Dreams: A tool for the design of dynamically reconfigurable embedded and modular systems

Dynamically Reconfigurable Systems are attracting a growing interest, mainly due to the emergence of novel applications based on this technology. However, commercial tools do not provide enough flexibility to design solutions, while keeping an acceptable design productivity. In this paper, a novel design flow is proposed, targeting dynamically reconfigurable systems. It is fully supported by a tool called Dreams, which is able to implement flexible systems, starting from a set of netlists corresponding to the modules, as well as a system description provided by the user. The tool automatically post-processes the nets, implementing a solution for the communications between reconfigurable regions, as well as the handling of routing conflicts, by means of a custom router. Since the design process of every module and the static system are independent, the proposed flow is compatible with system upgrade at run-time. In this paper, a use case corresponding to the design of a highly regular and parallel mesh-type architecture is described, in order to show the architectural flexibility offered by the tool.

Hammer: an Android based application for end-user industrial robot programming

This paper presents a novel tablet based end-user interface for industrial robot programming (called Hammer). This application makes easier to program tasks for industrial robots like polishing, milling or grinding. It is based on the Scratch programming language, but specifically design and created for Android OS. It is a visual programming concept that allows non-skilled programmer operators to create programs. The application also allows to monitor the tasks while it is being executed by overlapping real time information through augmented reality. The application includes a teach pendant screen that can be customized according to the operator needs at every moment.

A Fuzzy approach based on dynamic programming and metaheuristics for selecting safeguards for risk management for information systems

In this paper we focus on the selection of safeguards in a fuzzy risk analysis and management methodology for information systems (IS). Assets are connected by dependency relationships, and a failure of one asset may affect other assets. After computing impact and risk indicators associated with previously identified threats, we identify and apply safeguards to reduce risks in the IS by minimizing the transmission probabilities of failures throughout the asset network. However, as safeguards have associated costs, the aim is to select the safeguards that minimize costs while keeping the risk within acceptable levels. To do this, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems.

A proposal for enhancing the motivation in students of computer programming

Computer programming is known to be one of the most difficult courses for students in the first year of engineering. They are faced with the challenge of abstract thinking and gaining programming skills for the first time. These skills are acquired by continuous practicing from the start of the course. In order to enhance the motivation and dynamism of the learning and assessment processes, we have proposed the use of three educational resources namely screencasts, self-assessment questionnaires and automated grading of assignments. These resources have been made available in Moodle which is a Learning Management System widely used in education environments and adopted by the Telecommunications Engineering School at the Universidad Politécnica de Madrid (UPM). Both teachers and students can enhance the learning and assessment processes through the use of new educational activities such as self-assessment questionnaires and automated grading of assignments. On the other hand, multimedia resources such as screencasts can guide students in complex topics. The resources proposed allow teachers to improve their tutorial actions since they provide immediate feedback and comments to students without the enormous effort of manual correction and evaluation by teachers specially taking into account the large number of students enrolled in the course. In this paper we present the case study where three proposed educational resources were applied. We describe the special features of the course and explain why the use of these resources can both enhance the students? motivation and improve the teaching and learning processes. Our research work was carried out on students attending the "Computer programming" course offered in the first year of a Telecommunications Engineering degree at UPM. This course is mandatory and has more than 450 enrolled students. Our purpose is to encourage the motivation and dynamism of the learning and assessment processes.

Hybrid wind-diesel power plant valuation and optimization using real option theory and dynamic programming

El sistema de energía eólica-diesel híbrido tiene un gran potencial en la prestación de suministro de energía a comunidades remotas. En comparación con los sistemas tradicionales de diesel, las plantas de energía híbridas ofrecen grandes ventajas tales como el suministro de capacidad de energía extra para "microgrids", reducción de los contaminantes y emisiones de gases de efecto invernadero, y la cobertura del riesgo de aumento inesperado del precio del combustible. El principal objetivo de la presente tesis es proporcionar nuevos conocimientos para la evaluación y optimización de los sistemas de energía híbrido eólico-diesel considerando las incertidumbres. Dado que la energía eólica es una variable estocástica, ésta no puede ser controlada ni predecirse con exactitud. La naturaleza incierta del viento como fuente de energía produce serios problemas tanto para la operación como para la evaluación del valor del sistema de energía eólica-diesel híbrido. Por un lado, la regulación de la potencia inyectada desde las turbinas de viento es una difícil tarea cuando opera el sistema híbrido. Por otro lado, el bene.cio económico de un sistema eólico-diesel híbrido se logra directamente a través de la energía entregada a la red de alimentación de la energía eólica. Consecuentemente, la incertidumbre de los recursos eólicos incrementa la dificultad de estimar los beneficios globales en la etapa de planificación. La principal preocupación del modelo tradicional determinista es no tener en cuenta la incertidumbre futura a la hora de tomar la decisión de operación. Con lo cual, no se prevé las acciones operativas flexibles en respuesta a los escenarios futuros. El análisis del rendimiento y simulación por ordenador en el Proyecto Eólico San Cristóbal demuestra que la incertidumbre sobre la energía eólica, las estrategias de control, almacenamiento de energía, y la curva de potencia de aerogeneradores tienen un impacto significativo sobre el rendimiento del sistema. En la presente tesis, se analiza la relación entre la teoría de valoración de opciones y el proceso de toma de decisiones. La opción real se desarrolla con un modelo y se presenta a través de ejemplos prácticos para evaluar el valor de los sistemas de energía eólica-diesel híbridos. Los resultados muestran que las opciones operacionales pueden aportar un valor adicional para el sistema de energía híbrida, cuando esta flexibilidad operativa se utiliza correctamente. Este marco se puede aplicar en la optimización de la operación a corto plazo teniendo en cuenta la naturaleza dependiente de la trayectoria de la política óptima de despacho, dadas las plausibles futuras realizaciones de la producción de energía eólica. En comparación con los métodos de valoración y optimización existentes, el resultado del caso de estudio numérico muestra que la política de operación resultante del modelo de optimización propuesto presenta una notable actuación en la reducción del con- sumo total de combustible del sistema eólico-diesel. Con el .n de tomar decisiones óptimas, los operadores de plantas de energía y los gestores de éstas no deben centrarse sólo en el resultado directo de cada acción operativa, tampoco deberían tomar decisiones deterministas. La forma correcta es gestionar dinámicamente el sistema de energía teniendo en cuenta el valor futuro condicionado en cada opción frente a la incertidumbre. ABSTRACT Hybrid wind-diesel power systems have a great potential in providing energy supply to remote communities. Compared with the traditional diesel systems, hybrid power plants are providing many advantages such as providing extra energy capacity to the micro-grid, reducing pollution and greenhouse-gas emissions, and hedging the risk of unexpected fuel price increases. This dissertation aims at providing novel insights for assessing and optimizing hybrid wind-diesel power systems considering the related uncertainties. Since wind power can neither be controlled nor accurately predicted, the energy harvested from a wind turbine may be considered a stochastic variable. This uncertain nature of wind energy source results in serious problems for both the operation and value assessment of the hybrid wind-diesel power system. On the one hand, regulating the uncertain power injected from wind turbines is a difficult task when operating the hybrid system. On the other hand, the economic profit of a hybrid wind-diesel system is achieved directly through the energy delivered to the power grid from the wind energy. Therefore, the uncertainty of wind resources has increased the difficulty in estimating the total benefits in the planning stage. The main concern of the traditional deterministic model is that it does not consider the future uncertainty when making the dispatch decision. Thus, it does not provide flexible operational actions in response to the uncertain future scenarios. Performance analysis and computer simulation on the San Cristobal Wind Project demonstrate that the wind power uncertainty, control strategies, energy storage, and the wind turbine power curve have a significant impact on the performance of the system. In this dissertation, the relationship between option pricing theory and decision making process is discussed. A real option model is developed and presented through practical examples for assessing the value of hybrid wind-diesel power systems. Results show that operational options can provide additional value to the hybrid power system when this operational flexibility is correctly utilized. This framework can be applied in optimizing short term dispatch decisions considering the path-dependent nature of the optimal dispatch policy, given the plausible future realizations of the wind power production. Comparing with the existing valuation and optimization methods, result from numerical example shows that the dispatch policy resulting from the proposed optimization model exhibits a remarkable performance in minimizing the total fuel consumption of the wind-diesel system. In order to make optimal decisions, power plant operators and managers should not just focus on the direct outcome of each operational action; neither should they make deterministic decisions. The correct way is to dynamically manage the power system by taking into consideration the conditional future value in each option in response to the uncertainty.

An Empirical Comparison of Reuse in Embedded and Nonembedded Systems

High-quality software, delivered on time and budget, constitutes a critical part of most products and services in modern society. Our government has invested billions of dollars to develop software assets, often to redevelop the same capability many times. Recognizing the waste involved in redeveloping these assets, in 1992 the Department of Defense issued the Software Reuse Initiative. The vision of the Software Reuse Initiative was "To drive the DoD software community from its current "re-invent the software" cycle to a process-driven, domain-specific, architecture-centric, library-based way of constructing software.'' Twenty years after issuing this initiative, there is evidence of this vision beginning to be realized in nonembedded systems. However, virtually every large embedded system undertaken has incurred large cost and schedule overruns. Investigations into the root cause of these overruns implicates reuse. Why are we seeing improvements in the outcomes of these large scale nonembedded systems and worse outcomes in embedded systems? This question is the foundation for this research. The experiences of the Aerospace industry have led to a number of questions about reuse and how the industry is employing reuse in embedded systems. For example, does reuse in embedded systems yield the same outcomes as in nonembedded systems? Are the outcomes positive? If the outcomes are different, it may indicate that embedded systems should not use data from nonembedded systems for estimation. Are embedded systems using the same development approaches as nonembedded systems? Does the development approach make a difference? If embedded systems develop software differently from nonembedded systems, it may mean that the same processes do not apply to both types of systems. What about the reuse of different artifacts? Perhaps there are certain artifacts that, when reused, contribute more or are more difficult to use in embedded systems. Finally, what are the success factors and obstacles to reuse? Are they the same in embedded systems as in nonembedded systems? The research in this dissertation is comprised of a series of empirical studies using professionals in the aerospace and defense industry as its subjects. The main focus has been to investigate the reuse practices of embedded systems professionals and nonembedded systems professionals and compare the methods and artifacts used against the outcomes. The research has followed a combined qualitative and quantitative design approach. The qualitative data were collected by surveying software and systems engineers, interviewing senior developers, and reading numerous documents and other studies. Quantitative data were derived from converting survey and interview respondents' answers into coding that could be counted and measured. From the search of existing empirical literature, we learned that reuse in embedded systems are in fact significantly different from nonembedded systems, particularly in effort in model based development approach and quality where the development approach was not specified. The questionnaire showed differences in the development approach used in embedded projects from nonembedded projects, in particular, embedded systems were significantly more likely to use a heritage/legacy development approach. There was also a difference in the artifacts used, with embedded systems more likely to reuse hardware, test products, and test clusters. Nearly all the projects reported using code, but the questionnaire showed that the reuse of code brought mixed results. One of the differences expressed by the respondents to the questionnaire was the difficulty in reuse of code for embedded systems when the platform changed. The semistructured interviews were performed to tell us why the phenomena in the review of literature and the questionnaire were observed. We asked respected industry professionals, such as senior fellows, fellows and distinguished members of technical staff, about their experiences with reuse. We learned that many embedded systems used heritage/legacy development approaches because their systems had been around for many years, before models and modeling tools became available. We learned that reuse of code is beneficial primarily when the code does not require modification, but, especially in embedded systems, once it has to be changed, reuse of code yields few benefits. Finally, while platform independence is a goal for many in nonembedded systems, it is certainly not a goal for the embedded systems professionals and in many cases it is a detriment. However, both embedded and nonembedded systems professionals endorsed the idea of platform standardization. Finally, we conclude that while reuse in embedded systems and nonembedded systems is different today, they are converging. As heritage embedded systems are phased out, models become more robust and platforms are standardized, reuse in embedded systems will become more like nonembedded systems.

Simulating spin systems on IANUS, an FPGA-based computer

We describe the hardwired implementation of algorithms for Monte Carlo simulations of a large class of spin models. We have implemented these algorithms as VHDL codes and we have mapped them onto a dedicated processor based on a large FPGA device. The measured performance on one such processor is comparable to O(100) carefully programmed high-end PCs: it turns out to be even better for some selected spin models. We describe here codes that we are currently executing on the IANUS massively parallel FPGA-based system.

An hybrid parallel algorithm for solving tridiagonal linear systems versus the Wang’s method in a Cray T3D BSP computer

In this paper we describe an hybrid algorithm for an even number of processors based on an algorithm for two processors and the Overlapping Partition Method for tridiagonal systems. Moreover, we compare this hybrid method with the Partition Wang’s method in a BSP computer. Finally, we compare the theoretical computation cost of both methods for a Cray T3D computer, using the cost model that BSP model provides.

An application of fuzzy logic for hardware/software partitioning in embedded systems

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases.

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.

Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).

Spanish Nursing Students' Practical Experience of Computer Applications in Nursing

This study aimed to determine the level of computer practical experience in a sample of Spanish nursing students. Each student was given a Spanish language questionnaire, modified from an original used previously with medical students at the Medical School of North Carolina University (USA) and also at the Education Unit of Hospital General Universitario del Mar (Spain). The 10-item self-report questionnaire probed for information about practical experience with computers. A total of 126 students made up the sample. The majority were female (80.2%; n=101). The results showed that just over half (57.1%, n=72) of the students had used a computer game (three or more times before), and that only one third (37.3%, n=47) had the experience of using a word processing package. Moreover, other applications and IT-based facilities (e.g. statistical packages, e-mail, databases, CD-ROM searches, programming languages and computer-assisted learning) had never been used by the majority of students. The student nurses' practical experience was less than that reported for medical students in previous studies.

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described.

Flexible processing architecture for maintaining QoS in embedded systems applications

Comunicación presentada en las V Jornadas de Computación Empotrada, Valladolid, 17-19 Septiembre 2014