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Light-Emitting Artificial Synapses for Neuromorphic Computing

Research 2023年第2期2022卷 213-218页

作者： Chen Zhu Wen Huang Wei Li Xuegong Yu Xing'ao Li Collge of Integrated Circuit Science and Engineering Nanjing University of Posts and TelecommunicationsNanjing210023China New Energy Technology Engineering Laboratory of Jiangsu Province School of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China State Key Laboratory of Silicon Materials Zhejiang UniversityHangzhou 310027China College of Electronic and Optical Engineering College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China

As the key connecting points in the neuromorphic computing systems,synaptic devices have been investigated substantially in recent *** optoelectronic synaptic devices with optical outputs is becoming attractive due to... 详细信息

关键词： computing connecting attractive

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Speech cryptography algorithms: utilizing frequency and time domain techniques merging

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Journal of Ambient Intelligence and Humanized Computing 2024年第10期15卷 3617-3649页

作者： Faragallah, Osama S. Farouk, M. El-sayed, Hala S. El-bendary, Mohsen A. M. Department of Information Technology College of Computers and Information Technology Taif University Al- Hawiya21974 Saudi Arabia Department of Computer Science and Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Menoufia University Shebin El-kom32511 Egypt Department of Electronics Technology Faculty of Technology and Education Helwan University Cairo Egypt

Due to the importance security of sensitive speech and audio signals such as the countries president’s call phone, this research work presents an efficient audio cryptosystem. Audio speech communications play main and essential role in the human daily life, covering many important fields ranging from simple personal private information up to top secret, sensitive and critical information. It is difficult to prevent the eavesdropping by unauthorized person especially 1-D audio scrambling methods, it is built by simple algorithms and has low security level. So, there is a dire need for enhancing audio cryptosystem to be more secure against different types of attacks. In this work, the enhanced audio cryptosystem is proposed, the first approach enhances multiple key audio cryptosystem utilizing the chaotic baker map through enhancing the confusion level for both encryption/decryption algorithms employing time and transform domains. Simulation experiments revealed that the proposed boosted audio cryptosystem on time and transform domains is secure and robust against different types of attacks. The boosted multiple key based audio crypto-system is performed by increasing confusion and enlarging the secret-key space. Results of the simulation experiments revealed that the proposed multiple secret key-enhanced confusion audio cryptosystem is secure and robust with respect to different types of attacks. Moreover, results for transform domains show better results than results for time domain for the three proposed boosted audio cryptosystem. Also, the third proposed audio cryptosystem DWT-based shows the best results among all other proposed audio cryptosystem. Due to the results, robustness and reliability of proposed technique are higher the existing audio cryptographic system. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Cryptography

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A Novel Quantifiable Weight Model for the Digital Twin technology in Enterprise

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Procedia computer science 2024年 247卷 933-942页

作者： Yanchao Li College of Computer Science Guangdong University of Science and Technology Dongguan China

The performance evaluation involves a variety of composite factors, and most factors are subjectively determined by the people, so the fuzziness of this evaluation is inevitable. This paper firstly focuses on this issue based on the dynamic influence of digital twin technology input for different kinds of digital twin technology activities. Then this paper carries out a detailed analysis and research on the main core and criterion of digital twin technology. Thirdly this paper puts forward the following suggestions so as to give full play to the role of digital twin technology input of three different kinds in activities. Lastly this paper constructs a novel quantifiable weight model of performance evaluation by applying fuzzy mathematics theory. The calculation results indicate that this model can keep an eye on the evolution rule of digital twin technology to timely change the system are the optimal plans and measures based on weight analysis, and their weights are 0.336, 0.334 and 0.330 respectively, and the rationality of quantifiable weight behavior has a significant impact on the technology development.

关键词： Quantifiable weight model digital twin technology main core

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vGPUPCD: Design and Implementation of Virtual GPU on Private Cloud Desktop 2

vGPUPCD: Design and Implementation of Virtual GPU on Private...

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2nd IEEE International Conference on Educational technology, ICET 2022

作者： Gui, Ting Zhou, Shenfeng Gu, Binbin Chen, Jian Li, Dong Lv, Shengjun Zhijiang College of Zhejiang University of Technology Computer Lab Center Shaoxing China Zhijiang College of Zhejiang Information Engineering University of Technology Shaoxing China

ISBN: (数字)9781665485531

ISBN: (纸本)9781665485531

This paper proposes to design and implement GPU virtualization on the Private Cloud Desktop merged into the Teaching and Practical Platform of Zhijiang college of zhejiang university of technology to fill in the blanks to create and apply rending 3D applications. The analysis of the performance of the vGPUPCD has been also studied in this paper and some properties has to be tested on the experiments: 1) The video memory size of vGPU has been interaction with the memory size and the amounts of CPU cores and the minimum resources requirement has to be evaluated by experiments that the memory size is the memory size of system demand and add to the twice of the GPU video memory size and the 1G video memory of vGPU is equipped with 2 virtual CPU cores and so on. 2) The GPU memory usage of vGPUPCD has consumed 10% more than the local PCs if the same performance are reflected when opening or rending the 3D applications because vGPU has occupied no more than 10% of the GPU memory usage through the cloud desktop is powered on. © 2022 IEEE.

关键词： Graphics processing unit

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Research on Images Recognition based on Deep Convolution Neural Network 8

Research on Images Recognition based on Deep Convolution Neu...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Chang, Xiaobo Zhao, Linlin Wang, Jinxiang Yanbian University College of Engineering Yanji China Yanbian University Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanji China College of Engineering Yanbian University Computer Science and Technology Yanji China

ISBN: (纸本)9798350302455

Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method. © 2023 IEEE.

关键词： Convolution

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Cramér-Rao Lower Bound and Fairness Optimization in STAR-RIS Assisted ISAC Systems 100

Cramér-Rao Lower Bound and Fairness Optimization in STAR-RI...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Ruidong Cao, Yuwen Ohtsuki, Tomoaki He, Jiguang Donghua University College of Information Science and Technology Shanghai China Keio University Department of Information and Computer Science Yokohama Japan The Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331517786

This paper studies user fairness of an integrated sensing and communication (ISAC) system adopting both simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NO-MA). Note that optimizing user fairness of an ISAC system is critical, since the performance trade-off in terms of spectrum resource utilization between communication users and sensing users is hard to balance and improve, particularly at the low signal-to-noise ratio (SNR) regimes. However, such optimization problem is a coupled, non-convex, and is NP-hard in general. To solve this challenging problem, a low-complexity algorithm based on both the successive convex approximation and the semi-deterministic programming techniques is proposed. Notably, the proposed approach can maximize the signal-to-interference-plus-noise ratio (SINR) for the weakest communication user, while guaranteeing the superior sensing performance characterized by the cramér-rao lower bound (CRLB) for the sensing user. Simulation results demonstrate that our approach is capable of enabling superior sum-rate performance than the STAR-RIS with orthogonal multiple access (OMA), the conventional-RIS with NOMA, and the conventional-RIS with OMA schemes. © 2024 IEEE.

关键词： Cramer-Rao bounds

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An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

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computers, Materials & Continua 2024年第10期81卷 1789-1808页

作者： Umar Zaman Junaid Khan Eunkyu Lee Sajjad Hussain Awatef Salim Balobaid Rua Yahya Aburasain Kyungsup Kim Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Environmental IT Engineering Chungnam National UniversityDaejeon34134Republic of Korea Autonomous Ship Research Center Samsung Heavy IndustriesDaejeon34051Republic of Korea ICAROS Center(PRISMA Lab) University of Naples Federico IINaples80131Italy Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazan45142Saudi Arabia

Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman *** research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant *** were trained using key navigational parameters:latitude,longitude,speed,and *** aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction *** GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.

关键词： Trajectory prediction AIS data smart vessel deep learning LSTM GRU

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Lark: Verified Cross-Domain Access Control for Trusted Execution Environments

Lark: Verified Cross-Domain Access Control for Trusted Execu...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Fanlang Zeng Zhuoruo Zhang Rui Chang Chenyang Yu Zijun Zhang Yongwang Zhao College of Computer Science and Technology Zhejiang University Hangzhou China

Trusted Execution Environments (TEEs) play a crucial role in embedded systems, IoT, and cloud computing. However, their security issues are a major concern, particularly related to defects or improper implementations in access control mechanisms. Such issues can result in severe problems like privilege escalation and unintended memory accesses during inter-domain communication. Moreover, employing mathematical methods for rigorous security guarantees is *** address these challenges, we propose Lark, a cross-domain access control for TEEs, which is modeled and verified in Isabelle/HOL. Lark applies orthogonal access control attributes on memory to decouple access permissions of different privilege levels. Additionally, it enforces strict access permission checks for inter-domain communications. For a strict security guarantee, Lark is formalized and verified in Isabelle/HOL, with 84 definitions and 35 lemmas containing ∼1,600 lines of code. The machine-checkable proofs demonstrate that Lark ensures memory isolation and information flow security. We identify and resolve an inter-domain communication issue within an open-source TEE, and develop a prototype that implements the access control features of Lark. Exhaustive evaluations on real-world applications demonstrate that Lark introduces less than 5% performance overhead.

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Glaucoma Screening Using Vessel Segmentation

Glaucoma Screening Using Vessel Segmentation

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Shaik, Fahimuddin Khan, Mudassir Karimullah, Shaik Ahmed, Sheik Jamil Annamacharya Institute of Technology and Sciences Department of Ece Andhra Pradesh Rajampet India King Khalid University Department of Computer Science at College of Science & Arts Abha Saudi Arabia Presidency University School of Computer Science and Information Science India

ISBN: (纸本)9798350309140

The presence of glaucoma, a persistent eye ailment resulting in visual impairment, highlights the crucial necessity for timely identification. Historically, the main method for evaluating intraocular pressure (IOP) has been through tonometry. However, this technique can sometimes cause an increase in pressure, which poses a potential risk of harm to the optic nerve. An approach with greater potential involves assessing the optic nerve head in retinal fundus images using three-dimensional fundus photos, although this method is currently not widely accessible and is associated with significant expenses. In order to address these difficulties, the process of identifying glaucoma is increasingly dependent on Vessel Segmentation. This paper presents the application of Vessel Segmentation in the context of glaucoma screening, with a specific emphasis on 2D fundus pictures. The segmentation technique entails categorizing each superpixel as either a disc or a non-disc based on histograms and center-surround statistics for optic disc segmentation. Position information is taken into account for the segmentation of the optic cup. Expert professionals manually define the boundaries of the optic disc and cup to assess the efficacy of the proposed segmentation techniques. The proper determination of the cup-to-disc ratio (CDR), which is essential for diagnosing glaucoma, relies on correctly distinguishing the segmented optic disc and optic cup in color retinal fundus camera pictures. © 2024 IEEE.

关键词： Gabor filters

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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