This paper proposes a new controlling technique of asynchronous sigma delta modulation with characteristic of one-cycle response. This technique can reject power source perturbations in one cycle period and reduce the peaks of harmonic with one side random hysteresis technique. The proposed method was analyzed, designed, and experimented in a full bridge inverter. The distortion of output voltage and the harmonic peaks were used to measure the performance of the proposed technique. The experimental results show that the proposed technique can reduce the peak of harmonic up to 0.42 p.u and the harmonic distortion 5.9% at the ripple of 20% of power source when comparing with convention asynchronous sigma delta modulation.

This paper presents a technique for the variation of hysteresis band in delta-sigma modulation. A sinusoidal, and a random hystersis band are combined to achieve an optimal performance in terms of constant switching frequency and the harmonic spikes. The sinusoidal hysteresis band technique produces a constant switching frequency while the random hysteresis band suppresses the harmonic spikes. The effects of various variations of hysteresis band on the harmonic spectrum characteristic were described. The technique is experimented in a single-phase inverter and the harmonic peaks and the distortion of output voltage were used to measure the performance of the proposed technique.

Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.