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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Ruan, Zhuo Zhang, Yuejun Guo, Shuaicheng Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

ISBN: (纸本)9798331509453

The security issues in high-density memory have attracted increasing attention, and how to defend Row Hammer (RH) has become a research hotspot. To address the issue of significant storage overhead in defending RH attacks, this paper proposes a defense method based on dual-layer counting with Resistive Random Access Memory (RRAM). Initially, this method investigates the transition from low-resistance state to high-resistance state in RRAM, and utilizes the resistance variation process of RRAM to represent the number of row activations, achieving the functionality of counting row activations using a single RRAM;Secondly, a group counter is formed by an RRAM that one end connected to multiple memory rows and a sensitive amplifier that monitors the resistance variation of the RRAM. This group counter completes the aggregated counting of the row groups formed by multiple memory rows, completing the aggregation counting of the row groups composed of multiple memory rows;Finally, a row counter is constructed using RRAM and a sensitive amplifier placed at the end of a single memory row to accurately count the memory rows that exceed the counting range of the previous stage, achieving protection against RH. The dual-layer counter composed of row counter and group counter is connected to DRAM and tested under TSMC28nm technology. The experimental results demonstrate that the two counters can work together to protect against RH attacks without affecting the original Dynamic Random Access Memory (DRAM) timing logic. © 2024 IEEE.

关键词： RRAM

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Classification of Football Player Actions Using Sensing Data 3

Classification of Football Player Actions Using Sensing Data

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Hirasawa, Yukito Kinoshita, Yuichiro Kamarudin, Latifah Munirah Zakaria, Ammar University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia

ISBN: (纸本)9798350374766

This study classifies the actions of football players using sensing data acquired from wearable sensors attached to players and the ball. More than 800 sensing data with the labels of five types of player actions were created as a dataset. The neural networks were trained using 19 input items created by considering time-series variations in player and ball locations. The trained neural network model demonstrated a classification accuracy of 84.0%. The model successfully obtained sufficient accuracy for all types of *** results demonstrate that the sensing data and created input items can be effectively utilized for classifying the actions of football player. © 2024 IEEE.

关键词： Wearable sensors

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Reconfigurable Low-Overhead SoC for Arrhythmia Detection Based on Cyclic Convolutional Neural Networks 9

Reconfigurable Low-Overhead SoC for Arrhythmia Detection Bas...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Shi, Hanyu Zhang, Yuejun Wang, Ying Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

ISBN: (纸本)9798331509453

Cardiovascular disease continue to pose a major threat to the lives and health of humans across the world. How to achieve portable and high-precision detection has become a research hotspot in preventing cardiovascular diseases. To address the issue of high overhead and difficulty in reconfigurable in high-precision artificial intelligence (AI) detection. This paper proposes a reconfigurable low-overhead arrhythmia detection system on chip (SoC) solution based on cyclic convolutional neural networks (CNN). The solution first involves researching the algorithms and structures of convolutional neural networks. This solution first studies the algorithms and structures of convolutional neural networks. Using cyclic CNN to classify arrhythmia, and reusing convolution and pooling modules to reduce hardware overhead. Then, by flexibly configuring the hyperparameter registers within the cyclic CNN-IP core, combined with the RISC-V instruction programming CPU method, achieved the reconfigurable CNN on SoC. Finally, in the digital backend clock gating and multi-threshold voltage techniques are adopted to reduce chip power consumption by 64.3%. In this paper, under the TSMC 65nm process the chip layout design is completed. With a supply voltage of 1V and an operating frequency of 100MHz, the classification accuracy for arrhythmias in the MIT-BIH dataset reaches 97.07%, with a chip area of 3.68 mm2 and a power consumption of 2.86 mW. © 2024 IEEE.

关键词： System-on-chip

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An Efficient Multi-Vector-Based Model Predictive Current Control for PMSM Drive

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power electronics and Applications 2024年第1期9卷 79-89页

作者： Sun, Jun Yang, Yong Chen, Rong Zhang, Xinan Lim, Chee Shen Rodriguez, Jose School of Rail Transportation Soochow University Suzhou215131 China University of Western Australia Department of Electrical Electronic and Computer Engineering PerthWA6009 Australia School of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China Universidad San Sebastian Santiago Faculty of Engineering Santiago8370146 Chile

To address the problems of high current harmonics, large torque ripples, and heavy computational burden in the finite control set model predictive control (FCS-MPC), this paper proposes an efficient multi-vector model predictive current control (MPCC) scheme for permanent magnet synchronous motor (PMSM) drive. Firstly, a simple pre-selection method based on the trace of the stator current increment is proposed to obtain the candidate optimal voltage vectors. This pre-selection method avoids the heavy computational burden of evaluating all voltage vectors and is easy to implement. Then, to further reduce the torque ripples and current harmonics, the dwelling time of each voltage vector is achieved in inverse proportion to its cost function. Compared to the standard means, the proposed scheme is able to obtain great performance while greatly decreasing the computational burden and complexity. And its effectiveness is experimentally validated through comparative assessments. © 2017 CPSS.

关键词： Vectors

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Secure Encryption and Decryption Circuit on 8T1C Structure-Based Charge Accumulation and Dissipation 9

Secure Encryption and Decryption Circuit on 8T1C Structure-B...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Zhang, Shutong Wang, Pengjun Zhou, Ziyu Zhang, Yuejun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

ISBN: (纸本)9798331509453

To solve the large resource overhead and risk problems of traditional encryption and decryption technologies. This paper presents a secure computing scheme of 8T1C structure based on charge accumulation and dissipation which is used to implement low-cost encryption and decryption circuits. Firstly, the scheme uses the characteristics of capacitor charge accumulation and dissipation to complete the XOR encryption. Decryption of data is performed by dynamically charging and discharging the load capacitor. Secondly the 8T1C SRAM unit structure is designed in combination with the Static Random-Access Memory Physical Unclonable Function (SRAM PUF) structure. The encryption and decryption operations are built into the storage unit to reduce the hardware resource overhead, by integrating additional MOS tubes and capacitors. Finally, the encrypted data is reshaped by the sense amplifier and stored in the same unit. The function of safe data storage is realized through hardware reuse of circuit resources. The security encryption and decryption circuit is simulated and verified In the TSMC 65nm process. The experimental results show that the designed encryption and decryption circuit structure can confuse the traces of the original data and recover the original information content in the decryption process. PUF key uniqueness is 50.31%, randomness is 99.9%, and it has passed the NIST randomness test. Under the working environment of -50°C 125°C and 0.8V to 1.5V, the worst bit error rate is 7.6%, which can be applied to the security encryption field of the Internet of Things. © 2024 IEEE.

关键词： Static random access storage

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Insertion of Additional Image Charges in Method of Images for Small Ground Planes to Improve Prediction in Two-Dimensional Electrostatic Problems

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Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 193-200页

作者： Vetharatnam, Gobi Ghanbarisabagh, Mohammad Senjuti, Sifat Ara Department of Electrical and Electronic Engineering Lee Kong Chian Faculty of Engineering and Science University Tunku Abdul Rahman Cheras Selangor Kajang Malaysia Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences Islamic Azad University North Tehran Branch Hakimieh Tehran Iran

Traditional method of images is routinely applied in electrostatic problems with infinite ground planes. However, practical situations almost always deal with finite ground planes. This causes the prediction to be inexact. For the first time, the differences between them are analyzed and a new model for the image is proposed, for small ground planes in two-dimension. In this model, two additional charges are introduced at either side of the ground plane to improve the prediction. This model predicts the potential with less error compared with the traditional method of images. © 2022, The Institution of Engineers (India).

关键词： Forecasting

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Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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Advanced dairy cow monitoring: enhanced detection with precision 3D tracking

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Multimedia Tools and Applications 2025年第18期84卷 19117-19146页

作者： Wang, Ranran Li, Yingxiu Yue, Peng Yuan, Chunhong Tian, Fuyang Lu, Xin College of Mechanical and Electronic Engineering Shandong Agricultural University Shandong Tai’an271018 China Department of Computer Science Faculty of Engineering University of Sheffield SheffieldS1 4DP United Kingdom Faculty of Agriculture Iwate University Iwate Morioka020-8550 Japan Faculty of Science and Engineering Iwate University Iwate Morioka020-8550 Japan

Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. © The Aut

关键词： K-means clustering

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