This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

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Abstract-An electronically reconfigurable transmitarray device at 12 GHz is presented in this work. This paper highlights the functioning of this kind of device and thoroughly examines the proposed reconfigurable transmitarray. The architecture is discussed along with the design and selection of all the constituting elements and the prototypes for all of them. In order to add reconfigurability to the transmitarray structure, 360° reflective phase shifters were designed, prototyped and validated for direct application. Eventually, a demonstrative prototype for an active transmitarray with phase shifters was assembled, and radiation pattern measurements were taken in an anechoic chamber to demonstrate the capabilities of this structure.

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