Browsing by Author "Wang, Peng-Hua"

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A General Structure of Linear-Phase FIR Filters With Derivative Constraints
(2017-07-01)
Yu, Bo-You
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Wang, Peng-Hua
;
Chen, Po-Ning
;
電機工程學系
;
電信工程研究所
In this paper, a general structure of linear-phase finite impulse response filters, whose frequency responses satisfy given derivative constraints imposed upon an arbitrary frequency, is proposed. It is comprised of a linear combination of parallelly connected subfilters, called the cardinal filters, with weighted coefficients being the successive derivatives of the desired frequency response at the constrained frequency. An advantage of such a cardinal filters design is that only the weighted coefficients are relevant to the desired frequency response but not the cardinal filters; hence, a dynamic adjustment of the filter system becomes feasible. The key to derive the coefficients of cardinal filters is the determination of the power series expansion of certain trigonometric-related functions. By showing the elaborately chosen trigonometric-related functions satisfy specific differential equations, recursive formulas for the coefficients of cardinal filters are subsequently established, which make efficient their computations. At last, a simple enhancement of the cardinal filters design by incorporating the mean square error minimization is presented through examples.
A General Structure of Type-III FIR Filters with Derivative Constraints
(2017-01-01)
Yu, Bo-You
;
Wang, Peng-Hua
;
Chen, Po-Ning
;
電信工程研究所
;
Institute of Communications Engineering
In this paper, a general structure of type-III FIR filters with derivative constraints is proposed. It consists of a linear combinations of basic sub-filters, called cardinal filters, with weighting coefficients equal to derivatives of the target frequency response at omega = 0. Closed-form expressions for these cardinal filters are derived and their filter coefficients are shown to have a simple recursive relation. Numerical experiments confirm that the proposed design is much more numerically stable than the solution obtained by solving the derivative constraints.
Consistent Sparse Representations of EEG ERP and ICA Components Based on Wavelet and Chirplet Dictionaries
(2010)
Qiu, Jun-Wei
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Zao, John K.
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Wang, Peng-Hua
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Chou, Yu-Hsiang
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資訊工程學系
a randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.
Cooperative Radio Source Positioning and Power Map Reconstruction: A Sparse Bayesian Learning Approach
(2015-06-01)
Huang, Din-Hwa
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Wu, Sau-Hsuan
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Wu, Wen-Rong
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Wang, Peng-Hua
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電機資訊學士班
It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization in cognitive radio systems. To achieve that, secondary users (SUs) must know the locations and signal strength distributions (SSDs) of primary users\' base stations (PUBSs). Recently, a group sparse total least squares method was developed to cooperatively sense the PUBSs\' signal strength and estimate their locations. It approximates PUBSs\' power decay with a path loss model (PLM), assumes PUBSs\' locations on some grid points, and then accomplishes the estimation tasks. However, the parameters of the PLM have to be known in advance, and the accuracy of the location estimation is bounded by the resolution of the grid points, which limit its practical applications. In this paper, we propose a sparse Bayesian learning method to solve the problems. We use a Laplacian function to model the power decay of a PUBS and then derive learning rules to estimate corresponding parameters. The distinct features of the proposed method are that most parameters are adaptively estimated, and little prior information is needed. To further enhance the performance, we incorporate source number detection methods in the proposed algorithm such that the number of the PUBSs can be precisely detected, facilitating the estimation of PUBSs\' locations and SSDs. Moreover, the proposed algorithm is modified into a recursive mode to adapt to SUs\' mobility and time-variant observations. Simulations show that the proposed algorithm has good performance, even when the spatial measurement rate is low.
Cooperative Spectrum Sensing and Locationing: A Sparse Bayesian Learning Approach
(2010)
Huang, D-H Tina
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Wu, Sau-Hsuan
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Wang, Peng-Hua
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傳播研究所
;
Institute of Communication Studies
Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource management in cognitive radio. Furthermore, the proposed algorithm can still roughly reconstruct the power propagation map of the primary transmitters even when the measurement rate is below the lower bound for which compressive sensing (CS) can reconstruct signals with the l(1)-norm optimization method. Compared with the typical CS and Bayesian CS algorithms, simulation results show that average mean squared errors (MSE) of the estimated power propagation map are lower with the proposed algorithm. Besides, the computational complexity is also lower owing to bases pruning. The MSE of the location estimation are also shown to demonstrate the capability of the proposed algorithm.
GENERAL EXPRESSIONS OF DERIVATIVE-CONSTRAINED LINEAR-PHASE TYPE-I FIR FILTERS
(2013-01-01)
Wang, Peng-Hua
;
Yu, Bo-You
;
Chen, Po-Ning
;
電信工程研究所
;
Institute of Communications Engineering
In this paper, we propose a novel structure of linear-phase type-I FIR filters. The structure consists of a linear combination of some basic filters, called the cardinal filters. The weighting coefficients are exactly the derivatives of the amplitude response at omega = 0. We solve a closed-form recurrence relationship between the filter coefficients. Implementation of the cardinal filters is discussed.
Simple median-based EP PP scheme for enhancement of reconstructed Bayer colour filter array images
(2016-12)
Yang, Yi-Hong
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Wang, Peng-Hua
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Chen, Po-Ning
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電機工程學系
;
Department of Electrical and Computer Engineering
In this study, the median-based edge-preserving (EP) modification is revisited and has been shown to improve effectively the quality of a reconstructed Bayer colour filter array image in terms of the composite peak signal-to-noise ratio (CPSNR) performance index. Along this research direction, the authors propose an EP-modified signal-correlation-based (SCB) post-processing (PP), called EP-SCB, as an enhancement to any existing interpolation method. The Bayer image reconstruction system they consider thus consists of two operational phases. The first phase performs an initial estimation of the missing red, green and blue colours by using an existing interpolation method, whereas the second phase applies EP-SCB PP. Since a certain class of images may not fulfil the premise of having small variations in local colour difference, which is assumed by SCBtype interpolation, thereby resulting in a deterioration in CPSNR after EP-SCB PP, a threshold test on local variance ratio is also devised to conditionally switch off the second phase. Experimental results show that the EP-SCB PP with variance-ratio test gives a worst average CPSNR than the original interpolation methods tested in none of the Kodak and IMAX image groups experimented.
Simple median-based EP PP scheme for enhancement of reconstructed Bayer colour filter array images
(2016-12)
Yang, Yi-Hong
;
Wang, Peng-Hua
;
Chen, Po-Ning
;
電機工程學系
;
Department of Electrical and Computer Engineering
In this study, the median-based edge-preserving (EP) modification is revisited and has been shown to improve effectively the quality of a reconstructed Bayer colour filter array image in terms of the composite peak signal-to-noise ratio (CPSNR) performance index. Along this research direction, the authors propose an EP-modified signal-correlation-based (SCB) post-processing (PP), called EP-SCB, as an enhancement to any existing interpolation method. The Bayer image reconstruction system they consider thus consists of two operational phases. The first phase performs an initial estimation of the missing red, green and blue colours by using an existing interpolation method, whereas the second phase applies EP-SCB PP. Since a certain class of images may not fulfil the premise of having small variations in local colour difference, which is assumed by SCBtype interpolation, thereby resulting in a deterioration in CPSNR after EP-SCB PP, a threshold test on local variance ratio is also devised to conditionally switch off the second phase. Experimental results show that the EP-SCB PP with variance-ratio test gives a worst average CPSNR than the original interpolation methods tested in none of the Kodak and IMAX image groups experimented.
Sufficient condition on hypothesis statistics for suboptimality of the identical-quantizer parallel distributed detection system
(2018-01-01)
Yang, Yi-Hong
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Wang, Peng-Hua
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Chen, Po-Ning
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電信工程研究所
;
Institute of Communications Engineering
A challenge in designing an optimal parallel distributed detection system is its exponentially increasing number of possible combinations of local quantization rules with respect to n local sensors. Instead, one may focus on a much easier task of finding the best identical-quantizer system (IQS). This raises an important query about when the simple best IQS is globally optimal in Bayes detection error, particularly under a finite number of sensors. We consider this issue by asking the different but relevant question of when the best IQS is only suboptimal. By numerical experiments over the simplest system setting with ternary local observations and binary local quantizers, we find and prove that for every even n >= 2, if the output statistics of a local quantizer are symmetric with respective to the two hypotheses, the corresponding IQS design can never be optimal in the sense of minimizing the Bayes detection error subject to equal prior probabilities. Our analysis also indicates that asymptotically, one can almost halve the detection error of an IQS by replacing one of the identical local quantizers with a different one when the two hypothesis distributions place most of their probability masses, respectively, on two different local observation outcomes.
Type II, III, and IV Linear-Phase FIR Structures Based on Cardinal Filters
(2019-11-01)
Wang, Peng-Hua
;
Yu, Bo-You
;
Chen, Po-Ning
;
電機工程學系
;
Department of Electrical and Computer Engineering
Novel structures of type II, III, and IV linear-phase finite impulse response (FIR) systems are proposed. It is constituted of a linear combination of basic sub-filters, called , with weighting coefficients equal to the derivatives of the system amplitude response at a prescribed frequency. Since the cardinal filters can be synthesized via recursive closed-form expressions, regardless of the desired system amplitude response, the proposed structure provides a universal design for arbitrary derivative-constrained linear-phase FIR filters. Design examples of maximally flat filters show that further improvement by combining derivative constraints with the least squared error optimization technique can be obtained.