45 resultados para Reconfigurable architectures


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There are some approaches that take advantage of unused computational resources in the Internet nodes - users´ machines. In the last years , the peer-to-peer networks (P2P) have gaining a momentum mainly due to its support for scalability and fault tolerance. However, current P2P architectures present some problems such as nodes overhead due to messages routing, a great amount of nodes reconfigurations when the network topology changes, routing traffic inside a specific network even when the traffic is not directed to a machine of this network, and the lack of a proximity relationship among the P2P nodes and the proximity of these nodes in the IP network. Although some architectures use the information about the nodes distance in the IP network, they use methods that require dynamic information. In this work we propose a P2P architecture to fix the problems afore mentioned. It is composed of three parts. The first part consists of a basic P2P architecture, called SGrid, which maintains a relationship of nodes in the P2P network with their position in the IP network. Its assigns adjacent key regions to nodes of a same organization. The second part is a protocol called NATal (Routing and NAT application layer) that extends the basic architecture in order to remove from the nodes the responsibility of routing messages. The third part consists of a special kind of node, called LSP (Lightware Super-Peer), which is responsible for maintaining the P2P routing table. In addition, this work also presents a simulator that validates the architecture and a module of the Natal protocol to be used in Linux routers

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Robots are present each time more on several areas of our society, however they are still considered expensive equipments that are restricted to few people. This work con- sists on the development of control techniques and architectures that make possible the construction and programming of low cost robots with low programming and building complexity. One key aspect of the proposed architecture is the use of audio interfaces to control actuators and read sensors, thus allowing the usage of any device that can produce sounds as a control unit of a robot. The work also includes the development of web ba- sed programming environments that allow the usage of computers or mobile phones as control units of the robot, which can be remotely programmed and controlled. The work also includes possible applications of such low cost robotic platform, including mainly its educational usage, which was experimentally validated by teachers and students of seve- ral graduation courses. We also present an analysis of data obtained from interviews done with the students before and after the use of our platform, which confirms its acceptance as a teaching support tool

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In academia, it is common to create didactic processors, facing practical disciplines in the area of Hardware Computer and can be used as subjects in software platforms, operating systems and compilers. Often, these processors are described without ISA standard, which requires the creation of compilers and other basic software to provide the hardware / software interface and hinder their integration with other processors and devices. Using reconfigurable devices described in a HDL language allows the creation or modification of any microarchitecture component, leading to alteration of the functional units of data path processor as well as the state machine that implements the control unit even as new needs arise. In particular, processors RISP enable modification of machine instructions, allowing entering or modifying instructions, and may even adapt to a new architecture. This work, as the object of study addressing educational soft-core processors described in VHDL, from a proposed methodology and its application on two processors with different complexity levels, shows that it s possible to tailor processors for a standard ISA without causing an increase in the level hardware complexity, ie without significant increase in chip area, while its level of performance in the application execution remains unchanged or is enhanced. The implementations also allow us to say that besides being possible to replace the architecture of a processor without changing its organization, RISP processor can switch between different instruction sets, which can be expanded to toggle between different ISAs, allowing a single processor become adaptive hybrid architecture, which can be used in embedded systems and heterogeneous multiprocessor environments

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In this work, we propose a new approach to Interactive Digital Television (IDTV), aimed to explore the concepts of immersivity. Several architectures have been proposed to IDTV, but they did not explore coherently questions related to immersion. The goal of this thesis consists in defining formally what is immersion and interactivity for digital TV and how they may be used to improve user experience in this new televisive model. The approach raises questions such as the appropriate choice of equipment to assist in the sense of immersion, which forms of interaction between users can be exploited in the interaction-immersion context, if the environment where an immersive and interactive application is used can influence the user experience, and which new forms of interactivity between users, and interactivity among users and interactive applications can be explored with the use of immersion. As one of the goals of this proposal, we point out new solutions to these issues that require further studies. We intend to formalize the concepts that embrace interactivity in the brazilian system of digital TV. In an initial study, this definition is organized into categories or levels of interactivity. From this point are made analisis and specifications to achieve immersion using DTV. We pretend to make some case studies of immersive interactive applications for digital television in order to validate the proposed architecture. We also approach the use of remote devices anda proposal of middleware architecture that allows its use in conjunction with immersive interactive applications

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ln this work, it was deveIoped a parallel cooperative genetic algorithm with different evolution behaviors to train and to define architectures for MuItiIayer Perceptron neural networks. MuItiIayer Perceptron neural networks are very powerful tools and had their use extended vastIy due to their abiIity of providing great resuIts to a broad range of appIications. The combination of genetic algorithms and parallel processing can be very powerful when applied to the Iearning process of the neural network, as well as to the definition of its architecture since this procedure can be very slow, usually requiring a lot of computational time. AIso, research work combining and appIying evolutionary computation into the design of neural networks is very useful since most of the Iearning algorithms deveIoped to train neural networks only adjust their synaptic weights, not considering the design of the networks architecture. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of a promising solution, acceIerating the evolutionary process. Finally, individuaIs and evolution behavior can be exclusive on each copy of the genetic algorithm running in each task enhancing the diversity of populations

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This work aims to present how the reconfigurable microstrip antennas and frequency selective surfaces can be used to operate at communication systems that require changing their operation frequency according to system requirements or environmental conditions. The main purpose is to present a reconfigurable circular microstrip antenna using a parasitic ring and a reconfigurable dipole frequency selective surface. Thereupon there are shown fundamental topics like microstrip antennas, PIN diodes and the fundamental theory of reconfigurable antennas and frequency selective surfaces. There are shown the simulations and measurements of the fabricated prototypes and it is done an analysis of some parameters like the bandwidth and radiation pattern, for the antennas, and the transmission characteristics, for the frequency selective surface. Copper strips were used in place of the diodes for proof of the reconfigurability concept

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Blind Source Separation (BSS) refers to the problem of estimate original signals from observed linear mixtures with no knowledge about the sources or the mixing process. Independent Component Analysis (ICA) is a technique mainly applied to BSS problem and from the algorithms that implement this technique, FastICA is a high performance iterative algorithm of low computacional cost that uses nongaussianity measures based on high order statistics to estimate the original sources. The great number of applications where ICA has been found useful reects the need of the implementation of this technique in hardware and the natural paralelism of FastICA favors the implementation of this algorithm on digital hardware. This work proposes the implementation of FastICA on a reconfigurable hardware platform for the viability of it's use in blind source separation problems, more specifically in a hardware prototype embedded in a Field Programmable Gate Array (FPGA) board for the monitoring of beds in hospital environments. The implementations will be carried out by Simulink models and it's synthesizing will be done through the DSP Builder software from Altera Corporation.

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This work proposes hardware architecture, VHDL described, developed to embedded Artificial Neural Network (ANN), Multilayer Perceptron (MLP). The present work idealizes that, in this architecture, ANN applications could easily embed several different topologies of MLP network industrial field. The MLP topology in which the architecture can be configured is defined by a simple and specifically data input (instructions) that determines the layers and Perceptron quantity of the network. In order to set several MLP topologies, many components (datapath) and a controller were developed to execute these instructions. Thus, an user defines a group of previously known instructions which determine ANN characteristics. The system will guarantee the MLP execution through the neural processors (Perceptrons), the components of datapath and the controller that were developed. In other way, the biases and the weights must be static, the ANN that will be embedded must had been trained previously, in off-line way. The knowledge of system internal characteristics and the VHDL language by the user are not needed. The reconfigurable FPGA device was used to implement, simulate and test all the system, allowing application in several real daily problems

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This work treats of an implementation OFDMA baseband processor in hardware for LTE Downlink. The LTE or Long Term Evolution consist the last stage of development of the technology called 3G (Mobile System Third Generation) which offers an increasing in data rate and more efficiency and flexibility in transmission with application of advanced antennas and multiple carriers techniques. This technology applies in your physical layer the OFDMA technical (Orthogonal Frequency Division Multiple Access) for generation of signals and mapping of physical resources in downlink and has as base theoretical to OFDM multiple carriers technique (Orthogonal Frequency Division Multiplexing). With recent completion of LTE specifications, different hardware solutions have been developed, mainly, to the level symbol processing where the implementation of OFDMA processor in base band is commonly considered, because it is also considered a basic architecture of others important applications. For implementation of processor, the reconfigurable hardware offered by devices as FPGA are considered which shares not only to meet the high requirements of flexibility and adaptability of LTE as well as offers possibility of an implementation quick and efficient. The implementation of processor in reconfigurable hardware meets the specifications of LTE physical layer as well as have the flexibility necessary for to meet others standards and application which use OFDMA processor as basic architecture for your systems. The results obtained through of simulation and verification functional system approval the functionality and flexibility of processor implemented

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This work shows a theoretical analysis together with numerical and experimental results of transmission characteristics from the microstrip bandpass filters with different geometries. These filters are built over isotropic dielectric substrates. The numerical analysis is made by specifical commercial softwares, like Ansoft Designer and Agilent Advanced Design System (ADS). In addition to these tools, a Matlab Script was built to analyze the filters through the Finite-Difference Time-Domain (FDTD) method. The filters project focused the development of the first stage of filtering in the ITASAT s Transponder receptor, and its integration with the others systems. Some microstrip filters architectures have been studied, aiming the viability of implementation and suitable practical application for the purposes of the ITASAT Project due to its lowspace occupation in the lower UHF frequencies. The ITASAT project is a Universityexperimental project which will build a satellite to integrate the Brazilian Data Collect System s satellite constellation, with efforts of many Brazilian institutes, like for example AEB (Brazilian Spatial Agency), ITA (Technological Institute of Aeronautics), INPE/CRN (National Institute of Spatial Researches/Northeastern Regional Center) and UFRN (Federal University of Rio Grande do Norte). Comparisons were made between numerical and experimental results of all filters, where good agreements could be noticed, reaching the most of the objectives. Also, post-work improvements were suggested.

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Industrial automation networks is in focus and is gradually replacing older architectures of systems used in automation world. Among existing automation networks, most prominent standard is the Foundation Fieldbus (FF). This particular standard was chosen for the development of this work thanks to its complete application layer specification and its user interface, organized as function blocks and that allows interoperability among different vendors' devices. Nowadays, one of most seeked solutions on industrial automation are the indirect measurements, that consist in infering a value from measures of other sensors. This can be made through implementation of the so-called software sensors. One of the most used tools in this project and in sensor implementation are artificial neural networks. The absence of a standard solution to implement neural networks in FF environment makes impossible the development of a field-indirect-measurement project, besides other projects involving neural networks, unless a closed proprietary solution is used, which dos not guarantee interoperability among network devices, specially if those are from different vendors. In order to keep the interoperability, this work's goal is develop a solution that implements artificial neural networks in Foundation Fieldbus industrial network environment, based on standard function blocks. Along the work, some results of the solution's implementation are also presented

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The last years have presented an increase in the acceptance and adoption of the parallel processing, as much for scientific computation of high performance as for applications of general intention. This acceptance has been favored mainly for the development of environments with massive parallel processing (MPP - Massively Parallel Processing) and of the distributed computation. A common point between distributed systems and MPPs architectures is the notion of message exchange, that allows the communication between processes. An environment of message exchange consists basically of a communication library that, acting as an extension of the programming languages that allow to the elaboration of applications parallel, such as C, C++ and Fortran. In the development of applications parallel, a basic aspect is on to the analysis of performance of the same ones. Several can be the metric ones used in this analysis: time of execution, efficiency in the use of the processing elements, scalability of the application with respect to the increase in the number of processors or to the increase of the instance of the treat problem. The establishment of models or mechanisms that allow this analysis can be a task sufficiently complicated considering parameters and involved degrees of freedom in the implementation of the parallel application. An joined alternative has been the use of collection tools and visualization of performance data, that allow the user to identify to points of strangulation and sources of inefficiency in an application. For an efficient visualization one becomes necessary to identify and to collect given relative to the execution of the application, stage this called instrumentation. In this work it is presented, initially, a study of the main techniques used in the collection of the performance data, and after that a detailed analysis of the main available tools is made that can be used in architectures parallel of the type to cluster Beowulf with Linux on X86 platform being used libraries of communication based in applications MPI - Message Passing Interface, such as LAM and MPICH. This analysis is validated on applications parallel bars that deal with the problems of the training of neural nets of the type perceptrons using retro-propagation. The gotten conclusions show to the potentiality and easinesses of the analyzed tools.

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The seismic method is of extreme importance in geophysics. Mainly associated with oil exploration, this line of research focuses most of all investment in this area. The acquisition, processing and interpretation of seismic data are the parts that instantiate a seismic study. Seismic processing in particular is focused on the imaging that represents the geological structures in subsurface. Seismic processing has evolved significantly in recent decades due to the demands of the oil industry, and also due to the technological advances of hardware that achieved higher storage and digital information processing capabilities, which enabled the development of more sophisticated processing algorithms such as the ones that use of parallel architectures. One of the most important steps in seismic processing is imaging. Migration of seismic data is one of the techniques used for imaging, with the goal of obtaining a seismic section image that represents the geological structures the most accurately and faithfully as possible. The result of migration is a 2D or 3D image which it is possible to identify faults and salt domes among other structures of interest, such as potential hydrocarbon reservoirs. However, a migration fulfilled with quality and accuracy may be a long time consuming process, due to the mathematical algorithm heuristics and the extensive amount of data inputs and outputs involved in this process, which may take days, weeks and even months of uninterrupted execution on the supercomputers, representing large computational and financial costs, that could derail the implementation of these methods. Aiming at performance improvement, this work conducted the core parallelization of a Reverse Time Migration (RTM) algorithm, using the parallel programming model Open Multi-Processing (OpenMP), due to the large computational effort required by this migration technique. Furthermore, analyzes such as speedup, efficiency were performed, and ultimately, the identification of the algorithmic scalability degree with respect to the technological advancement expected by future processors

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This paper analyzes the performance of a parallel implementation of Coupled Simulated Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel processing is an efficient form of information processing with emphasis on exploration of simultaneous events in the execution of software. It arises primarily due to high computational performance demands, and the difficulty in increasing the speed of a single processing core. Despite multicore processors being easily found nowadays, several algorithms are not yet suitable for running on parallel architectures. The algorithm is characterized by a group of Simulated Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs on a single thread executed by different processors. In the analysis of parallel performance and scalability, these metrics were investigated: the execution time; the speedup of the algorithm with respect to increasing the number of processors; and the efficient use of processing elements with respect to the increasing size of the treated problem. Furthermore, the quality of the final solution was verified. For the study, this paper proposes a parallel version of CSA and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions. For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained are shown and discussed observing the analysis of the metrics. The conclusions of the paper characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the parallel scalability and parallel efficiency

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The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures