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International Symposium on Antennas Propagation and EM Theory

作者： Lingyang Kong Yufa Sun Tao Sheng Key Lab. of Intelligent Computing & Signal Processing Ministry of Education Anhui University

ISBN: (纸本)9781467317993

The TD-SCDMA relay (repeater) provide network operators with a cost-effective means of extending coverage to areas of low signal strength. The maximum gain of a TD-SCDMA repeater is, however, limited by the amount of isolation that exists between the donor and coverage antennas. The radio echoes from coverage antennas not only create interference in the incoming signal from the base station, but also cause instability of the repeater. In this paper, we present an interference cancellation system (ICS) for TD-SCDMA networks, which can eliminate these problems. Various characteristic features of the ICS are illustrated using the MATlab simulation results.

关键词： TD-SCDMA Relay Repeater ICS

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A consistent pixel-wise blur measure for partially blurred images

A consistent pixel-wise blur measure for partially blurred i...

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IEEE International Conference on Image processing

作者： Xianyong Fang Feng Shen Yanwen Guo Christian Jacquemin Jian Zhou Shanchun Huang Anhui University Hefei Anhui CN Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei China State Key Lab. for Novel Software Technology Nanjing University Nanjing China AMI LIMSI-CNRS Orsay France

Despite numerous efforts on blur measurement of partially blurred images, there still lacks an effective blur measure that is both pixel-wise and locally sharp consistent. The paper proposes a novel method with two contributions to overcome this limitation: 1) A new pixel-based blur metric, Multi-resolution Singular Value (MSV), which leverages the average singular value of high frequency bands to measure the blur of each pixel, and 2) a locally continuous strategy, maximum-likelihood estimation (MLE) based refinement, that ensures local continuity by imposing the local sharp consistency on pixel blur in a local correcting process. Experimental results show that our method is effective to smoothly measure the partially blurred images without local discontinuity.

关键词： Maximum likelihood estimation Equations Frequency measurement Image edge detection Mathematical model Wavelet transforms

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Estimation and Correction of I/Q Imbalance in Wideband Zero-IF Receiver

Estimation and Correction of I/Q Imbalance in Wideband Zero-...

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Information Technology and Computer Application (ITCA), International Conference on

作者： Shujie Guan Delin Cai Bin Wu Key Lab of Computing Intelligent and Signal Processing (Ministry of Education) Anhui University China Measurement and Control Communication R & D Center The 38th Research Institute of China Electronics Technology Group China

ISBN: (数字)9781728164946

ISBN: (纸本)9781728164953

In this paper, a time domain compensation method is proposed for in-phase (I)/quadrature (Q) mismatch occurring in a wideband Zero-IF radio architecture. First, training sequences are used for error estimation and the FIR filter are developed to reduce the imbalance between the IQ paths. Then, we perform channel equalization on the signal to solve the channel distortion still in the passband. The simulation results of the point-frequency continuous wave (CW) signal and the Radar Chirp signals (LFM) prove the effectiveness of the method. We have established a receiver system level hardware implement platform to test the practicality of the method. The test results show that the method can reduce the IRR by about 29dB.

关键词： Receivers Finite impulse response filters Wideband Time-domain analysis Hardware Radar OFDM

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Edge-guided non-local fully convolutional network for salient object detection

arXiv

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arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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A Novel Method for Rapidly Solving Wideband RCS by Combining UCBFM and Compressive Sensing

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Progress In Electromagnetics Research C 2022年 124卷 33-42页

作者： Wang, Zhonggen Li, Chenwei Sun, Yufa Nie, Wenyan Wang, Pan Lin, Han School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Key Lab of Intelligent Computing & Signal Processing Anhui University Hefei230039 China School of Mechanical and Electrical Engineering Huainan Normal University Huainan232001 China

While analyzing wideband electromagnetic scattering problems using ultra-wideband characteristic basis function method (UCBFM), the reconstruction of a reduced matrix and the recalculation of an impedance matrix at each frequency point cost a large amount of time. To overcome this issue, a novel method that combines UCBFM with compressive sensing (CS) is proposed in this paper to rapidly analyse the wideband RCS. The proposed method makes the ultra-wideband characteristic basis functions (UCBFs) generated at the highest frequency as the sparse basis, introduces the CS theory, randomly extracts several rows from the original matrix as the measurement matrix, utilizes the corresponding excitation vector as the measurement value, and then employs the recovery algorithm, through which the solution of target induced current can be obtained. Due to partial filling of impedance matrix and efficient recovery algorithm, the wideband RCS computation time of the object is significantly reduced using the proposed method. Furthermore, the numerical simulation results show that the computation efficiency for the target wideband RCS can be further enhanced compared with that of the stand-alone UCBFM. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Computational efficiency

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Author classification using transfer learning and predicting stars in co-author networks

Author classification using transfer learning and predicting...

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作者： Abbasi, Rashid Kashif Bashir, Ali Chen, Jianwen Mateen, Abdul Piran, Jalil Amin, Farhan Luo, Bin School of Information and Communication Engineering University of Electronics Science and Technology of China Chengdu China Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom Department of Computer Science Federal Urdu University of Arts Science and Technology Islamabad Pakistan Computer Engineering Department Sejong University Yeungnam Korea Republic of Department of Information and Communication engineering Yeungnam University Yeungnam Korea Republic of Key Lab of Intelligent Computing and Signal Processing of MOE & School of Computer and Technology Anhui University China

The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning;consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods. © 2020 John Wiley & Sons Ltd

关键词： Stars

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

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International Conference on Computational Intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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Multiple attribute group decision making based on interval type-2 fuzzy cross-entropy and ranking value

Multiple attribute group decision making based on interval t...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Liangli Du Ying Peng Junjun Mao Dengbao Yao School of Mathematical Science Anhui University Hefei PR China Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei PR China Economics School Anhui University Hefei PR China

ISBN: (纸本)9781538629024

In this paper, we focus our attention on the issue of MAGDM under interval type-2 fuzzy environment where all the information provided by the decision makers is characterized by IT2FSs and the information about the attribute weights are partly known and completely unknown. Firstly, some basic concepts are introduced. Then, a new definition of the cross entropy which consider three aspects of fuzziness, hesitancy and interval information is given. Interval type-2 fuzzy entropy is proposed on the basis of the cross entropy. Furthermore, two ranking-entropy optimal models of each alternative are built. A new approach to multiple attribute group decision making with IT2FSs based on interval type-2 fuzzy cross-entropy and ranking value is shown. Finally, we give a practical example to verify the feasibility of our method and make some comparisons with other previous methods. The results verify the validity and effectiveness of our proposed method.

关键词： decision making entropy fuzzy set theory

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An efficient implementation of double precision 1-D FFT for GPUs Using CUDA

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Journal of Information and Computational Science 2012年第2期9卷 387-394页

作者： Liu, Yanjun Guo, Licai Luo, Bin Zhang, Xingyi School of Computer Science and Technology Anhui University Hefei 230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China

Fast Fourier Transform (FFT) is a well known and widely used tool in many scientific and engineering fields. CUFFT, which is the NVIDIA's FFT library included in the CUDA toolkit, supports double precision FFTs. However, the implementation of CUFFT is not very efficient. In this paper, we implement an efficient double-precision Cooley-tukey algorithm for GPUs using CUDA. Some programming techniques are employed to exploit the hardware characteristics. These techniques include on-chip shared memory utilization, removing redundant computation, and coalescing the global memory access. Experiments show that the performance of our 1-D FFT is as fast as CUFFT. Furthermore, the performance of our FFT implementation is more than twice faster than CUFFT for small input sizes. Copyright © 2012 Binary Information Press.

关键词： Graphics processing unit

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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