Search

Start Over Creator Temel, Turgay

1. A new digital cochlea model neuro-spike representation of auditory signals and its application to classification of bat-like biosonar echoes

Creator:
Temel, Turgay
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Audio processing systems, pattern recognition and classification, cochlea model, and spike coding
Language:
English
Description:
For an improved neuro-spike representation of auditory signals within cochlea models, a new digital ARMA-type low-pass filter structure is proposed. It is compared to more conventional AR-type counterpart on a classification of biosonar echoes, in which echoes from various tree species insonified with a bat-like chirp call are converted to biologically plausible feature vectors. Next, parametric and non-parametric models of the class-conditional densities are built from the echo feature vectors. The models are deployed in single-shot and sequential-decision classification algorithms. The results indicate that the proposed ARMA filter structure offers an improved single-echo classification performance, which leads to faster sequential-decision making than its AR-type counterpart.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

2. An improved odor recognition system using learning vector quantization with a new discriminant analysis

Creator:
Temel, Turgay and Karlik, Bekir
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Olfactory system, classification, odor recognition, pre-processing, and learning vector quantization
Language:
English
Description:
A new pre-processing algorithm for improved discrimination of odor samples is proposed. The pre-processed odor sample outputs from two sensors are input using a learning-vector quantization (LVQ) classifier as a means of odor recognition to be employed within electronic nose applications. The proposed algorithm brings out highly scattered classes while minimizing the within-class scatter of the samples given an odor class. LVQ is observed to operate robustly and reliably in terms of variation of parameters of interest, mainly a learning parameter. Due to the increased performance along with computational simplicity and robustness, the scheme is suitable to sample-by-sample identification of olfactory sensory data and can be easily adapted to hierarchical processing with other sensory data in real-time.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public