Multi-Channel System Identification with Perfect Sequences– Theory and Applications–

2008 ◽  
pp. 171-198 ◽  
Author(s):  
Christiane Antweiler
Keyword(s):  
System Identification ◽  
Channel System ◽  
Perfect Sequences

scholarly journals Nonlinear system identification using Wiener basis functions and multiple-variance perfect sequences

2019 ◽  
Vol 160 ◽  
pp. 137-149 ◽  
Author(s):  
Alberto Carini ◽  
Simone Orcioni ◽  
Alessandro Terenzi ◽  
Stefania Cecchi
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Basis Functions ◽  
Perfect Sequences

A Community Based High Risk Register for Hearing Loss

1982 ◽  
Vol 47 (4) ◽  
pp. 373-375 ◽  
Author(s):  
James L. Fitch ◽  
Thomas F. Williams ◽  
Josephine E. Etienne
Keyword(s):  
Hearing Loss ◽  
System Identification ◽  
High Risk ◽  
Language Learning ◽  
Education Program ◽  
Communication System ◽  
Auditory Signal ◽  
Early Age ◽  
Community Based ◽  
Children With Hearing Loss

The critical need to identify children with hearing loss and provide treatment at the earliest possible age has become increasingly apparent in recent years (Northern & Downs, 1978). Reduction of the auditory signal during the critical language-learning period can severely limit the child's potential for developing a complete, effective communication system. Identification and treatment of children having handicapping conditions at an early age has gained impetus through the Handicapped Children's Early Education Program (HCEEP) projects funded by the Bureau of Education for the Handicapped (BEH).

Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

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