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27th International Conference on information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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Polarization-induced photocurrent switching effect in heterojunction photodiodes

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Nano Research 2023年第4期16卷 5503-5510页

作者： Dingbo Chen Yu-Chang Chen Guang Zeng Yu-Chun Li Xiao-Xi Li Dong Li Chao Shen Nan Chi Boon S.Ooi David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and EngineeringFudan UniversityShanghai 200433China Photonics Laboratory Division of ComputerElectricaland Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The unipolar photocurrent in conventional photodiodes(PDs)based on photovoltaic effect limits the output modes and potential versatility of these devices in *** photodiodes with photocurrent switching are emerging as a promising solution for obtaining photoelectric devices with unique and attractive functions,such as optical logic ***,we design an all-solid-state chip-scale ultraviolet(UV)PD based on a hybrid GaN heterojunction with engineered bipolar polarized electric *** introducing the polarization-induced photocurrent switching effect,the photocurrent direction can be switched in response to the wavelength of incident light at 0 V *** particular,the photocurrent direction exhibits negative when the irradiation wavelength is less than 315 nm,but positive when the wavelength is longer than 315 *** device shows a responsivity of up to−6.7 mA/W at 300 nm and 5.3 mA/W at 340 nm,*** particular,three special logic gates in response to different dual UV light inputs are demonstrated via a single bipolar PD,which may be beneficial for future multifunctional UV photonic integrated devices and systems.

关键词： GaN heterostructure ultraviolet(UV)photodiodes bipolar photocurrent optical logic device

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An innovation prediction of DNA damage of melanoma skin cancer patients using deep learning 14

An innovation prediction of DNA damage of melanoma skin canc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramakrishnan, Rakesh Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. Maheswaran, S. University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

There are now a variety of intriguing options for the study of genetic data thanks to recent developments in artificial neural networks and deep learning. In this study, we use a deep learning-based prediction model to find possible DNA damage in individuals with melanoma skin cancer. We create a convolutional neural network (CNN) model to forecast the DNA damage susceptibility of melanoma cancer cells using a publically available genome sequencing dataset. This model preprocesses the genomic data, extracts features, and categorises them. Comparing the results of our CNN model with those of a traditional logistic regression model, we find that our CNN reported superior performance in identifying differences between healthy and cancerous samples with an accuracy of nearly 96%. The model can be used to augment the standard clinical diagnosis of melanoma, which only uses visual assessment and histology. By intervening sooner, clinicians can put forward more personalized and informed plans of care and surveillance for each melanoma patient, reducing medical costs and improving the quality of patient diagnosis. © 2023 IEEE.

关键词： Neural network models

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On Covert Communication Performance with Outdated CSI in Wireless Greedy Relay systems

On Covert Communication Performance with Outdated CSI in Wir...

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作者： Bai, Jiaqing He, Ji Chen, Yanping Shen, Yulong Jiang, Xiaohong Future University Hakodate School of System Information Science Hakodate041-8655 Japan School of Computer Science and Technology Xidian University Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China Department of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an710121 China

Communication performance relies largely on the availability of channel state information (CSI). This paper investigates the impact of outdated CSI on the achievable covert communication performance in a two-hop wireless relay system under two typical covert transmission schemes of rate-control transmission (RCT) and power-control transmission (PCT). We first apply the typical channel feedback delay model to determine the statistical distribution of outdated CSI, based on which we then develop theoretical models to depict the inherent relationship between outdated CSI and the fundamental covert performance metrics in terms of detection error probability (DEP) and covert rate (CR). With the help of these models, we further explore the optimization of DEP subject to the CR constraint as well as the optimization of CR subject to the DEP constraint to reveal the max-min DEP performance and the maximal CR performance with the outdated CSI. Finally, extensive numerical results are provided to illustrate the impact of outdated CSI on the covert communication performance. © 2005-2012 IEEE.

关键词： Power control

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The three dimensional dosimetry imaging for automated eye cancer classification using transfer learning model 14

The three dimensional dosimetry imaging for automated eye ca...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mohammed, Murtuza Ali Mohammed, Vazeer Ali Ramakrishnan, Rakesh Mohammed, Mehmood Ali Logeshwaran, J. Maheswaran, S. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

computer vision methods utilizing transfer learning can offer a promising approach for automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest advances in AI have encouraged researchers to use deep learning models for automated cancer classification. This paper presents a novel approach using three-dimensional dosimetry imaging for automated ocular cancer classification using transfer learning. The approach uses a combination of dataset augmentation methods. The approach is adapted from the popular Inception V3 CNN architecture. Two different feature extraction approaches, namely mean and maximum pooling, are compared while extracting features from the output of the CNN layers. The extracted features are fed to the classifier which uses the SVM with a linear kernel as its decision system. The effectiveness of this method is evaluated on a set of 20 healthy ocular images and 26 suspicious ocular images. The overall classification accuracy achieved with this approach is 94.85%. The results obtained demonstrate that this approach has a promising potential for automated ocular cancer classification with an appropriate transfer learning model. © 2023 IEEE.

关键词： Classification (of information)

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Yulianto, Semi Krisnanik, Erly Hartawan, Muhammad Syarif Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia University of Pembangunan Nasional Veteran Information System Department Jakarta12450 Indonesia University of Krisnadwipayana Faculty of Technology Jakarta13077 Indonesia Asia e University Ict Department Malaysia

ISBN: (纸本)9798350307306

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain. © 2023 IEEE.

关键词： Cybersecurity

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Deep Learning Network for Vision-Based UAV Recognition

Deep Learning Network for Vision-Based UAV Recognition

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Prabhakar, Telagarapu Almutairi, Maryam Almutairi, Hissah Vasamsetti, Subrahmanyam Rao, M. V. Raghavendra Kunda, Aruna GMR Institute of Technology Vizianagaram District Andhra Pradesh Rajam532127 India College of Sciences and Humanities in Hawtat Bani Tamim Department of Information System Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia College of Computer and Information Technology Department of IS and IT Jazan University Jazan Saudi Arabia Godavari Institute of Engineering & Technology Department of Automobile Engineering Rajahmundry India

ISBN: (纸本)9781665462631

Unmanned aerial vehicles (UAVs) could be used carelessly or intentionally, which poses a serious risk to aviation safety. This makes automated UAV detection and tracking a crucial component of aerial security systems. Radar, radio frequency, and visible- and thermal-infrared imaging are often used technologies for UAV detection. Deep neural networks (DNNs) for the image-based object recognition have recently made advancements that make it possible to leverage For this detecting and tracking challenge, visual data. Through the encoder-decoder structure network, the suggested technique first estimates the distribution line area. The interference is eliminated using sampling clustering and image processing operations. Finally, based on the discovered distribution line, the UAV direction of tracking of the power line is determined. The approach can achieve a frame inspection speed of over 78 ms, a heading error of deviation range of (-1.53°, 1.36°), and a tracking rate of almost 99%. The results of the network and inspection technique dataset test demonstrate that the approach presented in this research may be used to check UAV distribution lines efficiently, swiftly, and accurately. © 2023 IEEE.

关键词： Aircraft detection

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, Engineering, Science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Analyzing Different Models for Driver Behavior Detection Using EEG Data

Analyzing Different Models for Driver Behavior Detection Usi...

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International Conference on Communications (COMM)

作者： Sama Hussein Al-Gburi Kanar Alaa Al-Sammak Ion Marghescu Claudia Cristina Oprea Khattab M. Ali Alheeti Nadia Ali Abbas Almosa Doctoral School of Electronics Telecommunications and Information Technology POLITEHNICA Bucharest Bucharest Romania Telecommunications Department POLITEHNICA Bucharest Bucharest Romania Department of Computer Networking System College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq Networking Department College of Information Technology University of Babylon Iraq Babylon Iraq

ISBN: (数字)9798350355956

ISBN: (纸本)9798350355963

Ensuring road safety is becoming more and more significant for researchers and for all the society. One of the main issues in this area considers driver drowsiness. Therefore, the focus of this paper is on the comprehensive analysis and comparison of three models of Machine Learning Algorithms Deep Neural Network (DNN), Logistic Regression, and Random Forest. In this case we target the classification of Alertness State of drivers into ‘Alert’ and ‘Drowsy’ type by using a designed data set. The ultimate goal is to gain insights into the intricate factors that shape driver behavior. This study used a benchmark EEG dataset. The data preprocessing methods included careful handling of missing values through imputation techniques and the transformation of categorical variables to improve their suitability for machine learning models. When assessing the effectiveness of each model, four key metrics are taken into account: precision, recall, F1 score, and accuracy. In this study, we used a EEG dataset. The study yielded several significant findings regarding the performance of the models employed. The DNN model reached an accuracy of 0.88, indicative of a strong performance nonetheless slightly lower than the Random Forest model, which achieved the highest accuracy at 0.94. The Logistic Regression model had the lowest overall accuracy at 0.83. These findings recommend that random forest models could be principally beneficial for developing reliable, real-time drowsiness detection systems, potentially improving road safety.

关键词： Logistic regression Analytical models Accuracy Artificial neural networks Brain modeling Electroencephalography Road safety Data models Random forests Vehicles

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Unleashing Fine-Coarse Curve Perception Via Trunk-Branch Perturbation

Unleashing Fine-Coarse Curve Perception Via Trunk-Branch Per...

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IEEE International Conference on Image Processing

作者： Yunxiang Cao Li Chen Yubo Wang Zhida Feng Xiaoming Liu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Segmenting intricate curve structures like retinal blood vessels, encompassing both fine and coarse details, remains a significant challenge. This work proposes a novel module that divides these complex curve structures into trunks and branches, fuses the input as auxiliary information, and optimizes the breakpoints of different curve parts through losses. In order to balance the redundant information that may lead to model overfitting, a unique feature perturbation strategy is introduced after the backbone decoding process to enhance the model’s robustness to complex curve structure segmentation tasks. Experiments show that this method can effectively distinguish different topological structures of blood vessels and maintain high segmentation accuracy even at blood vessel intersections or breakpoints, which holds immense potential for diverse future applications in image segmentation.

关键词： Measurement Image segmentation Fuses Perturbation methods Blood vessels Retina Robustness

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