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Smart Harvesting based on Image Processing

Smart Harvesting based on Image Processing

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2020 International computer Symposium, ICS 2020

作者： Joseph, S.P Wijerathna, L.L.M.C. Epa, K.G.R.D. Egalla, E.K.W.A.P.K. Abeygunawardhana, P.W.K. De Silva, R Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Faculty of Computing Malabe10115 Sri Lanka Sri Lanka Institute of Information Technology Dep. of Software Engineering Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9781728192550

Vision device is a critical component of fruit harvesting mobile robot which is designed for recognizing and harvesting of tomato fruits. The system of vision device of the moving robot, for a dedicated path identification with the use of colour detection algorithms and contour detection algorithms could be stated as an additional usage of the image-based technology. When the robot operates in real life, varying environmental parameters would not affect its activity, since the robot will be functioning mainly indoors, specifically in a greenhouse. Robust fruit segmentation algorithms for the visual system and the fruit plucking mechanism using a readymade robot arm with a soft gripper where it operates with basic hardware components like motors, controlled by a microcontroller with the intervention of kinematic theories, is being used with the goal of collecting fruit objects efficiently in the natural world. Plucking only the ripe and healthy object fruit without damaging both the fruit and the tree is the intended task of the system. The aim is to reduce human effort by promoting automation concepts in agriculture in developing countries like Sri Lanka with great potential, where agriculture plays a vital role in the development of the economy while indoor plantation and harvesting mechanism are also being developing to cater to the economy of the country as well. The way that this system is implemented will be discussed in this paper. © 2020 IEEE.

关键词： Fruits

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Secure Images Transmission Through OFDM System

Secure Images Transmission Through OFDM System

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Jenan Ayad Ahmed Sadiq Basma Makki Zainab Ali Abbood Hanan j. Abdulkareem Zaid Khudhur Hussein Computer Technology Engineering dep Al-Esraa University College Baghdad Iraq Medical Instrumentation Technical Engineering dep. Al-Esraa University College Baghdad Iraq

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

OFDM (Orthogonal Frequency Division Multiplexing) Next-generation wireless communication networks rely heavily on the OFDM system to transmit digital information in a more effective method as well as other traditional methods. Using mobile devices, digital document data such as videos, images, and texts can be easily shared and copied without the owner's permission. In this case, information security is a primary consideration. There are several advantages to using chaotic systems, such as their randomness, which makes them far more secure than traditional encryption methods, such as RSA (AES, DES). An efficient method for transmitting secure images across an AWGN channel has been developed because of this research. The dual-sided encryption and scrambling method for an OFDM system proposed in this paper is described in detail. Here, MATLAB is used to design an OFDM system that employs high-efficiency scrambling and a chaotic map for encryption to achieve high-security image transfer. Signal to noise ratio as well as bit error rate measurements are utilized to gauge the clear of image transmission. Using entropy, histogram analysis, NPCR, and UACI for testing encryption quality.

关键词： Wireless communication Chaotic communication OFDM Image communication System performance Encryption Noise measurement

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zuwaina Alkhyeli Ayesha Alshehhi Mazna Alhemeiri Salma Aldhanhani Khalil AlBalushi Fatima Ali AlNuaimi Abdelkader Nasreddine Belkacem Dep. Computer Science and Software Engineering CIT UAE University Al Ain UAE Dep. Information Systems and Security CIT UAE University Al Ain UAE Dep. Computer & Network Engineering CIT United Arab Emirates University Al Ain UAE TII Autonomous Robotics Research Center Abu Dhabi UAE

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user’s brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope.

关键词： Privacy Authentication Passwords Feature extraction Brain-computer interfaces Electroencephalography Security

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Enhancing Industrial 4.0 Connectivity: A D2D-Based Algorithm for Blind Spot Mitigation in 5G Future Networks Enabled Smart Industry

Enhancing Industrial 4.0 Connectivity: A D2D-Based Algorithm...

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IEEE Globecom Workshops

作者： Muhammad Farhan Khan Adeel Iqbal Atif Shakeel Adnan Rashid Dirk Pesch Confirm Centre for Smart Manufacturing University College Cork Cork Ireland Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan Dep. of Electrical & Information Engineering Politecnico di Bari Bari Italy

The Industry 4.0 is characterized by the integration of advanced and emerging technologies such as Fifth Generation (5G) and Next-Generation Networks & beyond, Artificial Intelligence (AI), Internet-of- Things (IoT), Machine Learning (ML), Robotics, Automation, Blockchain, Cloud Computing, Edge Computing, Augmented Reality (AR), Virtual Reality (VR) and many others. Despite the widespread adoption of 5G, Wireless Fidelity (Wi-Fi) routers, and other wireless technologies in the industry, there are still areas with limited or no coverage, commonly known as blind spots or coverage holes. The Device-to-Device (D2D) communication enables direct communication without a central entity, known as a coordinator or base station. By leveraging D2D communication, IoT nodes can serve as relays to deal with blind spots. In this research, we propose a novel D2D communication-based algorithm to cope with the challenge of blind spots in IoT -based smart industries. The algorithm focuses on nodes situated in blind spots by establishing connections to the network through nearby IoT nodes that function as relays. We demonstrate through simulations that our approach can effectively remove blind spots, improve coverage, increase throughput, and enhance overall system efficiency.

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Prediction of Glycemic Event in Emergency Section Patients Using Machine Learning

Prediction of Glycemic Event in Emergency Section Patients U...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Rotbei, Sayna Soler, Pablo Matías Merino-Barbancho, Beatriz Tourab, Hania Anchuelo, Arturo Corbatón García, Luis Picazo Forés, Ricardo Mesanza Matus, Laura Mariel Albert, Ricardo Muñoz Andicoechea, Aitor Odiaga Panadero, Raquel Piñero Díez, María Ángeles San Martín Sánchez, Ainhoa Burzaco Barrón, Rosana Soriano Irimia, Andrea Esculano, Esther Ruescas Vinceixo, Mireia Cramp Chaib, Fahd Beddar Fico, Giuseppe Botta, Alessio Dep. Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Emergency Dep. Hospital Clinico San Carlos Madrid Spain Universidad Politécnica de Madrid Spain Hospital de Fuenlabrada Spain Hospital de la Ribera Spain Hospital de Galdakao y Hospital de Gernika Spain Clínica Universitaria de Navarra Spain Hospital de Basurto Spain Hospital San Pedro de Logroño Spain Hospital Universitario Central de Asturias Spain Hospital Universitario de Vinalopó Spain Hospital Joan XXIII Spain Hospital de Soria Spain

ISBN: (纸本)9798350350548

Effective diabetes management is crucial to preventing serious complications in patients. This study employs machine learning techniques to analyze data from 11 Spanish hospital emergency dep.rtments, with the goal of improving the quality of life for individuals with type I and type II diabetes. Our model demonstrates a prediction accuracy of up to 95% for hypo- and hyperglycemia, and highlights significant insights into antidiabetic treatments. The results were validated using a decision tree and correlation plots, showing relationships between key variables. This approach shows potential for large-scale prediction of glycemic events and provides valuable support for clinical decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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Speech Emotion Recognition Using Deep Learning Techniques and Augmented Features

Speech Emotion Recognition Using Deep Learning Techniques an...

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International Conference on Electrical engineering, computer Science and Informatics (EECSI)

作者： Shahed Mohammadi Niloufar Hemati Ali Hashemi Haniye Zandiye Computer Science & Systems Engineering Dep. Ayandegan University Mazandaran Iran Islamic Azad University Central Tehran Branch Tehran Iran Qom University of Technology Qom Iran

This paper presents a study on speech emotion recognition using deep learning techniques. The focus is on utilizing the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS) datasets. The proposed approach involves preprocessing the audio signals by converting them into a sequence of numbers and normalizing them using max normalization. Furthermore, the signals are augmented using three methods: noise with a 0.015 noise rate, pitch adjustment, and a combination of noise with a 0.03 noise rate and pitch adjustment. The extraction of audio features includes a combination of two features, namely Mel Frequency Cepstrum Coefficients (MFCC) and Root Mean Square (RMS) energy, which provide distinct information from each other. These features are then used to train a CNN for emotion classification. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing emotions from speech signals. The proposed approach achieves an accuracy of 93.6% on the RAVDESS dataset and 99.9% on the TESS dataset. These results highlight the potential of the proposed approach in practical applications such as speech therapy and human-computer interaction.

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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TAXINOMISIS: A cloud – based platform for risk profiling and patient specific management of the carotid artery disease

TAXINOMISIS: A cloud – based platform for risk profiling an...

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

作者： Vassilis D. Tsakanikas Panagiotis K. Siogkas Vassiliki T. Potsika Antonis I. Sakellarios Dimitris S. Pleouras Vassiliki I. Kigka Themis P. Exarchos Igor B. Koncar Dimitrios I. Fotiadis Dep. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute Foundation for Research and Technology Hellas (FORTH)

Carotid Artery Disease is a complex multi-disciplinary medical condition causing strokes and several other disfunctions to individuals. Within this work, a cloud - based platform is proposed for clinicians and medical doctors that provides a comprehensive risk assessment tool for carotid artery disease. It includes three modeling levels: baseline data-driven risk assessment, blood flow simulations and plaque progression modeling. The proposed models, which have been validated through a wide set of studies within the TAXINOMISIS project, are delivered to the end users through an easy-to-use cloud platform. The architecture and the dep.oyment of this platform includes interfaces for handling the electronic patient record, the 3D arterial reconstruction, blood flow simulations and risk assessment reporting. TAXINOMISIS, compared with both similar software approaches and with the current clinical workflow, assists clinicians to treat patients more effectively and more accurately by providing innovative and validated *** Relevance - Asymptomatic carotid artery disease is a prevalent condition that affects a significant portion of the population, leading to an increased risk of stroke and other cardiovascular events. Early detection and appropriate treatment of this condition can significantly reduce the risk of adverse outcomes and improve patient outcomes. The development of a software tool to assist clinicians in the assessment and management of asymptomatic patients with carotid artery disease is therefore of great clinical relevance. By providing a comprehensive and reliable assessment of the disease and its risk factors, this tool will enable clinicians to make informed decisions regarding patient management and treatment. The impact of this tool on patient outcomes and the reduction of healthcare costs will be of great importance to both patients and the healthcare system.

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Fluorescence Spectroscopy Characterization of Electrophoretically dep.sited ZnO Nanoparticles on Aluminum, Silicon, and APTES Functionalized Silicon Substrates

Fluorescence Spectroscopy Characterization of Electrophoreti...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022

作者： Hazzazi, Fawwaz Young, Alex Daniels-Race, Theda Division of Electrical and Computer Engineering Louisiana State University Baton RougeLA70803 United States Dep. of Electrical Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States

ISBN: (数字)9781510649682

ISBN: (纸本)9781510649675

Zinc oxide (ZnO) nanopowder-based nanoparticles (NPs) with a mean diameter of less than 100 nm were mixed with synthesized electrophoretic dep.sition (EPD) solutions under different concentrations and dep.sited onto silicon (Si), 3-Aminopropyl-Triethoxysilane (APTES) functionalized silicon (APTES-Si), and aluminum (Al) substrates. The wavelength ranges and intensity of each structure's emission spectrum are determined using fluorescence spectroscopy. We will describe our findings considering our hypothesis that the underlying substrate conductivity and concentrations of ZnO nanoparticles in the electrophoretic dep.sition solutions are significant components in achieving higher intensity fluorescence of ZnO thin films required for devices. This study shows that ZnO NPs with higher concentrations dep.sited on a conductive substrate emit higher emission intensity. The presence of visible emission spectra for ZnO NPs dep.sited on all three substrate types show potential for future optical device applications employing the next generation of nanoscale electronic materials. © 2022 SPIE.

关键词： ZnO nanoparticles

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Toward an EEG-Based System for Monitoring Cognitive Load in Neurosurgeons

Toward an EEG-Based System for Monitoring Cognitive Load in ...

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Roberta Ayadi Giovanni Carone Nicolò Castelli Anna Della Calce Irene Del Chicca Mirco Frosolone Ludovica Gargiulo Giovanna Mastrati Nicola Moccaldi Marco Nalin Alessandro Perin Mauro Picciafuoco Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy IRCCS Carlo Besta Neurological Institute ab medica S.p.A. ISTC National Research Council CNR Institute of Cognitive Sciences and Technologies Rome Italy Dep. of Engineering for Innovation University of Salento Lecce Italy

In this study, a method combining statistical and machine learning approaches is proposed to select the most informative EEG features for the detection of the cognitive load linked to fine motor activities, during a Purdue Pegboard Test (PPT). The proposed method is validated by an experimental case study on neurosurgeons monitored by means of a wearable electroencephalographic (EEG) acquisition system during PPT execution at four increasing levels of complexity. EEG features of cognitive workload related to fine motor activity are identified by a Speraman rank correlation analysis and Friedman test. The EEG features selected by the statistical approach are provided as input to the machine learning algorithms. Classification accuracy is the metric adopted to validate the results of feature selection. Among the tested classifiers, the k-Nearest Neighbor (k-NN) reaches 53.3 ± 4.5% average accuracy in detecting the four levels of cognitive load.

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