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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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Retention Accelerated Testing for 3D QLC NAND Flash Memory: Characterization, Analysis, and Modeling

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第7期44卷 2779-2788页

作者： Yang, Shaoqi Zhang, Meng Zhan, Xuepeng Guo, Peng Zhao, Xiaohuan Yang, Guangkuo Guo, Xinyi Wu, Jixuan Wu, Fei Chen, Jiezhi Shandong University School of Information Science and Engineering Qingdao266237 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Shandong Sinochip Semiconductors Co. Ltd China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China

Three-dimensional (3D) NAND flash memory has become quite popular and is now widely used in data centers and mobile devices due to its outstanding storage density and cost-effectiveness. Larger storage capacity is made possible by 3D quad-level cell (QLC) NAND flash memory with the charge-trap (CT) structure, which stores four bits in each cell. However, data reliability is sacrificed in exchange for greater capacity. The lifespan of data retention is crucial for non-volatile storage. Thus, an important role is played by the Arrhenius model, which is widely used for lifespan prediction and high-temperature acceleration testing. Interestingly, we discover that the conventional Arrhenius model is inaccurate after analyzing the data retention properties of 3D QLC NAND flash memory. An empirical model is proposed for changing the apparent activation energy (Ea) based on the influence of different parameters, in order to accurately predict data lifespan and perform accelerated experiments. This developed model provides a temperature-and cycle-related parameter table for Ea, which is useful for high-temperature acceleration testing examinations. Simultaneously, we observe a linear connection between the 40°C data retention time mapping and the other temperatures. We evaluate the effects of the modified Ea model and the classic Arrhenius model with the epitaxial data and conclude that the former can reduce the error by approximately 70% to a maximum. © 1982-2012 IEEE.

关键词： Activation energy

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Enhancing CCTV Violence Detection: A Comparative Study of Deep Learning Models for Violence Detection in Surveillance Videos 8

Enhancing CCTV Violence Detection: A Comparative Study of De...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Rodrigues, Kenneth Dsouza, Glen Phansopkar, Omkar Bide, Pramod Sardar Patel Institute of Technology Department of Computer Science and Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798331531539

With the large number of CCTV cameras located worldwide, ensuring people's safety has become much easier. Despite this, it is impossible to keep track of 100s of CCTV cameras simultaneously. Therefore, deep learning methods of violence detection from surveillance footage have been proposed to reduce human intervention. This solution presents a comparison of the performance of three-dimensional (3D) Convolutional Neural Networks (CNNs), VGG networks, and MobileNet models combined with Long Short-Term Memory (LSTM) units and Multi-Stream Networks to detect violence in CCTV video footage, providing a comprehensive analysis of these models based on accuracy, hardware requirements, and other performance metrics essential to real-world applications. The majority of the existing systems are trained on violent footage from sports or movies, the models in this solution are trained on a combination of datasets consisting of CCTV footage which provides an accurate representation of real-world surveillance footage, thus improving model accuracy. Existing research does not study the effect of combining very deep CNN models like VGG with advanced techniques like multi-stream models. This solution explores the impact of integrating such models to create a more robust solution. All the models proposed and evaluated have produced accurate results and are capable of identifying violence based on surveillance footage, with an accuracy of up to 98.46%. Unlike other solutions that do not take into account the memory and prediction time of different models, this solution identifies the ideal model depending on the available resources and infrastructure, helping make a decision based on real-world requirements. © 2024 IEEE.

关键词： Convolutional neural networks

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Benchmarking the Performance of Pre-trained LLMs across Urdu NLP Tasks 1

Benchmarking the Performance of Pre-trained LLMs across Urdu...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Tahir, Munief Hassan Shams, Sana Fiaz, Layba Adeeba, Farah Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798891762015

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of 7 prominent LLMs: GPT-3.5-turbo, Llama 2-7B-Chat, Llama 3.1-8B, Bloomz 3B, Bloomz 7B1, Ministral-8B and Whisper (Large, medium and small variant) across 17 tasks using 22 datasets, 13.8 hours of speech, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analyzed. Our experiments show that SOTA models currently outperform encoder-decoder models in majority of Urdu NLP tasks under zero-shot settings. However, comparing Llama 3.1-8B over prior version Llama 2-7B-Chat, we can deduce that with improved language coverage, LLMs can surpass these SOTA models. Our results emphasize that models with fewer parameters but richer language-specific data, like Llama 3.1-8B, often outperform larger models with lower language diversity, such as GPT-3.5, in several tasks. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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computer Modeling in engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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Optimizing Power Consumption for Business Networks through Cooperative Communication in Mixed Wireless Environments

Optimizing Power Consumption for Business Networks through C...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Sanjay, C.P. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of business networks, the imperative to optimize power consumption has become paramount, particularly within the context of mixed wireless environments. This study introduces a novel approach to enhancing energy efficiency through cooperative communication strategies, tailored for heterogeneous networks that comprise a mix of cellular, Wi-Fi, and other wireless technologies. The core proposition of this research lies in leveraging the inherent diversity and redundancy in mixed wireless environments to facilitate energy-efficient communication. By employing advanced cooperative communication techniques, such as relay-assisted transmission and network coding, the proposed method significantly reduces the power required for data transmission and reception across various network segments. The methodology encompasses a comprehensive framework that models the network as a unified system, enabling the identification and exploitation of opportunities for power savings without compromising communication reliability or throughput. A set of sophisticated algorithms underpins the proposed framework, ensuring optimal decision-making regarding routing, resource allocation, and mode selection in real-time. The effectiveness of the proposed approach is validated through rigorous simulation studies, which demonstrate substantial improvements in power efficiency compared to conventional communication strategies. The results highlight the potential of cooperative communication in achieving sustainable and energy-efficient business networks, paving the way for greener and more cost-effective operations in mixed wireless environments. © 2024 IEEE.

关键词： Business Networks Cooperative Communication Energy Efficiency Mixed Wireless Environments Resource Allocation

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SNR-Responsive Communication: Turbo Codes and BCH in a Dynamic HARQ Scheme for Enhanced Efficiency

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SN computer science 2025年第5期6卷 1-13页

作者： Chintha, Gnana Jyothi Prathapa, Chandana Sai Sri Hasitha Namburi, Yaswanth Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangaluru 575025 India

Wireless communication systems face many challenges due to fluctuating channel conditions resulting in variable error rates and demands robust error management tactics. Traditional Automatic Repeat Request (ARQ) methods suffer from high communication overhead and latency and are inefficient in handling retransmissions. Although traditional Hybrid Automatic Repeat Request (HARQ) is effective, its widespread use is limited by high implementation costs. This underscores the need for adaptive systems that dynamically respond to changing channel conditions based on Signal-to-Noise ratio (SNR). This paper proposes the Dynamic HARQ (D-HARQ), which switches between ARQ, HARQ with Bose–Chaudhuri–Hocquenghem codes, and HARQ with turbo codes based on channel conditions during the transmission. Further, this paper incorporates a selective soft-combining technique by estimating the receiver end’s SNR. The main goal is to achieve the maximum possible throughput even at very low SNR values while maintaining optimal complexity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive HARQ BCH codes Error correction Selective soft combining SNR estimation Throughput optimization Turbo codes

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Improving Micro-expression Recognition using Multi-sequence Driven Face Generation

Improving Micro-expression Recognition using Multi-sequence ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yuan Zhong, Chongju Huang, Pinyi Cai, Wangyang Wang, Lei School of Computer Science and Engineering Central South University China School of Computer and Communication Engineering Changsha University of Science and Technology China

ISBN: (纸本)9798350368741

Micro-expression (ME) recognition holds great potential for revealing true human emotions. A significant barrier to effective ME recognition is the lack of sufficient annotated ME video data because MEs are subtle and involuntary facial expressions that are very hard to capture. To address this issue, data augmentation techniques, such as ME migration based on a driven video, have been employed to enrich training samples. Considering that MEs can be complex facial movements involving multiple action unit (AU) changes, we propose a novel ME generation approach that enables the creation of more realistic facial sequences by fusing MEs from multiple videos rather than just single driven video. To enhance the effectiveness of multi-sequence ME transfer, we adapt the thin plate spline motion model and improve traditional face alignment methods to better suit the model, facilitating multi-sequence driven ME generation. In our experiments, we conduct a downstream ME recognition task using models trained on our augmented ME sequences to demonstrate the effectiveness of our approach on the SAMM, SMIC, and CASME II datasets. The results confirm that our proposed approach outperforms state-of-the-art (SOTA) augmentation and generation methods in terms of F1 score and recognition accuracy. © 2025 IEEE.

关键词： face alignment micro-expression generation thin plate spline motion model video fusion

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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