process Automation (RPA). © 2023 IEEE.

关键词： Association relationship data Mining Electric Power Marketing Inspection method and Power Supply

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Analyzing BER Performance of 2x2, 3x3 and 4x4 MIMO Concatenated 8-PSK with and Without TCM Coding over Fading Channel Using Simulation Method 3

Analyzing BER Performance of 2x2, 3x3 and 4x4 MIMO Concatena...

引用

3rd IEEE international conference on signal, Control and Communication, SCC 2023

作者： Abdaljlil, Sana Ahmed Zerek, Amer R. Boiprav, Olga Elmezwghi, Albatool A. Sabratha University Sabratha Libya Libya Open University Tripoli Libya Belarusian State University of Informatics and Radioelectronics Information Protection Deprt. Minsk Belarus Electronic Technology Colleg Communications Department Tripoli Libya

ISBN: (纸本)9798350326390

M-ary Phase Shift Keying (M-ary PSK) is a critical digital modulation scheme used in modern wireless communication systems. It is considered one of the most important M-ary carrier techniques, as it efficiently transmits digital data over wireless channels. The utilization of a single symbol to encode multiple bits of data is instrumental in achieving enhanced data rates and spectral efficiency. This attribute plays a pivotal role in bolstering system performance and optimizing data rates without the need for expanding channel bandwidth. A modern technique employed in wireless communication systems, specifically multi-input multi-output (MIMO) systems, involves the use of orthogonal space-time block codes (OSTBC) to accomplish this objective. This work focuses on 2x2, 3x3 and 4x4 (MIMO systems concatenated with 8- phase shift keying (8-PSK) modulation, with and without trellis-coded modulation (TCM) coding. The performance of these systems is evaluated using simulation method. The results can guide the development of more efficient and reliable wireless communication systems. Therefore, the achieved outcome of 8-PSK modulation with TCM demonstrates superior Bit Error Rate (BER) performance when compared to employing uncoded 8-PSK. © 2023 IEEE.

关键词： Phase shift keying

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Artificial Intelligence-Based Analysis of Biological signals and Attention Assessment Methods 24

Artificial Intelligence-Based Analysis of Biological Signals...

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3rd international Symposium on Robotics, Artificial Intelligence and Information Engineering, RAIIE 2024

作者： Li, Zhijing Li, Hong Jiang, Yiping Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400718311

Biological information processing plays a pivotal role in modern life, with applications ranging from health monitoring to personalized medicine. This study endeavors to delve into a method employing artificial neural networks for the analysis of biological signals, specifically focusing on assessing individual attention levels. By harnessing physiological data such as heart rate variability and skin conductance, coupled with the utilization of artificial neural networks for data analysis and modeling, a more comprehensive understanding of individual attention states can be attained. In the experimental phase, a diverse pool of individuals was tested, and the experimental results were validated using recorded videos. The findings underscore the efficacy of the artificial neural network-based method in assessing individual attention levels, achieving an accuracy rate of 76.9%. This research not only introduces novel methodologies but also opens avenues for leveraging artificial intelligence technology in biological signal analysis and attention assessment. Future research endeavors could delve deeper into exploring additional features of biological signals and enhancing model performance to bolster assessment accuracy and practical applicability. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial neural networks Attention assessment Biological signal analysis Wearable technology

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Unsupervised Anomaly Detection to handle Imbalanced datasets using Auto encoders for ECG signal Classification 3

Unsupervised Anomaly Detection to handle Imbalanced Datasets...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Gayathiri, R.R. Nirmala Devi, M. Amrita School of Engineering Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350300710

data Imbalance is a prominent as well as challenging problem in the real-world datasets. Even though there are many methods to rectify this problem using supervised algorithms, data imbalance issue in an unsupervised manner is still under research. In this study two auto encoders - a sparse auto encoder (SAE) and convolution neural network bi-directional long short-term memory (CNN Bi-LSTM) auto encoder are proposed to handle the data imbalance issue by unsupervised anomaly detection technique. Entropy Weight Method (EWM) is used for feature weighting and feature reduction purpose. Four open-source datasets are considered for the study. The SAE network performed well with entropy weighted feature vectors of each dataset except for the time series dataset. On the other hand, the CNN Bi-LSTM network worked well for the time series dataset. The f1-score of each dataset with varied percentage of features reduced by EWM is studied. From the results it is also evident that considering 75% and above features could give good f1-score of 90% and higher. Thus, this unsupervised method of rectifying the data imbalance issue is an effective one. More generalization to the networks can be done so that one network can give enhanced performance to all the datasets available. © 2023 IEEE.

关键词： Long short-term memory

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Automatic Categorization of Emails into Folders Based on the Content of the Messages 3

Automatic Categorization of Emails into Folders Based on the...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Anuradha, T. Jasphin Jeni Sharmila, P. Kanimozhiraman Kalaiselvi, K. Shruthi, C.K. Jose Anand, A. Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600097 India Chennai Institute of Technology Department of Cse Tamil Nadu Kancheepuram600069 India Kcg College of Technology Department of Mathematics Tamil Nadu Chennai600097 India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamil Nadu Chennai602105 India Veltech High-tech Dr Rangarajan Dr.sakunthala Engineering College Department of It Avadi Tamil Nadu Tiruvallur600062 India Kcg College of Technology Department of Ece Chennai600097 India

ISBN: (纸本)9798350361186

This task is related to the automatic classification of e-mail messages into folders based on the subject of the messages. Basically, it's about automating the process that sorts messages into different mailboxes. Appropriate message representation techniques, considering their particularities, and machine learning techniques for automatic classification will be studied. This work will be based on the available Enron Message which contains the entire inbox of one company's employees, 500,000 messages from 150 employees. © 2024 IEEE.

关键词： Support vector machines

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