Physics-Based Correction of Extracted Conductance Parameters of Nonlinear Microwave Semiconductor Devices

Nonlinear models of microwave semiconductor devices are usually evaluated from small-signal high-frequency measurements of differential parameters, performed at many bias points. A linear equivalent circuit is extracted at each bias point, and this is used to construct the nonlinear model. This procedure does not take into account the effects of slow phenomena as heating of the semiconductor or trapping/detrapping of carriers, produced by biasing. Thus, the parameters extracted from multi-bias S-parameter measurements do not fulfill simple consistency requirements. This paper presents a new method to extract a dynamic model from the measurements of the S-parameters through a physics-based correction of the extracted conductance parameters.     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Metrological characterization of a diagnostic test extending the Receiving Operating Curve analysis using Supplement 2 recommendations

Receiving Operating Curve (ROC) analysis is a powerful and statistical accepted method to assess the performance of a diagnostic test. ROC curve plots true positive rate against false positive rate, evaluated on a certain population. Instrumental and model uncertainty contributions can strongly affect the performance of the ROC analysis especially in the evaluation of performance metrics such as Area Under ROC (AUC) and Optimal Operating Points. Supplement 2 reports detailed instructions to handle and propagate uncertainty through a Multiple Input Multiple Output system, in case of correlate output variables, such as TPR and FPR. After a detailed revision of the existing literature, the present paper describes and applies a novel methodology, totally framed in the GUM and its supplements, to represent and propagate the uncertainty contributions estimated in a medical context, throughout the ROC analysis, providing new concepts such as ROC confidence region and Optimal Operating Region.     

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.