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Image matching algorithm based on SIFT using color and exposure information

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Journal of Systems engineering and Electronics 2016年第3期27卷 691-699页

作者： Yan Zhao Yuwei Zhai Eric Dubois Shigang Wang School of Communication engineering Jilin University School of electrical engineering and computer science University of Ottawa

Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.

关键词： scale invariant feature transform(SIFT) image matching color exposure

Time-varying linear control for tiltrotor aircraft

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Chinese Journal of Aeronautics 2018年第4期31卷 632-642页

作者： Jing ZHANG Liguo SUN Xiangju QU Liuping WANG School of Aeronautic science and engineering Beihang University School of electrical and computer engineering RMIT University

Tiltrotor aircraft have three flight modes： helicopter mode, airplane mode, and transition mode. A tiltrotor has characteristics of highly nonlinear, time-varying flight dynamics and inertial/-control couplings in its transition mode. It can transit from the helicopter mode to the airplane mode by tilting its nacelles, and an effective controller is crucial to accomplish tilting transition missions. Longitudinal dynamic characteristics of the tiltrotor are described by a nonlinear Lagrangeform model, which takes into account inertial/control couplings and aerodynamic *** commands for airspeed velocity and attitude in the transition mode are calculated dynamically by visiting a command library which is founded in advance by analyzing the flight envelope of the tiltrotor. A Time-Varying Linear（TVL） model is obtained using a Taylorexpansion based online linearization technique from the nonlinear model. Subsequently, based on an optimal control concept, an online optimization based control method with input constraints considered is proposed. To validate the proposed control method, three typical tilting transition missions are simulated using the nonlinear model of XV-15 tiltrotor aircraft. Simulation results show that the controller can be used to control the tiltrotor throughout its operating envelop which includes a transition flight, and can also deal with vertical gust disturbances.

关键词： Constrained optimal control Inertia/control couplings Tiltrotor aircraft Time-varying control Transition mode

Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

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Chinese Physics B 2012年第12期21卷 161-167页

作者：王申全冯健赵青 College of Information science and engineering Northeastern University Department of electrical and computer engineering University of Alberta

In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks （CRNNs） with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique （the reciprocally convex combination method）, less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities （LMIs）. Two numerical examples show that our results are better than the existing ones.

关键词： recurrent neural networks stochastic delay mean-square stability linear matrix inequal- ity

Complete Boolean Satisfiability Solving Algorithms Based on Local Search

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Journal of computer science & Technology 2013年第2期28卷 247-254页

作者：郭文生杨国武 William N.N.Hung Xiaoyu Song School of computer science and engineering University of Electronic Science and Technology of China Synopsys Inc. Department of electrical and computer engineering Portland State University

Boolean satisfiability （SAT） is a well-known problem in computer science, artificial intelligence, and operations research. This paper focuses on the satisfiability problem of Model RB structure that is similar to graph coloring problems and others. We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure. We translate clauses into a graph with exclusive sets and relative sets. In order to reduce search depth, we determine search order using vertex weights and clique in the graph. The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks, especially in those large benchmark instances.

关键词： Boolean satisfiability set clique local search complete search

BAD-FM:Backdoor Attacks Against Factorization-Machine Based Neural Network for Tabular Data Prediction

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Chinese Journal of Electronics 2024年第4期33卷 1077-1092页

作者： Lingshuo MENG Xueluan GONG Yanjiao CHEN College of electrical engineering Zhejiang University School of computer science Wuhan University

Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components(e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e.,HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100%at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.

关键词： Adaptation models Systematics Frequency modulation Finance Predictive models Prediction algorithms Data models

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

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Journal of Systems engineering and Electronics 2014年第5期25卷 735-741页

作者： Hamidreza Hosseinzadeh Department of electrical and computer engineering Science and Research BranchIslamic Azad University

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

关键词： automatic modulation classification (AMC) tracking performance evaluation passive-aggressive (PA) classifier self- training cognitive radio (CR).

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 447-457页

作者： Naresh Malla Ujjwol Tamrakar Dipesh Shrestha Zhen Ni Reinaldo Tonkoski electrical engineering and computer science Department South Dakota State University IEEE

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic *** active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation ***,CCVSI with traditional controllers have a limited transient and steady state *** this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and *** proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q *** can generate adaptive control actions to compensate the PI *** proposed system was simulated under different nonlinear(three-phase full wave rectifier) load *** performance of the proposed approach was compared with the traditional *** have also included the simulation results without connecting the traditional PI control based power inverter for reference *** online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.

关键词： Adaptive dynamic programming(ADP) current controlled voltage source power inverter(CCVSI) online learning based controller neural networks shunt active filter(SAF) total harmonic distortion(THD)

A Semi-Tensor Product Based All Solutions Boolean Satisfiability Solver

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Journal of computer science & Technology 2023年第3期38卷 702-713页

作者：潘鸿洋储飞 Faculty of electrical engineering and computer science Ningbo UniversityNingbo 315211China

Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates all possible solutions. In this paper, a semi-tensor product (STP) based AllSAT solver is proposed. The solver can solve instances described in both the conjunctive normal form (CNF) and circuit form. The implementation of our method differs from incremental enumeration because we do not add blocking conditions for existing solutions, but rather compute the matrices to obtain all the solutions in one pass. Additionally, the logical matrices support a variety of logic operations. Results from experiments with MCNC benchmarks using CNF-based and circuit-based forms show that our method can accelerate CPU time by 8.1x (238x maximum) and 19.9x (72x maximum), respectively.

关键词： all solutions Boolean satisfiability(AllSAT) semi-tensor product of matrices conjunctive normal form(CNF)satisfiability circuit satisfiability

TuLP: A Family of Lightweight Message Authentication Codes for Body Sensor Networks

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Journal of computer science & Technology 2014年第1期29卷 53-68页

作者：龚征 Pieter Hartel Svetla Nikova 唐韶华朱博 School of computer science South China Normal University Faculty of electrical engineering Mathematics and Computer Science University of Twente Department of ESAT/SCD-COSIC Katholieke Universiteit Leuven IEEE School of computer science and engineering South China University of Technology Department of electrical and computer engineering University of Waterloo

A wireless sensor network （WSN） commonly whilst a body sensor network （BSN） must be secured with requires lower level security for public information gathering, strong authenticity to protect personal health information. In this paper, some practical problems with the message authentication codes （MACs）, which were proposed in the popular security architectures for WSNs, are reconsidered. The analysis shows that the recommended MACs for WSNs, e.g., CBC- MAC （TinySec）, OCB-MAC （MiniSec）, and XCBC-MAC （SenSee）, might not be exactly suitable for BSNs. Particularly an existential forgery attack is elaborated on XCBC-MAC. Considering the hardware limitations of BSNs, we propose a new family of tunable lightweight MAC based on the PRESENT block cipher. The first scheme, which is named TukP, is a new lightweight MAC with 64-bit output range. The second scheme, which is named TuLP-128, is a 128-bit variant which provides a higher resistance against internal collisions. Compared with the existing schemes, our lightweight MACs are both time and resource efficient on hardware-constrained devices.

关键词： message authentication code body sensor network low-resource implementation