An adaptive signal compression system with pre-specified reconstruction quality and compression rate

2006 ◽  
Vol 81 (2) ◽  
pp. 99-105
Author(s):  
M.Borahan Tümer ◽  
Mert C. Demir
Keyword(s):  
Compression Rate ◽  
Signal Compression ◽  
Compression System ◽  
Reconstruction Quality ◽  
Adaptive Signal

Content Adaptive Signal Compression for Remote SHM and NDT Operation

2015 ◽  
Author(s):  
MARCO SCHAARSCHMIDT ◽  
CLEMENS WESTERKAMP ◽  
ALEXANDER HENNEWIG ◽  
DENNIS PIEPER ◽  
HOLGER SPECKMANN ◽  
...  
Keyword(s):  
Signal Compression ◽  
Adaptive Signal ◽  
Content Adaptive

Adaptive Signal Compression in Intermediate Frequency Band for DRoF Mobile Fronthaul

2019 ◽  
Vol 31 (8) ◽  
pp. 579-582 ◽  
Author(s):  
D. M. Mathe ◽  
B. Neto ◽  
R. S. Oliveira ◽  
A. L. J. Teixeira ◽  
J. C. W. A. Costa
Keyword(s):  
Frequency Band ◽  
Intermediate Frequency ◽  
Signal Compression ◽  
Adaptive Signal

scholarly journals Implementation of an electrical signal compression system using sparce representation

2018 ◽  
Author(s):  
Mateus Mostaro de Oliveira ◽  
Leandro Rodrigues Manso Silva ◽  
Carlos Augusto Duque ◽  
Luciano Manhes de Andrade Filho ◽  
Paulo Fernando Ribeiro
Keyword(s):  
Electrical Signal ◽  
Signal Compression ◽  
Compression System

scholarly journals A new speech compression method

2004 ◽  
Vol 17 (3) ◽  
pp. 391-404
Author(s):  
Miranda Nafornita ◽  
Alexandru Isar ◽  
Dorina Isar
Keyword(s):  
Speech Processing ◽  
Linear Prediction ◽  
Compression Rate ◽  
Acoustic Model ◽  
Speech Compression ◽  
Compression Method ◽  
Threshold Detector ◽  
Compression System ◽  
Orthogonal Transform ◽  
Very High

In this paper a new speech compression method is presented. The traditional speech compression method is based on linear prediction. The compression method, proposed in this paper, is based on the use of an orthogonal transform, the discrete cosine packets transform. This method is well suited for the speech processing, taking into account the sine model of this kind of signals and because this transform converges asymptotically to the Karhunen-Lofleve transform. After the computation of the discrete cosine packets transform, the coefficients obtained are processed with a threshold detector, who keeps only the coefficients superior to a given threshold. This way the number of non zero coefficients is reduced doing the compression. The next block of the compression system is the quantization system. This is build following the speech psycho-acoustic model. The proposed compression method is transparent, the compression rate obtained is important and the operations number and the memory volume used are not very high.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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