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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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A Proposal of digital Image Steganography and Forensics Based on the Structure of File Storage 11th

A Proposal of Digital Image Steganography and Forensics Base...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Liu, Chen Department of Computer Information and Cyber Security Jiangsu Police Institute NanjingJiangsu210031 China Engineering Research Center of Electronic Data Forensics Analysis NanjingJiangsu210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department NanjingJiangsu210031 China

ISBN: (纸本)9789811665530

Steganography is a technology for transmitting secret messages in public channel by embedding secret information into a digital carrier. It has a wide range of applications in the Internet security thus has attracted great interests. Among all kinds of digital carriers, digital image has outstanding performance. To the best of our knowledge, the researches on image steganography mainly focus on the use of the feature of digital image in the spatial or frequency domain, while there are few researches on hiding information through the file format structure. Taking JPEG image standard as an example, this paper analyzes the file format structure as well as the compression procedure and reports a novel proposal of image steganography and forensics. The proposal uses the redundant channel between any marker segments in the JPEG format structure to embed the secret information. The validity of the proposal has been proved by experiments, and further study has shown that this proposal has no limitation on the hiding capacity and has good practicability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

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IEEE Transactions on Automatic Control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

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A 7-Channel Bio-Signal Analog Front End Employing Single-End Chopping Amplifier Achieving 1.48 NEF

A 7-Channel Bio-Signal Analog Front End Employing Single-End...

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European Conference on Solid-State Circuits (ESSCIRC)

作者： Renze Gan Liangjian Lyu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle WA USA

This paper presents a high noise efficiency readout circuit for multi-channel bio-signal acquisition. The prototype is implemented in a 65 nm CMOS LP process, which achieves a power consumption of 2.06 $\mu$W/channel, a noise efficiency factor (NEF) of1.48, a common-mode rejection ratio (CMRR) of 105 dB at 50 Hz, and a crosstalk of -65dB by employing these new techniques, including 1) the use of single-ended amplifiers as the unit of low noise amplifier (LNA) for lower NEF, 2) single-end chopping techniques for single-ended amplifiers for suppressing low-frequency noise and offset, 3) digital-assisted DC servo loop (DDSL) for fast settling.

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Optimal Operation of an Integrated Electricity-heat Energy system Considering Flexible Resources Dispatch for Renewable Integration

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Journal of Modern Power systems and Clean Energy 2021年第4期9卷 699-710页

作者： Wei Wang Shuhao Huang Guangming Zhang Jizhen Liu Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated,leading to great challenges in power *** renewable power curtailment is especially numerous in the integrated electricity-heat energy system(IEHES)on account of electricity-heat *** flexible resources(FRs)on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this *** the energy supply side,three kinds of FRs based on electricity-heat coordination are modeled and *** the load side,the shiftable electricity demand resource is *** this basis,the solution for FRs participating in IEHES dispatch is given,with goals of maximizing the renewable penetration ratio and lowering operation *** scenarios are performed,and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the *** contribution degrees of different FRs for renewable integration are also explored.

关键词： Integrated electricity-heat energy system(IEHES) renewable penetration flexible resource(FR) electricity-heat coordination shiftable demand load

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Zexiang Shao Shibin Zhang Sachin Kumar School of Cyberspace Security Chengdu University of Information Technology Chengdu China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu China Department of Computer Science and Engineering Ajay Kumar Garg Engineering College India

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally.

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 6

A Cloud-based Distributed Computing Approach for Extracting ...

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6th International Conference on Algorithms, Computing and systems, ICACS 2022

作者： Didachos, Christos Kintos, Dionisis Panagiotis Fousteris, Manolis Gerogiannis, Vassilis C. Son, Le Hoang Kanavos, Andreas Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Patras Greece Department of Digital Systems University of Thessaly Larissa Greece Vnu Information Technology Institute Vietnam National University Hanoi Viet Nam Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules' chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab"approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame. © 2022 ACM.

关键词： Big data

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Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automobile in Obstacle Avoidance Scenarios 2

Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automo...

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2nd International Workshop on Intelligent systems, IWIS 2022

作者： Nguyen, Tan-Thien-Nien Phan, Thanh-Danh Duong, Minh-Thien Nguyen, Chi-Tam Ly, Hong-Phong Le, My-Ha Hcmc University of Technology and Education Intelligent System Laboratory Ho Chi Minh City Viet Nam Soongsil University Department of Information and Telecommunication Engineering Seoul Korea Republic of University of Information Technology Computer Engineering Embedded Club Ho Chi Minh City Viet Nam Hcmc University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

Obstacle dodging and overtaking are the pivotal tasks ensuring safety for self-driving automobiles. Multi-sensors fusion is the must-required condition to explore the entire surrounding information. This paper proposes a novel frontal dynamic car dodging strategy for automobiles with the left-hand side steering wheel by fusion of a camera and 2D LiDAR features. To begin with, we improve the LiteSeg model to extract the segmented mask, which can determine the drivable area and the avoiding direction. In addition to a camera, 2D LiDAR is used to check the scene information on the right side, which the camera's range cannot cover. As for point clouds output of 2D LiDAR, we adopt the Adaptive Breakpoint Detection (defined as ABD) algorithm to cluster the objects in a scanning plane. Subsequently, the RANSAC algorithm forms a straight line from the clustered point clouds to determine the boundary of the right-side obstacle. Besides, we compute the distance from LiDAR to the estimated straight line to maintain a safe distance when overtaking. Last but not least, the post-processing results of the two devices are fused to decide on the obstacle dodging and overtaking. The comprehensive experiments reveal that our self-driving automobile could perform well on the university campus in diverse scenarios. © 2022 IEEE.

关键词： Cameras

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Energy Management and Optimization of Vehicle-to-grid systems for Wind Power Integration

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CSEE Journal of Power and Energy systems 2021年第1期7卷 172-180页

作者： Wei Wang Liu Liu Jizhen Liu Zhe Chen the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China the School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China with the Department of Energy Technology Aalborg University9220 AalborgDenmark

An approach to smoothing the fluctuations of largescale wind power is investigated using vehicle-to-grid(V2G)***,an energy management and optimization system is designed and *** using the wavelet packet decomposition method,the target grid-connected wind power,the required electric vehicle(EV)power,and supercapacitor power are *** energy management model for EVs is then developed by introducing a knapsack problem that can evaluate the needs of an EV ***,an optimized dispatch strategy for EVs and wind power is developed by using a dynamic programming method.A case study demonstrates that the energy management and optimization method for V2G systems achieves noticeable performance improvements over benchmark techniques.

关键词： Dynamic programming electric vehicle knapsack problem wavelet packet decomposition wind power integration

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Geometric Constellation Shaping for Wireless Optical Intensity Channels: An Information-Theoretic Approach

arXiv

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arXiv 2024年

作者： Zhou, Suhua Li, Tianqi Fang, Zhaoxi Zhou, Jing Zhang, Wenyi School of Automation Beijing Information Science and Technology University Beijing100192 China National Computer System Engineering Research Institute of China Beijing100083 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China Henan Key Laboratory of Visible Light Communications Zhengzhou450002 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All 2b levels (b ∈ N) of the obtained constellation can be represented by a basic level and b + 2 bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple 16-level LDPC coded modulation scheme show that a shaping gain of 0.65 dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution. © 2024, CC BY-NC-ND.

关键词： digital to analog conversion

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