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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ziyang Yu Huanhuan Song Hong Wen Yonghuang Liu Wen Li School of Aeronautics and Astronautics University of Elec. Science and Tech. of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC China

Due to the broadcast nature of wireless communication, it is susceptible to eavesdropping by unauthorized users. Artificial noise, as an important physical layer security technique, is different from traditional cryptography-based communication security mechanisms with high computational complexity, and cannot be cracked by the rapidly advancing computing power. The theoretical research on the security of artificial noise-assisted multiple input multiple output (MIMO) communication systems is quite comprehensive. However, since artificial noise is deeply dependent on the accurate instantaneous channel feedback, its practical application effect still lacks further experiment verification in actual MIMO systems. Based on LabVIEW and universal software radio peripheral (USRP) software defined radio technology, this paper presents a hardware simulation platform of artificial noise-assisted MIMO communication systems. The experimental data obtained from this platform demonstrates that artificial noise can effectively enhance system security with a slight influence on the system reliability.

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Developing Interpretable Machine Learning for Forward Kinematics of Robotic Arms

Developing Interpretable Machine Learning for Forward Kinema...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Sai Teja Kanneganti Jin–Song Pei Dean Frederick Hougen School of Computer Science University of Oklahoma Norman OK USA School of Elec. & Comp. Engrg. University of Oklahoma Norman OK USA

ISBN: (纸本)9781728190495

Machine learning (ML) is becoming increasingly sought after in diverse domains. Unfortunately for this objective, most ML research has focused on improving performance on evaluation metrics such as accuracy. However, to make important decisions, ML models need to be interpretable. We propose an approach to approximate kinematics of a robotic arm using interpretable artificial neural networks (ANNs). This work is based on approximating nonlinear functions where domain knowledge and visually observable features of the data are used to design ANNs. After analyzing existing work, we present a feasibility study approximating the kinematics of a simplified robotic arm and extend the work to multiple hidden layers. We generalize the existing work and extend its use for a different application, noting the challenges that arise while extending this work to multiple hidden layers.

关键词： Measurement Kinematics Machine learning Artificial neural networks Manipulators Computational intelligence

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Mobile robot navigation method based on improved Q-learning algorithm 36

Mobile robot navigation method based on improved Q-learning ...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Tan, Zhiyong Wang, Tao Yu, Yao University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Key Lab. of Knowledge Automat. for Indust. Processes of Min. of Educ. Sch. of Automat. and Elec. Eng. Beijing100083 China

ISBN: (纸本)9781665437127

In recent years, autonomous navigation of mobile robots has become one of the hot topics in research. An improved Q-learning method (IQL) combining action selec.ion strategy and Q function update method is proposed. A new exploration strategy of staged greedy exploration is used in IQL. In addition, the idea of backtracking is used to update the Q value to speed up the convergence speed. Experiments conducted in a grid environment show that compared with the classic algorithm, the algorithm converges faster and the program execution time is shorter. © 2021 IEEE.

关键词： Reinforcement learning

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Mitigating Catastrophic Forgetting in Deep Transfer Learning for Fingerprinting Indoor Positioning

Mitigating Catastrophic Forgetting in Deep Transfer Learning...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Heng Pan Shuang Wei Di He Zhuoling Xiao Shintaro Arai College of Info. Mech. and Elec. Eng. Shanghai Normal University Shanghai China Shanghai Key Laboratory of Navigation and Location-based Services Shanghai Jiao Tong University Shanghai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Okayama University of Science Okayama Japan

This letter proposes a deep-learning-based fingerprinting indoor positioning method, aiming to mitigate catastrophic forgetting in the process of depth transfer learning. Recently indoor positioning methods based on fingerprint have made great development. The deep transfer learning technique has been applied to transfer the positioning network among different scenarios. But catastrophic forgetting is a big challenge during the supervised transfer learning, which results in poor performance of fine-tuned network in source scenario. In order to solve this issue, the proposed method improves the fine-tuning learning procedure according to the importance of network parameters. It can adaptively control the ratio of network parameters by adding regularization factor to loss function. Simulation results show that the proposed method can effectively improve the positioning accuracy in source scenario without reducing the positioning accuracy in target scenario, especially for the transfer learning in multiple scenarios.

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Facial Expression Recognition using Hand-Crafted Features and Supervised Feature Encoding 2

Facial Expression Recognition using Hand-Crafted Features an...

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2nd International Conference on elec.rical, Communication and computer engineering, ICECCE 2020

作者： Noor, Jumana Daud, Muneeba Rashid, Raima Mir, Hira Nazir, Saima Velastin, Sergio A. Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan School of Elec. Eng. and Computer Science Queen Mary University of London and Zebra Technologies Corp. London United Kingdom

ISBN: (纸本)9781728171166

Facial Expression Recognition (FER) is an emerging field of research because of the latest advancement in the computing field making the user's experience a clear priority. The proposed framework focuses on facial expression recognition using state-of-the-art hand-crafted feature extraction algorithms. First, pre-processing of images is applied to reduce noise and faces are then extracted using Viola Jones algorithm. Then, salient features are extracted from the region of interest using different feature extraction methods. We compare the performance of methods such as Speed-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Histogram of Oriented Gradients (HOG) and Harris Corner Detection. The high-dimensional extracted features are quantized using supervised k-mean clustering. Finally, the proposed method is evaluated on the publicly available CK+ dataset and the model suggested achieves 90.79 per cent predictive accuracy. Angry, disgust, fear, happy, sadness, surprise, contempt are the seven different emotions to be recognized in CK+ dataset. © 2020 IEEE.

关键词： Feature extraction

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A PUF-based Scheme for Identify Trusted Mobile Phone Accessories

A PUF-based Scheme for Identify Trusted Mobile Phone Accesso...

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Li, Liang Huang, Zhao Chen, Yin Du, Maofan Wang, Quan Liu, Jinhui School of Cyber Engineering Xidian University xi'an China School of Mechanical Elec. and Info. Engineering Shandong University Weihai China Graduate School of Media GovernanceKeio University Kanagawa Japan School of Syber Engineering Xidian University X'ian China School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9781728189543

The rapid diffusion of mobile communication technology and smart phone devices has raised the concerns about the security problems of mobile phone accessories. Despite a few effort has been made in this aspect, there are still some deficiencies such as poor compatibility and limited application scope. This article presents a trusted mobile phone accessory identification scheme. Our method uses physically unclonable function (PUF) as hardware security primitives which are embedded in mobile phone accessories to generate digital signatures, so that they can be applied to identify authorized mobile phone accessories. We finally implement the proposed scheme on a field programmable gate array (FPGA) platform. Experimental results show that our solution can effectively identify trusted mobile phone accessories. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Evaluation of Response Time for Linked Data applications using CoAP and HTTP 2

Evaluation of Response Time for Linked Data applications usi...

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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Akre, Vishwesh Shahzad, Muhammad Nawaz, Asif Parkash, Om Ul Rehman, Anis Ali Khattak, Hasan Khan, Doulat Ahmed, Sheeraz Mohindru, Pankaj Higher Colleges of Technology Dubai Women's College Dubai United Arab Emirates Mns University of Engg. Tech. Deptt. of Elec. Engg. and Tech. Multan Pakistan Higher College of Technology Department of Electrical Engineering Dubai United Arab Emirates Comsats University Department of Computer Science Islamabad Pakistan Islamabad Pakistan Capital University of Science Technology Department of Computer Science Islamabad Pakistan Iqra National University Department of Computer Science Peshawar Pakistan Punjabi University Electronics Communications Engineering Patiala India

ISBN: (纸本)9781665429214

In internet of things we deal with real life objects so we use Constrained Application Protocol (CoAP) instead of the conventional HTTP (in web). Current Research in the WOT (Web of Things) generally takes things in broader sense of physical objects into consideration. Usually these considered things can be (but not necessarily) tagged objects (RFID, Barcodes, Image Recognition, NFC, QR codes) to Wireless Sensor Networks (WSN), vehicles and consuming elec.ronics. This project aims to compare response time of the sensors (sensing temperature of CPU) if the request is to be launched from different type of resources from desktop, android or raspberry pi. © 2021 IEEE.

关键词： HTTP

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Stabilization of THz waves using chaos supremacy

Stabilization of THz waves using chaos supremacy

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2023 International Conference on Smart-Green Technology in elec.rical and Information Systems, ICSGTEIS 2023

作者： Kuwashima, Fumiyoshi Jarrahi, Mona Cakmakyapan, Semih Wada, Kenji Haraguchi, Masanobu Kawakami, Yuki Moriyasu, Takeshi Morikawa, Osamu Kurihara, Kazuyoshi Kitahara, Hideaki Furuya, Takashi Nakajima, Makoto Tani, Masahiko Fukui University of Technology Dep. of Electr. and Electron. Eng. Fukui Japan Ucla Electrical and Computer Eng. Los AngelesCA90095 United States Osaka Metropolitan University Dep. of Physics and Electronics Osaka Japan Tokushima Univ. Industrial and Social Science Grad. Sch. of Tech. Tokushima Japan Fukui Col. Natl. Inst. of Tech. Dep. of Elec. and Inf. Eng. Fukui Japan University of Fukui Faculty of Engineering Fukui Japan Japan Coast Guard Academy Liberal Arts Kure Japan University. of Fukui School of Education Fukui Japan FIR-UF University of Fukui Fukui Japan Osaka Univ. Inst. of Laser Engineering Osaka Japan

ISBN: (纸本)9798350382822

We apply the chaos supremacy of laser chaos system to the high stabilization of THz waves. Stability was demonstrated by down converting the optical beats between laser longitudinal modes in the THz domain to the radio frequency domain and comparing a conventional free running CW laser diode to a chaotically oscillating laser diode. Because of their exceptional stability, chaotically oscillating lasers are attractive options for optical pump sources in terahertz time-domain spectroscopy systems. © 2023 IEEE.

关键词： Stabilization

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Reinforcement Causal Structure Learning on Order Graph

arXiv

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arXiv 2022年

作者： Yang, Dezhi Yu, Guoxian Wang, Jun Wu, Zhengtian Guo, Maozu School of Software Shandong University Jinan China SDU-NTU Joint Centre for AI Research Shandong University Jinan China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China College of Elec. & Inf. Eng. Beijing University of Civil Engineering and Architecture Beijing China

Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose Reinforcement Causal Structure Learning on Order Graph (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Use of the Lévy Flight Firefly Algorithm in 2D McClellan Unconstrained Transform Adaptive Filters 63

Use of the Lévy Flight Firefly Algorithm in 2D McClellan Un...

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63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020

作者： Hussain, M. Jenkins, W.K. Radhakrishnan, C. Pennsylvania State University School of Electrical and Computer Engineering University ParkPA United States University of Illinois Dept. of Elec. and Comp. Engr. Urbana-ChampaignIL United States

ISBN: (纸本)9781538629161

Recently published results demonstrated that bio-inspired Lévy Flight Firefly Algorithms (LFFAs) can be effectively applied to IIR adaptive filter structures, non-linear adaptive structures, adaptive filters using second order coupled form sections, and adaptive lattice-ladder structures which have been well known for many decades. This paper shows how the LFFA can be used in 2D McClellan Unconstrained Transform adaptive filters so the transform domain parameters can be adjusted to accurately approximate the frequency domain contours when no a priori contour information is available. It appears that this is the first time that the LFFA has been used in 2D adaptive filtering. © 2020 IEEE.

关键词： Adaptive filters

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