A concept for modern virtual telecommunication engineering office

High-performance modern Internet allows internal delivery and complement of attractive (mobile) services in the same way and QoS that are in the LANs. The world economics is widely characterized nowadays via the stable trends that the large and mid-range companies and authorities let in ever greater extent to outsource own engineering services via external smaller service providers. A concept for a modern virtual telecommunication engineering office under use of Service-Oriented Architectures and Cloud Computing technologies has been offered. Multiple use cases for virtual telecommunication engineering office have been discussed. As a significant example, the CANDY Framework and Online Platform have been examined. The important development trends for the CAD for network planning regarding to the tool integration and effective access optimization have been discussed. The CANDY system has been represented as an exhibit at CeBIT 2007,2008, 2011 in Hannover

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students' interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students' evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students' performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final graThe authors wish to acknowledge the support of the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the Grants "Projeto Estrategico-UI 252-2011-2012'' reference PEst-OE/EME/UI0252/2011, "Ph.D. Scholarship Grant'' reference SFRH/BD/85672/2012, and the support of Parallel Planes Lda.info:eu-repo/semantics/publishedVersio

Localization and tracking of parameterized objects in point clouds

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 43-46).This thesis focuses on object recognition and tracking from three dimensional point cloud renderings of dense range and bearing data. Sensors like laser range-finders and depth cameras have become increasingly popular in autonomous robotic applications. A common task is to locate and track specific objects of interest located somewhere in the point cloud. This often introduces a tedious network of heuristics to build objects from identified primitives or an intractable high dimensional search space. Through a parameterized object model and certain relaxation functions, a likelihood based view of the data can be used to accomplish these goals with increased performance and reliability. Improvements in mathematics and convergence properties have shown that this method can be realized in real time.by Robert Truax.S.M

Musemo: Express Musical Emotion Based on Neural Network

Department of Urban and Environmental Engineering (Convergence of Science and Arts)Music elicits emotional responses, which enable people to empathize with the emotional states induced by music, experience changes in their current feelings, receive comfort, and relieve stress (Juslin & Laukka, 2004). Music emotion recognition (MER) is a field of research that extracts emotions from music through various systems and methods. Interest in this field is increasing as researchers try to use it for psychiatric purposes. In order to extract emotions from music, MER requires music and emotion labels for each music. Many MER studies use emotion labels created by non-music-specific psychologists such as Russell???s circumplex model of affects (Russell, 1980) and Ekman???s six basic emotions (Ekman, 1999). However, Zentner, Grandjean, and Scherer suggest that emotions commonly used in music are subdivided into specific areas, rather than spread across the entire spectrum of emotions (Zentner, Grandjean, & Scherer, 2008). Thus, existing MER studies have difficulties with the emotion labels that are not widely agreed through musicians and listeners. This study proposes a musical emotion recognition model ???Musemo??? that follows the Geneva emotion music scale proposed by music psychologists based on a convolution neural network. We evaluate the accuracy of the model by varying the length of music samples used as input of Musemo and achieved RMSE (root mean squared error) performance of up to 14.91%. Also, we examine the correlation among emotion labels by reducing the Musemo???s emotion output vector to two dimensions through principal component analysis. Consequently, we can get results that are similar to the study that Vuoskoski and Eerola analyzed for the Geneva emotion music scale (Vuoskoski & Eerola, 2011). We hope that this study could be expanded to inform treatments to comfort those in need of psychological empathy in modern society.clos

Supply chain responsiveness for a large retailer

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73).A large U.S. based retailer underwent a large, complex multi-year supply chain network transformation. This transformation resulted in significant savings in logistics costs. Additionally, the regional distribution center that was introduced as part of this transformation as a new node between the supplier and the store became the decision making center for placing purchase orders with suppliers and for receiving and shipping the purchase order to individual stores. This resulted in longer lead times causing a change in the in-store units held and therefore, directly impacting the net sales. This thesis focuses on establishing the relationship between the stores performance and lead-time, review-time combinations in both supply chain networks, the original direct to store and the new regional distribution based networks.by Sunil Anand and Xiaobei Song.M.Eng.in Logistic

Alignment model for trunk road network maintenance outsourcing

Road maintenance outsourcing is now the foremost strategy by which road authorities procure maintenance works. Despite growing application of road maintenance outsourcing, there are conflicting estimates on the effective­ness of road maintenance outsourcing and shortage of appropriate models to align over optimistic expectations of road authorities from road maintenance outsourcing with substantiated benefits. This paper investigates the efficacy of road maintenance outsourcing. In this paper, the different variants of road maintenance outsourcing and road maintenance works are evaluated with a SWOT analysis and a comprehensive literature review respectively. In addition, a road main­tenance outsourcing alignment model based on a decision tree and Balance Score Card is proposed and illustrated with a Nigerian trunk road network authority as a case study. The result of the SWOT analysis and comprehensive literature review establishes fresh insight into road maintenance outsourcing dynamics. The presented road maintenance outsourc­ing alignment model provides adequate pathways that could assist road authorities identify the most appropriate road maintenance outsourcing variant for road maintenance procurement. In addition it aligns actual benefits and expectations of road maintenance outsourcing and facilitates development of SMART metrics for effective assessment of road main­tenance outsourcing. The proposed model is applicable across other infrastructures. First published online: 01 Sep 201

A tool for predicting the thermal performance of a diesel engine

This paper presents a thermal network model for the simulation of the transient response of diesel engines. The model was adjusted by using experimental data from a completely instrumented engine run under steady-state and transient conditions. Comparisons between measured and predicted material temperatures over a wide range of engine running conditions show a mean error of 7◦C. The model was then used to predict the thermal behavior of a different engine. Model results were checked against oil and coolant temperatures measured during engine warm-up at constant speed and load, and on a New European Driving Cycle. Results show that the model predicts these temperatures with a maximum error of 3◦C.Torregrosa, AJ.; Olmeda González, PC.; Martín Díaz, J.; Romero Piedrahita, CA. (2011). A tool for predicting the thermal performance of a diesel engine. Heat Transfer Engineering. 32(10):891-904. doi:10.1080/01457632.2011.548639S891904321