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A Frequency Domain Clutter Suppression Approach for Passive Radar

IEEE SENSORS JOURNAL 2024年第9期24卷 14916-14929页

作者： Sun, Quande Shan, Tao Feng, Yuan He, Sicong Bai, Xia Zhang, Hongchi Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Aeronaut Comp Technol Res Inst ACTRI Xian 710068 Shanxi Peoples R China

Passive radar (PR) often suffers from the direct-path and multipath clutter interference, causing the target echo to be submerged by clutter, so the clutter must be suppressed at first. A novel approach to suppress clutter effectively is proposed. First, the reference and surveillance signals are divided into multiple short segments, and then the segmented signals are transformed from time domain to frequency domain. Subsequently, clutter suppression is performed on each frequency bin. Finally, the surveillance signal after clutter suppression is transformed back to the time domain. Compared with conventional clutter suppression methods, the proposed method does not require the high-order computation adopted by adaptive filtering, thus reducing the computational complexity significantly. Furthermore, the proposed method is not confined to orthogonal frequency division multiplex (OFDM) signal, making it widely applicable. The superiority of the proposed method is demonstrated by application to simulated and experimental data.

关键词： Clutter Surveillance OFDM Sensors Passive radar Frequency-domain analysis Correlation Clutter suppression computational complexity frequency domain passive radar (PR)

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Scheduling jobs in open shops with limited machine availability

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RAIRO-OPERATIONS RESEARCH 2002年第2期36卷 149-156页

作者： Blazewicz, J Formanowicz, P Poznan Univ Technol Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Inst Bioorgan Chem PL-61704 Poznan Poland

In this paper, open shop scheduling problems with limited machine availability are studied. Such a limited availability of machines may appear in many real-life situations, e.g. as preventive maintenance activities. Three types of jobs are distinguished: nonpreemptable, resumable and preemptable. An operation of a resumable job if not completed before a non-availability period of a machine may be suspended and continued without additional cost when the machine becomes available. In the paper, results are given for the scheduling problems associated with the three types of jobs. For preemptable jobs polynomial-time algorithms based on the two-phase method are proposed.

关键词： limited machine availability computational complexity two-phase method mathematical programming

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Model predictive current control for multilevel CHB-PMSM system with lower calculation

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IET ELECTRIC POWER APPLICATIONS 2020年第6期14卷 1089-1096页

作者： Yan, Yan Liu, Fushun Shi, Tingna Zhang, Guozheng Wang, Zhiqiang Xia, Changliang Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tiangong Univ Sch Elect Engn & Automat Tianjin 300387 Peoples R China

Due to the large number of voltage vectors in the multilevel inverter, the traditional multilevel model predictive control has a problem of heavy online computing burden. In this study, a model predictive current control strategy for multilevel-cascaded H-bridge inverter-permanent-magnet synchronous motor (CHB-PMSM) system is proposed. By deeply analysing the relationship between current vector and increment of the voltage vector, the candidate voltage vector sets in dynamic and steady state are optimised. Besides, the number of candidate voltage vectors is reduced to two regardless of the levels of CHB inverter, avoiding all voltage vectors participating in the calculation. Consequently, the experimental results on the CHB-PMSM system verify that the proposed strategy can significantly reduce the computational complexity and make the motor system obtain good dynamic and steady-state performance.

关键词： predictive control synchronous motors electric current control machine control permanent magnet motors invertors CHB inverter multilevel CHB-PMSM system heavy online computing burden model predictive current control strategy multilevel-cascaded H-bridge inverter-permanent-magnet synchronous motor system current vector candidate voltage vector sets computational complexity steady-state performance

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CLASSIFICATION OF LINEARLY NONSEPARABLE PATTERNS BY LINEAR THRESHOLD ELEMENTS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第2期6卷 318-331页

作者： ROYCHOWDHURY, VP SIU, KY KAILATH, T UNIV CALIF IRVINE DEPT ELECT & COMP ENGNIRVINECA 92717 STANFORD UNIV INFORMAT SYST LABSTANFORDCA 94305

Learning and convergence properties of linear threshold elements or perceptrons are well understood for the case where the input vectors (or the training sets) to the perceptron are linearly separable. Little is known, however, about the behavior of the perceptron learning algorithm when the training sets are linearly nonseparable. In this paper we present the first known results on the structure of linearly nonseparable training sets and on the behavior of perceptrons when the set of input vectors is linearly nonseparable. More precisely, we show that using the well known perceptron learning algorithm a linear threshold element can learn the input vectors that are provably learnable, and identify those vectors that cannot be learned without committing errors. We also show how a linear threshold element can be used to learn large linearly separable subsets of any given nonseparable training set. In order to develop our results, we first establish formal characterizations of linearly nonseparable training sets and define learnable structures for such patterns. We also prove computational complexity results for the related learning problems. Next, based on such characterizations, we show that a perceptron does the best one can expect for linearly nonseparable sets of input vectors and learns as much as is theoretically possible.

关键词： Vectors Convergence computational complexity computational modeling Machine learning NASA Information systems Laboratories

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Anomaly Detection Methods for Categorical Data: A Review

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ACM COMPUTING SURVEYS 2019年第2期52卷 1–35页

作者： Taha, Ayman Hadi, Ali S. Cairo Univ Fac Comp & Informat 5 Dr Ahmed Zewail St Giza 12613 Egypt Amer Univ Cairo POB 74 New Cairo 11835 Egypt Cornell Univ Ithaca NY USA

Anomaly detection has numerous applications in diverse fields. For example, it has been widely used for discovering network intrusions and malicious events. It has also been used in numerous other applications such as identifying medical malpractice or credit fraud. Detection of anomalies in quantitative data has received a considerable attention in the literature and has a venerable history. By contrast, and despite the widespread availability use of categorical data in practice, anomaly detection in categorical data has received relatively little attention as compared to quantitative data. This is because detection of anomalies in categorical data is a challenging problem. Some anomaly detection techniques depend on identifying a representative pattern then measuring distances between objects and this pattern. Objects that are far from this pattern are declared as anomalies. However, identifying patterns and measuring distances are not easy in categorical data compared with quantitative data. Fortunately, several papers focussing on the detection of anomalies in categorical data have been published in the recent literature. In this article, we provide a comprehensive review of the research on the anomaly detection problem in categorical data. Previous review articles focus on either the statistics literature or the machine learning and computer science literature. This review article combines both literatures. We review 36 methods for the detection of anomalies in categorical data in both literatures and classify them into 12 different categories based on the conceptual definition of anomalies they use. For each approach, we survey anomaly detection methods, and then show the similarities and differences among them. We emphasize two important issues, the number of parameters each method requires and its time complexity. The first issue is critical, because the performance of these methods are sensitive to the choice of these parameters. The time complexity is also

关键词： computational complexity data mining holo entropy intrusion detection systems mixed data outliers detection nominal data novelty detection semi-supervised learning Shannon entropy supervised learning unsupervised learning

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Multiwindow discrete Gabor transform using parallel lattice structures

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IET SIGNAL PROCESSING 2020年第7期14卷 420-426页

作者： Li, Rui Tao, Liang Keung Kwan, Hon Anhui Sci & Technol Univ Coll Informat & Network Engn 1501 Huangshan Rd Bengbu 233100 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol 111 Jiulong Rd Hefei 233100 Anhui Peoples R China Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

The multiwindow discrete Gabor transform (M-DGT) is an effective time-frequency analysis tool to analyse time-varying signals containing components with multiple frequencies. In this study, fast block time-recursive methods for computing the M-DGT coefficients of a signal and the reconstruction of the signal from the transform coefficients are presented with steps as listed, respectively, in Algorithms 1 and 2, and their implementations using unified parallel lattice structures are also given. The proposed algorithms consisting of Algorithms 1 and 2 for respective forward and inverse transforms are compared to (i) those of the existing serial algorithms in terms of computational complexity and time;and (ii) those of the existing parallel algorithms in terms of hardware complexity. The results indicate that the proposed algorithm is fast in computing M-DGT coefficients of a signal and reconstructing the signal with a reduced hardware complexity.

关键词： discrete transforms parallel algorithms computational complexity signal reconstruction time-frequency analysis transforms signal sampling existing serial algorithms computational complexity existing parallel algorithms M-DGT coefficients multiwindow discrete Gabor effective time-frequency analysis tool time-varying signals time-recursive methods transform coefficients unified parallel lattice structures respective forward transforms inverse transforms

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SINGLE ROW ROUTING ON MULTILAYERS

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 1995年第1期50卷 126-131页

作者： DINGLE, A SUDBOROUGH, IH UNIV TEXAS COMP SCI PROGRAM RICHARDSON TX 75083 USA

A common problem in VLSI is automating the routing of wires between pins in a circuit. Several specifications of the routing problem exist. One class of these problems, known as the single row routing problem, involves routing wires when the pins are laid out along a straight line. To prevent electrical interference, the wires are laid out in tracks parallel to the row of pins and distinct wires are prohibited from crossing. Formally, the single row routing problem, known to be NP-complete, involves determining the feasibility of any wiring in the minimum number of tracks. When wires may be routed on more than one layer the problem of determining the feasibility of a wiring in a minimum number of layers but with an arbitrary number of parallel tracks is NP-complete. A long-standing open problem has been the complexity of the single row routing problem on multilayers when the number of parallel tracks per layer is fixed. We show that this version of the problem is also NP-complete. (C) 1995 Academic Press, Inc.

关键词： computational complexity

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Extension of the forward-backward method using spectral acceleration for the fast analysis of large array problems

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IEE PROCEEDINGS-MICROWAVES ANTENNAS AND PROPAGATION 2000年第3期147卷 167-172页

作者： Chou, HT Yuan Ze Univ Dept Elect Engn Chung Li 320 Taiwan

The analysis of large array problems has been a very difficult task because of the limitations of currently available numerical methods such as method of moments (MoM), especially in the situation where nonperiodic structures are present. The recently available approach of the forward-backward method using the novel spectral acceleration (FBM/NSA) algorithm, previously developed for the modelling of scattering problems from large rough surfaces, has tremendous potential to overcome the current limitations in exact numerical studies. The extension of this FBM/NSA approach with O(N) in computational complexity is presented for treating large array problems. Similarly to its applications in rough surface problems, the efficiency and accuracy of this approach are validated and demonstrated by numerical results in comparison with rigorous MoM solutions.

关键词： Antenna theory numerical methods electromagnetic wave scattering FBM/NSA algorithm spectral acceleration computational complexity Antenna arrays fast analysis large array problems antenna theory forward-backward method antenna arrays Other numerical methods numerical analysis Electromagnetic wave propagation

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Laguerre escalator lattice and feedforward/feedback active noise control

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SIGNAL PROCESSING 2007年第4期87卷 725-738页

作者： Veena, S. Narasimhan, S. V. Natl Aerosp Lab Aerosp Elect & Syst Div DSP Syst Lab Bangalore 5600012 Karnataka India

In this paper, a new Laguerre Escalator Lattice structure for an adaptive filter is proposed. This new structure orthogonalizes a nonstationary signal with computational efficiency. In this, the escalator lattice orthogonalizes a nonstationary signal and the Laguerre structure provides the computational efficiency. Further, its nonstationary orthogonalization has been exploited for active noise control (ANC). In the feedforward ANC (FFANC), the new orthogonalization of the input is used both for the main path identification and for the noise canceller for the secondary path identification. In the feedback ANC (FBANC), it is used to predict the nonstationary primary noise field component from the residual noise, for deriving the desired error for the secondary path identification. For the system identification with a nonstationary input, the proposed structure has a significantly improved performance both in terms of convergence speed and error, over that of the Laguerre lattice. Its application to FFANC for a nonstationary noise filed results in a very good performance both in terms of convergence speed and the error magnitude over that of Laguerre lattice. In FBANC, the ability of the new adaptive filter to accurately predict the nonstationary primary noise component from the error for the noise canceller improves the secondary path identification. This results in a significant improvement in ANC performance of about 5 dB over that one uses Laguerre lattice predictor. Further, the escalator realization by Laguerre structure reduces the computations significantly (by about 50%). (c) 2006 Elsevier B.V. All rights reserved.

关键词： nonstationarity escalator structure backward prediction error computational complexity Laguerre structure

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Fair redistricting is hard

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THEORETICAL COMPUTER SCIENCE 2019年 791卷 28-35页

作者： Kueng, Richard Mixon, Dustin G. Villar, Soledad CALTECH Inst Quantum Informat & Matter Pasadena CA 91125 USA Ohio State Univ Dept Math 231 W 18th Ave Columbus OH 43210 USA NYU Courant Inst Math Sci New York NY USA NYU Ctr Data Sci New York NY USA

Gerrymandering is a long-standing issue within the U.S. political system, and it has received scrutiny recently by the U.S. Supreme Court. In this note, we prove that deciding whether there exists a fair redistricting among legal maps is NP-hard. To make this precise, we use simplified notions of "legal" and "fair" that account for desirable traits such as geographic compactness of districts and sufficient representation of voters. The proof of our result is inspired by the work of Mahanjan, Minbhorkar and Varadarajan that proves that planar k-means is NP-hard. (C) 2019 Elsevier B.V. All rights reserved.

关键词： NP-hard computational complexity Gerrymandering

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