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Advancements in ECU Identification and Fingerprinting for Enhanced Cybersecurity in Vehicle Networks

Advancements in ECU Identification and Fingerprinting for En...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： Nuthan Manish Ratnam Dokka School of Engineering Central University of Karnataka Gulbarga Karnataka India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This paper explores novel techniques for identifying Electronic Control Units (ECUs) and detecting unauthorized messages in vehicle networks. By leveraging signal processing, machine learning, and spectral analysis, the proposed system achieves a high accuracy rate of 98.76% in classifying known ECUs and identifying rogue messages. Additionally, the paper discusses future research directions for dynamic online classification and network complexity optimization.

关键词： Analytical models Accuracy Frequency-domain analysis machine learning Fingerprint recognition Complexity theory Object recognition Vehicle dynamics Computer security Spectral analysis

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Early Diagnosis of Parkinson's Disease with Speech Pronunciation Features Based on XGBoost Model 2

Early Diagnosis of Parkinson's Disease with Speech Pronuncia...

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2nd IEEE international conference on Software Engineering and Artificial Intelligence (SEAI) / 7th international Workshop on Pattern Recognition (IWPR)

作者： Wang, Xiumei Chen, Xinghong Wang, Qinga Chen, Guannan Fujian Normal Univ Coll Photon & Elect Engn Fuzhou Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

Speech disorder is one of the early symptoms of Parkinson's disease. In order to accurately diagnose the Parkinson's disease in early time, we propose a Parkinson's disease diagnosis method based on machine learning. XGBoost algorithm was applied to detect and classify the speech signals of Parkinson's disease patients. The performance of this method has been evaluated on several metrics based on the UCI Parkinson database, and in experiments, support vector machines, random forests, and neural networks have also been used to detect and classify the speech signal collected from Parkinson's patients. Experimental results show that the performance of XGBoost algorithm is better than other algorithms, the accuracy of Parkinson's disease diagnosis is 96%, precision is 100%, AUC is 0.97, F1_Score is *** thus contributes to the better understanding of the Parkinson disease and possibly the further analyze of the speech feature.

关键词： Parkinson's disease speech disorder XGBoost machine learning

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Research on signal processing Methods for Gas Ultrasonic Flowmeters

Research on Signal Processing Methods for Gas Ultrasonic Flo...

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machine learning, Cloud Computing and Intelligent Mining (MLCCIM), international conference on

作者： Yong Wan China Coal Technology and Engineering Group Chongqing Research Institute Chongqing China State Key laboratory of gas disaster monitoring and emergency technology Chongqing China

On the basis of the difficulty in determining the feature point in the signal processing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signal processing method is proposed to determine a feature wave of the signal accurately. Thus the propagation time of ultrasonic signal is calculated,and the gas flow rate is measured *** method is realized real time in a signal processing system with DSP and FPGA as dualcore for the gas ultrasonic *** judging the “step”, the error caused by noise interference is eliminated,and the stability of the measurement results is enhanced. With piecewise linear calibration,the non-linear error is corrected,and the measurement accuracy of the system is improved. The experimental results of gas flow calibration verify the effectiveness of the method and the system.

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Employing Blockchain and machine learning for Monitoring the Accumulation and Dispensation of Covid-19 Vaccine 2nd

Employing Blockchain and Machine Learning for Monitoring the...

引用

2nd international conference on signal and Data processing, ICSDP 2022

作者： Singh, Suraj Mahadevan, Anbazhagan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819914098

Fair vaccine distribution is a humanitarian priority. Despite this scientific breakthrough, inoculation of the global population itself is an enormous challenge. Even though the vaccination process for SARS-CoV-2 (COVID-19) started globally just after a year of outbreak, it still continues to present significant challenges. One such challenge was gaining and maintaining public trust about the viability of the antibody and the effectiveness of inoculation itself. However, vaccines’ adequacy is dictated by the accessibility and receptiveness of the dissemination framework, which is divided among the collaborators. Most disseminated records, such as the blockchain, provide an open, public, and permanent framework for a variety of uses. In this work, we propose a framework that uses the Blockchain mechanism for vaccines’ safety, security, transparency, and traceability. The Blockchain technology employed in this proposed endeavor ensures and manages vaccine registration, storage, and delivery. Furthermore, machine learning (ML) based prediction is also used for predicting the future trends of the COVID-19 vaccination. A feedback system is also provided to keep track of the number of vials in case a patient has any negative effects. In many aspects, such as good efficiency and scalability, the proposed work shows promising results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A deep learning approach of heartbeat classification for the single-lead ECG signals and inter-patient paradigm 2

A deep learning approach of heartbeat classification for the...

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2nd international conference on signal Image processing and Communication, ICSIPC 2022

作者： Xiao, Hanguang Zhang, Banglin Xia, Qingling Jiang, Bin Ran, Zhiqiang Zhu, Xiuhong Song, Wangwang School of Artificial Intelligence Chongqing University of Technology Chongqing401135 China

ISBN: (纸本)9781510657694

The corresponding arrhythmia often occurs before the onset of cardiovascular disease (CVD), electrocardiogram (ECG) can more intuitively detect any abnormality in the heartbeat as a sign of arrhythmia. There are many traditional ECG classification methods, but these methods are constrained human costs and inaccuracy since they rely on manually extracting features, and cannot fully mine the deep pathological information hidden in the data. Consequently, a novel deep learning model based on single-lead ECG signals and inter-patient paradigm is developed to improve the shortcomings of traditional ECG heartbeat classification. The residual connection is adopted in the proposed method to improve classification accuracy and alleviate the gradient disappearance issue. Heartbeat complexes that included a targeted heartbeat and an adjacent heartbeat is selected as the input of the model. The MIT-BIH arrhythmia database is employed to valid proposed method. Besides, a focal loss function is used to address the classes imbalance of the database. The experimental results show that the positive predictive values of the proposed classification method for N, S, V, and F are 99.10%, 96.80%, 61.32%, and 95.36%, respectively. In addition, the sensitivity values are 95.83%, 95.54%, 90.43%, and 84.79%;the specificity values are 92.96%, 99.88%, 96.05%, and 99.97%, respectively. Compared with the art-of-state inter-patient ECG heartbeat classification approaches, our proposed approach achieved better results. Therefore, the proposed deep learning model of heartbeat classification is effective and feasible for the single-lead ECG signals and inter-patient paradigm. © 2022 SPIE.

关键词： Electrocardiograms

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High-performance UV detector based on freestanding AlGaN film by electrochemical etching technology 2

High-performance UV detector based on freestanding AlGaN fil...

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2nd international conference on Optical Communication and Optical Information processing, OCOIP 2024

作者： Yang, Yuqing Wang, Xingfu Zhang, Lu Wu, Shuanghong School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China No.4 Section 2 North Jianshe Road Chengdu610054 China Faculty of Engineering South China Normal University Taoyuan East Road Nanhai District Foshan528225 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Ultraviolet (UV) photodetector (PD) is a device that converts optical signal into electrical signal, serving as a critical component in optoelectronic systems. Due to the leakage effect caused by defects of substrate and buffer layer, the traditional III-nitride semiconductors material UV PDs usually have a large dark current, which seriously restricts the performance of the UV PDs. Herein, freestanding Al0.1Ga0.9N UV PD was prepared by electrochemical (EC) etching technology. Based on the freestanding Al0.1Ga0.9N UV detector, the photoelectric performance was charactered. The freestanding Al0.1Ga0.9N UV PD shows a low dark current below of 1 nA owing to the removing of redundant buffer layer, and exhibits a high responsivity of 0.61 A/W, a high detectivity of 4.1 × 1012 Jones. The time response demonstrates that the rise time and decay time of the Al0.1Ga0.9N UV PD are merely 0.291 and 0.007 s. The Al0.1Ga0.9N PD demonstrates a stable and rapid response time at 325 nm under a low bias of 2 V. This research offers a method for the fabrication and transfer of Al0.1Ga0.9N UV PD, facilitating the development of novel functional devices, including AlGaN heterogeneously integrated devices and flexible devices. © 2025 SPIE.

关键词： Electrochemical etching

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Comparative Study of machine learning Algorithms to Forecast Weather Conditions

Comparative Study of Machine Learning Algorithms to Forecast...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Prashant Ahlawat Abhinav Tripathi Ishika Thakur Ankush Yadav Purushottam Kumar Department of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The article focuses on improving weather forecasting by comparing several machine-learning algorithms. In this study, a standard weather condition dataset was considered for the analysis. The dataset undergoes necessary pre-processing techniques like exploration, analysis, and modeling. The structured data was then trained through popular machine learning algorithms like Logistic Regression, Random Forest, Decision Trees, XGBoost, Support Vector machine (SVM), and Naïve Bayes. The models are evaluated by prediction accuracy, confusion matrix, and error rate. Among the implemented models, the Naïve Bayes model stood as the best model for the specific dataset for the prediction. This research assesses the effects of climate change on monsoon dynamics and long-term trends by assisting policymakers, researchers, and stakeholders in understanding weather conditions through machine learning models.

关键词： Support vector machines Rain Accuracy machine learning algorithms Biological system modeling Weather forecasting signal processing algorithms Predictive models Bayes methods Random forests

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System Modeling and machine learning in Prediction of Metastases in Lung Cancer

System Modeling and Machine Learning in Prediction of Metast...

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16th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Swierniak, Andrzej Kozlowska, Emilia Fujarewicz, Krzysztof Borys, Damian Wilk, Agata Smieja, Jaroslaw Suwinski, Rafal Department of Systems Biology and Engineering Silesian University of Technology Akademicka 16 Gliwice44-100 Poland The 2nd Radiotherapy and Chemotherapy Clinic M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Wybrzeze Armii Krajowej 15 Gliwice Poland

The aim of this paper is to present goals and preliminary results of our project devoted to system engineering approach in prediction of metastases in lung cancer. More specifically we consider existing and develop new methods of system modeling, machine learning, signal processing and intelligent control to find biomarkers enabling prediction of risk of tumor spread and colonization of distant organs in non-small-cell lung carcinoma basing on clinical data and medical images. The results could bring us knowledge about the dynamics and origin of metastatic dissemination of lung cancer. By dynamics, we understand when and where a tumor will disseminate, and by origin we mean dissemination path (directly from original tumor or through lymphatic nodes). This information is very valuable for clinicians, as it could guide the personalized treatment of lung cancer patients. The results will elucidate important issues concerning prediction of individual progress of cancer and treatment outcome in oncology. They will provide both theoretical and simulation tools to support decision making and diagnostics in oncology, on the basis of individual patient state. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Lung cancer

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Electromyography Gestures Sensing with Deeply Quantized Neural Networks 2

Electromyography Gestures Sensing with Deeply Quantized Neur...

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2nd Edition IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Pau, Danilo Pietro Randriatsimiovalaza, Marc Dimbiniaina STMicroelectronics System Research and Applications Agrate Brianza Italy

ISBN: (纸本)9798350300802

Electromyography measures muscle electrical activity as the response to a nerve's stimulation. Recent developments in deep learning have also been applied to process bio-signals. The development of Deeply Quantized Neural Networks offers the opportunity to design low complex machine learning inferences. They target very low bit-depth representations for weights, biases, and activations. However, they could be highly affected by decreased performances if pushed to one-bit representation. This paper investigated hand gesture classification using electromyography signals. To accomplish this task at the lowest bit depth to save memory and complexity assets, was challenging from the accuracy perspective. One objective was to build a highly accurate model to classify the hand gestures while fulfilling the limited memory and computation latency constraints of a low-cost microcontroller, according to the model complexity. The classification accuracy achieved was 93.94% on the Italian Sign Language dataset and 83.70% on the Gestures Master dataset. The models were characterized on two STM32 microcontrollers in terms of execution time. On STM32H7, the 8-bits model ran around 8.306ms/inference. It was 11 ms faster than a fp32 based convolutional neural network. On STM32G4 instead, the 8-bits model was executed around 40 ms/inference while 102 ms for the fp32 model. Furthermore, these models have been profiled by ***, an experimental tool to deploy pre-trained neural networks into super integrated inertial sensor processing unit, requiring less hardware resources than an off-the-shelf microcontroller. On the Italian Sign Language dataset and the Gestures Master dataset the maximum RAM was 29.17 KiB while the FLASH 21.13KiB with a computational complexity of 1,451,413 multiply and accumulates at 10 MHz. © 2023 IEEE.

关键词： deep quantization electromyography Hand gesture classification intelligent signal processing unit microcontrollers

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A Network Intrusion Detection System Based on Ensemble machine learning Techniques 2

A Network Intrusion Detection System Based on Ensemble Machi...

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2nd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2021

作者： Parkar, Prachiti SAP Fieldglass SAP Labs India Bangalore India

ISBN: (纸本)9781665442992

Technology is growing exponentially than ever before;it helps us to make unforeseen progress but at the same time these advancements also pose a grave danger to software systems in terms of security. While paving a path for advancements, these developments are simultaneously lending a helping hand to hackers to hack into systems illegally and misuse the data. While the hackers cannot be stopped, their activities can be identified and prevented. In this paper, I have proposed a simple and effective technique to detect network intrusions and protect valuable data by using machine learning algorithms. The novelty in this approach is that the proposed system used the same classifiers in feature selection which were used later for the main classification step, this proved to be fundamental in achieving high accuracy. Moreover, I have successfully simulated attacks in real-time to test our application. Finally, I concluded with fetched results and insights for future research. © 2021 IEEE.

关键词： Cybersecurity

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