• Journal of Biomedical Engineering Research
• /
• v.28 no.3
• /
• pp.429-440
• /
• 2007

A coded excitation has been studied to improve the performance for ultrasound imaging in term of SNR, imaging frame rate, contrast to tissue ratio, and so forth. However, it requires a complicated arbitrary waveform transmitter for each active channel that is typically composed of a multi-bit Digital-to-Analog Converter (DAC) and a linear power amplifier (LPA). Not only does the LPA increase the cost and size of a transmitter block, but it consumes much power, increasing the system complexity further and causing a heating-up problem. This paper proposes an optimized 1.5bit fourth order sigma-delta modulation technique applicable to design an efficient arbitrary waveform generator with greatly reduced power dissipation and hardware. The proposed SDM can provide a required SQNR with a low over-sampling ratio of 4. To this end, the loop coefficients are optimized to minimize the quantization noise power in signal band while maintaining system stability. In addition, the decision level for the 1.5 bit quantizer is optimized for a given input waveform, which results in the SQNR improvement of more than 5dB. Computer simulation results show that the SQNR of a FM(frequency modulated) signal generated by using the proposed method is about 26dB, and the peak side-lobe level (PSL) of its compressed waveform on receive is -48dB.

• Journal of Computing Science and Engineering
• /
• v.5 no.2
• /
• pp.111-120
• /
• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

• Journal of Korea Multimedia Society
• /
• v.6 no.3
• /
• pp.419-428
• /
• 2003

Several methods have been studied to prune the parasitic branches that cause unfortunately from thinning a shape to get its skeleton. We found that the symmetric path finding method was most efficient because it followed the boundary pixels of the shape just once. In this paper, its extended method is proposed to apply to removing the noise branches that protrude out of the boundary of a segmented or extracted shape in a given image. The proposed method can remove a noise branch with one-pixel width and also remove the noise branch that includes a round shape called a noise blob. The method uses a 4-8-directional boundary-following technique to determine symmetric paths and finds noise branches with noise blobs by detecting quasi-symmetric paths. Its time complexity is a linear function of the number of boundary pixels. Interactively selectable parameters are used to define various types of noise branches flexibly, which are the branch - size parameter and the blob-size parameter. Experimental results for a practical shape and various artificial shapes showed that the proposed method was very useful for simplifying the shapes.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.3
• /
• pp.63-69
• /
• 2012

Given art gallery P with n vertices, the maximum (sufficient) number of mobile guards is${\lfloor}n/4{\rfloor}$ for simple polygon and${\lfloor}(3n+4)/16{\rfloor}$ for simple orthogonal polygon. However, there is no polynomial time algorithm for minimum number of mobile guards. This paper suggests polynomial time algorithm for the minimum number of mobile guards. Firstly, we obtain the visibility graph which is connected all edges if two vertices can be visible each other. Secondly, we select vertex u with ${\Delta}(G)$ and v with ${\Delta}(G)$ in $N_G(u)$ and delete visible edges from u,v and incident edges. Thirdly, we select $w_i$ in partial graphs and select edges that is the position of mobile guards. This algorithm applies various art galley problems with simple polygons and orthogonal polygons art gallery. As a results, the running time of proposed algorithm is linear time complexity and can be obtain the minimum number of mobile guards.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.4
• /
• pp.708-717
• /
• 2017

Tracking of sound-location is widely used in various area such as intelligent CCTV, video conference and voice commander. In this paper we introduce the real-time sound-location tracking method for smart lecture system using TDoA(Time Difference of Arrival) with orthogonal microphone array on the ceiling. Through discussion on some models of TDoA detection, cross correlation method using linear microphone array is proposed. Orthogonal array with 5 microphone could detect omni direction of sound-location. For real-time detection we adopt the threshold of received energy for eliminating no-voice interval, signed cross correlation for reducing computational complexity. The detected azimuth angles are processed using median filter for lowering the angle deviation. The proposed system is implemented with high performance MCU of TMS320F379D and MEMs microphone module and shows the accuracy of 0.5 and 6.5 in degree for white noise and lectured voice, respectively.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.533-545
• /
• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.5
• /
• pp.981-989
• /
• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.87-97
• /
• 2004

In this paper, we propose an algorithm for interlaced-to-progressive conversion by the weighted summation of the information collected from spatial do-interlacing method, in which the weighted edge based line average is applied, and the temporal method in which the motion compensation is employed by using hi-directional BMA (block matching algorithm). We employed time-recursive and motion adaptive processing as motion detection is involved. Also, a median filter is used to deal with limitation of the linear summation in which only an intermediate of values being involved is determined. The main goal of the approach is to overcome the shortcomings of each of the do-interlacing techniques without significant increment of the computational complexity, and the proposed method is apt to implement in hardware for real-time processing.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.11
• /
• pp.54-59
• /
• 1999

In this paper, a generalized auto-regressive(AR) model is proposed for linear prediction based on adaptive spatio-temporal support region(ASTSR). The conventional AR model suffers from the drawback that the prediction error increases in the edge region because the rectangular support region of the edge does not satisfy the stationary assumption. Thus, the proposed approach puts an emphasis on the formulation of a spatio-temporally adaptive support region for the AR model, called ASTSR. The ASTSR consists of two parts: the adaptive spatial support region(ASSR) connected with edges and the adaptive temporal support region(ATSR) related to temporal discontinuities. The AR model based on ASTSR not only produces more accurate model parameters but also reduces the computational complexity in the motion picture restoration.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.4
• /
• pp.105-110
• /
• 2012

The CCA (canonical correlation analysis) is a well known analysis tool that measures the linear relationship between two variable sets and it can be used for blind source separation (BSS). In previous works, a blind source separation scheme based on the CCA and auto regression was proposed. Unfortunately, the proposed scheme requires high signal-to-noise ratio for successful source separation. In this paper, we propose an improved BSS scheme based on the CCA and auto regression by eliminating the main diagonal elements of auto covariance matrix. Compared to the previously proposed BSS scheme, the proposed BSS scheme not only offers better source separation performance but also requires low computational complexity.