130,478 research outputs found
Indoor radio channel modeling at D-band frequencies
No full textThis paper presents indoor radio channel measurements and models at D-band frequencies. A Line-of-Sight (LOS) alpha-beta-gamma path loss model is created based on indoor measurements up to 8.5 m in a laboratory and office room, resulting in a floating intercept alpha of 34.2 dB, PL exponent beta 1.9 and frequency dependency gamma 1.9. The penetration losses for wood, acrylic, polyvinyl chloride (PVC) and glass are measured, resulting in a respective loss of 8, 3.5, 3 and 12 dB/cm. Furthermore, attenuation due to desk objects obstructing the LOS path is found to range from 3 to 10 dB for one or more universal serial bus (USB) cables, and 8 to 13 dB for a computer keyboard and mouse. A laptop screen completely blocks the LOS path. Therefore, we measured the attenuation of the reflected path when the LOS path is blocked, and conclude that desk objects provide valid fallback paths
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Three-stages concatenated Machine Learning model for SFN prediction
No full textThe single frequency network (SFN) has been assumed worldwide by telecommunication operators to save radio frequency resources and homogenize the network. Its applications have transcended the digital terrestrial television and digital radio to become part of the key techniques of the broadband and broadcast convergence for LTE-A, 5G and beyond. However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected up-performance and quality of service. This paper aims to propose a machine learning model to predict the SFN performance from the legacy MFN parameters. The model is based on regression and classification machine learning algorithms concatenated in three consecutive stages to predict SFN electric-field strength, modulation error ratio and gain. The training and test processes are performed with a dataset of 389 samples from an SFN/MFN trial in Ghent, Belgium. The best performance is obtained with concatenating gradient boosting, random forest, and linear regression, which allows predicting the SFN electric-field strength with an R2 of 92%, the modulation error ratio with 95%, and SFN gain with 87% from only MFN and position data. Besides, the model allows classifying the data points according to positive or negative SFN gain with an accuracy of 93%
Outdoor channel modeling at D-band frequencies for future fixed wireless access applications
No full textFixed wireless access networks at millimeter wave frequencies enable an alternative to fiber-optic installations for providing high-throughput Internet connectivity. In this letter, we present outdoor channel measurements at D-band frequencies ranging from 120 GHz to 165 GHz, contributing to the design of future fixed wireless access networks. We measure angular path loss (PL) for both Line-of-Sight (LOS) and non-Line-of-Sight (NLOS) scenarios and calculate angular spread. We also measure building reflection loss for different angles and building facades. Directional LOS PL equals free-space PL, whereas omnidirectional PL is slightly lower. The angular spread of the LOS measurements is 19.7 degrees. The omnidirectional NLOS PL model has a higher PL and the angular spread increases to 54.4 degrees. Losses up to 11 dB should be taken into account for reflection on a fiber cement or building brick facade. and up to 15.6 dB and 18.5 dB for roughcast and stone bricks. Even though wireless communication via the direct path is preferred, reflected paths can enable high-throughput wireless communication if the direct path is obstructed
From MFN to SFN: Performance Prediction Through Machine Learning
No full textIn the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Plets: a product line of model-based testing tools
No full textSoftware testing is recognized as a fundamental activity for assuring software quality. Furthermore, testing is also recognized as one of the most time consuming and expensive activities of software development process. A diversity of testing tools has been developed to support this activity, including tools for Model-based Testing (MBT). MBT is a testing technique to automate the generation of testing artifacts from the system model. This technique presents several advantages, such as, lower cost and less effort to generate test cases. Therefore, in the last years a diversity of commercial, academic, and open source tools to support MBT has been developed to better explore these advantages. In spite of the diversity of tools to support MBT, most of them have been individually and independently developed from scratch based on a single architecture. Thus, they face difficulties of integration, evolution, maintenance, and reuse. In another perspective, Software Product Lines (SPL) offers possibility of systematically generating software products at lower costs, in shorter time, and with higher quality. The main contribution of this Ph. D thesis is to present a SPL for testing tools that support MBT (PLeTs) and an automated environment to support the generation of these tools (PlugSPL). Furthermore, our strategy was initially applied to generate some MBT testing tools which were applied in two examples of use performed in collaboration of an IT company. Based on the feedback from the examples of use we can infer that SPL can be considered a relevant approach to improve productivity and reuse during generation of MBT testing tools. Moreover, we also performed an experimental study carried out to evaluate the effort to use an MBT tool derived from our SPL to generate test scripts and scenarios. Thus, the results point out that the effort to generate test scripts, when compared with a Capture and Replay based tool, was reduced considerably.O teste de software é uma atividade fundamental para garantir a qualidade de software. Além disso, teste de software é uma das atividades mais caras e demoradas no processo de desenvolvimento de software. Por esta razão, diversas ferramentas de teste foram desenvolvidas para apoiar esta atividade, incluindo ferramentas para Teste Baseado em Modelos (TBM). TBM é uma técnica de teste para automatizar a geração de artefatos de teste a partir de modelos do sistema. Esta técnica apresenta diversas vantagens, tais como, menor custo e esforço para gerar casos de teste. Por este motivo, nos últimos anos, diversas ferramentas para TBM foram desenvolvidas para melhor explorar essas vantagens. Embora existam diversas ferramentas TBM, a maioria delas tem o seu desenvolvimento baseado em um esforço individual, sem a adoção de técnicas de reuso sistemático e com base em uma única arquitetura, dificultando a integração, evolução, manutenção e reutilização dessas ferramentas. Uma alternativa para mitigar estes problemas é adotar os conceitos de Linhas de Produto de Software (LPS) para desenvolver ferramentas de TBM. LPS possibilitam gerar sistematicamente produtos a custos mais baixos, em menor tempo e com maior qualidade. A principal contribuição desta tese de doutorado é apresentar uma LPS de ferramentas de teste que suportam TBM (PLeTs) e um ambiente automatizado para apoiar a geração dessas ferramentas (PlugSPL). Além disso, esta tese apresenta uma abordagem para gerar ferramentas para TBM, que foram aplicadas em dois exemplos de uso. Com base nos resultados obtidos nos exemplos de uso, podemos inferir que LPS pode ser considerada uma abordagem relevante para melhorar a produtividade e o reuso durante a geração de ferramentas de TBM. Além disso, também foi realizado um estudo experimental com o objetivo de avaliar o esforço para se utilizar uma ferramenta derivada da PLeTs para geração de scripts de teste. Os resultados apontaram que o esforço para gerar scripts de teste foi reduzido consideravelmente, quando comparado com a uma ferramenta de Capture and Replay
Plets: a product line of model-based testing tools
No full textO teste de software é uma atividade fundamental para garantir a qualidade de software. Além disso, teste de software é uma das atividades mais caras e demoradas no processo de desenvolvimento de software. Por esta razão, diversas ferramentas de teste foram desenvolvidas para apoiar esta atividade, incluindo ferramentas para Teste Baseado em Modelos (TBM). TBM é uma técnica de teste para automatizar a geração de artefatos de teste a partir de modelos do sistema. Esta técnica apresenta diversas vantagens, tais como, menor custo e esforço para gerar casos de teste. Por este motivo, nos últimos anos, diversas ferramentas para TBM foram desenvolvidas para melhor explorar essas vantagens. Embora existam diversas ferramentas TBM, a maioria delas tem o seu desenvolvimento baseado em um esforço individual, sem a adoção de técnicas de reuso sistemático e com base em uma única arquitetura, dificultando a integração, evolução, manutenção e reutilização dessas ferramentas. Uma alternativa para mitigar estes problemas é adotar os conceitos de Linhas de Produto de Software (LPS) para desenvolver ferramentas de TBM.LPS possibilitam gerar sistematicamente produtos a custos mais baixos, em menor tempo e com maior qualidade. A principal contribuição desta tese de doutorado é apresentar uma LPS de ferramentas de teste que suportam TBM (PLeTs) e um ambiente automatizado para apoiar a geração dessas ferramentas (PlugSPL). Além disso, esta tese apresenta uma abordagem para gerar ferramentas para TBM, que foram aplicadas em dois exemplos de uso. Com base nos resultados obtidos nos exemplos de uso, podemos inferir que LPS pode ser considerada uma abordagem relevante para melhorar a produtividade e o reuso durante a geração de ferramentas de TBM. Além disso, também foi realizado um estudo experimental com o objetivo de avaliar o esforço para se utilizar uma ferramenta derivada da PLeTs para geração de scripts de teste. Os resultados apontaram que o esforço para gerar scripts de teste foi reduzido consideravelmente, quando comparado com a uma ferramenta de Capture and Replay.Software testing is recognized as a fundamental activity for assuring software quality. Furthermore, testing is also recognized as one of the most time consuming and expensive activities of software development process. A diversity of testing tools has been developed to support this activity, including tools for Model-based Testing (MBT). MBT is a testing technique to automate the generation of testing artifacts from the system model. This technique presents several advantages, such as, lower cost and less effort to generate test cases. Therefore, in the last years a diversity of commercial, academic, and open source tools to support MBT has been developed to better explore these advantages. In spite of the diversity of tools to support MBT, most of them have been individually and independently developed from scratch based on a single architecture. Thus, they face difficulties of integration, evolution, maintenance, and reuse. In another perspective, Software Product Lines (SPL) offers possibility of systematically generating software products at lower costs, in shorter time, and with higher quality.The main contribution of this Ph. D thesis is to present a SPL for testing tools that support MBT (PLeTs) and an automated environment to support the generation of these tools (PlugSPL). Furthermore, our strategy was initially applied to generate some MBT testing tools which were applied in two examples of use performed in collaboration of an IT company. Based on the feedback from the examples of use we can infer that SPL can be considered a relevant approach to improve productivity and reuse during generation of MBT testing tools. Moreover, we also performed an experimental study carried out to evaluate the effort to use an MBT tool derived from our SPL to generate test scripts and scenarios. Thus, the results point out that the effort to generate test scripts, when compared with a Capture and Replay based tool, was reduced considerably
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