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CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation Systems

CLEAR: An Efficient Traffic Sign Recognition Technique for C...

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IEEE International Conference on Utility and Cloud computing (UCC)

作者： Meghna Rai Bhawna Khosla Yash Dhawan Himanshi Kharotia Neeraj Kumar Ajay Bandi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Northwest Missouri State University USA

In this modern era, the popularity of autonomous systems has increased manifold because they are replacing people in various jobs and are expected to become the backbone of modern society. Specifically, self-driving vehicles and robots are gaining popularity in a wide range of applications. However, traffic sign detection, a critical component of intelligent transportation systems, remains quite challenging since it needs to be done quickly, with high precision, and with high dependability. A fast, real-time, robust automatic traffic sign detection and recognition can support and relieve the driver and significantly increase driving safety and comfort. Out of many algorithms and frameworks available for traffic sign identification, i.e., object detection, one of the most popular ones is You Only Look Once (YOLO) since it provides accurate results with minimal background errors in most real-time processing tasks and has excellent learning capabilities. Motivated by this, this research article provides a novel framework, CLEAR, for traffic sign identification in adverse climate conditions using YOLOv5. We compare different models’ speed, accuracy, and other metrics on a Traffic Sign Dataset obtained from the Open Image Dataset V6. Experimental results demonstrated that the proposed CLEAR model achieved the best performance with a Mean Average Precision of 0.73392 and Recall of 0.74194 compared to the existing schemes.

关键词： Training Measurement Real-time systems Data models Usability Task analysis Tuning

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Baldur: A Power-Efficient and Scalable Network Using All-Optical Switches 26

Baldur: A Power-Efficient and Scalable Network Using All-Opt...

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26th IEEE International symposium on high performance computer architecture (HPCA)

作者： Jokar, Mohammad Reza Qiu, Junyi Chong, Frederic T. Goddard, Lynford L. Dallesasse, John M. Feng, Milton Li, Yanjing Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Illinois Dept Elect & Comp Engn 1406 W Green St Urbana IL 61801 USA

ISBN: (数字)9781728161495

ISBN: (纸本)9781728161495

We present the first all-optical network, Baldur, to enable power-efficient and high-speed communications in future exascale computing systems. the essence of Baldur is its ability to perform packet routing on-the-fly in the optical domain using an emerging technology called the transistor laser (TL), which presents interesting opportunities and challenges at the system level. Optical packet switching readily eliminates many inefficiencies associated with the crossings between optical and electrical domains. However, TL gates consume high power at the current technology node, which makes TL-based buffering and optical clock recovery impractical. Consequently, we must adopt novel (bufferless and clock-less) architecture and design approaches that are substantially different from those used in current networks. At the architecture level, we support a bufferless design by turning to techniques that have fallen out of favor for current networks. Baldur uses a low-radix, multi-stage network with a simple routing algorithm that drops packets to handle congestion, and we further incorporate path multiplicity and randomness to minimize packet drops. this design also minimizes the number of TL gates needed in each switch. At the logic design level, a non-conventional, length-based data encoding scheme is used to eliminate the need for clock recovery. We thoroughly validate and evaluate Baldur using a circuit simulator and a network simulator. Our results show that Baldur achieves up to 3,000X lower average latency while consuming 3.2X-26.4X less power than various state-of-the-art networks under a wide variety of traffic patterns and real workloads, for the scale of 1,024 server nodes. Baldur is also highly scalable, since its power per node stays relatively constant as we increase the network size to over 1 million server nodes, which corresponds to 14.6X-31.0X power improvements compared to state-of-the-art networks at this scale.

关键词： optical computing all-optical network exascale computing datacenter network

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the Proposal of Fuzzy Observation and Detection of Massive Data DDOS Attack threat 15th

The Proposal of Fuzzy Observation and Detection of Massive D...

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15th National Conference of Operational and Systems Research, BOS 2018 and 17th International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN 2018

作者： Zarzycki, Hubert Apiecionek, Lukasz Czerniak, Jacek M. Ewald, Dawid Department of Computer Science Casimir the Great University in Bydgoszcz ul. Chodkiewicza 30 Bydgoszcz85-064 Poland University of Information Technology and Management Copernicus ul. Inowroclawska 56 Wroclaw53-648 Poland

ISBN: (纸本)9783030470234

this article presents a potential implementation of OFN notation for discovering and protecting network from Distributed Denial of Service attacks. DDoS attacks are able to block web servers and could be launched from any place in the network. In this article some real experimental results are presented. the prepared network and DDoS attack tool was used for collecting massive data of IP packets, then OFN implementation was used for discovering attacks. As a results, the authors present a problem and tool which, once implemented in IP network, could deal with DDoS attack using OFN notation. © 2021, Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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Complex Human Activities Recognition Based on high performance 1D CNN Model

Complex Human Activities Recognition Based on High Performan...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Raman Maurya T. Hui Teo Shi Hui Chua Hwang-Cherng Chow I-Chyn Wey Engineering Product Development Singapore University of Technology and Design Singapore Engineering Product Development Science Mathematics and Technology Singapore University of Technology and Design Singapore Department of Electronic Engineering Chang Gung University Taiwan Department of Electrical Engineering Chang Gung University Taiwan

ISBN: (纸本)9781665465007

Human activity recognition (HAR) is an emerging scientific research field that has wide area of applications in different fields such as healthcare, social-sciences and human-computer interaction etc. In many cases, humans perform very complex physical activities that needs to be tracked in order to improve well-being, quality of life and health. In this study, a method for complex HAR based on One dimensional (1D) CNN model using tri-axis accelerometer sensor data was proposed. the sensor data was collected from a smartwatch for three complex human activities which are studying, playing games and mobile scrolling. 1D CNNs provides high accuracy as well as less computational complexity in performing HAR. the proposed 1D CNN model was trained and optimized on a self-prepared dataset in Python. the adapted model provides an accuracy of 98.28 %. A preliminary study shows that the proposed model could effectively recognize the intended activities as a baseline for extending future work in the HAR area.

关键词： Adaptation models Wearable Health Monitoring Systems Computational modeling Games Real-time systems Human activity recognition Resource management

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MANET Routing Protocol performance Comparison: DSDV, AODV, and DSR

MANET Routing Protocol Performance Comparison: DSDV, AODV, a...

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Innovative Approaches in Smart Technologies (ISAS), International symposium on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Tanupriya Choudhury Ketan Kotecha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Mobile Ad hoc Networks (MANETs) may self-configure and rely on wireless connections rather than a permanent infrastructure; hence this research delves into their intricacies. this research aims to better understand how MANETs function. MANETs are characterized by their high degree of dynamism and adaptability to changes in network architecture. these networks are made up of mobile nodes that are independent and can perform the functions of both a host and a router. Having said that, this flexibility does provide a number of substantial issues, notably in the area of routing. the unexpected and fluid nature of MANET topology, which is related to the mobility of the nodes, makes routing a hard process. the effectiveness of the network is strongly reliant on the efficacy of the routing protocols that are implemented; therefore this is an important consideration. Our study analyses the capabilities of three popular MANET routing protocols: DSDV, AODV, and DSR. these protocols must be compared using end-to-end latency, packet delivery percentage, and lost packet ratio across different network densities and mobility patterns. In our simulations, we seek to replicate a wide variety of situations that can occur in the real world by altering the number of nodes, the connection counts, and the pause periods. these simulations are carried out with the help of the Network Simulator 2 (version 2.35), a programme that is well-known for its use in network research. Additionally, the graphical representation of the data is carried out with the assistance of gnuplot. Our first findings suggest that AODV has a significant advantage over DSDV and DSR when it comes to handling the dynamic networking environment of MANETs more effectively. this is especially true in situations characterised by high node mobility and variable network density. this research not only highlights the necessity of selecting the appropriate routing protocol in MANETs, but it also offers a thorough framework fo

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Modelling and analysis of vienna rectifier for more electric aircraft applications using wide band-gap materials 15

Modelling and analysis of vienna rectifier for more electric...

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15th International Engineering and computing Research Conference, EURECA 2021

作者： Loong, Ling Jin Vaithilingam, Chockalingam Aravind Rajendran, Gowthamraj Muneeswaran, Venkatkumar High Impact Research Lab School of Computer Science and Engineering Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia George Green Institute for Electromagnetics Research School of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom

this paper presents a comprehensive study on the switching effects of wide bandgap devices and the importance of power electronics in an aircraft application. Silicon (Si), silicon carbide (SiC), and gallium nitride (GaN) are wide bandgap devices that act as a power electronic switch in the AC-DC converter for More Electric Aircraft (MEA) applications. therefore, it is important to observe their converting efficiency to identify the most suitable wide bandgap device among three devices for AC-DC converters in aircraft applications to provide high efficiency and high-power density. In this study, the characteristics of Si, SIC, and GaN devices are simulated using PSIM software. Also, this paper presents the performance of the Vienna rectifier for aircraft application. the Vienna rectifier using Si, SiC, and GaN devices are simulated using PSIM software for aircraft application. GaN with Vienna rectifier provides better performance than Si and SiC devices for aircraft applications among the three devices. It gives high efficiency, high power density, low input current thD to meet IEEE-519 standard, and high-power factor at mains. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Gallium nitride

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Advances in Yoga Training Tools: A CNN-RF Based Pose Identification System

Advances in Yoga Training Tools: A CNN-RF Based Pose Identif...

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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A hybrid model that is presented in this article is called CNN-RF which uses convolutional Neural Networks in conjunction with Random Forest algorithms. there is a novel model that supports the task of yin yoga from the video motion data in real-time, with precision and speed. the model deals with the problem of accurate identification of body images in yoga due to the ease of its solution. It uses a deep learning approach in order to extract distinctive features, which are combined and further categorized by the ensemble learning algorithm. With these techniques, a situational quotient of 94 is reachable. 115%. the sum of the performance of the five two poses was excellent (the maximum accuracy of 94). Surprisingly, the MNEs have shifted the marketing of their goods and services according to China’s new domestic market (with a $75 \%$ market share), focusing on recall (with a $98 \%$ effectiveness rate). Support Vector Machine (SVM, 99% precision and 95% recall, scores around 98%), and F1-scores (above 94%), signifies its importance. the outcomes obtained at the confusion matrix prove we have a good classification accuracy and, mostly, observed confusion between postures that are similar is little. they call attention to the issues to improve the future services. Error rate showed a considerable decrease during the training stages, from the error rate of $18 \%$ dropping to $4 \%$, demonstrating very high quality of the training in 10 epochs. the model’s reliability in statistical and the speed of gaining training points makes it feasible to be applied in yoga training systems, health monitoring, and rehabilitation machines. this brings the essential e-bikes features, user motivation and accident reduction. the above work provides a solid basis for AI and physical wellness to merge by proposing an approximation method that can enhance safety and effectiveness in yoga training with the help of technology.

关键词： Training Support vector machines Accuracy Error analysis Streaming media Feature extraction Real-time systems Convolutional neural networks Random forests Monitoring

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Distinctive Zucchini Classification in India: Leveraging CNN and RNN Models

Distinctive Zucchini Classification in India: Leveraging CNN...

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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Rishabh Sharma Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the proposed study works at three types of zucchini-Arka Sumeet, Arka Anamika, and Ahkra Manik-in India. It uses a mix of Convolutional Neural Networks (CNN) and recurrent Neural Networks (RNN) models to classify them. A big set of $\mathbf{1 1, 0 0 0}$ high-quality pictures showing different stages of zucchini growth was gathered and cleaned up to make all the data match. the suggested new design, a mix of CNN and RNN, was made to find complex plant pictures. It can also understand how the zucchini grows over time better by looking at its patterns. the model is very good, with a success rate of $\mathbf{9 6. 9 \%}$. It does an excellent job telling zucchini types apart. Measurement tools like precision, recall, and F1-score along with specificity show if the model is good at correctly classifying things. the Confusion Matrix helps us understand where mistakes happen in classification, so can improve models better. this study shows how the mix of CNN-RNN is important for the proper grouping of zucchini types. It gives a strong base to help precision farming uses. the usefulness of the model in farming choices, getting rid of diseases, and planning specific crop strategies shows its real importance. More progress is needed in making neural networks better, adding more data, and fixing models. this will make them flexible for all kinds of farming situations and improve their performance. this study shows a good, solid way to classify types of zucchini in India. It could have important effects on broader farming tech uses.

关键词： Recurrent neural networks Computational modeling Crops Solids Data models Planning Convolutional neural networks Farming Diseases Strain

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ParkinSense: A Fuzzy Inference System designed to aid in diagnosing Parkinson’s Disease

ParkinSense: A Fuzzy Inference System designed to aid in dia...

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International Conference on computing and Networking Technology (ICCNT)

作者： Aaryan Mahadik Veena Trivedi Kunal Khubchandani Raj Tandon Bhavika Valecha TE Computer Engg VESIT Chembur CMPN Dept Faculty VESIT Chembur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that poses significant challenges in early detection and precise classification of its severity. this research paper introduces a machine learning and fuzzy logic based approach to address these challenges by developing a predictive model for Parkinson’s disease detection and severity classification. the study utilizes a diverse dataset comprising clinical information and speech features from individuals with and without Parkinson’s disease. through comprehensive data preprocessing and feature engineering, the paper aims to extract relevant patterns and biomarkers associated with the disease and to predict a probability of the disease using a fuzzy approach. A variety of machine learning algorithms, including but not limited to support vector machines, random forests, and neural networks, are employed to train and evaluate the model. the focus is on achieving high accuracy, sensitivity, and specificity in detecting the presence of Parkinson’s disease. Additionally, the model is designed to classify the severity levels of Parkinson’s disease, providing valuable insights into disease progression. the findings aim to enhance our understanding of the disease’s underlying mechanisms and facilitate more targeted and personalized treatment strategies. the proposed model’s performance is assessed through rigorous validation techniques, including cross-validation and independent testing. the paper aspires to bridge the gap between machine learning advancements and the pressing clinical needs in Parkinson’s disease diagnosis and severity assessment, fostering a more informed and proactive approach to patient care. this research investigates Parkinson’s disease detection through machine learning, incorporating features such as shimmer and frequency analysis. By employing advanced algorithms, the study aims to enhance diagnostic precision by examining subtle vocal and movement characteristics associated with P

关键词： Fuzzy logic Support vector machines Machine learning algorithms Pressing Sensitivity and specificity Predictive models Feature extraction Random forests Diseases Testing

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Machine Learning Vulnerabilities at the Edge: A Study of Multi-stage Gray-box Attacks*

Machine Learning Vulnerabilities at the Edge: A Study of Mul...

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International Conference on computing and Networking Technology (ICCNT)

作者： Ayush Sinha Ishan Shrivastava Nachiket Tapas O. P. Vyas C3iHub Indian Institute of Technology Kanpur U.P India Department of Information Technology Indian Institute of Information Technology Allahabad Devghat Jhalwa Prayagraj India Department of Computer Science and Engineering Chhattisgarh Swami Vivekanand Technical University Bhilai Chhattisgarh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the growing use of machine learning (ML) models at the network edge has created new opportunities for adversaries to exploit vulnerabilities. Adversarial machine learning (AML) attacks manipulate input data, causing misclassification in sectors such as healthcare, autonomous vehicles, and finance. this research provides a pre-emptive analysis of a multi-stage gray-box attack, which can achieve results similar to a white-box attack. Unlike white-box attacks, this gray-box attack uses real-time edge data to train a surrogate model from the targeted ML model. the adversary then adjusts input data for the surrogate model to identify classifier weaknesses and design a more potent attack. Detection of this attack is challenging because it does not require full access to the targeted classifier’s parameters or training data. We examine a real life scenario, each reflecting distinct adversarial real-time data instances. the surrogate model is chosen from six ML models trained on the complete dataset. Simulations show that the multi-stage gray-box attack misleads the ML classifier, achieving a high evasion rate with less data compared to a white-box attack. Our findings reveal the susceptibility of ML classifiers at the network edge to multi-stage gray-box attacks, highlighting potential impacts on performance and reliability. We also evaluate strategies to address these vulnerabilities, including robust encryption algorithms and advanced anomaly detection techniques.

关键词： Training Image edge detection Prevention and mitigation Training data Medical services Data models Real-time systems Adversarial machine learning Reliability Glass box

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