This paper proposes the use of independent component analysis (ICA) method for learning features from radio frequency (RF) ultrasonic signals. Conventional feature extractors usually suffer from limitations caused by some of their assumptions about the structure of imaged organ and the interaction between ultrasonic signal and tissue. ICA, on the other hand, is a data-driven approach which learns efficient representation of data by maximizing independence of some basis functions that describe the important structures of the data. It has less restrictive considerations about formation of data and as a consequence, could adapt itself to the characteristics of data with different natures. These features of ICA make it a proper candidate for dealing with the problem of feature extraction from medical ultrasound signals where variations in tissue structures and data acquisition conditions are considerable. Experimental results on raw backscattered RF signals of prostate gland show favorable performance of ICA as compared with the conventional methods designed for extracting feature from RF signals.

Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

It is well known that Meyer's model for the MOST implemented in SPICE does not guarantee charge conservation, while Ward's model seems somewhat complex to be used as a standard for the transient analysis of all the MOS circuits. In this work, a new simple MOST model for the transient analysis of MOS integrated circuits is presented. The model was developed by performing the numerical integration of the gate current as a first step without introducing the concept of a mean constant capacitance. The new model has been implemented in the program SGS-SPICE. All benchmark tests indicate that it conserves the charge at all the terminals of the MOS transistor.

This paper summarizes the inadequacies of present MOSFET models as applied to analog circuit design and, in some cases, proposes solutions. Both efficient models suitable for CAD and more complex models are considered. Problem areas discussed include poor modeling of the moderate inversion region, poor modeling of the surface potential in strong inversion, ambiguous use of "threshold" voltages and poor expressions for them, poor modeling of the drain small-signal conductance, very poor modeling of intrinsic small-signal capacitances, inadequate small-signal equivalent circuit topologies, and poor implementation of known correct ideas in some CAD programs, including the dependence of the effective mobility on the substrate potential, the modeling of thermal noise in the nonsaturation region, and the modeling of ion-implanted devices.

Explicit and accurate formulations for the surface potential against quasi-Fermi potential of the minority carriers in a long-channel MOS transistor are found. Moreover, it is shown that, for a variety of process parameters, these simple formulations, together with a suitable rearrangement of the Brews' model, can be fruitfully adopted for an accurate modeling of the MOST characteristics in all the operating regimes (weak inversion, moderate inversion, strong inversion).