• Journal of information and communication convergence engineering
• /
• v.14 no.4
• /
• pp.215-221
• /
• 2016

We consider a design of a combined quantizer and estimator for distributed systems wherein each node quantizes its measurement without any communication among the nodes and transmits it to a fusion node for estimation. Noting that the quantization partitions minimizing the estimation error are not independently encoded at nodes, we focus on the parameter regions created by the partitions and propose a cluster-based quantization algorithm that iteratively finds a given number of clusters of parameter regions with each region being closer to the corresponding codeword than to the other codewords. We introduce a new metric to determine the distance between codewords and parameter regions. We also discuss that the fusion node can perform an efficient estimation by finding the intersection of the clusters sent from the nodes. We demonstrate through experiments that the proposed design achieves a significant performance gain with a low complexity as compared to the previous designs.

• Journal of information and communication convergence engineering
• /
• v.12 no.4
• /
• pp.215-220
• /
• 2014

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.

• Journal of the Korea Society for Simulation
• /
• v.22 no.2
• /
• pp.53-62
• /
• 2013

Cloud environment is one in the field of distributed computing and it consists of physical nodes and virtual nodes. In distributed cloud environment, an optimal path search is that each node to perform a search for an optimal path. Synchronization of each node is required for the optimal path search via fast data transmission because of real-time environment. Therefore, a quantization technique is required in order to guarantee QoS(Quality of Service) and search an optimal path. The quantization technique speeds search data transmission of each node. So a main server can transfer data of real-time environment to each node quickly and the nodes can perform to search optimal paths smoothly. In this paper, we propose the quantization technique to solve the search problem. The quantization technique can reduce the total data transmission. In order to experiment the optimal path search system which applied the quantized data transmission, we construct a simulation of cloud environment. Quantization applied cloud environment reduces the amount of data that transferred, and then QoS of an application for the optimal path search problem is guaranteed.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.4
• /
• pp.217-223
• /
• 2005

In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.207-208
• /
• 2018

본 논문에서는 기계학습의 하나인 Learning Vector Quantization 알고리즘을 이용하여 컨트롤러 순서를 정하는 모델을 제안하였다. 제안한 모델은 모든 컨트롤러 정보를 수집하여 Learning Vector Quantization의 LVQ1와 LVQ2 기법을 이용하여 컨트롤러의 순서를 정한다. 이를 통해, 효율적인 컨트롤러 동기화가 이뤄질 것으로 기대된다.

• Journal of information and communication convergence engineering
• /
• v.16 no.3
• /
• pp.142-147
• /
• 2018

We present a practical design algorithm for quantizers at nodes in distributed systems in which each local measurement is quantized without communication between nodes and transmitted to a fusion node that conducts estimation of the parameter of interest. The benefits of vector quantization (VQ) motivate us to incorporate the VQ strategy into our design and we propose a low-complexity design technique that seeks to assign vector codewords into sets such that each codeword in the sets should be closest to its associated local codeword. In doing so, we introduce new distance metrics to measure the distance between vector codewords and local ones and construct the sets of vector codewords at each node to minimize the average distance, resulting in an efficient and independent encoding of the vector codewords. Through extensive experiments, we show that the proposed algorithm can maintain comparable performance with a substantially reduced design complexity.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37A no.12
• /
• pp.1043-1053
• /
• 2012

In distributed video coding (DVC) systems, the complexity of encoders is greatly reduced by removing the motion estimation operations in encoders, since the correlation between frames is utilized in decoders. The transmission of parity bits is requested through the feedback channel, until the related errors are corrected to decode the Wyner-Ziv frames. The requirement to use the feedback channel limits the application of DVC systems. In this paper, we propose an efficient method to remove the feedback channel in DVC systems. First, a simple side information generation method is proposed to calculate the amount of parity bits in the encoder, and it is shown that the proposed method yields good performance with low complexity. Then, by calibrating the theoretical entropy with three parameters, we can calculate the amount of parity bits in the encoder and remove the feedback channel. Moreover, an adaptive method to determine quantization parameters for key frames is proposed. Extensive computer simulations show that the proposed method yields better performance than conventional methods.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.558-564
• /
• 2018

In distributed estimation where each node can collect only partial information on the parameter of interest without communication between nodes and quantize it before transmission to a fusion node which conducts estimation of the parameter, we consider a novel quantization technique employed at local nodes. It should be noted that the performance can be greatly improved if each node can transmit its measurement to one designated node (namely, head node) which can quantize its estimate using the total rate available in the system. For this case, the best strategy at the head node would be simply to partition the parameter space using the generalized Lloyd algorithm, producing the global codewords, one of which is closest to the estimate is transmitted to a fusion node. In this paper, we propose an iterative design algorithm that seeks to efficiently assign the codewords into each of quantization partitions at nodes so as to achieve the performance close to that of the system with the head node. We show through extensive experiments that the proposed algorithm offers a performance improvement in rate-distortion perspective as compared with previous novel techniques.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.2067-2070
• /
• 2002

In this paper, a VLSI architecture for transform and quantization module, which consists of 2D-DCT, quantization, AC/DC prediction block, scan conversion, inverse quantization and 2D-IDCT, is presented. The architecture of the module is designed to handle a macroblock data within 1064 cycles and suitable for MPEG-4 video codec handling CIF image formats. Only single 1-D DCT/IDCT cores are used for the design instead of 2-D DCT/IDCT, respectively. 1-bit serial distributed arithmetic architecture is adopted for 1-D DCT/IDCT to reduce the hardware area in this architecture. As the result, the maximum utilization of hardware can be achieved, and power consumption can be minimized. The proposed design is operated on 27MHz clock. The experimental results show that the accuracy of DCT and IDCT meet the IEEE specification.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.8
• /
• pp.27-31
• /
• 2013

In this paper, we propose a new quantization method using fuzzy C-means clustering(FCM) for lumbar ultrasound image recognition. Unlike usual histogram based quantization, our method first classifies regions into 10 clusters and sorts them by the central value of each cluster. Those clusters are represented with different colors. This method is efficient to handle lumbar ultrasound image since in this part of human body, the brightness values are distributed to doubly skewed histogram in general thus the usual histogram based quantization is not strong to extract different areas. Experiment conducted with 15 real lumbar images verified the efficacy of proposed method.