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We propose the concept and report the development of an ultra-broadband photonics-based RF front-end, as well as its experimental digital performance analysis up to 38 GHz. The proposed RF front-end applies optical frequency multiplication based on the external modulation technique and the optical nonlinear effect four-wave mixing for enabling a combination of photonicassisted RF upconversion and amplification, regardless of frequency operation. We present ultra-broadband operation and frequency tunability from DC to 38 GHz by simultaneously performing RF conversion and amplification in the optical domain. The experimental results of a successful implementation in a real wireless-optical network demonstrate the RF front-end applicability in the three potential frequency bands for 5G networks, namely, 6.0, 28, and 38 GHz. Furthermore, a detailed digital performance investigation using different modulation formats is presented based on diverse RF and optical parameters, including signal-to-noise ratio, RF gain, and error vector magnitude. Low phase noise, spectral purity, and distortion absence are also observed for all frequency bands. [ABSTRACT FROM PUBLISHER]

The pectoral muscle represents a predominant density region in most medio-lateral oblique (MLO) views of mammograms; its inclusion can affect the results of intensity-based image processing methods or bias procedures in the detection of breast cancer. Local analysis of the pectoral muscle may be used to identify the presence of abnormal axillary lymph nodes, which may be the only manifestation of occult breast carcinoma. We propose a new method for the identification of the pectoral muscle in MLO mammograms based upon a multiresolution technique using Gabor wavelets. This new method overcomes the limitation of the straight-line representation considered in our initial investigation using the Hough transform. The method starts by convolving a group of Gabor filters, specially designed for enhancing the pectoral muscle edge, with the region of interest containing the pectoral muscle. After computing the magnitude and phase images using a vector-summation procedure, the magnitude value of each pixel is propagated in the direction of the phase. The resulting image is then used to detect the relevant edges. Finally, a post-processing stage is used to find the true pectoral muscle edge. The method was applied to 84 MLO mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database. Evaluation of the pectoral muscle edge detected in the mammograms was performed based upon the percentage of false-positive (FP) and false-negative (EN) pixels determined by comparison between the numbers of pixels enclosed in the regions delimited by the edges identified by a radiologist and by the proposed method. The average FP and FN rates were, respectively, 0.58% and 5.77%. Furthermore, the results of the Gabor-filter-based method indicated low Hausdorif distances with respect to the hand-drawn pectoral muscle edges, with the mean and standard deviation being 3.84 ± 1.73 mm over 84 images. [ABSTRACT FROM AUTHOR]