• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.327-336
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• 2004

In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.120-125
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• 2009

For effective noise removal prior to video processing, noise power or noise variance of an input video sequence needs to be found exactly, but it is actually a very difficult process. This paper presents an accurate noise variance estimation algorithm based on motion compensation between two adjacent noisy pictures. Firstly, motion estimation is performed for each block in a picture, and the residue variance of the best motion-compensated block is calculated. Then, a noise variance estimate of the picture is obtained by adaptively averaging and properly scaling the variances close to the best variance. The simulation results show that the proposed noise estimation algorithm is very accurate and stable irrespective of noise level.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.27-39
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• 2016

This paper derives and analyzes a novel block fast Fourier transform (FFT) based joint detection algorithm. The paper compares the performance and complexity of the novel block-FFT based joint detector to that of the Cholesky based joint detector and single user detection algorithms. The novel algorithm can operate at chip rate sampling, as well as higher sampling rates. For the performance/complexity analysis, the time division duplex (TDD) mode of a wideband code division multiplex access (WCDMA) is considered. The results indicate that the performance of the fast FFT based joint detector is comparable to that of the Cholesky based joint detector, and much superior to that of single user detection algorithms. On the other hand, the complexity of the fast FFT based joint detector is significantly lower than that of the Cholesky based joint detector and less than that of the single user detection algorithms. For the Cholesky based joint detector, the approximate Cholesky decomposition is applied. Moreover, the novel method can also be applied to any generic multiple-input-multiple-output (MIMO) system.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.857-864
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• 2002

Channel coding is one of the most important things to improve digital communications. In this paper, we analyze the performance of turbo code with block interleaver using hopping algorithm (i. e., non-linear interleaver) for high speed multi-media service. The input and output of conventional block interleaver is achieved by the order of column and row, but hoping algorithm is achieved by hopping the column and row that increase the minimum distance and average distance between the nearest data dually. To verify and compare the performance of an proposed method the computer simulation have been performed using turbo code in gaussian channel environment.

• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.37-43
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• 2005

This paper presents a hybrid control algorithm for the active noise control in the rectangular enclosure using an active/passive foam actuator. The hybrid control composes of the adaptive feedforward with feedback loop in which the adaptive feedforward control uses the well-known filtered-x LMS(least mean square) algorithm and the feedback loop consists of the sliding mode controller and observer. The hybrid control has its robustness for both transient and persistent external disturbances and increases the convergence speed due to the reduced variance of the jiltered-x signal by adding the feedback loop. The sliding mode control (SMC) is used to incorporate insensitivity to parameter variations and rejection of disturbances and the observer is used to get the state information in the controller deign. An active/passive smart foam actuator is used to minimize noise actively using an embedded PVDF film driven by an electrical input and passively using an absorption-foam. The error path dynamics is experimentally identified in the form of the auto-regressive and moving-average using the frequency domain identification technique. Experimental results demonstrate the effectiveness of the hybrid control and the feasibility of the smart foam actuator.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.281-287
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• 2021

Bioinformatics is formed with a blend of biology and informatics technologies and it employs the statistical methods and approaches for attending the concerning issues in the domains of nutrition, medical research and towards reviewing the living environment. The ceaseless growth of DNA sequencing technologies has resulted in the production of voluminous genomic data especially the DNA sequences thus calling out for increased storage and bandwidth. As of now, the bioinformatics confronts the major hurdle of management, interpretation and accurately preserving of this hefty information. Compression tends to be a beacon of hope towards resolving the aforementioned issues. Keeping the storage efficiently, a methodology has been recommended which for attending the same. In addition, there is introduction of a competent algorithm that aids in exact matching of small pattern. The DNA representation sequence is then implemented subsequently for determining 2 bases to 6 bases matching with the remaining input sequence. This process involves transforming of DNA sequence into an ASCII symbols in the first level and compress by using LZ77 compression method in the second level and after that form the grid variables with size 3 to hold the 100 characters. In the third level of compression, the compressed output is in the grid variables. Hence, the proposed algorithm S_Pattern DNA gives an average better compression ratio of 93% when compared to the existing compression algorithms for the datasets from the UCI repository.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• ETRI Journal
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• v.45 no.3
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• pp.418-432
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• 2023

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10^-3.