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6th International Conference on computer and Informatics engineering, IC2IE 2023

作者： Putro, Prasetyo Adi Wibowo Amelia, Fetty Pidanic, Jan Suhartanto, Heru Rahardjo, Imam Arif Imandeka, Ejo Universitas Indonesia Faculty of Computer Science Depok Indonesia Cryptographic Hardware Engineering Politeknik Siber Dan Sandi Negara Bogor Indonesia University of Pardubice Faculty of Electrical Engineering and Informatics Pardubice Czech Republic Universitas Negeri Jakarta Department of Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9798350345162

The use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn't discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory. © 2023 IEEE.

关键词： cybersecurity Kitchenham sensor SLR smart vehicles solution threat

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Enhancing Wound Care in Healthcare systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

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DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones

arXiv

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arXiv 2024年

作者： Lan, Gongjin Wu, Yu Hao, Qi The Department of Computer Science and Engineering Southern University of Science and Technology China The Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Southern University of Science and Technology China

Human pose estimation (HPE), particularly multi-person pose estimation (MPPE), has been applied in many domains such as human-machine systems. However, the current MPPE methods generally run on powerful GPU systems and take a lot of computational costs. Real-time MPPE on mobile devices with low-performance computing is a challenging task. In this paper, we propose a lightweight neural network, DIR-BHRNet, for real-time MPPE on smartphones. In DIR-BHRNet, we design a novel lightweight convolutional module, Dense Inverted Residual (DIR), to improve accuracy by adding a depthwise convolution and a shortcut connection into the well-known Inverted Residual, and a novel efficient neural network structure, Balanced HRNet (BHRNet), to reduce computational costs by reconfiguring the proper number of convolutional blocks on each branch. We evaluate DIR-BHRNet on the well-known COCO and CrowdPose datasets. The results show that DIR-BHRNet outperforms the state-of-the-art methods in terms of accuracy with a real-time computational cost. Finally, we implement the DIR-BHRNet on the current mainstream Android smartphones, which perform more than 10 FPS. The free-used executable file (Android 10), source code, and a video description of this work are publicly available on the page 1 to facilitate the development of realtime MPPE on smartphones. © 2024, CC BY.

关键词： Smartphones

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Towards No Penalty Control Hazard Handling 66

Towards No Penalty Control Hazard Handling

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2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Surya Balasubramanian, Linknath Rizkalla, Maher Lee, John J. Ytterdal, Trond Kumar, Mukesh Indiana University Purdue University Indianapolis Department of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Department of Electronic Systems Trondheim Norway Indian Institute of Technology Indore Department of Electrical Engineering Indore India

ISBN: (纸本)9798350302103

Achieving higher throughput is one of the most important requirements of a modern microcontroller. It is therefore not affordable for it to waste a considerable number of clock cycles in branch mispredictions. This paper proposes a novel mechanism that makes microcontrollers forgo branch predictors and thereby eliminate branch mispredictions. Further, we synthesized the proposed design with 7nm FinFET process and compared its latency with other designs to make sure that the microcontroller's operating frequency is not degraded by using this design. The proposed technique is implemented as five different modules that work together to forward required operands, resolve branches without prediction, and calculate the next instruction's address in the first stage of an in-order five stage pipelined micro-architecture. Since the address of successive instruction to a control transfer instruction is calculated in the first stage of pipeline, branch prediction is no longer necessary, thereby eliminating the clock cycle penalties occurred when using a branch predictor. The designed system was tested using test-bench with modifications to make the test-code compatible with the designed architecture. The designed architecture was able to successfully calculate the address of next correct instruction and fetch it without any wastage of clock cycles. Moreover, the latency of instruction fetch stage of pipeline integrated with the proposed architecture is 330 ps (3 GHz approx.) which is more than the operating frequencies of microcontrollers. Therefore, the proposed design can be integrated in high performance microcontrollers. © 2023 IEEE.

关键词： branch predictor control transfer instruction in-order five stage pipeline microcontroller mispredictions

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Trans-Mamba: The Cross-network of Transformer and Mamba for Traffic Flow Prediction 10

Trans-Mamba: The Cross-network of Transformer and Mamba for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Qian Jia, Haonan Ye, Junchen Feng, Jinyan Lan, Fayang Du, Bowen Huang, Runhe Beihang University Ccse Lab Beijing China Beihang University School of Transportation Science and Engineering Beijing China Guizhou Qiantong Zhilian Technology Co. Ltd Guizhou China Beihang University and Zhongguancun Laboratory Transportation Science and Engineering Beijing China Hosei University Faculty of Computer and Information Sciences Department of Computer Science Metropolis Tokyo Japan

ISBN: (纸本)9798331520861

Traffic flow prediction is a crucial aspect of intelligent transportation systems. It holds significant importance in optimizing traffic management and enhancing road safety. However, the task of traffic flow forecasting remains a significant challenge due to its inherent complexity, dynamic nature, and the presence of long-range dependencies. Existing graph neural network methods exhibit two primary limitations. Firstly, when static graphs are employed to capture spatial dependencies, they fail to account for the dynamics in these dependencies. Secondly, these methods struggle to effectively capture long-range spatial dependencies. Furthermore, attention-based methods, while potentially addressing some of these issues, require substantial computational and memory resources, leading to inefficiencies. We design a cross-network model that combines Transformer and Mamba architectures for traffic flow prediction, which efficiently captures dynamic long-range spatio-temporal dependencies. The model architecture is composed of three main components: a learnable spatio-temporal embedding layer that adaptively captures the unique features of different nodes and time;stacked spatio-temporal blocks, which include two temporal blocks and one spatial block arranged between them to effectively capture spatio-temporal dependencies;and a fully connected output layer that generates the final predictions. Experiments on three public real-world datasets demonstrate the effectiveness of TransMamba. Specifically, the model improves the mean absolute error (MAE) and root mean square error (RMSE) by 2.59% and 4.14%, respectively, on the PeMS08 dataset. Additionally, ablation studies confirm the effectiveness and efficiency of each component of the model. © 2024 IEEE.

关键词： Graph neural networks

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Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis 3

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Gupta, Eesha Jana, D. Little Femilin Deshmukh, Gopal B. Singla, Abhishek Krishna, K. Gopala Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations. © 2023 IEEE.

关键词： Clustering algorithms

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SRAM Vmin Scaling via Negative Wordline 66

SRAM Vmin Scaling via Negative Wordline

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2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Gopinath, Anoop Ytterdal, Trond Yadav, Avinash Lee, John Rizkalla, Maher Kumar, Mukesh Indiana University-Purdue University Indianapolis Department of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Department of Electronic Systems Trondheim Norway Indian Institute of Technology Indore Department of Electrical Engineering Indore India

ISBN: (纸本)9798350302103

The noise margin during the write operation in SRAMs is investigated by implementing a negative bitline voltage scheme in this paper. Negative bitline improves the write static noise margin of the SRAM bitcells during a write operation;however, this can cause degraded hold static noise margin of unaccessed cells in the array, called column half-select. Applying a negative wordline voltage on unaccessed rows in an array under negative bitline shows that the degraded hold static noise margin of unaccessed cells can be improved. Fundamentally, the dependence of negative bitline on Vmin,hold of unaccessed cells is negated by applying negative wordline. This allows for a more aggressive scaling of Vmin,hold, resulting in a 77.8% reduction in static power. By applying a complementary negative wordline voltage to counter the half-select condition in columns, the write static noise margin of cells in accessed rows was also boosted by 23% without any degradation in hold static margins of unaccessed cells. © 2023 IEEE.

关键词： Column half-select SRAM Vmin

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Multiple Instance Learning for Diabetic Retinopathy Detection 31

Multiple Instance Learning for Diabetic Retinopathy Detectio...

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31st Symposium of Advanced Database systems, SEBD 2023

作者： Avolio, Matteo Fuduli, Antonio Vocaturo, Eugenio Zumpano, Ester Department of Mathematics and Computer Science University of Calabria Rende Italy Department of Computer Engineering Modeling Electronics and Systems Sciences University of Calabria Rende Italy CNR-NANOTEC National Research Council Rende Italy

Diabetic Retinopathy (DR) is a complication of the diabetes, caused by a damage to the blood vessels in the light-sensitive tissue of the retina: consequently it affects the eyes, determining blindness and visual impairment. On the basis of the last report of the International Diabetes Federation, 537 million adults (20-79 years) were living with diabetes (1 in 10) in 2021, being the cause of 6.7 million deaths (1 every 5 seconds). Taking into account that the number of people affected by DR is predicted to increase to 643 million by 2030 and 700 million by 2045, it is clear that effective screening of potential DR patients is of utmost importance. While direct and indirect ophthalmoscopy are the main methods for evaluating DR, artificial intelligence is on the rise in vision care. DR is detectable by analyzing data from patients' fundus photographs, and is therefore a disease that artificial intelligence tools can effectively support. In this paper, we present some numerical results obtained in the classification between eye fundi of healthy individuals and of people with severe DR, using a Multiple Instance Learning approach. © 2023 CEUR-WS. All rights reserved.

关键词： Image processing

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption 14

Design and FPGA Implementation of the Henon Chaotic Map for ...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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