Precise detection of isolated words in noise by the linear prediction method

1988 ◽  
Vol 84 (S1) ◽  
pp. S15-S15 ◽  
Author(s):  
Hidefumi Kobatake ◽  
Katsuhisa Tawa
Keyword(s):  
Linear Prediction ◽  
Prediction Method

A Subspace Based Analysis of the Modified Forward-Backward Linear Prediction Method

1988 ◽  
Vol 34 (5) ◽  
pp. 408-415 ◽  
Author(s):  
V U Reddy ◽  
K Ajayakumari
Keyword(s):  
Linear Prediction ◽  
Prediction Method

A New Method for Discharge State Prediction of Micro-EDM Using Empirical Mode Decomposition

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Zhenyuan Jia ◽  
Lingxuan Zhang ◽  
Fuji Wang ◽  
Wei Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Linear Prediction ◽  
Electrical Discharge Machining ◽  
Model Identification ◽  
Prediction Method ◽  
Ar Model ◽  
Micro Edm ◽  
Multisensor Data Fusion ◽  
State Prediction ◽  
Mode Decomposition

The property of high frequency in micro-EDM (electrical discharge machining) causes the discharge states to vary much faster than in conventional EDM, and discharge states of micro-EDM have the characteristics of nonstationarity, nonlinearity, and internal coupling, all of this makes it very difficult to carry out stable control. Thus empirical mode decomposition is adopted to conduct the prediction of the discharge states obtained through multisensor data fusion and fuzzy logic in micro-EDM. Combined with the autoregressive (AR) model identification and linear prediction, the mathematical model for EDM discharge state prediction using empirical mode decomposition is established and the corresponding prediction method is presented. Experiments demonstrate that the new prediction method with short identification data is highly accurate and operates quickly. Even using short model identification data, the accuracy of empirical mode decomposition prediction can stably reach a correlation of 74%, which satisfies statistical expectations. Additionally, the new process can also effectively eliminate the lag of conventional prediction methods to improve the efficiency of micro-EDM, and it provides a good basis to enhance the stability of the control system.

Short-range forecast of atmospheric pollutants using non-linear prediction method

2008 ◽  
Vol 42 (31) ◽  
pp. 7284-7292 ◽  
Author(s):  
Valeriy N. Khokhlov ◽  
Alexander V. Glushkov ◽  
Nataliya S. Loboda ◽  
Yulia Y. Bunyakova
Keyword(s):  
Short Range ◽  
Linear Prediction ◽  
Prediction Method ◽  
Atmospheric Pollutants ◽  
Non Linear

Integration of Projection Histograms and Linear Prediction for Object Tracking

2012 ◽  
Author(s):  
Phaik Yong Yeoh ◽  
Syed Abdul Rahman Abu Bakar
Keyword(s):  
Motion Tracking ◽  
Linear Prediction ◽  
Tracking System ◽  
Prediction Method ◽  
Image Sequences ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Potential Applicability ◽  
Experimental Image ◽  
Actual Location

Kertas kerja ini mengemukakan satu sistem visual untuk menjejak objek bergerak yang dapat beroperasi secara masa–nyata, efisien dan tahan lasak, dengan menggunakan turutan imej yang diperolehi daripada satu kamera statik. Algoritma menjejak yang dikemukakan menggabungkan teknik Unjuran Histogram dan kaedah Ramalan Linar untuk mencapai kelajuan menjejak yang lebih tinggi. Unjuran Histogram dilakukan untuk mendapatkan lokasi sebenar bagi objek yang dijejak, manakala kaedah Ramalan Linar disertakan dalam algoritma menjejak yang dikemukakan untuk meramal lokasi bagi objek bergerak dalam imej seterusnya, berdasarkan beberapa ukuran centroid sebelumnya. Hasil gabungan teknik Unjuran Histogram dan tertib kedua Ramalan Linar telah membolehkan algoritma yang dikemukakan untuk menjejak objek bergerak dengan tepat. Potensi and kecekapan bagi algoritma menjejak yang dikemukakan telah dibuktikan oleh keputusan menjejak yang baik pada beberapa turutan imej eksperimen. Kata kunci: menjejak pergerakan, Unjuran Histogram, Ramalan Linar, perbezaan bingkai In this paper, a real–time, efficient and robust visual tracking system for a single moving object using image sequences captured by a stationary camera is presented. The proposed tracking algorithm integrates the Projection Histograms technique with the Linear Prediction method in order to achieve a faster tracking speed. The Projection Histograms technique is applied to obtain the actual location of the tracked target, whereas the Linear Prediction method is incorporated in the proposed tracking algorithm to predict the location of the moving object in the next frame based on its several past centroid measurements. The Projection Histograms technique coupled with a second order Linear Prediction method has enabled the proposed algorithm to accurately track a single moving object. The potential applicability and efficiency of the proposed tracking algorithm has been validated by good tracking results on several experimental image sequences. Key words: Motion tracking, projection histograms, linear prediction, frame differencing

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