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Quadcopter 3d-printer frame static optimization

AIP Conference Proceedings 2023年第1期2977卷

作者： Khalid Mohsin Ali Mauwafak Ali Tawafik Alaa Adbdulhady Jaber Department of Computer Engineering Technology Faculty of Information Technology Imam Ja’afar Alsadiq University Baghdad Iraq Mechanical Department University of Technology Baghdad Iraq Dep. Of refrigeration & air conditioning Alnukhba University College Baghdad Iraq

The quadcopter is an advanced intelligent type of helicopter with four rotors; Qc is despite its intelligence and the complexity of its electronic parts, it is distinguished by the simplicity of the structure and its symmetry, and the four sides are equal in size, weight, and power of the motors. At present, the design of the quadcopter has diversified into a structure that takes the shape of a plus sign, others take a structure shaped like a letter H or hybrid H, and one that is the most common that takes the letter X, which we will work on. The most susceptible portion of the quadcopter to failure is the arms because they are sensitive to multiple types of stress, including fracture due to collision or fall and failure due to overloading. The goal of this study is to improve the design of the quadruple arm so that it can reliably lift heavy loads. The quad frame was designed with a size of 60 cm and a weight of 2 kg. eight structures of the same material (PLA), the same size wheelbase and the same weight, but in different arm shapes, these frames designed and analyzed statically by the SolidWorks Simulation to find the stresses, deformations, and areas of weakness in each arms separately, assuming the same power of motors and the same size of the propeller (lifting force) and the gravity force. The results showed that the bearing strength of the arm dep.nds on the shape of the cross-sectional area along the length of the arm according to the laws of bending stress.

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An Overview on Big Data Mining Using Evolutionary Techniques

An Overview on Big Data Mining Using Evolutionary Techniques

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2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

作者： Alaeddin, Fadia Khalifeh, Ala' Darabkh, Khalid A. Princess Sumaya University for Technology Luminus Technical University College Computer Science Department Amman Jordan German Jordanian University Dep. of Electrical Eng. Amman Jordan University of Jordan Dept. of Computer Engineering Amman Jordan

ISBN: (纸本)9781728196732

Big Data processing suffers from several limitations due to its magnitude making it time consuming to implement any kind of analysis such as data mining. Evolutionary Algorithms (EAs) are metaheuristic optimization algorithms inspired by the natural behavior of the population evolution such as Genetic Algorithm, Artificial Bee Colony, Artificial Ant Colony and other swarm intelligence algorithms. EAs have recently been used to overcome Big Data limitations, especially memory consumption and the long execution time. This paper provides an overview of the recent research papers that utilize evolutionary algorithms to deal with the optimization problems related to Big Data mining such as clustering, classification and features' selection. © 2020 IEEE.

关键词： K-means clustering

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An Efficient Localization for Indoor Environment using Classification Algorithms

An Efficient Localization for Indoor Environment using Class...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

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Introducing network coding to RPL: The Chained Secure Mode (CSM)

arXiv

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arXiv 2020年

作者： Raoof, Ahmed Lung, Chung-Horng Matrawy, Ashraf Dep. of Systems and Computer Eng Carleton University Canada School of Information Technology Carleton University Canada

As the de facto routing protocol for many Internet of Things (IoT) networks nowadays, and to assure the confidentiality and integrity of its control messages, the Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM). While the PSM and ASM are intended to protect against external routing attacks and some replay attacks (through an optional replay protection mechanism), recent research showed that RPL in PSM is still vulnerable to many routing attacks, both internal and external. In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intra-flow Network Coding. The goal of CSM is to enhance RPL’s resilience against several routing attacks, with the ability to mitigate some of them. The security and performance of a proof-of-concept prototype of CSM were evaluated and compared against RPL in UM and PSM (with and without the optional replay protection) in the presence of Neighbor attack as an example. It showed that CSM has better performance and more enhanced security compared to the PSM with the replay protection. On the other hand, it showed a need for a proper recovery mechanism for the case of losing a control message. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

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Extremum Seeking Control for Beam Steering using Hypersurfaces

Extremum Seeking Control for Beam Steering using Hypersurfac...

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IEEE International Conference on Communications Workshops, ICC

作者： Nouman Ashraf Marios Lestas Taqwa Saeed Hamidreza Taghvaee Sergi Abadal Andreas Pitsillides Christos Liaskos Dep. of Electrical Engineering Frederick University Dep. of Computer Science University of Cyprus Nicosia Cyprus Dep. of Computer Architecture Universitat Politècnica de Catalunya Barcelona Spain Institute of Computer Science Foundation of Research and Technology Hellas Heraklion Greece

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Hypersurface, a software defined metasurface (SDM) paradigm constitutes a revolutionary technology aiming at offering programmatic control over all aspects of a propagating wave, altering its direction, power, polarization and phase. In the absence of line of sight (LOS) between a transmitter and a receiver, HSF can provide seamless connectivity via programmatic reflection. However, such an application requires fine tuning of the metasurface reconfiguration parameters, which may not be effective when done in an open loop fashion due to model uncertainties. In this work, we consider a feedback-based formulation of the problem, which involves maximization of the received power and propose the use of Extremum Seeking Control (ESC) for the controller implementation due to fact that it is model-free and adheres to the maximization problem. Extensive simulations indicate that the proposed scheme is successful in guiding the impinging wave to the receiver within reasonable time even in the presence of time varying delays incurred by message propagation. In addition, a discrete time implementation is considered, investigating its limitations as we increase the sampling time while also characterizing the traffic within the controller network.

关键词： Receivers Beam steering Transmitters Aerospace electronics computer science Software Delays

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GDPR compliance for blockchain applications in healthcare

arXiv

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arXiv 2020年

作者： Hasselgren, Anton Wan, Paul Kengfai Horn, Margareth Kralevska, Katina Gligoroski, Danilo Faxvaag, Arild Dep. of Neuromedicine and Movement Science NTNU Norwegian University of Science and Technology Norway Dep. of Manufacturing and Civil Engineering NTNU Norwegian University of Science and Technology Norway Dep. of Social- and Educational Science NTNU Norwegian University of Science and Technology Norway Dep. of Information Security and Communication Technology NTNU Norwegian University of Science and Technology Norway

The transparent and decentralized characteristics associated with blockchain can be both appealing and problematic when applied to a healthcare use-case. As health data is highly sensitive, it is also highly regulated to ensure the privacy of patients. At the same time, access to health data and interoperability is in high demand. Regulatory frameworks such as GDPR and HIPAA are, amongst other objectives, meant to contribute to mitigating the risk of privacy violations in health data. Blockchain features can likely improve interoperability and access control to health data, and at the same time, preserve or even increase, the privacy of patients. Blockchain applications should address compliance with the current regulatory framework to increase real-world feasibility. This exploratory work indicates that published proof-of-concepts in the health domain comply with GDRP, to an extent. Blockchain developers need to make design choices to be compliant with GDPR since currently, none available blockchain platform can show compliance out of the box. Copyright © 2020, The Authors. All rights reserved.

关键词： Health risks

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Performance analysis of ONOS and floodlight SDN controllers based on TCP and UDP traffic

Performance analysis of ONOS and floodlight SDN controllers ...

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2019 International Conference on computer, Control, Electrical, and Electronics engineering, ICCCEEE 2019

作者： Eljack, Arwa Hamad Hassan, Ahmed Hassan M. Elamin, Hala Hassan Mashreq University Dep. of Information Technology Khartoum Sudan Mashreq University Faculty of Engineering Khartoum Sudan

ISBN: (数字)9781728110066

ISBN: (纸本)9781728110066

Software-Defined Networking (SDN) is newly originated network architecture. SDN characterizes by flexible, dynamic, cost-effective, and convenient to manage. This article essentially provides two OpenFlow-enabled controllers, i.e., Floodlight controller and ONOS controller. A performance comparison of both controllers is tested over different network topologies (single, linear and tree). The analyzing of controllers based on some metrics such as transfer, Delay, bandwidth and jitter using an efficient network simulator called Mininet. The ONOS show better performance compared to Floodlight in both TCP and UDP traffic in network. © 2019 IEEE.

关键词： Controllers

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Feel-tire Unina: Development and Modeling of a Sensing System for Intelligent Tires 5

Feel-tire Unina: Development and Modeling of a Sensing Syste...

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5th International Forum on Research and Technologies for Society and Industry, RTSI 2019

作者： Breglio, Giovanni Irace, Andrea Marrazzo, Vincenzo Romano Riccio, Michele Romano, Luigi Strano, Salvatore Terzo, Mario University of Naples Federico II Dep. of Electrical Eng. and Information Naples Italy Chalmers University of Technology Dep. of Maritime and Mechanical Engineering Gothenburg Sweden University of Naples Federico II Dep. of Industrial Engineering Naples Italy

ISBN: (纸本)9781728138152

In recent years, a great deal of research has been devoted to develop intelligent tire concepts, aiming at increasing the effectiveness of the active safety system of ground vehicles. There are several areas of improvement regarding intelligent tires;among them, the possibility of correlating information provided by sensors with tire dynamics. However, many approaches are based on regression algorithms which need a large amount of data to be collected and can easily lead to misinterpretations due to the non-full-physical nature of the proposed methods. In this paper, the use of a cost-effective flex sensor for intelligent tire applications is presented. The proposed technology is based on a flexible ring tire model (FRTM) able to estimate the tire circumferential strain due to different load conditions. The flex sensor has been also combined with a a wireless data transmission and a suitable prototype has been realized. Also, a simplified analytical expression for describing the tire curvature according to the applied load has been derived in closed-form. Experimental results have been finally analyzed by employing the FRTM and an analytical procedure for correlating the tire curvature with the voltage outputs provided by the flex sensor is discussed. © 2019 IEEE.

关键词： Tires

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Efficient COVID-19 Identification Using Deep Learning for IoT

Efficient COVID-19 Identification Using Deep Learning for Io...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vivek Veeraiah A. Pankajam Ela Vashishtha Dharmesh Dhabliya P. Karthikeyan Radha Raman Chandan Department of R & D Computer Science Adichunchanagiri University Mandya Karnataka India Dep of Business Administration Avinashilingam Institute for Home Science and Higher Education for Women Tamil Nadu India Healthcare Consultant University of North Texas Health Science Center Texas USA Dept. of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India School of Management Studies KonguEngineering College Perundurai Erode Tamil Nadu India Dept. of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Jhalwa Prayagraj Uttar Pradesh India

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting.

关键词： COVID-19 Deep learning Patient monitoring Transfer learning Transportation Vaccines Internet of Things

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Deep Learning Approach for the Precision Medicine Track 28

Deep Learning Approach for the Precision Medicine Track

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28th Text REtrieval Conference, TREC 2019

作者： Consuegra-Ayala, Juan Pablo Stilo, Giovanni Celi, Alessandro Di Salle, Amleto University of Havana School of Math and Computer Science Cuba University of L'Aquila Dep. of Information Engineering Computer Science and Mathematics Italy

The paper describes the system presented by the University of L'Aquila in collaboration with the University of Havana - team named UNIVAQ - to the TREC 2019 Precision Medicine Track. The proposed solution, maps any kind of documents - Scientific Abstract, Clinical trials, and Topics - into a multi-dimensional common general representation. Each document is described by five primitive features. The values of each feature are extracted from the original documents using deep learning and machine learning text processing based techniques. To recognize Genes and Diseases, we have trained our models using the PubTator annotated corpus. Instead, to derive demographics information, we have trained the employed deep learning models using the documents -obtained from the Relevance and Raw judgements of the past edition of TREC Precision Medicine/Clinical Decision Support Track 2018- considered "relevant" or "partially relevant". The results of the Track clearly show that applying a system (as our) made solely by a tagging based approach to the Precision Medicine task, is not sufficient to achieve the performances gained by other systems presented in the TREC Precision Medicine Track 2019. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

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