Tran Thai Son

Low-order IIR filter bank design

The advantage of infinite-impulse response (IIR) filters over finite-impulse response (FIR) ones is that the former require a much lower order (much fewer multipliers and adders) to obtain the desired response specifications. However, in contrast with well-developed FIR filter bank design theory, there is no satisfactory methodology for IIR filter bank design. The well-known IIR filters are mostly derived by rather heuristic techniques, which work in only narrow design classes. The existing deterministic techniques usually lead to too high order IIR filters and thus cannot be practically used. In this paper, we propose a new method to solve the low-order IIR filter bank design, which is based on tractable linear-matrix inequality (LMI) optimization. Our focus is the quadrature mirror filter bank design, although other IIR filter related problems can be treated and solved in a similar way. The viability of our theoretical development is confirmed by extensive simulation.

LMI characterization for the convex hull of trigonometric curves and applications

In this paper, we develop a new linear matrix inequality (LMI) technique, which is practical for solutions of the general trigonometric semi-infinite linear constraint (TSIC) of competitive orders. Based on the new full LMI characterization for the convex hull of a trigonometric curve, it is shown that the semi-infinite optimization problem involving TSIC can be solved by an LMI optimization problem with additional variables of dimension just n, the order of the trigonometric curve. Our solution method is very robust which allows us to address almost all practical filter design problems. Unlike most previous works involving several complex mathematical tools, our derivation arguments are based on simple results of the convex analysis and some formal elementary transforms. Furthermore, many filter/filterbank design problems can be reformulated as the optimization of linear/convex quadratic objectives over the TSIC. Based on this reformulation, these problems can be equivalently reduced to LMI optimization problems with the minimal size. Our examples of designing up to 1200-tap filters verifies the viability of our formulation.

Car detection using multi-feature selection for varying poses

This paper presents a novel method of car detection by using the Adaboost algorithm, which is enhanced by the Quadratic Programming for feature extraction. In this paper, car is divided into many relevant features through their appearances in training samples such as wheel and window. We crop features of object in training images and utilize them for the Adaboost training. The results of the Adaboost training are many sets of weak classifiers corresponding to the relevant features. The Quadratic Programming is applied to set up the priority order of weak classifiers when they are combined together by their relevant positions for detection. In other words, we utilize the Adaboost as a kernel function for generating the stronger classifier, which is a linear combination of weak classifiers selected by the Quadratic Programming. The proposed method can provide a high accuracy of object detection by using a few hundred samples for training the Adaboost.

Efficient Large-Scale Filter/Filterbank Design via LMI Characterization of Trigonometric Curves

Many filter and filterbank design problems can be posed as the optimization of linear or convex quadratic objectives over trigonometric semi-infinite constraints. Recent advances in design methodology are based on various linear matrix inequality (LMI) characterizations of the semi-infinite constraints, and semidefinite programming (SDP) solutions. Despite these advances, the design of filters of several hundredth order, which typically arise in multicarrier communication and signal compression, cannot be accommodated. This hurdle is due mainly to the large number of additional variables incurred in the LMI characterizations. This paper proposes a novel LMI characterization of the semi-infinite constraints that involves additional variables of miminal dimensions. Consequently, the design of high-order filters required in practical applications can be achieved. Examples of designs of up to 1200-tap filters are presented to verify the viability of the proposed approach.

New linear-programming-based filter design

It is well known that the filter-design problem with mask constraints can be formulated as a semi-infinite program. There are two approaches toward the solution of this semi-infinite program. The first griding approach relaxes the semi-infinite constraint by refining it in the finite griding domain so it does not always guarantee global optimal and feasible solution. The second semi-definite programming (SDP)-based approach does guarantee the global optimal solution and excellently handles positive real constraints. However, the magnitude constraints are still persistent and not yet handled by SDP tool in an efficient manner. In this brief, a new tight polyhedral approximation for semi-infinite constrained domain is proposed. Based on it, we present a new linear-programming-based solution method for filter design, which unlike the griding approach yields global solution and unlike SDP based approach is practical for even long-tap filters. Simulation results confirm the viability of the proposed method.

Monotonic Optimization Based Decoding for Linear Codes

A new efficient method is developed for optimal maximum likelihood (ML) decoding of an arbitrary binary linear code based on data received from a Gaussian channel. The decoding algorithm is based on minimization of a difference of two monotonic objective functions subject to the 0-1 constraint of bit variables. The iterative process converges to the global optimal ML solution after a finite number of steps. The proposed algorithms computational complexity depends on the input sequence length k which is much less than the codeword length n, especially for codes with small code rates. The viability of the developed method is verified through simulations on different coding schemes.

Stereo Matching Algorithm Using a Simplified Trellis Diagram Iteratively and Bi-Directionally

This paper presents an approach that uses the Viterbi algorithm in a stereo correspondence problem. We propose a matching process which is visualized as a trellis diagram to find the maximum a posterior result. The matching process is divided into two parts: matching the left scene to the right scene and matching the right scene to the left scene. The last result of stereo problem is selected based on the minimum error for uniqueness by a comparison between the results of the two parts of matching process. This makes the stereo matching possible without explicitly detecting occlusions. Moreover, this stereo matching algorithm can improve the accuracy of the disparity image, and it has an acceptable running time for practical applications since it uses a trellis diagram iteratively and bi-directionally. The complexity of our proposed method is shown approximately as O(N2 × P), in which N is the number of disparity, and P is the length of the epipolar line in both the left and right images. Our proposed method has been proved to be robust when applied to well-known samples of stereo images such as random dot, Pentagon, Tsukuba image, etc. It provides a 95.7 percent of accuracy in radius 1 (differing by ±1) for the Tsukuba images.

Optimum multi-user detection by nonsmooth optimization

The optimum multiuser detection (OMD) is a discrete (binary) optimization. The previously developed approaches often relax it by a semi-definite program (SDP) and then employ randomization for searching the optimal solution around the solution of this relaxed SDP. In this paper, we show the limited capacity of this SDP program, which at the end cannot give a better solution than the simple linear minimum mean square error detector (LMMSE). Our departure point is to express the problem as quadratic minimization over quadratic equality constraint (QMQE) or concave quadratic minimization over a box of continuous optimization (CQOB). The QMQE allows us to develop a nonsmooth optimization algorithm to locate the global optimal solution of OMD, while CQOB facilities effective confirmation of the solutions found by QMQE. Our intensive simulation clearly shows that the algorithm outperforms all previously developed algorithms while the computational burden is essentially reduced.

Mask constrained beam pattern synthesis for large arrays

Antenna array pattern synthesis with mask constraints can be formulated as a convex optimization problem with semiinfinite trigonometric polynomial constraints. The current approach uses a Linear Matrix Inequality (LMI) characterization of the semi-infinite constraints to convert the original problem into a semidefinite programming (SDP) problem. However, an important drawback of this approach is the large number of additional variables incurred in the equivalent SDP representation, which in turn prohibits its use in the design of large antenna arrays that arise in many modern applications. This paper presents an efficient method for the synthesis of large antenna arrays via a novel LMI characterization of semi-infinite constraints that only involves a minimal number of additional variables. Subsequently, the design of patterns for arrays with hundreds of elements can be easily achieved on a standard personal computer using existing SDP solvers.

Monotonic optimization based decoding for linear codes

New efficient methods are developed for the optimal maximum-likelihood (ML) decoding of an arbitrary binary linear code based on data received from any discrete Gaussian channel. The decoding algorithm is based on monotonic optimization that is minimizing a difference of monotonic (d.m.) objective functions subject to the 0–1 constraints of bit variables. The iterative process converges to the global optimal ML solution after finitely many steps. The proposed algorithm’s computational complexity depends on input sequence length k which is much less than the codeword length n, especially for a codes with small code rate. The viability of the developed is verified through simulations on different coding schemes.