Teaching and learning Operational Amplifiers using a reconfigurable and expandable kit

Operational Amplifiers (OpAmps) are one of the most important integrated circuits in the area of electronics. These type of devices are widely adopted in the area since they allow the design of simple and/or complex analogue circuits without many efforts. It is therefore fundamental to create innovative educational solutions to facilitate their teaching and learning, and in particular the inclusion of more experimental work in a course curricula. For this purpose, it was designed and implemented a reconfigurable and expandable kit to teach and learn electronic circuits based on the OpAmp uA741. The kit comprises a software application and a hardware platform. The software application allows the simulation and the reconfiguration of real electronic circuits based on the OpAmp uA741 included in the hardware platform. For measuring and/or applying signals to a particular reconfigured circuit, users may establish automatic connections. In this paper it is described the features and functionalities provided by the kit, and an overview about the OpAmp uA741. At the end, some teachers’ opinions about their perceptions concerning a possible adoption of the kit in a real educational scenario are presented.N/

Modeling, Simulation and Emulation of Intelligent Domotic Environments

Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirement

Building trainable taggers in a web-based, UIMA-supported NLP workbench

Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench is intended for specialists and nontechnical audiences alike, and provides the ever expanding library of analytics compliant with the Unstructured Information Management Architecture, a widely adopted interoperability framework. We explore the flexibility of this framework by demonstrating workflows involving three processing components capable of performing self-contained machine learning-based tagging. The three components are responsible for the three distinct tasks of 1) generating observations or features, 2) training a statistical model based on the generated features, and 3) tagging unlabelled data with the model. The learning and tagging components are based on an implementation of conditional random fields (CRF); whereas the feature generation component is an analytic capable of extending basic token information to a comprehensive set of features. Users define the features of their choice directly from Argo’s graphical interface, without resorting to programming (a commonly used approach to feature engineering). The experimental results performed on two tagging tasks, chunking and named entity recognition, showed that a tagger with a generic set of features built in Argo is capable of competing with taskspecific solutions.

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

An Alternative Way of Teaching Operational Amplifiers Using a Reconfigurable and Expandable Kit

Early on, students must develop competences by implementing simple or complex electronic circuits with Operational Amplifiers (OpAmps). Traditionally, these skills were mainly developed in laboratory classes, but technology allows us to explore other and complementary ways of aiding students in this achievement. This paper presents a contribution to improve the way OpAmps are included in electronic engineering courses’ curricula. A reconfigurable and expandable kit to teach electronic circuits based on the OpAmp uA741 was designed and implemented. This kit comprises a software application locally interfaced with a hardware platform capable of running in a PC. This platform includes a circuit with the OpAmp uA741 able to reconfigure according to a set of parameters defined by a software application. Its reconfiguration capability also enables the establishment of automatic connections for measuring and for applying signals to a reconfigured circuit, plus the ability to simulate the same or other OpAmp-based circuits. This paper provides an overview about the OpAmp uA741 and its relevance in engineering education. After presenting the kit and make some considerations for its improvement, at the end a brief discussion about its implementation in education according to specific educational strategies and methodologies are provided.This work was supported in part by the Fundação para a Ciência e Tecnologia under Grant FCT-UID-EQU-04730-2013info:eu-repo/semantics/publishedVersio

The History of the iPad

The purpose of this paper is to review the history of the iPad and its influence over contemporary computing. Although the iPad is relatively new, the tablet computer is having a long and lasting affect on how we communicate. With this essay, I attempt to review the technologies that emerged and converged to create the tablet computer. Of course, Apple and its iPad are at the center of this new computing movement

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License