This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent's search (BS) to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.

To examine models of temporal resolution and to investigate the decision processes underlying the detection of a brief pause in a bandpass noise, psychometric functions for gap detection were measured at octave frequencies from 0.25 to 8 kHz. Three normal listeners were tested using a constant-stimulus procedure with a cued Yes-No paradigm. The Minimum Detectable Gap (MDG) estimated from the midpoint of the psychometric functions decreased systematically with increasing frequency. The slopes of the psychometric functions generally increased as the test frequency increased up to 2 kHz, but remained constant at the higher frequencies. Two models were investigated: an energy-detector model and a loudness-detector model. Both consisted of auditory filtering, a nonlinearity, and short-term integration. In the energy-detector model, the nonlinearity was a square law. In the loudness-detector model, it was a compressive power law. Using the usual Gaussian approximations, the energy-detector model fails at low frequencies because the probability distributions of short-term energy differ from Gaussian distributions. The probability distributions of short-term loudness closely follow Gaussian distributions. The loudness-detector model predicts the frequency dependence of the MDG quite accurately, except at 0.25 kHz. It also predicts psychometric functions that resemble the data at low frequencies, but the predicted slopes increase much less with frequency than the measured slopes. This result may indicate that the onset response to the trailing marker of the gap provides an important cue for detection of gaps with durations exceeding the MDG.

No abstract

No abstract

Statistical process control is widely used in industrial processes, service fields, among others. While parametric control charts are useful in certain processes, there is often a lack of enough knowledge about the process distribution. So, nonparametric control charts are needed in such situations. This paper develops a new nonparametric control chart based on the AnsariBradley nonparametric test and the effective change point model. Simulation results show that our proposed control chart is superior to other nonparametric control charts in monitoring process variability for most cases. Our proposed control chart is easy in computation, and powerful for monitoring process variability.

No abstract

No abstract

No abstract