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IEEE Journal on Flexible electronics 2024年第4期3卷 118-119页

作者： Nag, Anindya Ozioko, Oliver Kim, Woo Soo Andrews, Joseph Technische Universität Dresden Faculty of Electrical and Computer Engineering Dresden01062 Germany Dresden01069 Germany University of Derby Department of Electrical and Electronic Engineering DerbyDE22 1GB United Kingdom Simon Fraser University School of Mechatronic Systems Engineering BurnabyBCV5A 1S6 Canada University of Wisconsin-Madison Department of Mechanical Engineering and Department of Electrical and Computer Engineering MadisonWI53706 United States

Wearable sensing has recently been highly preferred due to its quick and accurate measurement of physiological parameters. These sensors have been devised using various polymers [1], [2] and nanomaterials [3], [4] suited for the chosen application. With the exponential growth of wearable electronics [5], [6], [7], there is a need to broaden their capabilities in terms of functionality and availability. Commercializing these wearable electronics needs further encouragement to use these sensors as point-of-care devices. Self-powered sensors [8], [9] are one of the growing aspects in the sector of wearable sensing. With the growing requirement for energy usage, self-powered sensing systems need to be developed to generate and harvest energy ubiquitously [10], [11]. This Special Issue highlights some of the published papers that work on using smart textiles and self-powered devices for efficient and sustainable sensing applications. © 2022 IEEE.

关键词： Wearable sensors

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Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

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computers, Materials & Continua 2023年第5期75卷 3011-3026页

作者： Amjad Hussain Zahid Muhammad Junaid Arshad Musheer Ahmad Naglaa F.Soliman Walid El-Shafai School of Systems and Technology University of Management and TechnologyLahore54700Pakistan Department of Computer Science University of Engineering and TechnologyLahore54700Pakistan Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

A substitution box(S-Box)is a crucial component of contemporary cryptosystems that provide data protection in block *** the moment,chaotic maps are being created and extensively used to generate these SBoxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical *** this paper,the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking *** projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their workingmakes both of these dynamic in *** tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic *** of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique *** analysis and exploration confirmed that the projected chaoticmap exhibits a significant amount of chaotic *** security assessment in terms of bijectivity,nonlinearity,bits independence,strict avalanche,linear approximation probability,and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several *** proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security *** comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.

关键词： Substitution-box chaotic map data security tweaking

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Cancellable template generation for speaker recognition based on spectrogram patch selection and deep convolutional neural networks

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International Journal of Speech Technology 2022年第3期25卷 689-696页

作者： El-Moneim, Samia A. Nassar, M.A. Dessouky, Moawad I. Ismail, Nabil A. El-Fishawy, Adel S. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications 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 Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

Nowadays, biometric systems have replaced password-or token-based authentication systems in many fields to improve the security level. However, biometric systems are also vulnerable to security threats. Unlike passwords, biometric templates cannot be replaced if lost or compromised. To deal with issue of compromising biometric templates, template protection schemes have evolved to make it possible to replace the biometric templates. A cancellable biometric scheme is such a template protection scheme that can replace a biometric template, when it is stolen or lost. The biometric used here is speech. It is important to preserve user confidentiality. Cancellable biometrics is a new notion addressed for this problem. This paper presents a scheme for cancellable speaker recognition based on spectrogram patch selection. The simulation results reveal that the suggested approach is practical, and it satisfies the desired criteria such as renewability, security and performance. The accuracy of the proposed cancellable speaker recognition scheme reaches 98.75% with a Convolutional Neural Network (CNN) composed of three layers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

关键词： Biometrics

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Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection

Using Explainable AI for EEG-based Reduced Montage Neonatal ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Udayantha, Dinuka Sandun Weerasinghe, Kavindu Wickramasinghe, Nima Abeyratne, Akila Wickremasinghe, Kithmin Wanigasinghe, Jithangi De Silva, Anjula Edussooriya, Chamira U. S. University of Moratuwa Department of Electronic and Telecommunication Engineering Sri Lanka University of British Columbia Department of Electrical and Computer Engineering Canada University of Colombo Faculty of Medicine Department of Paediatrics Sri Lanka

ISBN: (纸本)9781665410205

The neonatal period is the most vulnerable time for the development of seizures. Seizures in the immature brain lead to detrimental consequences, therefore require early diagnosis. The gold -standard for neonatal seizure detection currently relies on continuous video-EEG monitoring;which involves recording multi-channel electroencephalogram (EEG) alongside real-time video monitoring within a neonatal intensive care unit (NICU). However, video-EEG monitoring technology requires clinical expertise and is often limited to technologically advanced and resourceful settings. Cost-effective new techniques could help the medical fraternity make an accurate diagnosis and advocate treatment without delay. In this work, a novel explainable deep learning model to automate the neonatal seizure detection process with a reduced EEG montage is proposed, which employs convolutional nets, graph attention layers, and fully connected layers. Beyond its ability to detect seizures in real time with a reduced montage, this model offers the unique advantage of real-time interpretability. By evaluating the performance on the Zenodo dataset with 10-fold cross-validation, the presented model achieves an absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall, respectively. © 2024 IEEE.

关键词： Electroencephalography

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De'hubert: Disentangling Noise in a Self-Supervised Model for Robust Speech Recognition 48

De'hubert: Disentangling Noise in a Self-Supervised Model fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ng, Dianwen Zhang, Ruixi Yip, Jia Qi Yang, Zhao Ni, Jinjie Zhang, Chong Ma, Yukun Ni, Chongjia Chng, Eng Siong Ma, Bin Alibaba Group China Nanyang Technological University School of Computer Science and Engineering Singapore Xi'an Jiaotong University Faculty of Electronic and Information Engineering China

ISBN: (纸本)9781728163277

Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However, many current models are trained on a clean corpus from a single source, which tends to do poorly when noise is present during testing. Nonetheless, it is crucial to overcome the adverse influence of noise for real-world applications. In this work, we propose a novel training framework, called deHuBERT, for noise reduction encoding inspired by H. Barlow's redundancy-reduction principle. The new framework improves the HuBERT training algorithm by introducing auxiliary losses that drive the self- and cross-correlation matrix between pairwise noise-distorted embeddings towards identity matrix. This encourages the model to produce noise- agnostic speech representations. With this method, we report improved robustness in noisy environments, including unseen noises, without impairing the performance on the clean set. © 2023 IEEE.

关键词： Noise abatement

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High-Performance Rejection Sampling Hardware Circuit Design for Kyber 15

High-Performance Rejection Sampling Hardware Circuit Design ...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Wang, Yang Zhang, Huihong Zhang, Yuejun Wei, Hongshuai Wang, Pengjun Yuan, Tengfei Wang, Chengjie Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

CRYSTALS-Kyber is a powerful Post-Quantum Cryptography(PQC) with high resistance to quantum computer attacks. In order to optimize the core sampling structure of Kyber so as to improve the hardware execution efficiency, this paper proposes a high-performance rejection sampling hardware circuit design scheme for Kyber. The scheme first divides the input random numbers by width converter, and then combines four parallel sampling cores to shorten the sampling time period;then on the basis of the high parallelization of sampling cores, this work use the reorganization of random numbers and the bit-weighted value characteristics of binary numbers to design high-efficiency comparator to reduce the rejection rate, and reduce the sampling time and overhead;and finally, splice the valid inputs and set up a buffer to store the generated samples. The scheme is verified for its performance by testing under Taiwan Semiconductor Manufacturing Company(TSMC) 65nm process and FPGA implementation under Vivado platform. The experimental results demonstrate that the optimized sampling circuit reduces the rejection rate from 18.73% to 11.33% and shortens the sampling clock period by 80.84% compared to the basic implementation;comparing with the related work, it reduces the area by 67.77% while controlling the sampling time, and the consumption of hardware resources is greatly reduced, which improves the efficiency of the hardware execution of the Kyber rejection sampling. © 2023 IEEE.

关键词： CRYSTALS-Kyber high-efficiency comparator Post-Quantum Cryptography rejection sampling width converter

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A deep regression convolutional neural network using whole image-based inferencing for dynamic visual crowd estimation

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International Journal of Business Intelligence and Data Mining 2022年第1-2期22卷 100-114页

作者： Teoh, Shen Khang Yap, Vooi Voon Nisar, Humaira Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Malaysia Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Malaysia

As intelligent surveillance system applications become ubiquitous, automated crowd counting solutions must be made continually faster and accurate. This paper presents an improved convolutional neural network (CNN) architecture for accurate visual crowd counting in crowd images. Multi-column convolutional neural network (MCNN) is widely used in previous works to predict the density map for visual crowd counting. However, this method has limitations in predicting a quality density map. Instead, the proposed model is architected using powerful CNN layers, dense layers, and one regressor node with whole image-based inference. Therefore, it is less computationally intensive and inference speed can be increased. Tested on the mall dataset, the proposed model achieved 2.01 mean absolute error and 8.53 mean square error. Moreover, benchmarking on different CNN architectures has been conducted. The proposed model shows promising counting accuracy and reasonable inference speed against the existing state-of-art approaches. © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Neural Network-based System Identification: A Comprehensive FPGA Design and Implementation

Neural Network-based System Identification: A Comprehensive ...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Abdulnabi, Saif Hasan Mudhafar, Yousif Samer Kadhim, Ali Abdulhassan Mahdi, Mohammad Baqer Sojar, Hassan Hadi University of Kufa Faculty of Engineering Department of Electronic and Communications Engineering Najaf54001 Iraq College of Engineering University of Baghdad Department of Electrical Engineering Al-Jadriya Baghdad10001 Iraq Information Technology College Imam ja'Afar Al-Sadiq University Department of Computer Techniques Engineering Najaf Iraq College of Technical Engineering Computer Techniques Engineering Department The Islamic University Najaf Iraq

ISBN: (纸本)9798350350524

Neural networks are more commonly used to identify systems for system diagnostics without fully implementing and building the system. The project aims to design and implement the neural network identification system and implement it based on a Field Programmable Gate Array (FPGA) by interfacing between MATLAB / Simulink and Xilinx programs. The work was done thanks to God through two methods: The first through the simulation method through the coupling between MATLAB and Xilinx. While the second method was practically done by loading the simulation designs onto the FPGA. Work performance is measured by subtracting the value resulting from the actual system operation of the identification signal system and the simplified system and the difference between the simulation result and practical result. The use of FPGA with system identification in this project gives us a big advantage that simulation results are equal to practical results (difference is zero). © 2024 IEEE.

关键词： Simulink

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Ternary Multiply-Accumulate Circuit Based on Domino Structure 15

Ternary Multiply-Accumulate Circuit Based on Domino Structur...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Shi, Hanyu Zhang, Yuejun Zhang, Huihong Li, Qikang Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

In order to solve the problem of memory and computation bottleneck in neural networks and improve the efficiency of computation, this paper proposes a design scheme of ternary multiply-accumulate circuits based on domino structure. This paper first analyzes the principles of ternary logic and multiply-accumulate circuits, then introduces the design scheme of domino ternary literal arithmetic circuits, multiply circuits, and accumulate circuits, and finally realizes the ternary multiply-accumulate arithmetic by combining the above circuits. The scheme is tested under Taiwan Semiconductor Manufacturing Company(TSMC) 65nm process, and the results verify the accuracy of the arithmetic results. The two-bit multiply four-bit accumulate circuit operates at the supply voltage of 1.2V, the temperature of 27°C, and the clock frequency of 50MHz, and the average power dissipation of the circuit is measured to be 75.5uW, and the maximum delay of the circuit is 385ps. © 2023 IEEE.

关键词： domino circuit multiply-accumulate circuit ternary logic

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An Efficient Hash Computing Unit for Kyber Algorithm 15

An Efficient Hash Computing Unit for Kyber Algorithm

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Wei, Hongshuai Zhang, Yuejun Zhang, Huihong Wang, Yang Yuan, Tengfei Wang, Chengjie Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

In the post-quantum Kyber algorithm, the Hash computation directly affects the processing speed of key generation, noise polynomial, and key exchange processes. To address the issues of multiple types of Hash computations, complex variables, and high resource consumption, this paper proposes an efficient Hash computation unit. The proposed scheme first establishes independent data interaction units through the sponge construction, achieving higher parallelism by combining the logically merged Keccak-1600 permutation function. Subsequently, the central binomial distribution sampling and optimized rejection sampling are integrated into the Hash unit, resulting in a compact and highly flexible Hash computation unit. Finally, the scheme is tested and verified under the Taiwan Semiconductor Manufacturing Company (TSMC) 65nm process. The proposed method consumes only 36.4K equivalent logic gates, and experimental results show that the Hash computation cycle is reduced by up to 30%, rejection sampling cycle by 16%, making it more efficient compared to related implementations. © 2023 IEEE.

关键词： CRYSTALS-KYBER hash computation unit quantum-resistant cryptography sampler sponge

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