Wee Ser

Probabilistic neural-network structure determination for pattern classification

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.

Cross-updated active noise control system with online secondary path modeling

A good active noise control (ANC) system with online secondary path modeling should have the property that the operation of the ANC controller and the modeling of the secondary path are mutually independent. A new ANC system with online secondary path modeling is presented. Three cross-updated least mean square (LMS) adaptive filters are used to reduce mutual disturbances between the operation of the ANC controller and the modeling of the secondary path. Computer simulations have been conducted and the results show that the proposed method is able to produce superior performance compared to existing methods.

Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge

Abstract This paper presents a minutiae detection procedure based on adaptive tracing the gray-level ridge of the fingerprint image with piecewise linear lines of different length. The original fingerprint image is smoothed with an adaptive-oriented smoothing filter only at some selected points. This will greatly reduce the computational time. Each ridge in the skeleton is labeled with a number so that each detected minutia is associated with one or two ridge numbers, which is useful for post processing. We objectively assess the performance of this approach by using two large fingerprint databases.

A robust online secondary path modeling method with auxiliary noise power scheduling strategy and norm constraint manipulation

In many practical cases for active noise control (ANC), the online secondary path modeling methods that use auxiliary noise are often applied. However, the auxiliary noise contributes to residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, a sudden and large change in the secondary path leads to easy divergence of the existing online secondary path modeling methods. To mitigate these problems, this paper proposes a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation. The auxiliary noise power is scheduled based on the convergence status of an ANC system with consideration of the variation of the primary noise. The purpose is to alleviate the increment of the residual noise due to the auxiliary noise. In addition, the norm manipulation is applied to adaptive filters in the ANC system. The objective is to avoid over-updates of adaptive filters due to the sudden large change in the secondary path and thus prevent the ANC system from diverging. Computer simulations show the effectiveness and robustness of the proposed method.

Online fingerprint template improvement

This work proposes a technique that improves fingerprint templates by merging and averaging minutiae of multiple fingerprints. The weighted averaging scheme enables the template to change gradually with time in line with changes of the skin and imaging conditions. The recursive nature of the algorithm greatly reduces the storage and computation requirements of this technique. As a result, the proposed template improvement procedure can be performed online during the fingerprint verification process. Extensive experimental studies demonstrate the feasibility of the proposed algorithm.

An active noise control system using online secondary path modeling with reduced auxiliary noise

In an active noise control (ANC) system using the filtered-x least mean square (FxLMS) algorithm, an online secondary path modeling method that uses an injected auxiliary noise is often applied. Such a method allows quick and full-band signal-independent modeling. In addition, it is suitable for multisecondary path modeling. Normally, the larger the auxiliary noise, the faster an accurate model can be obtained. However, it increases the residual noise of the ANC system. To mitigate this problem, in this letter, a new online secondary path modeling method is proposed. Rather than fixed, the power of auxiliary noise is varied according to the working status of the ANC system. More specifically, the auxiliary noise is large before the ANC system converges, and becomes small when the system converges. Computer simulations show its effectiveness and robustness.

Beampattern Synthesis for Linear and Planar Arrays With Antenna Selection by Convex Optimization

A convex optimization based beampattern synthesis method with antenna selection is proposed for linear and planar arrays. Conjugate symmetric beamforming weights are used so that the upper and non-convex lower bound constraints on the beampattern can be convex. Thus, a mainlobe of an arbitrary beamwidth and response ripple can be obtained. This method can achieve completely arbitrary sidelobe levels. By minimizing a re-weighted objective function based on the magnitudes of the elements in the beamforming weight vector iteratively, the proposed method selects certain antennas in an array to satisfy the prescribed beampattern specifications precisely. Interestingly, a sparse array with fewer antennas (compared to other methods) is produced. This method can design non-uniformly spaced arrays with inter-element spacings larger than one half-wavelength, without the appearance of grating lobes in the resulting beampattern. Simulations are shown using arrays of up to a few hundred antennas to illustrate the practicality of the proposed method.

Robust Adaptive Beamformers Based on Worst-Case Optimization and Constraints on Magnitude Response

In this paper, novel robust adaptive beamformers are proposed with constraints on array magnitude response. With the transformation from the array output power and the magnitude response to linear functions of the autocorrelation sequence of the array weight, the optimization of an adaptive beamformer, which is often described as a quadratic optimization problem in conventional beamforming methods, is then reformulated as a linear programming (LP) problem. Unlike conventional robust beamformers, the proposed method is able to flexibly control the robust response region with specified beamwidth and response ripple. In practice, an array has many imperfections besides steering direction error. In order to make the adaptive beamformer robust against all kinds of imperfections, worst-case optimization is exploited to reconstruct the robust beamformer. By minimizing array output power with the existence of the worst-case array imperfections, the robust beamforming can be expressed as a second-order cone programming (SOCP) problem. The resultant beamformer possesses superior robustness against arbitrary array imperfections. With the proposed methods, a large robust response region and a high signal-to-interference-plus-noise ratio (SINR) enhancement can be achieved readily. Simple implementation, flexible performance control, as well as significant SINR enhancement, support the practicability of the proposed methods.

Predictive fine granularity successive elimination for fast optimal block-matching motion estimation

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. A new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger subblocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEA/MSEA.

Linear Aperiodic Array Synthesis Using an Improved Genetic Algorithm

A novel algorithm on beam pattern synthesis for linear aperiodic arrays with arbitrary geometrical configuration is presented in this paper. Linear aperiodic arrays are attractive for their advantages on higher spatial resolution and lower sidelobe. However, the advantages are attained at the cost of solving a complex non-linear optimization problem. In this paper, we explain the Improved Genetic Algorithm (IGA) that simultaneously adjusts the weight coefficients and inter-sensor spacings of a linear aperiodic array in more details and extend the investigations to include the effects of mutual coupling and the sensitivity of the Peak Sidelobe Level (PSL) to steering angles. Numerical results show that the PSL of the synthesized beam pattern has been successfully lowered with the IGA when compared with other techniques published in the literature. In addition, the computational cost of our algorithm can be as low as 10% of that of a recently reported genetic algorithm based synthesis method. The excellent performance of IGA makes it a promising optimization algorithm where expensive cost functions are involved.