• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1274-1285
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• 1991

This paper presents a statistical convergence analysis of the normalized least mean square(NLMS)algorithm that employs a single-pole lowpass filter, In this algorithm the lowpass filter is used to adjust its output towards the estimated value of the input signal power recursively. The estimated input signal power so obtained at each time is then used to normalize the convergence parameter. Under the assumption that the primary and reference inputs to the adaptive filter are zero mean wide sense stationary, and Gaussian random processes, and further making use of the independence assumption. we derive expressions that characterize the mean and maen squared behavior of the filter coefficients as well as the mean squared estimation error. Conditions for the mean and mean squared convergence are explored. Comparisons are also made between the performance of the NLMS algorithm and that of the popular least mean square(LMS) algorithm Finally, experimental results that show very good agreement between the analytical and emprincal results are presented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.1
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• pp.64-72
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• 2001

This paper is on a design of the high-speed cipher IC using IDEA algorithm. The chip is consists of six functional blocks. The principal blocks are encryption and decryption key generator, input data circuit, encryption processor, output data circuit, operation mode controller. In subkey generator, the design goal is rather decrease of its area than increase of its computation speed. On the other hand, the design of encryption processor is focused on rather increase of its computation speed than decrease of its area. Therefore, the pipeline architecture for repeated processing and the modular multiplier for improving computation speed are adopted. Specially, there are used the carry select adder and modified Booth algorithm to increase its computation speed at modular multiplier. To input the data by 8-bit, 16-bit, 32-bit according to the operation mode, it is designed so that buffer shifts by 8-bit, 16-bit, 32-bit. As a result of simulation by 0.25 $\mu\textrm{m}$ process, this IC has achieved the throughput of 1Gbps in addition to its small area, and used 12,000gates in implementing the algorithm.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.149-160
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• 1997

This paper is concerned with the problem of speaker-adaptive speech synthes is method using a mapped codebook designed by fuzzy mapping on FLVQ (Fuzzy Learning Vector Quantization). The FLVQ is used to design both input and reference speaker's codebook. This algorithm is incorporated fuzzy membership function into the LVQ(learning vector quantization) networks. Unlike the LVQ algorithm, this algorithm minimizes the network output errors which are the differences of clas s membership target and actual membership values, and results to minimize the distances between training patterns and competing neurons. Speaker Adaptation in speech synthesis is performed as follow;input speaker's codebook is mapped a reference speaker's codebook in fuzzy concepts. The Fuzzy VQ mapping replaces a codevector preserving its fuzzy membership function. The codevector correspondence histogram is obtained by accumulating the vector correspondence along the DTW optimal path. We use the Fuzzy VQ mapping to design a mapped codebook. The mapped codebook is defined as a linear combination of reference speaker's vectors using each fuzzy histogram as a weighting function with membership values. In adaptive-speech synthesis stage, input speech is fuzzy vector-quantized by the mapped codcbook, and then FCM arithmetic is used to synthesize speech adapted to input speaker. The speaker adaption experiments are carried out using speech of males in their thirties as input speaker's speech, and a female in her twenties as reference speaker's speech. Speeches used in experiments are sentences /anyoung hasim nika/ and /good morning/. As a results of experiments, we obtained a synthesized speech adapted to input speaker.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.449-455
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• 2014

Detection of moving objects is a fundamental task in most of the computer vision applications, such as video surveillance, activity recognition and human motion analysis. This is a difficult task due to many challenges in realistic scenarios which include irregular motion in background, illumination changes, objects cast shadows, changes in scene geometry and noise, etc. In this paper, we propose an foreground extraction algorithm based on codebook, a database of information about background pixel obtained from input image sequence. Initially, we suppose a first frame as a background image and calculate difference between next input image and it to detect moving objects. The resulting difference image may contain noises as well as pure moving objects. Second, we investigate a codebook with color and brightness of a foreground pixel in the difference image. If it is matched, it is decided as a fault detected pixel and deleted from foreground. Finally, a background image is updated to process next input frame iteratively. Some pixels are estimated by input image if they are detected as background pixels. The others are duplicated from the previous background image. We apply out algorithm to PETS2009 data and compare the results with those of GMM and standard codebook algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.4
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• pp.194-208
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• 2008

In this paper, a multiple-input-multiple-output (MIMO) hybrid-automatic repeat request (HARQ) algorithm with antenna scheduling is proposed. It retransmits the packet using scheduled transmit antennas according to the state of the communication link, instead of retransmitting the packet via the same antennas. As a result, a combination of conventional HARQ systems, viz. chase combining (CC) and incremental redundancy (IR) are used to achieve better performance and lower redundancy. The proposed MIMO-OFDM HARQ system with antenna scheduling is shown to be superior to conventional MIMO HARQ systems, due to its spatial diversity gain.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.167-170
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• 2000

This paper describes a novel multi-operand radix-2 signed-digit(SD) adder. The novel multi-operand addition algorithm can eliminate carry propagation chain by dividing the input operands into even place part and odd place part, and adding them each. The multi-operand adder with this algorithm can add six operands in parallel, and is faster than the ordinary method of SD adder binary tree. A hardware model for proposed adder is shown which is implemented by the current-mode MOSFET circuit technology. Simulations have been made by SPICE in order to verify the function of the proposed circuit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1768-1789
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• 2015

The maximal signal-to-interference-plus-noise ratio (Max-SINR) algorithm for interference alignment (IA) has received considerable attention for its high sum rate achievement in the multiple-input multiple-output (MIMO) interference channel. However, its complexity may increase dramatically when the number of users approaches the IA feasibility bound, and the number of iterations and computational time may become unacceptable. In this paper, we study the properties of the Max-SINR algorithm thoroughly by presenting theoretical insight into the algorithm and by providing the potential of reducing the overall computational cost. Furthermore, a novel IA algorithm based on the principle direction search is proposed, which can converge more rapidly than the conventional Max-SINR method. In the proposed algorithm, it searches along the principle direction, which is found to approximately point to the convergence values, and can approach the convergence solutions rapidly. In addition, the closed-form solution of the optimal step size can be formulated in the sense of minimal interference leakage. Simulation results demonstrate that the proposed algorithm outperforms the conventional minimal interference leakage and Max-SINR algorithms in terms of the convergence rate while guaranteeing the high throughput of IA networks.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.67-73
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• 2005

Recently, the social requirement of personal identification techniques has been increasing. Fingerprint recognition is one of the biometries methods that has been widely used for this requirement. This paper proposes the fingerprint matching algorithm that uses the information of the ridge shapes of minutiae. In which, the data of the ridge shape are expressed in one-dimensional discrete-time signals. In our algorithm, we obtain one-dimensional discrete-time signals for ridge at every minutiae from input and registered fingerprints, and find pairs of minutia which have the similar ridge shape by comparing input fingerprint with registered fingerprint, thereafter we find candidates of rotation angle and moving displacement from the pairs of similar minutia, and obtain the final rotation angle and moving displacement value from those candidates set by using clustering method. After that, we align an input fingerprint by using obtained data, and calculate the matching rate by counting the number of corresponded pairs of minutia within the overlapped area of an input and registered fingerprints. As a result of experiment, false rejection rate(FRR) of $18.0\%$ at false acceptance rate(FAR) of $0.79\%$ is achieved.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.371-380
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• 2011

The noise caused by a variety of reasons worsens the quality of input image when we use the images reproducing device. The basic difficulty to solve this problem is that the noise and the signal are difficult to be distinguished. Contrast enhancement such as unsharp masking is one of the most important procedures to improve the quality of input images. The conventional unsharp masking enhances the images by adding their amplified high frequency components. The noise component of the input images, however, also tends to be amplified due to the nature of the unsharp masking. This paper considers the block approach for detecting niose and image feature of the input image so that the unsharp masking could be adaptively applied accordingly. Simulation results show that it is made possible to enhance contrast of the image without boosting up the noisy components by applying the proposed algorithm.