Signal detection using approximate Karhunen-Loeve expansions

Ruiz-Molina, Juan Carlos; Navarro-Moreno, J.; Oya, Antonia

doi:10.1109/18.923757

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Cited by 7 publications

(1 citation statement)

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“…If the signal is known, the best detection is a generalised matched filter, and if the signal contains unknown parameters, the best detection is a generalised likelihood ratio test (GLRT). However, both of that require pre‐processing the received signal prior to remove its correlation by the Karhunen–Loève (K‐L) transform or a pre‐whitening filter, which lead to a complicated calculation [1–3]. Transforming the received signal into the frequency domain for detection is simple, but it lacks the systematic analysis of the detection performance [4].…”

Section: Introductionmentioning

confidence: 99%

Frequency domain detection of sinusoidal signal under coloured Gaussian noise

Dai

Wang

2019

J. eng.

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Section: Introductionmentioning

confidence: 99%

Frequency domain detection of sinusoidal signal under coloured Gaussian noise

Dai

Wang

2019

J. eng.

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Making decisions on brain tumor diagnosis by soft computing techniques

Farías¹,

Santos²,

López³

2009

Soft Comput

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In this paper, a synergy of advanced signal processing and soft computing strategies is applied in order to identify different types of human brain tumors, as a help to confirm the histological diagnosis of experts and consequently to facilitate the decision about the correct treatment or the necessity of an operation. A computational tool has been developed that merges, on the one hand, wavelet transform to reduce the size of the biomedical spectra and to extract the main features, and on the other hand, Support Vector Machine and Neural Networks to classify them. The influence of some of the configuration parameters of each of those soft computing techniques on the clustering is analyzed. These two methods and another one based on medical knowledge are compared. The classification results obtained by these computational tools are promising specially taking into account that medical knowledge has not been considered and that the number of samples of each class is very low in some cases.

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