Optical pseudorandom signal analysis in spectral phase-encoded OCDMA communication system

2001 ◽  
Author(s):  
Chao Zuo ◽  
Wenhua Ma ◽  
Hongtu Pu ◽  
Jintong L. Lin
2014 ◽  
Vol 23 (2) ◽  
pp. 104-111 ◽  
Author(s):  
Mary Ann Abbott ◽  
Debby McBride

The purpose of this article is to outline a decision-making process and highlight which portions of the augmentative and alternative communication (AAC) evaluation process deserve special attention when deciding which features are required for a communication system in order to provide optimal benefit for the user. The clinician then will be able to use a feature-match approach as part of the decision-making process to determine whether mobile technology or a dedicated device is the best choice for communication. The term mobile technology will be used to describe off-the-shelf, commercially available, tablet-style devices like an iPhone®, iPod Touch®, iPad®, and Android® or Windows® tablet.


1982 ◽  
Vol 47 (4) ◽  
pp. 373-375 ◽  
Author(s):  
James L. Fitch ◽  
Thomas F. Williams ◽  
Josephine E. Etienne

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).


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


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