An adaptive prediction method for signal fusion

2017 ◽  
Author(s):  
Alexander Holland ◽  
Shadnaz Asgari
Keyword(s):  
Prediction Method ◽  
Signal Fusion ◽  
Adaptive Prediction

scholarly journals A PREDICTIVE CODING METHOD FOR LOSSLESS COMPRESSION OF IMAGES

2013 ◽  
Vol 7 (3) ◽  
pp. 683-685
Author(s):  
Anil Mishra ◽  
Ms. Savita Shiwani
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Video Processing ◽  
Prediction Method ◽  
Original Form ◽  
Minimum Entropy ◽  
Digital World ◽  
Adaptive Prediction ◽  
Continuous Tone ◽  
Spatial Redundancy

Images are an important part of today's digital world. However, due to the large quantity of data needed to represent modern imagery the storage of such data can be expensive. Thus, work on efficient image storage (image compression) has the potential to reduce storage costs and enable new applications.This lossless image compression has uses in medical, scientific and professional video processing applications.Compression is a process, in which given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed.Compression is efficient for storing and transmission purpose.In this paper we described a new lossless adaptive prediction based algorithm for continuous tone images. In continuous tone images spatial redundancy exists.Our approach is to develop a new backward adaptive prediction techniques to reduce spatial redundancy in a image.The new prediction technique known as Modifed Gradient Adjusted Predictor (MGAP) is developed. MGAP is based on the prediction method used in Context based Adaptive Lossless Image Coding (CALIC). An adaptive selection method which selects the predictor in a slope bin in terms of minimum entropy improves the compression performance.

scholarly journals A novel multi-step adaptive prediction method for chaotic time series

2006 ◽  
Vol 55 (4) ◽  
pp. 1666
Author(s):  
Meng Qing-Fang ◽  
Zhang Qiang ◽  
Mu Wen-Ying
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Adaptive Prediction

Chaotic Time Series Adaptive Prediction Based on Volterra Series

2014 ◽  
Vol 945-949 ◽  
pp. 2495-2498 ◽  
Author(s):  
Fang Dai ◽  
Gao Hua Liao
Keyword(s):  
Time Series ◽  
Real Time ◽  
Volterra Series ◽  
Prediction Method ◽  
Fault Prediction ◽  
Statistical Prediction ◽  
Neighborhood Search ◽  
Model Parameters ◽  
Current Time ◽  
Adaptive Prediction

At present, the mine has only realized the real-time monitoring of gas, but not the prediction of gas.There were some limitation of the traditional prediction method, such as modeling subjectivism and statistical prediction. Because it can dynamically adjust the parameters of the model, adaptive prediction method can get the current time according to the prediction error of data and the current time, real-time fault prediction model parameters, this is a very consistent with the prediction method for practical use.This paper presents the gas emission chaos time series method by using volterra series prediction, and on the basis to establish time-series prediction models. The results show that the method not only avoids the phase space reconstruction, but also avoid the points in the neighborhood search, in real-time, with very high efficiency.

A Self-Adaptive Prediction Algorithm for Cloud Workloads

2015 ◽  
Vol 7 (2) ◽  
pp. 65-76 ◽  
Author(s):  
Li Mao ◽  
Deyu Qi ◽  
Weiwei Lin ◽  
Chaoyue Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Dynamic Scheduling ◽  
Main Idea ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Adaptive Prediction ◽  
Self Adaptive

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

The Volterra adaptive prediction method based on matrix decomposition

2016 ◽  
Vol 19 (2) ◽  
pp. 363-377 ◽  
Author(s):  
Yanling Li ◽  
Yunpeng Zhang ◽  
Jing Wang ◽  
Bo Huang ◽  
Weixin Liu
Keyword(s):  
Prediction Method ◽  
Matrix Decomposition ◽  
Adaptive Prediction
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