Abstract
This paper presents the results of a preliminary study to examine the ability of post-silicon devices for analog processing. It is focused on the latest thin-film transistors (TFTs) with amorphous gallium-indium-zinc oxide (a-GIZO) as active layer. Three circuit configurations are presented: a differential pair and two multiplier topologies. Both triode and saturation regions of operation are included in the analysis, with the devices set to remain in strong accumulation. A neural model, which is developed based on the measured data of the TFTs, is used for the circuit simulations in the Cadence Virtuoso environment. The analog multipliers simulation results are compared against the expected functional results.

Abstract
This paper addresses a modeling and simulation methodology for analog circuit design with amorphous-GIZO thin-film transistors (TFTs). To reach an effective circuit design flow, with commercially available tools, a TFT model has been first developed with an artificial neural network (ANN). Multilayer perceptron with backpropagation algorithm has been adopted to model the static behavior of the TFT devices, for different aspect ratios. The model was then implemented in Verilog-A, to allow a quick instantiation in circuit. Simulations using Cadence Spectre are performed to validate the model. On a second phase, simulation results of basic analog circuits, with this ANN model, are verified against the actual functional results, namely an adder, subtractor, and current mirror circuit. Results demonstrate not only the ANN model accuracy and compatibility with dc and transient analysis, but also show the a-GIZO TFT capability to perform analog operations. © 2012 IEEE.

Abstract
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC field, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on t-statistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods. © 2012 IEEE.

Abstract
For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones. © 2012, Springer-Verlag London Limited.

Abstract

Abstract
Breast Cancer Conservative Treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as Breast Cancer Conservative Treatment. cosmetic results (BCCT. core) software tool. Although the satisfying results, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system as a low-cost tool, the incorporation of the Microsoft Kinect sensor in BCCT evaluations was studied. The aim with this work is to enable the simultaneous detection of breast contour and breast peak points using depth-map data. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. Additionally, comparatively to previous research, the procedure for detecting prominent points was automated.