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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

共 8829 条记录，以下是261-270 订阅

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Slow-moving Target Detection Method with FDA-MIMO Radar Based on Doppler-spreading Effect 2

Slow-moving Target Detection Method with FDA-MIMO Radar Base...

引用

2nd ieee International conference on Signal, information and data Processing, ICSIDP 2024

作者： Wang, Ding Zhang, Shunsheng Huang, Libing University of Electronic Science and Technology of China Research Institute of Electronic Science and Technology Chengdu China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798331515669

It is difficult to distinguish the Doppler frequency between clutter and slow-moving targets when airborne radar is looking down, which makes it difficult to perform detection. To solve this problem, an frequency diverse array multiple-input multiple-output (FDA-MIMO) radar slow-moving target detection method Doppler-spreading (DS) effect is proposed in this paper. FDA-MIMO radar clutter and ground moving target signal models are established. Using DS effect, clutter and slow-moving targets are separated in the Doppler domain, and then clutter suppression and target detection are effective. Simulation results confirm the efficacy of the proposed method. © 2024 ieee.

关键词： MIMO radar

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Blockchain-Powered Security Management System for Vehicular Communication 2

Blockchain-Powered Security Management System for Vehicular ...

引用

2nd ieee International conference on Emerging Research in Computational science, ICERCS 2024

作者： Rosariogilmary Jayasris Manvizhin IFET College of Engineering Dept of Information Technology Tamilnadu India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

As cognitive vehicle networks continue to evolve, safeguarding sensitive information related to radio frequency spectrum usage is crucial. This paper presents an innovative privacy-preserving framework that integrates blockchain technology, homomorphic encryption, and secure multi-party computation (SMPC) techniques. These elements work together to ensure the confidentiality and integrity of spectrum sensing data while promoting more efficient utilization of the spectrum. Homomorphic encryption allows for operations on encrypted data, ensuring that privacy is maintained during computations. This feature is essential for enabling secure data processing between vehicles and cloud servers without exposing sensitive information. Additionally, the use of blockchain technology facilitates secure and transparent interactions among vehicles and the cloud server, providing a tamper-resistant ledger for recording spectrum sensing results. This transparency builds trust and ensures data integrity. Furthermore, the application of SMPC techniques enhances accuracy in spectrum sensing by enabling multiple vehicles to collaborate on sensing tasks without disclosing their individual inputs. This collaborative effort maintains privacy while improving overall sensing accuracy. By integrating these advanced technologies, the proposed framework establishes a robust foundation for privacy-preserving spectrum sensing in cognitive vehicle networks. This approach not only fosters user trust but also ensures compliance with privacy regulations, paving the way for more secure and efficient operations in this emerging domain. © 2024 ieee.

关键词： Blockchain

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Digital Forensics Meets Blockchain: Enhancing Evidence Authenticity and Traceability 2

Digital Forensics Meets Blockchain: Enhancing Evidence Authe...

引用

2nd International conference on Cyber Resilience, ICCR 2024

作者： Al-E'mari, Salam Sanjalawe, Yousef Makhadmeh, Sharif Alqudah, Hamzah University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Cybersecurity Amman Jordan University Of Petra Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan

ISBN: (纸本)9798350394962

In the rapidly evolving landscape of cybercrime, ensuring the authenticity and traceability of digital evidence has become paramount. This paper explores the integration of blockchain technology with digital forensics as a novel approach to address these concerns. Blockchain, with its inherent characteristics of immutability and decentralization, can offer significant advancements in ensuring tamper-proof storage and chronological verification of forensic artifacts. By merging these two domains, we introduce a framework that not only bolsters the authenticity of digital evidence but also facilitates a robust traceability mechanism. Through a detailed case study, we demonstrate the practicality and effectiveness of this integrated approach, highlighting its potential to revolutionize current forensic methodologies. However, we also delve into the challenges posed, such as scalability, privacy, and legal implications, and suggest future research avenues. Leveraging blockchain technology in the realm of digital forensics has the potential to redefine evidence integrity and the forensic investigation landscape. © 2024 ieee.

关键词： Digital forensics

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Diabetic Prediction with Ensemble Model and Feature Selection Using information Gain Method 2

Diabetic Prediction with Ensemble Model and Feature Selectio...

引用

2nd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Roy, Shreya Priyadarshini Beulah.s, Angelin Kasat, Avani Pankajalakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350327533

Diabetes is a chronic medical condition characterized by high levels of blood glucose levels, there is no specific cure for it, there are only ways to control it. Predicting diabetes in its early stages is very crucial as it affects the overall health of the patients when not predicted early. Through this work an Ensemble method of Logistic Regression, SVM, Naive Bayes and Random Forest Classifiers has been implemented. The result obtained has thoroughly been analyzed and documented. Proposed ensemble model obtained an accuracy of 85%. © 2024 ieee.

关键词： Logistic regression

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Detecting Fish Species Through Mask RCNN 2

Detecting Fish Species Through Mask RCNN

引用

2nd International conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Rajeswari, A.M. Afreeth, S. Afsal Baaqir, A. Deepalakshmi, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350328202

Detection of underwater fish species has become more important through marine science research. Automatic detection of fish species would facilitate the development of marine science. There are several different methods that can be employed to determine fish species. Each of the available methods requires a substantial amount of training data to detect precisely. To overcome this, in this article, we report on the findings of an investigation that was carried out utilizing an deep learning algorithm known as Mask Region based Convolution Neural Network (RCNN). The investigation was conducted using a dataset containing images of various types and directions of fish found in different parts of the world. Mask RCNN is able to produce the highest quality segmentation mask possible for every occurrence and different fish species with a high degree of accuracy. The mask RCNN is able to recognize and categorize fish species with an overall accuracy of almost 97%. © 2024 ieee.

关键词： Fish

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Image Processing and Machine Learning Methods for Assessing Food Quality 2

Image Processing and Machine Learning Methods for Assessing ...

引用

2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Hasti, Hajipara V. Jatan, Sanghvi A. Nahar, Tapan Dubey, Vijay Parmar, Chandrashinh Marwadi University Department of Information and Communication Technology Rajkot India

ISBN: (纸本)9798350373110

This comprehensive review paper explores innovative strategies for quality assessment in agriculture and food industries, which offer greater efficiency and cost efficiency compared to traditional approaches. It highlights the use of sophisticated convolutional neural networks (CNNs) tailored specifically for wheat contamination detection with unsurpassed accuracy. Notably, an innovative noninvasive system employing machine learning achieved 92.57% accuracy when estimating apple freshness estimation. Hyperspectral imaging technology accurately classifies apple bruises with 97.33% accuracy, outclassing Yolov4-Tiny by 11%. An automated nondestructive scanning method evaluates meat quality while taking into account both its applications and challenges. Utilizing image processing and machine learning technologies, a cost-effective system has been created that accurately classifies and prices rice products, contributing greatly to product quality assessment methodologies as well as increasing competitiveness within agricultural and food technology industries. © 2024 ieee.

关键词： agricultural industry food quality machine learning non-invasive methods quality assessment

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Preliminary Results of UAV-Borne High-Squint SAR Sub-Aperture Imaging 2

Preliminary Results of UAV-Borne High-Squint SAR Sub-Apertur...

引用

2nd ieee International conference on Signal, information and data Processing, ICSIDP 2024

作者： Gao, Yibo Li, Linghao Ma, Xinnong Yan, Junjie Wu, Shiyu Liu, Yibo Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515669

Unmanned-aerial-vehicle (UAV) borne high-squint synthetic aperture radar (SAR) can flexibly provide two-dimensional (2-D) radar images of the oblique forward areas in advance. In 2024, a high-squint SAR data acquisition experiment using a high-band radar system mounted on a multi-rotor UAV was carried out successfully. The experiment is demonstrated in this paper, including SAR system introduction, experiment design, data processing and imaging results. Finally, a high-squint SAR sub-aperture image is obtained by using the method introduced in this paper. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Two-Stage Intrusion Detection Approach with SMOTE-Enhanced Attack Classification 2

Two-Stage Intrusion Detection Approach with SMOTE-Enhanced A...

引用

2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Balaji, Kausthub Chandrasekhar, D.V.S. Mauriya, Abhishek Srinivas, D.B. Nitte Meenakshi Institute of Technology Information Science and Engineering Bangalore India

ISBN: (纸本)9798350373110

Cyber security is a critical issue in today's world. Intrusion Detection Systems (IDSs) are important tools for protecting computer systems from attacks. However, traditional IDSs have a number of limitations. They are often rule-based, which can make them difficult to keep up with new attack methods. They can be easily bypassed by attackers who know how to evade the rules. Deep learning becomes a powerful new technology with the potential to overcome these limitations. Deep learning-based IDSs can learn to detect new attack methods without the need for explicit rules. They can be more difficult for attackers to evade. This paper presents a deep learning-based IDS that overcomes the limitations of traditional IDSs. The system is trained on a large dataset of labeled attack traffic, which allows it to learn to detect various attack methods. The proposed system is also evaluated on a dataset of attack traffic, and the results show that it is able to detect attacks with an accuracy of 98.422% and a 78% accuracy on a balanced dataset for identifying the type of attacks. © 2024 ieee.

关键词： Network intrusion

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Design and Implementation of Carbon Accounting information System Based on Blockchain 2

Design and Implementation of Carbon Accounting Information S...

引用

2nd International conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Wen, Yali Li, Jie Chen, Yansheng Postdoctoral Research Station in Applied Economics School of Accounting Nanjing China Nanjing University of Finance and Economics Guangdong University of Finance Guangzhou China Institute of Big Data and Artificial Intelligence Applications Guangdong Industry Polytechnic Guangzhou China

ISBN: (纸本)9798350373318

This paper explores the application of blockchain technology in carbon accounting information systems to enhance the security and trustworthiness of carbon emission data management. We introduce the significance of carbon accounting information systems and their challenges., analyze the potential advantages of blockchain technology in carbon emission data management., and discuss the crucial role of data encryption technology in ensuring the security and trustworthiness of carbon emission data. We construct the traceability and supervision of carbon emission data, the application of smart contracts in carbon trading, and the mathematical definition and model of data encryption technology to ensure the trustworthiness of carbon emission data. Through quantitative analysis, we provide a theoretical foundation for addressing carbon emission data's security and trustworthiness issues. Finally, we design and implement the application of blockchain technology in carbon accounting information systems using Python pseudo-code. This paper provides practical technical solutions for the information construction of carbon markets and the security management of carbon emission data. It makes contributions to the construction of theoretical definitions and mathematical models. © 2024 ieee.

关键词： Blockchain Carbon Accounting information System Supervision Traceability

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Detection of Oscillations in PMU data Based on Multisynchrosqueezing Transform for Sub/Supersynchronous Oscillations 2

Detection of Oscillations in PMU Data Based on Multisynchros...

引用

ieee 2nd International conference on Power science and technology (ICPST)

作者： Liu, Zhijian Tang, Cheng Dong, Hang Li, Ruixin Wang, Zizhuo Chen, Jikai Kunming Univ Sci & Technol Sch Elect Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9798350349047;9798350349030

The current power system often uses phasor measurement units (PMUs) for fault data extraction, which is of great value for the analysis of sub synchronous and super synchronous oscillations in the power grid. In order to monitor sub/super synchronous oscillations in the power system online, facilitating fault diagnosis and power grid recovery, this paper introduces an automatic detection method for sub/super synchronous oscillations based on Multi Synchronous Compression Transform (MSST). This method mainly utilizes data obtained from PMU. Through PMU data, time and spectral information can be extracted, enabling the MSST algorithm to effectively process non-stationary signals while reducing sensitivity to noise. The application of the MSST algorithm includes using ridge tracking method for signal modal extraction and reconstruction. Finally, use the Total Least Squares ESPRIT (TLS-ESPRIT) to calculate the parameters of these patterns. Through the detection and identification of sub/ultra synchronous oscillations in doubly fed wind turbines, it has been shown that this method has better detection accuracy and anti-interference ability compared to traditional methods in identifying and analyzing sub/ultra synchronous oscillations in power systems.

关键词： Multisynchrosqueezing Transform,PMU data TLSESPRIT method DFIG

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