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8th IEEE International Conference on advanced Robotics and Mechatronics, ICARM 2023

作者： Han, Zhicheng Gu, Jason Feng, Ying Institute of Advanced Technology University of Science and Technology of China Hefei230031 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

The task of safely navigating outdoor environ-ments presents significant difficulties for people with visual impairments. This paper aims to address this issue by proposing a wearable assistance system for visually impaired individuals in blind lane detection and following scenarios. The system consists of two stages: the first stage employs a fully convolutional network to detect blind lanes and an object detection network to identify obstacles. The second stage utilizes an improved artificial potential field method to achieve real-time path following based on the detection results. Our experiments involving subjects in dynamic outdoor environments demonstrate the robustness of our proposed method in navigating and avoiding obstacles under challenging outdoor conditions. © 2023 IEEE.

关键词： Object detection

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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A Review on the need of Clustering Techniques Used for Wireless Sensor Networks 2

A Review on the need of Clustering Techniques Used for Wirel...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bharany, Salil Rehman, Ateeq Ur Tariq Sadiq, M. Farrukh, Muhammad Alharbi, Meshal Hussain, Amjad Issa, Ghassan F. CET and Technology Department Amritsar India Government College University Department of Electrical Engineering Lahore Pakistan University of Brighton School of Architecture & Engineering United Kingdom Minhaj University Department of Computer Science Lahore Pakistan Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science Alkharj11942 Saudi Arabia School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

Wireless sensor networks have been implemented in various software to help gather and analyze data from the physical world. Wireless sensor networks are networks of multiple tiny sensors powered by low-energy batteries (WSNs). WSN lifetime is a critical factor. This is because, after WSNs have been deployed in a given application, the inaccessibility of the sensing nodes makes it impossible to recharge or replace the power source that is limited in energy. Until recently, WSNs were thought to be the same. All the network nodes have the same power, processing speed, and operational capacity. However, scientists have developed heterogeneous WSNs, in which the properties of individual nodes may vary, to extend the lifespan of networks. In order to extend a network's helpful life, energy-efficient protocols must be designed. Even though WSN has its own problems, it has brought a fresh perspective to studying real-time intelligent systems. WSN (Wireless Sensor Network) has gained significant popularity in the recent years, owing to its vast applications in real-time scenarios, such as disaster management, pollution monitoring, temperature monitoring, traffic monitoring, transport monitoring, healthcare monitoring, battlefield monitoring, and border security surveillance, to name a few. Numerous sensor nodes are employed in these applications. They are typically placed in the sensing field, where they automatically transfer data to the base station (BS) through energy transmission (i.e., battery in the sensor). In WSN, clustering is one method for optimizing power consumption at the sensor node. The sensor nodes are grouped and given a leader when they undergo clustering. This study overviews various WSN clustering algorithms, each categorized according to various clustering properties. © 2023 IEEE.

关键词： Sensor nodes

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Efficient multimodal cancelable biometric system based on steganography and cryptography

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Iran Journal of computer science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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An application of the shortest path algorithm for the identification of weak MHD mode

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Plasma science and Technology 2023年第8期25卷 19-27页

作者：黄卓毛飞越丁永华田微黄名响李达沈呈硕王能超梁云峰 the J-TEXT Team College of Computer Science South-Central Minzu UniversityWuhan 430074People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Forschungszentrum Jülich GmbH Institut für Energie-und Klimaforschung-PlasmaphysikPartner of the Trilateral Euregio Cluster(TEC)Jülich 52425Germany 不详

The identification of magnetohydrodynamic(MHD)modes is a crucial issue in the control of magnetically confined *** paper proposes a novel method for identifying the evolution of MHD modes from a signal with a low signal-to-noise *** proposed method generates a weighted directed graph from the time-frequency spectrum and calculates the evolution of the mode frequency by solving the shortest *** method addresses the limitations posed by the lack of data channels and the disturbance of noise in the estimation of mode frequency and yields much better results compared to traditional *** is demonstrated that,using this method,the evolution of an unlocked tearing mode was more accurately calculated on the J-TEXT *** method remains feasible even with a low signal-to-noise ratio of 0.5,as shown by its ***,with appropriate parameters,this method can be applied to not only signals with MHD modes,but also to general signals with continuous modes.

关键词： plasma physics magnetohydrodynamic pattern recognition

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Design of new resonant magnetic perturbation coils on the J-TEXT tokamak

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Plasma science and Technology 2023年第11期25卷 142-149页

作者：黄卓周松樊金荣李达饶波王能超丁永华毛飞越黄名响田微陈忠勇陈志鹏梁云峰 the J-TEXT Team College of Computer Science South-Central Minzu UniversityWuhan 430074People’s Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China Forschungszentrum Jülich GmbH Institut für Energie-und Klimaforschung-PlasmaphysikPartner of the Trilateral Euregio Cluster(TEC)Jülich D-52425Germany 不详

The resonant magnetic perturbation(RMP)system is a powerful auxiliary system on *** the J-TEXT tokamak,a set of new in-vessel coils is designed to enhance the amplitude of the *** new coils are designed to be two-turn saddle *** two-turn saddle coils have been optimized in terms of their structure,support,and protection components to overcome the limitations of the narrow in-vessel space,resulting in a compact coil module that can be accommodated in the *** verify the feasibility of this design,an electromagnetic simulation is performed to investigate the electrical parameters and the generated field of the coils.A multi-field coupled simulation is performed to investigate the capacity of heat *** a result of these efforts,the new RMP coils have been successfully installed on the J-TEXT *** has significantly enhanced the RMP amplitude and been widely applied in experiments.

关键词： fusion engineering tokamak resonant magnetic perturbation coil multi-field coupled simulation(Some fioures may appeari in colour only in the online iournal)

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Learning Stereo Depth Estimation with Bio-Inspired Spike Cameras

Learning Stereo Depth Estimation with Bio-Inspired Spike Cam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wang, Yixuan Li, Jianing Zhu, Lin Xiang, Xijie Huang, Tiejun Tian, Yonghong School of Electronic and Computer Engineering Peking University China School of Computer Science Peking University China Department of Electrical and Computer Engineering Shanghai Jiao Tong University China Peng Cheng National Laboratory China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Bio-inspired spike cameras, offering high temporal resolution spike streams, have brought a new perspective to address common challenges (e.g.,high-speed motion blur) in depth estimation tasks. In this paper, we propose a novel problem setting, spike-based stereo depth estimation, which is the first trail that explores an end-to-end network to learn stereo depth estimation with transformers for spike cameras, named Spike-based Stereo Depth Estimation Transformer (SSDEFormer). We first build a hybrid camera platform and provide a new stereo depth estimation dataset (i.e.,PKU-Spike-Stereo) with spatiotemporal synchronized labels. Then, we propose a novel spike representation to effectively exploit spatiotemporal information from spike streams. Finally, a transformer-based network is designed to generate dense depth maps without a fixed-disparity cost volume. Empirically, it shows that our approach is extremely effective on both synthetic and real-world datasets. The results verify that spike cameras can perform robust depth estimation even in cases where conventional cameras and event cameras fail in fast motion scenarios. © 2022 IEEE.

关键词： Cameras

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud Computing 4

A Probabilistic Public-Key Encryption with Ensuring Data Int...

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4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kara, Mostefa Karampidis, Konstantinos Papadourakis, Giorgos Laouid, Abdelkader Alshaikh, Muath University of El Oued LIAP Laboratory El Oued El Oued39000 Algeria Hellenic Mediterranean University Department of Electrical and Computer Engineering Greece College of Computing and Informatics Saudi Electronic University Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350300918

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated;in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key;The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud;thus, we realize verifiable partial homomorphic encryption. © 2023 IEEE.

关键词： Network security

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Multi-domain feature fusion for target color decoding from multichannel neural recordings of pigeons

Multi-domain feature fusion for target color decoding from m...

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2023 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Li, Hongxun Liu, Ruiqing Niu, Xiaoke Wang, Zhizhong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Department of Mechanical and Electrical Engineering Henan Light Industry Vocational College Zhengzhou450000 China

ISBN: (纸本)9781510666351

The entopallium is a high-level neural structure of the avian visual system that is related to the processing of color- and shape-related information during target recognition. The mechanism underlying it remains unclear and has drawn a considerable amount of research interest. Decoding has invariably been used in relevant studies to verify whether the extracted response-related features contain visual information. However, local field potentials (LFPs) often contain rich features. In this study, We combine the features of different domains to decode color information, including those in the time and frequency domains as well as features related to non-linear and functional network connectivity. To extract valuable response-related features, three types of feature selection methods are compared. The LFP data from the entopallium of a pigeon were recorded during periods of stimulus presentation in which two colors were randomly presented in each trial. The selected feature set was finally used to decode the colors by using several general classifiers. The results showed that the fusion of multi-domain features helped improve decoding performance, and the optimal feature set was a fusion of multiple domains of features. These results improve our understanding of the avian neural mechanisms involved in encoding color-related information. © 2023 SPIE.

关键词： Color

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