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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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A Secure and Trustworthy Biometric Data Ecosystem for Cross-border Suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kyriakou, Katerina Apostolaras, Apostolos Velentzas, Polychronis Benos, Georgios Koutsoukos, Konstantinos Symvoulidis, Chrysostomos Liang, Kaitai Shi, Zeshun Leonidis, Asterios Miniadou, Kyriaki Veroni, Eleni Evangelatos, Spyridon Papadopoulos, Georgios Th. Korakis, Thanasis University of Thessaly Department of Electrical and Computer Engineering Volos Greece The Centre for Research & Technology Certh Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft Netherlands Crete Greece Netcompany-Intrasoft S.A. Research & Innovation Development Luxembourg Luxembourg Hellenic Mediterranean University Dept. of Electronic Engineering Crete Greece Harokopio University of Athens Department of Informatics and Telematics Athens Greece

ISBN: (纸本)9798350362480

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs. © 2024 IEEE.

关键词： Biometrics Data Sovereignty Biometrics Data Space Cross-Border Suspect Identification Homomorphic Encryption Privacy Enhancing Technologies

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Evaluation of ARIMA, Facebook Prophet and a boosting algorithm framework for monthly precipitation prediction of a semi-arid district of north Karnataka, India 4

Evaluation of ARIMA, Facebook Prophet and a boosting algorit...

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4th International Conference on Emerging Research in Electronics, computer Science and technology, ICERECT 2022

作者： Chowdari, K.K. Barma, Surajit Deb Bhat, Nagaraj Girisha, R. Gouda, K.C. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumkur572103 India National Institute of Technology Karnataka Surathkal Department of Water Resources and Ocean Engineering Karnataka Mangalore575025 India SMVITM Bantakal Department of Computer Science and Engineering Karnataka Udupi574115 India PES College of Engineering Dept. of Computer Science and Engineering Karnataka Mandya571401 India CSIR Fourth Paradigm Inst Karnataka Bangalore560037 India

ISBN: (纸本)9781665456357

This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901-2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data. © 2022 IEEE.

关键词： Time series

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Agricultural Data Space: the METRIQA Platform and a Case Study in the CODECS project

Agricultural Data Space: the METRIQA Platform and a Case Stu...

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Federated Conference on computer Science and Information Systems (FedCSIS)

作者： Manlio Bacco Giovanna Maria Dimitri Alexander Kocian Paolo Barsocchi Antonino Crivello Gianluca Brunori Marco Gori Stefano Chessa Inst. of Information Science and Technologies (ISTI) National Research Council (CNR) Pisa Italy Dept. of Information Engineering and Mathematics (DIISM) University of Siena Italy Dept. Computer Science University of Pisa Italy Dept. of Agriculture Food and Environment University of Pisa Italy Dept. Computer Science University of Pisa Italy ISTI-CNR Italy

ISBN: (数字)9788396960160

ISBN: (纸本)9798350359718

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.

关键词： Industries Connectors computer science Data analysis Digital transformation Supply chains Companies Stakeholders Convergence Information systems

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;inst.gram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Using Object Detection to Select Grasp Type & Control Functional Electrical Stimulation for Hand Rehabilitation

Using Object Detection to Select Grasp Type & Control Functi...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Nikunj Arunkumar Bhagat Manikanta Ruppa Dept. of Electrical Engineering Indian Institute of Science Bengaluru India Dept. of Computer Science Indian Inst. Of Inf. Tech. & Mgmt Thiruvananthapuram India

Unilateral muscle weakness and hand paralysis is the most common outcome after a stroke. Functional electrical stimulation (FES) is effective in assisting hand prehension, but conventional stimulators require users to manually select the grasp type (e.g. by pressing a button), which is challenging for patients with severe paralysis. Also, patients need to frequently divert attention from their task to operate the stimulator, which is cumbersome and reduces their engagement in the therapy. In this study, we develop a novel deep learning-based object detection approach to select multiple grasp types and control an electrical stimulator, in order to assist grasping. Object detection was performed using a state-of-the-art YOLOv5 algorithm, which achieved above 93% mean average precision. The algorithm tracked the positions of the hand and objects and selected a grasp type based on the object nearest to the hand. Once the grasp type was selected, a custom-built FES stimulator was activated to execute pre-defined stimulation sequences and allow a person to grasp the nearest object. This contactless, vision-based solution is beneficial for patients opting for homebased rehabilitation since it doesn’t require additional setup time or help from caregivers. The future scope of this work includes testing the object detection-based FES on stroke patients and determining its efficacy in restoring hand movements.

关键词： Signal processing algorithms Object detection Pressing Stroke (medical condition) Signal processing Iron Paralysis

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Exploring Hot/Cold Data Separation for Garbage Collection Efficiency Enhancement on OCSSDs

Exploring Hot/Cold Data Separation for Garbage Collection Ef...

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International SoC Design Conference, ISOCC

作者： Yu-Shiang Tsai Shuo-Han Chen Yong-Cheng Liaw Cheng-Yueh Wu Dept. of Computer Science & Information Engineering National Taipei Uni.of Technology Taipei Taiwan Dept. of Computer Science Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan

Solid State Drives (SSDs) are widely used in various electronic devices and have gained a significant market share because SSDs do not have mechanical components, have fast access speeds and are less likely to be damaged by external forces. To fulfill the functionalities of conventional storage devices, the limitations of SSDs become prominent and require the Flash Translation Layer (FTL) to manage the NAND flash memory. Nevertheless, the conventional FTL functions without the assistance of the host system, which leads to an information gap and decreased efficiency. Recently, the concept of open-channel SSD (OCSSD) has emerged to allow direct host control over SSD internals. Leveraging the concept of OCSSD, this paper proposes a host-assisted garbage collection enhancement mechanism to facilitate the efficiency of garbage collection through hot/cold data separation, and the proposed mechanism can effectively reduce the number of garbage collections by an average of 60.8%.

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation

Delay Analysis in Closed-Loop EEG Phase-Triggered Transcrani...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yu-Cheng Chang Pin-Hsuan Chao Yan-Ming Kuan Chiu-Jung Huang Li-Fen Chen Wei-Chung Mao Tung-Ping Su Sin-Horng Chen Chun-Shu Wei Dept. of Electronics and Electrical Engineering NYCU Hsinchu Taiwan Inst. of Brain Science NYCU Hsinchu Taiwan Inst. of Biomedical Engineering (BME) NYCU Hsinchu Taiwan Dept. of Computer Science (CS) NYCU Hsinchu Taiwan Dept. of Psychiatry Cheng Hsin General Hospital Taipei Taiwan Dept. of CS and the Inst. of BME NYCU Hsinchu Taiwan

The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves inst.ntaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system’s transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.

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