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In this work, a new signal processing method was proposed in order to predict externally applied forces to human hands by deriving a relationship between the surface electromyographic (SEMG) signals and experimentally known forces. This relationship was investigated by analyzing the spectral features of the SEMG signals. SEMG signals were recorded from(More)
Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various(More)
In this paper, we present a connection between the discrete Gabor expansion and the evolutionary spectral theory. Including a scale parameter in the Gabor expansion, we obtain a new representation for deterministic signals that is analogous to the Wold-Cramer decomposition for non-stationary processes. The energy distribution resulting from the expansion is(More)
This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate(More)
In wireless communications, the channel is typically mod-eled as a random linear time-varying system that spreads the transmitted signal in both time and frequency due to multi-path and Doppler effects. In this paper, we show how time-frequency analysis can be used to model and estimate the channel of a multi-carrier spread spectrum (MC-SS) system with a(More)