A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.The research leading to these results has received funding from the Spanish Government and European FEDER funds (DPI2012-32390), the Valencia Regional Government (PROMETEO/2013/085) and the University of Alicante (GRE12-17)

Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping

Scherbart A, Timm W, Boecker S, Nattkemper TW. Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping. In: Sá de JM, ed. Artificial Neural Networks – ICANN 2007 - 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II. Lecture Notes in Computer Science. Vol 4669. Berlin: Springer; 2007: 90-99.In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by ν-Support Vector Regression and show how the LLM learning architecture provides a basis for peptide feature profiling and visualisation

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win

Mecanismos para controlo e gestão de redes 5G: redes de operador

In 5G networks, time-series data will be omnipresent for the monitoring of network metrics. With the increase in the number of Internet of Things (IoT) devices in the next years, it is expected that the number of real-time time-series data streams increases at a fast pace. To be able to monitor those streams, test and correlate different algorithms and metrics simultaneously and in a seamless way, time-series forecasting is becoming essential for the pro-active successful management of the network. The objective of this dissertation is to design, implement and test a prediction system in a communication network, that allows integrating various networks, such as a vehicular network and a 4G operator network, to improve the network reliability and Quality-of-Service (QoS). To do that, the dissertation has three main goals: (1) the analysis of different network datasets and implementation of different approaches to forecast network metrics, to test different techniques; (2) the design and implementation of a real-time distributed time-series forecasting architecture, to enable the network operator to make predictions about the network metrics; and lastly, (3) to use the forecasting models made previously and apply them to improve the network performance using resource management policies. The tests done with two different datasets, addressing the use cases of congestion management and resource splitting in a network with a limited number of resources, show that the network performance can be improved with proactive management made by a real-time system able to predict the network metrics and act on the network accordingly. It is also done a study about what network metrics can cause reduced accessibility in 4G networks, for the network operator to act more efficiently and pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização de métricas de rede. Com o aumento do número de dispositivos da Internet das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente e de maneira integrada, a previsão de séries temporais está a tornar-se essencial para a gestão preventiva bem sucedida da rede. O objetivo desta dissertação é desenhar, implementar e testar um sistema de previsão numa rede de comunicações, que permite integrar várias redes diferentes, como por exemplo uma rede veicular e uma rede 4G de operador, para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso, a dissertação tem três objetivos principais: (1) a análise de diferentes datasets de rede e subsequente implementação de diferentes abordagens para previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho e implementação de uma arquitetura distribuída de previsão de séries temporais em tempo real, para permitir ao operador de rede efetuar previsões sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão criados anteriormente e sua aplicação para melhorar o desempenho da rede utilizando políticas de gestão de recursos. Os testes efetuados com dois datasets diferentes, endereçando os casos de uso de gestão de congestionamento e divisão de recursos numa rede com recursos limitados, mostram que o desempenho da rede pode ser melhorado com gestão preventiva da rede efetuada por um sistema em tempo real capaz de prever métricas de rede e atuar em conformidade na rede. Também é efetuado um estudo sobre que métricas de rede podem causar reduzida acessibilidade em redes 4G, para o operador de rede atuar mais eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic

Pattern Mining for Label Ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog

Pattern mining for label ranking

Preferences have always been present in many tasks in our daily lives. Buying the right car, choosing a suitable house or even deciding on the food to eat, are trivial examples of decisions that reveal information, explicitly or implicitly, about our preferences. The recent trend of collecting increasing amounts of data is also true for preference data. Extracting and modeling preferences can provide us with invaluable information about the choices of groups or individuals. In areas like e-commerce, which typically deal with decisions from thousands of users, the acquisition of preferences can be a difficult task. For these reasons, artificial intelligence (in particular, machine learning) methods have been increasingly important to the discovery and automatic learning of models about preferences. In this Ph.D. project, several approaches were analyzed and proposed to deal with the LR problem. Most of which has focused on pattern mining methods.Algorithms and the Foundations of Software technolog