• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.647-649
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• 2004

When a controller is implemented by a one-chip processor with fixed-point operations, the finite alphabet problem usually occurs since parameters and signals should be taken in a finite set of values. This paper formulates PID finite alphabet PID control problem which combines the PID controller with the finite alphabet problem. We will propose a PID parameter tuning method based on an optimization algorithm under the finite alphabet condition. The PID parameters can be represented by a fixed-point representation, and then the problem is formulated as an optimization with constraints that parameters are taken in the finite set. Some simulation are to be performed to exemplify the performance of the PID parameter tuning method proposed in this paper.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.412-430
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• 2003

In many practical problems in signal processing and control, the signal values are often restricted to belong to a finite number of levels. These questions are generally referred to as "finite alphabet" problems. There are many applications of this class of problems including: on-off control, optimal audio quantization, design of finite impulse response filters having quantized coefficients, equalization of digital communication channels subject to intersymbol interference, and control over networked communication channels. This paper will explain how this diverse class of problems can be formulated as optimization problems having finite alphabet constraints. Methods for solving these problems will be described and it will be shown that a semi-closed form solution exists. Special cases of the result include well known practical algorithms such as optimal noise shaping quantizers in audio signal processing and decision feedback equalizers in digital communication. Associated stability questions will also be addressed and several real world applications will be presented.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.91-97
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• 1996

This paper proves by straightforward computation an interesting property of a recursive indexing: it preserves the entropy of a geometrically-distributes stationary memoryless source. This result is a pleasant surprise because the recursive indexing though one-to-one, is a symbol-to-string mapping and the entropy is measured in terms of the source symbols. This preservation of the entropy implies that the minimum average number of bits needed to represent a geometric memoryless source by the recursive indexing followed by a good binary encoder of a finite imput alphabet remains the same as that by a good encoder of an infinite input alphabet. Therefore, the recursive indexing theoretically keeps coding optimality intact. For this reason recursive indexing can provide an interface for a binary code with a finite code book that performs reasonably well for a source with an infinite alphabet.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.685-688
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• 2003

In this paper, a performance comparison between two blind signal estimation algorithms in a LTI channel is considered. The two algorithms, Iterative Least-Squares with Projection (ILSP) and a modified ILSP, are based on the finite-alphabet property of input symbols. This case typically arises in a multiple access system with a sensor array antenna at the receiving end. We start with the formulation of a maximum-likelihood (ML) estimation problem under an additive white Gaussian noise assumption. A blind ML estimator is derived with its iterative algorithm for calculation. Then we narrow down the consideration of this problem to QPSK case so that a modified algorithm is proposed for $\pi$/4-QPSK case. The modified version is compared with the original ILSP algorithm in terms of the rate of the convergence to global minima. A computer simulation shows that the modified algorithm gives a better performance. This result implies that the performance of the blind separation algorithms may be greatly improved by adopting a smart coding scheme with rich structure.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.1-11
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• 1998

We present somple block methods for blind maximal ratio combining (MRC) based on a maximum likelihood (ML) principle and finite alphabet properties (FAP) inherent in digital communication systems. The methods can provide accurate estimates of channel parameters even with a small subset of data, thus realizing nearly perfect combining. The channel parameters of diversity branches and the data sequence are estimated simultaneously by using an alternating projection technique. Two different methods, that is, (1) Joint combining and data sequence estimation(JC-DSE) method and (2) Pre-combining and blind phase estimation (PC-BPE) method are presented. Efficient initiallization schemes that can assure the convergence to the global optimum are also presented. Simulation results demonstrate the performance of two methods on the symbol error rate (SER) and the estimated accuracy of the channel parameters.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.265-267
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• 2004

A blind separation problem in a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system with finite-alphabet inputs is considered. A discrete-time matrix equation model is used to describe the input-output relation of the system in order to make full use of the advantages of modern digital signal processing techniques. At first, ambiguity problem is investigated. Then, based on the results of the investigation, a new identifiability condition is proposed for the case of an input-data set which is widely used in digital communication. A probability bound such that an arbitrary input matrix satisfies the identifiability condition is derived. Finally, Monte-Carlo simulation is performed to demonstrate the validity of our suggestions.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1780-1783
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• 2002

A problem of signal transmitted and received in OFDM systems is considered. In particular, an efficient solution to the problem of blind channel estimation based on Maximum Likelihood (ML) principle has been investigated. The paper proposes a new upper-bound cost, used in conjunction with a standard branch and bound integer programming technique for solving the ML problem. The tighter upper-bound cost exploits a finite-alphabet property of the transmitted signal. The proposed upper-bound cost was found to greatly speed up the ML algorithm, thus reducing computational complexity. Experimental results and discussion are included.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.67-71
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• 2009

Recently, the string non-inclusion related problems have been studied vigorously. Given a set of strings F over a constant size alphabet, consider a string x such that x does not include any string in F as a substring. We call x a Common Non-SuperString(CNSS for short) of F. Among the CNSS's of F, the longest one with finite length is called the Longest Common Non-SuperString(LCNSS for short) of F. In this paper, we first propose a new graph model using prefixes of F. Next, we suggest an O(N)-time algorithm for finding the LCNSS of F, where N is the sum of the lengths of all the strings in F.