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IEEE International Conference on Robotic Computing (IRC)

作者： Juann Kim Dongwhan Lee Youngseo Kim Heeyeon Shin Yeeun Heo Yaqin Wang Eric T. Matson Dept. Software Sangmyung University Cheonan Republic of Korea Software Convergence Kyung Hee University Yongin Republic of Korea Dept. Human Centered AI Sangmyung University Seoul Republic of Korea Computer Engineering Kyung Hee University Yongin Republic of Korea Software Engineering Soongsil University Seoul Republic of Korea Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9781665472616

Drones have been studied in a variety of industries. Drone detection is one of the most important task. The goal of this paper is to detect the target drone using the microphone and a camera of the detecting drone by training deep learning models. For evaluation, three methods are used: visual-based, audio-based, and the decision fusion of both features. Image and audio data were collected from the detecting drone, by flying two drones in the sky at a fixed distance of 20m. CNN (Convolutional Neural Network) was used for audio, and YOLOv5 was used for computer vision. From the result, the decision fusion of audio and vision-based features showed the highest accuracy among the three evaluation methods.

关键词： Deep learning Training Industries Computer vision Cameras Acoustics Convolutional neural networks

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Data-driven Distributed and Localized Model Predictive control

arXiv

引用

arXiv 2021年

作者： Alonso, Carmen Amo Yang, Fengjun Matni, Nikolai Dept. of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States Dept. of Computer and Information Sciences University of Pennsylvania PhiladelphiaPA United States Dept. of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States

Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized directly from trajectory data. Our method, data-driven Distributed and Localized Model Predictive control (D3LMPC), builds upon the data-driven System Level Synthesis (SLS) framework, which allows one to parameterize closed-loop system responses directly from collected open-loop trajectories. The resulting model-predictive controller can be implemented with distributed computation and only local information sharing. By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system. Moreover, we show that our algorithm enjoys theoretical guarantees for recursive feasibility and asymptotic stability. Finally, we also demonstrate the optimality and scalability of our algorithm in a simulation experiment. © 2021, CC BY.

关键词： Model predictive control

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Radio Frequency Power Transmission for Self-Sustaining Miniaturized IoT Devices: Survey and Experimental Evaluation

Radio Frequency Power Transmission for Self-Sustaining Minia...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM

作者： Louis Meile Anja Ulrich Philipp Mayer Michele Magno Dept. of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (数字)9781728170190

ISBN: (纸本)9781728170206

The Internet of Things (IoT) is a promising vision to cover a wide range of applications. However, battery-operated devices at the edge of the Internet, have a limited lifetime or involve significant maintenance effort. Energy Harvesting (EH) is one of the most investigated technologies to extend the battery lifetime. Nevertheless, the further miniaturization of devices poses challenges, mainly due to the small form factor needed. In particular, harvesting energy from miniaturized transducers (i.e., millimeter-sized solar panels) is significantly more challenging. Among other EH technologies, transmitting power wirelessly is growing in popularity as it can be controlled, and it is less unpredictable than environmental sources. This work investigates different technologies to supply small size and low power smart devices for IoT applications with wireless power transmission. The paper illustrates with experimental measurements, the power acquired with a commercial radiofrequency power transmission system from Powercaset. According to this evaluation, the RF power transfer to the battery ranged from mW to μW using millimeter-sized antennas up to meters of distance. Thus the system is an effective solution for the supply of small size IoT devices.

关键词： Radio frequency Batteries Power transmission Wireless communication Inductive charging Radio transmitters Receivers

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Semantic Mapping of Component Framework Interface Ontologies for Interoperability of Vehicle Applications

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Procedia Computer Science 2020年 170卷 813-818页

作者： Sangita De Michael Niklas Rooney Brian Juergen Mottok Premek Brada Dept. of Electrical Engineering and Information Technology OTH Regensburg 93053 Germany Dept. of Computer Science and Engineering University of West Bohemia Pilsen 30100 Czech Republic

Over the past few years, ontology merging, and ontology semantic alignment has gained significant interest as research topics in automotive application domain for finding solutions to semantic data heterogeneity. To accomplish the complex and novel vehicle service requirements such as autonomous driving, V2X (Vehicle-to-Vehicle communication), etc. the automotive applications involve collaborations of several platform-specific data from heterogeneous enterprises component frameworks and consequently there has been increase in data interoperability issues. At the application component level, data interoperability relies on the semantic alignment or mapping between the various component framework interfaces data models represented as XML schemas (XSD). With the XML schemas being the preferred standard for the interface description exchange between most of the automotive application domain components, however, the data interoperability between the semantically equivalent but structurally different data constructs of multiple heterogeneous XSDs stands as a challenge in the absence of an ontology-based approach. To confront this crucial requirement for data interoperability and to increase in effect the reuse of existing components through their interfaces, we propose an approach to semantically map the various component framework interface data models when expressed as ontology schemas, based on the exploration of semantic synergies. The transformation between XSD and RDF (Resource Description Framework) schema representations and the use of queries over the ontology schemas for semantic mapping are demonstrated including a real-world case study.

关键词： ontology xml xsd rdf model mapping semantic component interface

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Fanncortexm: An open source toolkit for deployment of multi-layer neural networks on arm cortex-m family microcontrollers : formance analysis with stress detection 5

Fanncortexm: An open source toolkit for deployment of multi-...

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5th IEEE World Forum on Internet of Things, WF-IoT 2019

作者： Magno, Michele Cavigelli, Lukas Mayer, Philipp Hagen, Ferdinand Von Benini, Luca Dept. of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781538649800

We present FANNCortexM, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight neural networks on ARM Cortex-M series microcontrollers. The toolkit takes a neural network trained with FANN and generates code targeted at execution on low-power microcontrollers either with a floating-point unit (i.e., ARM Cortex-M4F and M7F) or without a floating-point unit (i.e., ARM Cortex M0-M3). The toolkit is optimized in terms of memory and computational resources. We demonstrate its functionality on the basis of a sample application scenario performing stress detection on a wearable multi-sensor bracelet. Experimental results show a high classification accuracy of 96% for the target application scenario, and low latency of only a few microseconds while keeping the memory requirements (11kB flash storage, 36kB RAM) far below the limitations of the device. Power measurements show a power consumption of only 1.6mW, thus allowing continuous stress detection for 29 days. © 2019 IEEE.

关键词： Microcontrollers

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Anomaly detection from cyber threats via infrastructure to automated vehicle

arXiv

引用

arXiv 2021年

作者： Van Der Ploeg, Chris Smit, Robin Siagkris-Lekkos, Alexis Benders, Frank Silvas, Emilia Netherlands Organisation for Applied Scientific Research Integrated Vehicle Safety Group AT Helmond5700 Netherlands Mechanical Engineering Dept. Dynamics and Control Group Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Mechanical Engineering Dept. Control Systems Technology Group Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands

Using Infrastructure-to-Vehicle (I2V) information can be of great benefit when driving autonomously in highdensity traffic situations with limited visibility, since the sensing capabilities of the vehicle are enhanced by external sensors. In this research, a method is introduced to increase the vehicle's self-awareness in intersections for one of the largest foreseen challenges when using I2V communication: Cyber security. The introduced anomaly detection algorithm, running on the automated vehicle, assesses the health of the I2V communication against multiple cyber security attacks. The analysis is done in a simulation environment, using cyber-attack scenarios from the Secredas Project (Cyber Security for Cross Domain Reliable Dependable Automated Systems) and provides insights into the limitations the vehicle has when facing I2V cyber attacks of different types and amplitudes and when sensor redundancy is lost. The results demonstrate that anomalies injected can be robustly detected and mitigated by the autonomous vehicle, allowing it to react more safely and comfortably and maintaining correct object tracking in intersections. Copyright © 2021, The Authors. All rights reserved.

关键词： Crime

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Improving value-based e-government towards the achievement of smart government 5

Improving value-based e-government towards the achievement o...

引用

5th International Conference on Informatics and Computing, ICIC 2020

作者： Hermanto, Agus Binti Ibrahim, Rosziati Kusnanto, Geri University of 17 Agustus 1945 Universiti Tun Hussein Onn Malaysia Dept. of Informatics Engineering Surabaya Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia University of 17 Agustus 1945 Dept. of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9781728180472

The development of information technology in government so far is still experiencing problems with the lack of achievement of public value from e-government development. Therefore, this study aims to develop new perspectives at the macro level rather than the micro-level which has been widely used. The research method in this paper uses a value chain approach and digital transformation stages by strengthening technology and services that focus on the level of efficiency of government operational services which is increased from the micro level to the macro level and develops a reference model for increasing the value of services to achieve sustainable development goals. National. The result is the availability of models and frameworks that can be applied in e-government to build value-based e-government. © 2020 IEEE.

关键词： Smart city

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EEG-Based Discrimination Between Patients with MCI and Alzheimer

EEG-Based Discrimination Between Patients with MCI and Alzhe...

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

作者： Borhan Kazimipour Reza Boostani Afshin Borhani-Haghighi Sattam Almatarneh Mohammad Aljaidi CSE Dept. Monash University Melbourne Australia CSE and IT Dept. Electrical and Computer Engineering Faculty Shiraz University Shiraz Iran Neurology Department Namazi hospital Shiraz University of Medical Sciences Shiraz Iran Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Department of Computer Science Zarqa University Zarqa Jordan

There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.

关键词： Visualization Patient monitoring Fluctuations Independent component analysis Feature extraction Electroencephalography Real-time systems

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Highly Sensitive Photonic Crystal Fiber for Illegal Drugs Detection in THz Regime 11

Highly Sensitive Photonic Crystal Fiber for Illegal Drugs De...

引用

11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Bulbul, Abdullah Al-Mamun Awal, Md. Abdul Rahaman, Md. Ekhlasur Imam, Farjana Podder, Etu Ahmed, Md. Salim Hossain, Md. Bellal Mondal, Himadri Shekhar Iqbal, Faysal Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of ETE Gopalganj Bangladesh Electronics and Communication Engineering Discipline Khulna University Khulna Bangladesh School of Electrical and Information Engineering University of Sydney NSW Australia Bangladesh Army University of Engineering and Technology Dept.of EEE Natore Bangladesh

ISBN: (纸本)9781728168517

Misuse of incentive drugs momentarily and rapidly enhances the functioning of several organs, physiological system, nerves, etc.;the likeliness of sleep disorder;and the release of hormones in extreme level. Drug abuse not only creates momentary effects but also causes fatal damages and acute implications on human health in the long run. These effects imply the necessity of the detection of illegal drugs in an efficient, gentle and unfailing manner along with higher level of obviousness and accuracy. Regarding these as the prime targets, an illegal drug sensor based on PCF has been introduced in this paper. The sensor has been modelled to detect illegal drugs namely Amphetamine, Cocaine, Ketamine and Morphine. The model has been examined in THz regime using COMSOL Multiphysics v.5.4. The background material of the model is Zeonex. The study of the optical properties for the proposed sensor model suggests that the model exhibits an ignorable amount of effective material and confinement loss together with significant values for both effective area and relative sensitivity. The sensor model exhibits increased sensitivity of about 92.33%,90.72%, 94.91 %, and 97.84% at 1.6 THz in detecting Amphetamine, Cocaine, Ketamine and Morphine respectively. Furthermore, existing fabrication methods can be practiced to fabricate the proposed sensor. © 2020 IEEE.

关键词： Chemical detection

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Development of Three-Dimensional Convolutional Neural Network for Urban Flood Classification Using Synthetic Aperture Radar Multi-Temporal Image

Development of Three-Dimensional Convolutional Neural Networ...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Dodi Sudiana Indra Riyanto Mia Rizkinia Rahmat Arief Anton Satria Prabuwono Josaphat Tetuko Sri Sumantyo Ketut Wikantika Dept. of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia Center for Environmental Studies and Faculty of Engineering Universitas Budi Luhur Jakarta Indonesia Research Center for Remote Sensing National Research and Innovation Agency Jakarta Indonesia Department of Information Technology King Abdulaziz University Jeddah Saudi Arabia Center for Environmental Remote Sensing and Research Institute of Disaster Medicine Chiba University Chiba Japan Geodesy and Geomatics Engineering Center for Remote Sensing Institut Teknologi Bandung Bandung Indonesia

Urban flooding is a significant catastrophe due to its widespread impact on the population. Typically, floods occur concurrently with heavy rainfall, rendering the affected area obscured by clouds when observed through optical sensors on satellites. A novel approach is proposed in this study to address this issue, aiming to classify flooded urban areas using a remote sensing synthetic aperture radar (SAR) sensor on a satellite. Unlike optical sensors, SAR can penetrate clouds. The framework was developed using the 3D convolutional neural network (3DCNN) method to preserve the temporal variability, which processed multi-temporal SAR data from Sentinel-1 (S-1). The dataset used in this research comprised 24 S-1 scenes covering Jakarta Province, Indonesia, with Dual VV and VH polarization, between March 2019 and February 2020, divided into two co-event images, 18 pre-event images, and four post-event images. We employed hyper-parameters of 150 epochs, a batch size of 100, and a learning rate of 0.001, with a training/testing data split of 80/20 in the training phase. The 3DCNN achieved an average overall accuracy of 70.3%, with a maximum accuracy of 71.4%, and each epoch took 113 seconds on average to process. These results demonstrate the potential of the 3DCNN method to accurately estimate the extent of flooding and identify areas at risk of flooding, thereby aiding early detection and flood prevention efforts in other urban areas in the future.

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