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Robotics in Primary and Secondary Education - The Lego Mindstorms EV3 implementation 6

Robotics in Primary and Secondary Education - The Lego Minds...

6th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Samara, Elpida Angelidis, Pantelis Kotsiari, Aspasia Kilintzis, Pavlos Giorgos, Avlogiaris Dep. of Regional and Cross Border Development University of Western Macedonia Kozani Greece Dep. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dep. of IT Engineering University of Western Macedonia Kozani Greece Dep. Of Mechanical Engineering University of Western Macedonia Kozani Greece Dep. of Statistics and Insurance Science University of Western Macedonia Kozani Greece

ISBN: (纸本)9781665427425

The implementation of educational robotics has been increasingly popular over the recent years, highlighting the benefits that digital technology has brought to education. The ulterior aim of this paper is to investigate the level of acceptance of robotics' implementation on the educational process by the primary and secondary education students in Greece. This is conducted by first presenting Lego Mindstorms EV3 robotic device to the students, by implementing a step-by-step workshop, to introduce them to the basic characteristics of an educational robotic platform. Next, the students are answering a questionnaire indicating their experiences and remarks about the learning process. Their answers are being statistically processed to reveal the crucial points, regarding the implementation of educational robotics in the educational process. The study contains key elements regarding students' perceptions over educational robotics' sector, applied as a primary educational tool. © 2021 IEEE

关键词： Students

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Genetic Optimization Techniques for Enhancing Web Attacks Classification in Machine Learning

Genetic Optimization Techniques for Enhancing Web Attacks Cl...

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IEEE International Symposium on dep.ndable, Autonomic and Secure Computing (DASC)

作者： Ameera S. Jaradat Ahmad Nasayreh Qais Al-Na'amneh Hasan Gharaibeh Rabia Emhamed Al Mamlook Dep. of Computer Science Yarmouk University Irbid Jordan Dep. of Cyber Security and Cloud Computing Applied Science Private University Amman Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia

Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work dep.oyed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively.

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A New Implementation of SHA-3 Hash Function Using Seed Value

A New Implementation of SHA-3 Hash Function Using Seed Value

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Information Technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Ali H. Kashmar Ammar I. Shihab Wafaa S. Jawad Dept. of Computer Science College of Science University of Baghdad Baghdad Iraq Dep. Of Computer and Communication Faculty of Engineering Islamic University of Lebanon Beirut Lebanon

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

This paper aimed to suggest a new Secured Hash Algorithm 3 (SHA-3) with seed value implementation. Seed worth ensuring that there are fewer expectations collisions, despite an attacker selecting the data. Furthermore, attackers who combine seed values with hash functions are prevented from exploiting the properties of a hash function to construct many security attacks, such as denial of service attacks and dictionary attacks. A novel approach to enhanced SHA-3 diffusion was proposed here by XORing the seed value with $\theta$ step result after transforming the seed bits into a $5 \times 5 \times 64$ matrix and discarding the rest.

关键词： Hash functions Resistance Privacy Dictionaries Numerical analysis Buildings Vectors

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An Implementation and Design of a Digital Light Switch of Smart-Home Based on MQTT Broker 4

An Implementation and Design of a Digital Light Switch of Sm...

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4th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2021

作者： Tsao, Yung-Chung Wu, Chia-Chun Kuo, Yaw-Wen Tsai, Yin-Te Hsu, Chihcheng Wang, Renjie Chou, Polun Ho, Liulin Li, Chiyang Ko, Chihsheng National Chi Nan University Dep. of Electrical Engineering Puli Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung Taiwan National Central Univeristy Dep. of Information Management Chung-Li Taiwan College of Science and Technology National Chi Nan University Taiwan

ISBN: (纸本)9781665423076

Home appliances are designed and manufactured for the market. Most users find that APPs are installed on smartphones or tablets to control home appliances with individual APP. Even light-bulbs with different brands or models need exclusive control software to corresponding light-bulbs. Thus, the smart and intelligent technology for smart-home is difficult to use due to different brands home-appliances from different manufactures. It lacks a standard to coordinate the control way like traditional light-bulbs-based E27 specs. For the light-bulbs of home appliances as an example, the traditional light-bulbs-based E27 lamp-holder have different fixtures and power-supply. Thus, new light-bulbs of home appliances for smart-home need a complex control-way besides electronic power to achieve smart functions and remote-control based on APPs on users' smart-phone. In this research, we try to integrate the internet of thing and message queue telemetry transport (MQTT) to design a mechanism and to implement a prototype web system to achieve an instant and visualized digital light switch of smart-home to solve the problems. For the validation of this research, we used a NodeMCU-32 microcontroller unit as a micro-controller to integrate with WS2812B LED strips to design light tube and light-bulb as the proof of concept (PoC) to demonstrate it in smart-home. © 2021 IEEE.

关键词： Internet of things

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Deep Learning Technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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Networked multi-sensor rollator with AI-assisted fall detection

Networked multi-sensor rollator with AI-assisted fall detect...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Markus Schwarz Vincent Gauda Markus Kaiser Henrik K. J. Kuttruff Jonas Rentzsch Maximilian Thür Dep. of Health Care Hochschule Niederrhein Univ. of Applied Sciences Krefeld Germany Dep. of Electrical Engineering and Computer Science Hochschule Niederrhein Univ. of Applied Sciences Krefeld Germany

This contribution describes a networked and energy-autonomous rollator, primarily designed to increase the users safety. To achieve this, a multi-sensor approach is used to provide sufficient data for classification of activity patterns, detection of long term changes in agility as well as fall detection, supported by AI technology. For data economy, networking of the rollator in outdoor areas is limited to the transmission of emergency situations via mobile radio to inform caregivers. With the user's consent, analysis of medium-and long-term changes in movement and activity behaviour can also be transmitted as an option.

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EEG-Based Stress Assessment During Robot Assisted Surgery. Comparison of Statistical Methods with Machine Learning

EEG-Based Stress Assessment During Robot Assisted Surgery. C...

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

作者： Pasquale Arpaia Irene Del Chicca Matteo De Luca Ludovica Gargiulo Giovanna Mastrati Nicola Moccaldi Elisa Morganti Marco Nalin Mauro Picciafuoco Francesca Pierro Filippo Rigoni Riccardo Rigoni Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy ab medica S.p.A. Cerro Maggiore MI Italy Dep. of Electronics Information and Bioengineering (DEIB) Polytechnic University of Milan Milan Italy Dep. of Computer Science (D-INFK) ETH Zurich Zurich Switzerland

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This study proposes a adaptive pipeline (feature extraction, feature selection, and classification) for the processing of electroencephalographic (EEG) signals to assess Mental Workload (MWL) during training sessions with a surgical robot. Unlike previous studies in this field, EEG signals were acquired by a dry eight-electrode device to maximize usability and comfort for the users. Fourteen medical students were EEG monitored while performing four different tasks on a simulation platform incorporated into the Da Vinci System. The proposed pipeline was compared with typical machine-learning approaches based on handcraft feature extraction approaches. The results confirmed the effectiveness of the proposed solution as a further contribution to EEG-based monitoring of robotic surgery for adaptive training.

关键词： Training Medical robotics Pipelines Surgery Machine learning Feature extraction Electroencephalography Biomedical monitoring Usability Monitoring

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM)

Parallel and hierarchically-distributed Shoreline Alert Mode...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Ciro Giuseppe de Vita Gennaro Mellone Aniello Florio Catherine Alessandra Torres Charles Diana Di Luccio Marco Lapegna Guido Benassai Giorgio Budillon Raffaele Montella Dep. of Science and Technologies University of Naples “Parthenope” Naples Italy Dep. of Computer Science and Engineering University of Madrid Carlos III Madrid Spain Dept. of Mathematics and Applications University of Naples “Federico II” Naples Italy

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.

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A Novel Gpu-Based Approach for Embedded Narmax/Frols System Identification

SSRN

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SSRN 2023年

作者： Soudré, Marlon Marques Ayala, Helon Vicente Hultmann Melo, Alba Cristina M.A. Llanos, Carlos H. University of Brasilia Dep. of Mechanical Engineering DF Brasilia70910-900 Brazil Pontifical Catholic University of Rio de Janeiro Dep. of Mechanical Engineering RJ Rio de Janeiro22231-180 Brazil University of Brasilia Dep. of Computer Science DF Brasilia70910-900 Brazil

Identifying system dynamics is an essential task for several research areas that deal with real systems intended for purposes such as control, monitoring, or fault detection. However, the nonlinear characteristics of most real systems have led to the development of techniques with high computational complexity, which can make their use unfeasible in systems with restrictions, such as embedded systems. In this case, few studies have been done on how best to map nonlinear system identification techniques to embedded system platforms which may have parallelism capabilities, such as those incorporating GPUs. This work proposes a novel software architecture for parallel model term selection and estimation for Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) models, addressed to GPU-based embedded platforms. Namely, we devise a new parallel strategy to perform Forward Regression Orthogonal Least Squares (FROLS) in nonlinear black-box system identification. The architecture presented was tested in the new NVIDIA Jetson Developer Kit, which contains a 128-core Maxwell-GPU, and compared to serial versions of the algorithm run in standard Intel Core i7 CPU and ARM A57. Using a theoretical example, we defined a parallelization strategy and validated it with an electro-mechanical positioning system representing time-invariant nonlinear systems. As a result, the time spent in the proposed parallel GPU algorithm is more than 24.5x faster, and the energy-efficiency is 22x better if compared to its serial version. This represents a significant improvement in speedup and energetic demand, allowing NARMAX/FROLS models to be constructed in real-time using low-cost and low-power embedded platforms. © 2023, The Authors. All rights reserved.

关键词： Electric power utilization

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Deep Learning Technique for Real-Time Forest Fire Detection ...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Nidhi Gupta Sujata Chakravarty Satyabrata Dash Dept. of Computer Science & Engineering Centurion University of Technology & Management Odisha India Dep. of Computer Science and Engineering Gandhi Institute of Technology & Management (GITAM Deemed to be University) Andhra Pradesh India

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.

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