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Improved gene selection and classification method using quantum particle swarm optimization and projection extreme learning machine

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Journal of Computational and Theoretical Nanoscience 2015年第12期12卷 5781-5788页

作者： Zhang, Xinman Liang, Mujia Xu, Xuebin Jia, Pukun Chen, Sushing MOE Key Lab for Intelligent Networks and Network Security School of Electronics and Information Engineering Xi'An Jiaotong University Xi'an710049 China Guangdong xi'An Jiaotong University Academy Guangdong 528000 China School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer and Information Science and Engineering University of Florida 32611 United States

In order to improve and accelerate the speed of gene selection and classification, an optimal and intelligent method for microarray cancer data is investigated in this paper. Based on new machine learning method theory, projection extreme learning machine (PELM) is studied in searching key gene of disease-producing, combined of quantum particle swarm optimization (QPSO) intelligent search strategy. The method has stronger search ability and quicker convergence speed than other current methods. Experiments are performed on the four microarray datasets: colon, leukemia, lymphoma, and prostate cancers. We apply quantum particle swarm optimization (QPSO) as an effective selection method and projection extreme learning machine (PELM) for classification to reduce the dimensionality of microarray datasets. The resulting feature sets contain significantly smaller subsets of important genes in very rapid computing time, with higher test accuracy and a better evaluation index by calculating the mutual information. © 2015 American Scientific Publishers All rights reserved.

关键词： Genes

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Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

Robust Multi-Exposure Image Fusion: A Structural Patch Decom...

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作者： Ma, Kede Li, Hui Yong, Hongwei Wang, Zhou Meng, Deyu Zhang, Lei Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computing. Hong Kong Polytechnic University Hong Kong Hong Kong Institute for Information and System Sciences and Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Inst. for Info. and Syst. Sci. and Min. of Educ. Key Lab. of Intelligent Networks and Netwk. Secty. Xi'an Jiaotong University Xi'an710049 China

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts. Second, it handles RGB color channels jointly, and thus produces fused images with more vivid color appearance. Third and most importantly, the direction of the signal structure component in the patch vector space provides ideal information for ghost removal. It allows us to reliably and efficiently reject inconsistent object motions with respect to a chosen reference image without performing computationally expensive motion estimation. We compare the proposed algorithm with 12 MEF methods on 21 static scenes and 12 deghosting schemes on 19 dynamic scenes (with camera and object motion). Extensive experimental results demonstrate that the proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes. Moreover, it maintains a lower computational cost compared with the state-of-The-Art deghosting schemes.11The MATlab code of the proposed algorithm will be made available online. Preliminary results of Section III-A [1] were presented at the IEEE International Conference on Image Processing, Canada, 2015. © 1992-2012 IEEE.

关键词： Image fusion

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Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment

Hybrid Deep Learning based Attack Detection and Classificati...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Jaya Dipti Lal Shahnawaz Ayoub Prashant D Hakim S. Prabagar Vijay Kumar Dwivedi Mohit Tiwari Department of Electronics and Communication Engineering Shri G S Institute of Technology and Science Indore Madhya Pradesh India Department of Computer Science Shri Venkateshwara University Gujrala Amroha Uttar Pradesh India Department of Information Technology Thakur College of Science and Commerce Mumbai Maharashtra India Department of Information Science Engineering Networks and IoT Lab School of Computer Science and Engineering & Information Science Presidency University Bengaluru Karnataka India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur Chhattisgarh India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes.

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Deep Reinforcement Learning for Energy-Efficient Fresh Data Collection in Rechargeable UAV-assisted IoT networks

Deep Reinforcement Learning for Energy-Efficient Fresh Data ...

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IEEE Conference on Wireless Communications and Networking

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Ronghui Hou School of Cyber Engineering Xidian University Xi’an China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an China

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

关键词： Simulation Wireless power transfer Data collection Autonomous aerial vehicles Energy efficiency Scheduling Trajectory

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Discrete-Time Distributed Optimization for Linear Uncertain Multi-Agent Systems

Discrete-Time Distributed Optimization for Linear Uncertain ...

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IEEE Conference on Decision and Control

作者： Tong Liu Michelangelo Bin Ivano Notarnicola Thomas Parisini Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The distributed optimization algorithm proposed by J. Wang and N. Elia in 2010 has been shown to achieve linear convergence for multi-agent systems with single-integrator dynamics. This paper extends their result, including the linear convergence rate, to a more complex scenario where the agents have heterogeneous multi-input multi-output linear dynamics and are subject to external disturbances and parametric uncertainties. Disturbances are dealt with via an internal-model-based control design, and the interaction among the tracking error dynamics, average dynamics, and dispersion dynamics is analyzed through a composite Lyapunov function and the cyclic small-gain theorem. The key is to ensure a small enough stepsize for the convergence of the proposed algorithm, which is similar to the condition for time-scale separation in singular perturbation theory.

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Next-Generation Satellite IoT networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer

Next-Generation Satellite IoT Networks: A HAPS-Enabled Solut...

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

作者： Ethan Fettes Pablo G. Madoery Halim Yanikomeroglu Gunes Karabulut Kurt Colin Bellinger Stéphane Martel Khaled Ahmed Sameera Siddiqui Department of Systems and Computer Engineering Non-Terrestrial Networks (NTN) Lab Carleton University Canada Department of Electrical Engineering Poly-Grames Research Center Polytechnique Montréal Montréal Canada National Research Council Canada Canada Satellite Systems MDA Canada Defence Research and Development Canada Canada

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

关键词： Limiting Satellite broadcasting Optical buffering Optical fiber networks Network architecture Internet of Things Optical sensors Proposals Surges Next generation networking

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Queueing stability and CSI probing of a TDD wireless network with interference alignment

Queueing stability and CSI probing of a TDD wireless network...

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作者： Deghel, Matha Assaad, Mohamad Debbah, Mérouane Ephremides, Anthony TCL Chair on 5G Lab-oratoire de Signaux et Systèmes CNRS UMR8506 CentraleSupélec Gif-sur-Yvette91192 France Large Systems and Networks Group CentraleSupélec Gif-sur-Yvette France Mathematical and Algorithmic Sciences Laboratory Huawei Technologies France Boulogne–Billancourt92100 France Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland at College Park College ParkMD20742 United States

This paper characterizes the performance in terms of queueing stability of a network composed of multiple MIMO transmitter-receiver pairs taking into account the dynamic traffic pattern and the probing/feedback cost. We adopt a centralized scheduling scheme that selects a number of active pairs in each time-slot. We consider that the transmitters apply interference alignment (IA) technique if two or more pairs are active, whereas in the special case where one pair is active point-to-point MIMO singular value decomposition (SVD) is used. We consider a time-division duplex (TDD) system where transmitters acquire their channel state information (CSI) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity (i.e. imperfect case). Under this setting, we characterize in this paper the stability region of the system under both the imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap between these two resulting regions. Further, under each case we provide a centralized probing policy that achieves the max stability region. These stability regions and scheduling policies are given for the symmetric system, where all the path loss coefficients are equal to each other, as well as for the general system. For the symmetric system, we provide the conditions under which IA yields a queueing stability gain compared to SVD. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate numbers of pairs, consequently we propose an approximate policy that has a reduced complexity but that achieves only a fraction of the system stability region. A characterization of this fraction is provided. © 2017 IEEE.

关键词： Channel state information

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Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

arXiv

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

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Satellite-Assisted UAV Data Collection for Information Freshness in IoRT networks

Satellite-Assisted UAV Data Collection for Information Fresh...

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IEEE Conference on Wireless Communications and Networking

作者： Weijie Zhou Mengjie Yi Yan Zhang Xijun Wang Juan Liu State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi'an Shaanxi China School of Cyber Engineering Xidian University Xi'an China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Utilizing UAVs and satellites can offer an effective means to collect data for the Internet of remote things (IoRT) networks. However, due to the limited energy of UAVs and the high cost of satellite communication, ensuring the reduction of UAV energy consumption and communication costs while collecting fresh data poses a significant challenge. In this paper, we explore the issue of data gathering in IoRT networks with the assistance of UAVs and satellites. The UAV gathers data from sensor nodes (SNs) and decides whether to relay the collected data via satellite or send it directly to the data processing center. We handle this problem by formulating it as a Markov decision process to minimize the combined weighted sum of the average age of information, the energy consumption of the UAV, and communication costs through the implementation of a compound-action proximal policy optimization (CPPO) method. It can handle the compound actions of the UAV. This approach simultaneously optimizes the UAV's path, SN scheduling, and transmission decisions. Simulation results demonstrate that our algorithm can achieve better performance compared to baseline methods.

关键词： Energy consumption Satellites Costs Simulation Markov decision processes Autonomous aerial vehicles Scheduling

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LPD communication: A sequential change-point detection perspective

arXiv

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arXiv 2019年

作者： Huang, Ke-Wen Wang, Hui-Ming Towsley, Don Vincent Poor, H. School of Electronics and Information Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi710049 China College of Information and Computer Sciences University of Massachusetts AmherstMA01002 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In this paper, we establish a framework for low probability of detection (LPD) communication from a sequential change-point detection (SCPD) perspective, where a transmitter, Alice, wants to hide her signal transmission to a receiver, Bob, under the surveillance of an adversary, Willie. The new framework facilitates to model LPD communication and further evaluate its performance under the condition that Willie has no prior knowledge on when the transmission from Alice starts and that Willie wants to detect the existence of the communication as quickly as possible in real-time manner. We consider three different sequential tests for Willie, i.e., the Shewhart test, the cumulative sum (CUSUM) test, and the Shiryaev-Roberts (SR) test, to model the detection procedure. Communication is said to be covert if it stops before detection by Willie with high probability. Covert probability defined as the probability that Willie is not alerted during the communication procedure is investigated. We formulate an optimization problem aimed at finding the transmit power and transmission duration such that the total amount of information that can be transmitted is maximized subject to a high covert probability. Under Shewhart test, closed-form approximations of the optimal transmit power and transmission duration are derived, which well approximate the solutions obtained from exhaustive search. As for CUSUM and SR tests, we provide an effective algorithm to search the optimal solution. Numeric results are presented to show the performance of LPD communication. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability

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