scholarly journals High-efficiency Vector Quantization Codebook Search Algorithms for Extended Adaptive Multi-ratewideband Audio Coder

2019 ◽  
Vol 31 (1) ◽  
pp. 153
Author(s):  
Cheng-Yu Yeh ◽  
Hsiang-Yueh Lai
Keyword(s):  
Vector Quantization ◽  
High Efficiency ◽  
Search Algorithms ◽  
Audio Coder

Dynamic Allocation of Radio Frequency in the Battlefield Environment

2012 ◽  
Vol 197 ◽  
pp. 496-501 ◽  
Author(s):  
Jing Xue Liu ◽  
Zhi Bo Li ◽  
Hong Li
Keyword(s):  
Genetic Algorithms ◽  
Radio Frequency ◽  
High Efficiency ◽  
Simulation Experiment ◽  
Mathematic Model ◽  
Spectrum Management ◽  
Search Algorithms ◽  
Dynamic Allocation ◽  
Set Up ◽  
Battlefield Environment

Dynamic allocation of Radio Frequency (Hereafter called “RF”) is critical in the battlefield spectrum management. The article analyzes the conventional method of RF dynamic allocation in the battlefield environment, and set up the mathematic model of the RF dynamic allocation by using the results of spectrum detected in battlefield. It designs the algorithms with the combination of Genetic Algorithms and Tabu Search Algorithms. The simulation experiment proves the high efficiency of hybrid algorithms and it suit for solving the RF dynamic allocation problem in the battlefield environment.

Application of SOM-Based Fuzzy Systems in Voltage Security Margin Estimation

2001 ◽  
Vol 5 (3) ◽  
pp. 157-162
Author(s):  
Mu-Chun Su ◽  
◽  
Eugene Lai ◽  
Chee-Yuen Tew ◽  
Chih-Wen Liu ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Fuzzy Systems ◽  
High Efficiency ◽  
Simulated Data ◽  
Operating Conditions ◽  
Feature Maps ◽  
Voltage Security ◽  
Significant Research ◽  
Som Algorithm ◽  
Current Operating

In recent years, many significant research efforts have been devoted to voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. In this paper we propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. The SOM-based fuzzy systems use the Kohonen’s self-organizing feature map (SOM) algorithm, not only for its vector quantization feature, but also for its topological property. The vector quantization feature of feature maps is used to search a good supply of most representative cluster centers. Then the topology-preserving feature is fully utilized to select a set of most influential rules so as to contribute to the computation of system outputs. The proposed technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency.

High-speed closest codeword search algorithms for vector quantization

1995 ◽  
Vol 43 (3) ◽  
pp. 323-331 ◽  
Author(s):  
Chang-Hsing Lee ◽  
Ling-Hwei Chen
Keyword(s):  
Vector Quantization ◽  
High Speed ◽  
Search Algorithms

High-Efficiency Image-Coding Method Using Vector Quantization and Block Coding

1989 ◽  
Vol 20 (9) ◽  
pp. 100-108
Author(s):  
Tomoyuki Nonomura ◽  
Kazumi Yamashita ◽  
Kiyotsugu Satoh
Keyword(s):  
Vector Quantization ◽  
Image Coding ◽  
High Efficiency ◽  
Block Coding ◽  
Coding Method

scholarly journals Self-Configuring (1 + 1)-Evolutionary Algorithm for the Continuous p-Median Problem with Agglomerative Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 130
Author(s):  
Lev Kazakovtsev ◽  
Ivan Rozhnov ◽  
Guzel Shkaberina
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
High Efficiency ◽  
Location Theory ◽  
Search Algorithms ◽  
Neighborhood Search ◽  
Local Search Algorithms ◽  
Median Problem ◽  
Data Volume ◽  
Automatic Grouping

The continuous p-median problem (CPMP) is one of the most popular and widely used models in location theory that minimizes the sum of distances from known demand points to the sought points called centers or medians. This NP-hard location problem is also useful for clustering (automatic grouping). In this case, sought points are considered as cluster centers. Unlike similar k-means model, p-median clustering is less sensitive to noisy data and appearance of the outliers (separately located demand points that do not belong to any cluster). Local search algorithms including Variable Neighborhood Search as well as evolutionary algorithms demonstrate rather precise results. Various algorithms based on the use of greedy agglomerative procedures are capable of obtaining very accurate results that are difficult to improve on with other methods. The computational complexity of such procedures limits their use for large problems, although computations on massively parallel systems significantly expand their capabilities. In addition, the efficiency of agglomerative procedures is highly dependent on the setting of their parameters. For the majority of practically important p-median problems, one can choose a very efficient algorithm based on the agglomerative procedures. However, the parameters of such algorithms, which ensure their high efficiency, are difficult to predict. We introduce the concept of the AGGLr neighborhood based on the application of the agglomerative procedure, and investigate the search efficiency in such a neighborhood depending on its parameter r. Using the similarities between local search algorithms and (1 + 1)-evolutionary algorithms, as well as the ability of the latter to adapt their search parameters, we propose a new algorithm based on a greedy agglomerative procedure with the automatically tuned parameter r. Our new algorithm does not require preliminary tuning of the parameter r of the agglomerative procedure, adjusting this parameter online, thus representing a more versatile computational tool. The advantages of the new algorithm are shown experimentally on problems with a data volume of up to 2,000,000 demand points.

A Fast Image Encoding Algorithm Based on the Pyramid Structure of Codewords

2011 ◽  
pp. 214-226
Author(s):  
Ahmed A. Radwan ◽  
Ahmed Swilem ◽  
Mamdouh M. Gomaa
Keyword(s):  
Vector Quantization ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Search Procedure ◽  
Image Encoding ◽  
Fast Search ◽  
Pyramid Structure ◽  
Speed Up ◽  
Simulation Results ◽  
Full Search

This article presents a very simple and efficient algorithm for codeword search in the vector quantization encoding. This algorithm uses 2-pixel merging norm pyramid structure to speed up the closest codeword search process. The authors first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that, the proposed search algorithm reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.

Sign in / Sign up

Export Citation Format

Share Document