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Low-Noise,High-Gain 28 GHz LNA Design Using Multi-Objective Optimization with NSGA-Ⅱ and MOPSO 认领 引用
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作者 Spandana Saggurthi Anand Nayyar +1 位作者 Sk Hasane Ahammad Sumendra Yogarayan 《Computers, Materials & Continua》 SCIE EI 2026年第9期691-709,共19页
This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms... This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms.The objectives of the paper include simultaneous minimization of noise figure(NF)and power consumption while maximizing gain under matching and stability constraints.Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology,an optimization framework was created in Python,with the passive components LG,LS,LD,LOUT,and COUT chosen to be the variables optimized.The NSGA-Ⅱ optimized design achieves 1.7 dB NF,17 dB gain,and 4.7 mW DC power,while MOPSO achieves 1.8 dB NF,17.1 dB gain,and 5.0 mW power.NSGA-Ⅱ provides improved Pareto diversity and slightly better output matching,whereas MOPSO reduces computational time by 24%with comparable RF performance.The results demonstrate effective multi-objective design-space exploration and controlled algorithm benchmarking at the schematic-level for mm-wave LNA design. 展开更多
关键词 LNA mm-wave multi-objective optimization NSGA-Ⅱ MOPSO internet of things(IoT) S-parameters gain and noise figure
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Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence 认领 引用 被引量：1
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作者 Shachar Bar P.W.C.Prasad Md Shohel Sayeed 《Computers, Materials & Continua》 SCIE EI 2024年第10期1-23,共23页
Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(I... Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution. 展开更多
关键词 Anomaly detection artificial intelligence cyber security data privacy deep learning federated learning industrial internet of things internet of things intrusion detection system machine learning
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Modified Neural Network Used for Host Utilization Predication in Cloud Computing Environment 认领 引用 被引量：1
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作者 Arif Ullah Siti Fatimah Abdul Razak +1 位作者 Sumendra Yogarayan Md Shohel Sayeed 《Computers, Materials & Continua》 SCIE EI 2025年第3期5185-5204,共20页
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ... Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets. 展开更多
关键词 Cloud computing datacenter virtual machine(VM) predication algorithm
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Enhancing Vehicle Overtaking System via LoRa-Enabled Vehicular Communication Approach 认领 引用
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作者 Kwang Chee Seng Siti Fatimah Abdul Razak Sumendra Yogarayan 《Computer Systems Science & Engineering》 2025年第1期239-258,共20页
Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their sur... Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard. 展开更多
关键词 Connected vehicle internet of things LoRa vehicle overtaking vehicle-to-cloud vehicle-to-vehicle V2V
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Guest Editorial:Special issue on advances in representation learning for computer vision 认领 引用
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作者 Andrew Beng Jin Teoh Thian Song Ong +1 位作者 Kian Ming Lim Chin Poo Lee 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期1-3,共3页
Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional ... Deep learning has been a catalyst for a transformative revo-lution in machine learning and computer vision in the past decade.Within these research domains,methods grounded in deep learning have exhibited exceptional performance across a spectrum of tasks.The success of deep learning methods can be attributed to their capability to derive potent representations from data,integral for a myriad of downstream applications.These representations encapsulate the intrinsic structure,fea-tures,or latent variables characterising the underlying statistics of visual data.Despite these achievements,the challenge per-sists in effectively conducting representation learning of visual data with deep models,particularly when confronted with vast and noisy datasets.This special issue is a dedicated platform for researchers worldwide to disseminate their latest,high-quality articles,aiming to enhance readers'comprehension of the principles,limitations,and diverse applications of repre-sentation learning in computer vision. 展开更多
关键词 spite computer representation
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Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation 认领 引用
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作者 Parthasarathi Manivannan Palaniyappan Sathyaprakash +3 位作者 Vaithiyashankar Jayakumar Jayakumar Chandrasekaran Bragadeesh Srinivasan Ananthanarayanan Md Shohel Sayeed 《Computers, Materials & Continua》 SCIE EI 2024年第12期4327-4347,共21页
Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remain... Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems. 展开更多
关键词 EfficientNetV2B3 multi-level knowledge distillation RestNet50V2 weather classification
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Dual-Modal Drowsiness Detection to Enhance Driver Safety 认领 引用
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作者 Yi Xuan Chew Siti Fatimah Abdul Razak +1 位作者 Sumendra Yogarayan Sharifah Noor Masidayu Sayed Ismail 《Computers, Materials & Continua》 SCIE EI 2024年第12期4397-4417,共21页
In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems.This study aims to enhance road safety by propos... In the modern world,the increasing prevalence of driving poses a risk to road safety and necessitates the development and implementation of effective monitoring systems.This study aims to enhance road safety by proposing a dual-modal solution for detecting driver drowsiness,which combines heart rate monitoring and face recognition technologies.The research objectives include developing a non-contact method for detecting driver drowsiness,training and assessing the proposed system using pre-trained machine learning models,and implementing a real-time alert feature to trigger warnings when drowsiness is detected.Deep learning models based on convolutional neural networks(CNNs),including ResNet and DenseNet,were trained and evaluated.The CNN model emerged as the top performer compared to ResNet50,ResNet152v2,and DenseNet.Laboratory tests,employing different camera angles using Logitech BRIO 4K Ultra HD Pro Stream webcam produces accurate face recognition and heart rate monitoring.Real-world vehicle tests involved six participants and showcased the system’s stability in calculating heart rates and its ability to correlate lower heart rates with drowsiness.The incorporation of heart rate and face recognition technologies underscores the effectiveness of the proposed system in enhancing road safety and mitigating the risks associated with drowsy driving. 展开更多
关键词 Drowsy advanced driver assistance system driver safety on-the-road experiments
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New Ethereum-based distributed PKI with a reward-and-punishment mechanism 认领 引用
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作者 Chong-Gee Koa Swee-Huay Heng Ji-Jian Chin 《Blockchain(Research and Applications)》 EI CSCD 2025年第1期14-26,共13页
This paper explores the critical role of Public Key Infrastructure(PKI)in ensuring the security of electronic transactions,particularly in validating the authenticity of websites in online environments.Traditional Cen... This paper explores the critical role of Public Key Infrastructure(PKI)in ensuring the security of electronic transactions,particularly in validating the authenticity of websites in online environments.Traditional Centralised PKIs(CPKIs)relying on Certificate Authorities(CAs)face a significant drawback due to their susceptibility to a single point of failure.To address this concern,Decentralised PKIs(DPKIs)have emerged as an alternative.However,both centralised and decentralised approaches encounter specific challenges.Researchers have made several attempts using blockchain-based PKI,which implements a reward and punishment mechanism to enhance the security of traditional PKI.Most of the attempts are focused on CA-based PKI,which still suffers from the risk of a single point of failure.Inspired by ETHERST,which is a blockchainbased PKI that implements Web of Trust(WoT)with reward and punishment,we introduce ETHERST version 3.0,with improvements in its secure level algorithm that enhances trustworthiness measurement.Comparative simulations between ETHERST version 2.0 and ETHERST version 3.0 reveal the superior performance of the latter in trustworthiness measurement and ensure the higher security of a virtual community.The new simulation algorithm with different node type definitions and assumptions presents results through tables and graphs,showing that ETHERST version 3.0 outperforms ETHERST version 2.0.This research contributes to advancing the field by introducing an innovative PKI solution with enhanced trustworthiness and security features. 展开更多
关键词 Blockchain Ethereum PKI Security
Two-Factor Cancelable Biometrics Authenticator 认领 引用
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作者 Ying-Han Pan Andrew T.B.J David N.C.L 《Journal of Computer Science & Technology》 SCIE EI 2007年第1期54-59,共6页
Biometrics-based authentication system offers advantages of providing high reliability and accuracy.However the contemporary authentication system is impuissance to compromise.If a biometrics data is compromised,it ca... Biometrics-based authentication system offers advantages of providing high reliability and accuracy.However the contemporary authentication system is impuissance to compromise.If a biometrics data is compromised,it cannot be replaced and rendered unusable.In this paper,a cancelable biometrics-based authenticator is proposed to solve this irrevocability issue.The proposed approach is a two-factor authentication system,which requires both of the random data and facial feature in order to access the system.In this system,tokenized pseudo-random data is coupled with momentbased facial feature via inner product algorithm.The output of the product is then discretized to generate a set of private binary code,coined as 2factor-Hashing code,which is acted as verification key.If this biometrics-based verification key is compromised,a new one can be issued by replacing a different set of random number via token replacement.Then,the compromised one is rendered completely useless.This feature offers an extra protection layer against biometrics fabrication since the verification code is replaceable.Experimental results demonstrate that the proposed system provides zero Equal Error Rate in which there is a clear separation in between the genuine and the imposter distribution populations. 展开更多
关键词 cancelable biometrics face recognition Geometric moments pseudo Zernike moment
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