scholarly journals A Robust Apnea Period Detection Method in Changing Sleep Posture by Average Mutual Information of Heartbeat and Respiration

2009 ◽  
Vol 45 (1) ◽  
pp. 11-17
Author(s):  
Yosuke Kurihara ◽  
Kajiro Watanabe ◽  
Kazuyuki Kobayashi ◽  
Tanaka Tanaka
Keyword(s):  
Mutual Information ◽  
Detection Method ◽  
Average Mutual Information ◽  
Period Detection ◽  
Sleep Posture

scholarly journals Optimal community detection method based on average mutual information

2019 ◽  
Vol 49 (5) ◽  
pp. 613-629
Author(s):  
Dong LI ◽  
Mingquan CHENG ◽  
Yang XU ◽  
Feng YUAN ◽  
Yinan CHEN ◽  
...  
Keyword(s):  
Mutual Information ◽  
Community Detection ◽  
Detection Method ◽  
Average Mutual Information

Capacity Analysis for Correlated Multi-Hop MIMO Channels under Colored Noise

2015 ◽  
Vol 1 ◽  
pp. 41
Author(s):  
Nguyen N. Tran ◽  
Ha X. Nguyen
Keyword(s):  
Computational Complexity ◽  
Mutual Information ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Maximization Problem ◽  
Capacity Analysis ◽  
Mimo Channels ◽  
Average Mutual Information ◽  
Channel Output

A capacity analysis for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels is presented in this paper. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noises are colored. First, by invoking Karush-Kuhn-Tucker condition for the optimality of convex programming, we derive the optimal source symbol covariance for the maximum mutual information between the channel input and the channel output when having the full knowledge of channel at the transmitter. Secondly, we formulate the average mutual information maximization problem when having only the channel statistics at the transmitter. Since this problem is almost impossible to be solved analytically, the numerical interior-point-method is employed to obtain the optimal solution. Furthermore, to reduce the computational complexity, an asymptotic closed-form solution is derived by maximizing an upper bound of the objective function. Simulation results show that the average mutual information obtained by the asymptotic design is very closed to that obtained by the optimal design, while saving a huge computational complexity.

Input Vector Identification and System Model Construction by Average Mutual Information

2000 ◽  
Author(s):  
Paul B. Deignan ◽  
Peter H. Meckl ◽  
Matthew A. Franchek ◽  
Salim A. Jaliwala ◽  
George G. Zhu
Keyword(s):  
Mutual Information ◽  
System Model ◽  
Input Output ◽  
Output Data ◽  
Average Mutual Information ◽  
Virtual Sensors ◽  
Input Signals ◽  
Model Independent ◽  
Intelligent Model ◽  
Selection Of

Abstract A methodology for the intelligent, model-independent selection of an appropriate set of input signals for the system identification of an unknown process is demonstrated. In modeling this process, it is shown that the terms of a simple nonlinear polynomial model may also be determined through the analysis of the average mutual information between inputs and the output. Average mutual information can be thought of as a nonlinear correlation coefficient and can be calculated from input/output data alone. The methodology described here is especially applicable to the development of virtual sensors.

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