• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.104-110
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• 2008

In this paper, a novel 2-level-search sphere decoding algorithm for multiple-input multiple-output (MIMO) detection and its VLSI implementation are presented. The proposed algorithm extends the search space by concurrently performing symbol detection on 2 level of the tree search. Therefore, the possibility of discarding good candidates can be avoided. Simulation results demonstrate the good performance of the proposed algorithm in terms of bit-error-rate (BER). From the proposed algorithm, an efficient very large scale integration (VLSI) architecture which incorporates low-complexity and fixed throughput features is proposed. The proposed architecture supports many modulation techniques such as BPSK, QPSK, 16-QAM and 64-QAM. The sorting block, which occupies a large portion of hardware utilization, is shared for different operating modes to reduce the area. The proposed hardware implementation results show the improvement in terms of area and BER performance compared with existing architectures.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.80-87
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• 2010

The method of function synthesis is essential when we control the systems not known their characteristic, by predicting the function to satisfy a relation between input and output from the given pairs of input-output data. In general the most systems operate non-linearly, it is easy to come about problem is composed with combinations of parameter, constant, condition, and so on. Genetic programming is proposed by one of function synthesis methods. This is a search method of function tree to satisfy a relation between input and output, with appling genetic operation to function tree to convert function into tree structure. In this paper, we indicate problems of a function synthesis method by an existing genetic programming propose four type of new improved method. In other words, there are control of function tree growth, selection of local search method for early convergence, effective elimination of redundancy in function tree, and utilization of problem characteristic of object, for preventing function from complicating when the function tree is searched. In case of this improved method, we confirmed to obtain superior structure to function synthesis method by an existing genetic programming in a short period of time by means of computer simulation for the two-spirals problem.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.9
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• pp.243-250
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• 2021

Recently, with the development of cloud computing, interest in database outsourcing is increasing. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose a parallel range query processing algorithm that supports privacy protection. The proposed algorithm uses the Paillier encryption system to support data protection, query protection, and access pattern protection. To reduce the operation cost of a checking protocol (SRO) for overlapping regions in the existing algorithm, the efficiency of the SRO protocol is improved through a garbled circuit. The proposed parallel range query processing algorithm is largely composed of two steps. It consists of a parallel kd-tree search step that searches the kd-tree in parallel and safely extracts the data of the leaf node including the query, and a parallel data search step through multiple threads for retrieving the data included in the query area. On the other hand, the proposed algorithm provides high query processing performance through parallelization of secure protocols and index search. We show that the performance of the proposed parallel range query processing algorithm increases in proportion to the number of threads and the proposed algorithm shows performance improvement by about 5 times compared with the existing algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2188-2208
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• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.109-121
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• 2007

This paper focuses on algorithms based on the evolution, which is applied to various optimization problems. Especially, among these algorithms based on the evolution, we investigate the simple genetic algorithm based on Darwin's evolution, the Lamarckian algorithm based on Lamark's evolution and the Baldwin algorithm based on the Baldwin effect and also Investigate the difference among them in the biological and engineering aspects. Finally, through this comparison, we suggest a new algorithm to find more various solutions changing the genotype or phenotype search space and show the performance of the proposed method. Conclusively, the proposed method showed superior performance to the previous method which was applied to the constrained minimum spanning tree problem and known as the best algorithm.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1939-1944
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• 2003

This paper presents a global path planning algorithm based on a visibility graph method, and applies additionally various constraints for constructing the reduced visibility graph. The modification algorithm for generating the rounded path is applied to the globally shortest path of the visibility graph using the robot size constraint in order to avoid the obstacle. In order to check the visibility in given 3D map data, 3D CAD data with VRML format is projected to the 2D plane of the mobile robot, and the projected map is converted into an image for easy map analysis. The image processing are applied to this grid map for extracting the obstacles and the free space. Generally, the tree size of visibility graph is proportional to the factorial of the number of the corner points. In order to reduce the tree size and search the shortest path efficiently, the various constraints are proposed. After short paths that crosses the corner points of obstacles lists up, the shortest path among these paths is selected and it is modified to the combination of the line path and the arc path for the mobile robot to avoid the obstacles and follow the rounded path in the environment. The proposed path planning algorithm is applied to the mobile robot LCAR-III.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.253-261
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• 2005

In this paper, we propose an efficient block-based disparity estimation algorithm fur multiple view image coding in EE2 and EE3 in 3DAV. The proposed method emphasizes on visual quality improvement to satisfy the requirements for multiple view generation. Therefore, we perform an adaptive disparity estimation that constructs variable blocks by considering given image features. Examining neighboring features around desired block search range is set up to decrease complexity and additional information than only using quad-tree coding through applying binary-tree and quad-tree coding by taking into account stereo image feature having big disparity. The experimental results show that the proposed method improves PSNR about 1 to 2dB compared to existing other methods and decreases computational complexity up to maximum 68 percentages than FBMA.