We demonstrate the resonance wavelength and quality factor dependence of 50nm defect-hole placement within photonic crystal L3 microcavities. Proper placement of defect-holes leads to a 12% increase in photonic crysta...
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Vehicular Ad hoc Networks (VANETs) are a special type of Mobile Ad hoc Networks (MANETs), made by vehicles communicating among themselves, and by vehicles communicating to devices located in the margins of roads and h...
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Vehicular Ad hoc Networks (VANETs) are a special type of Mobile Ad hoc Networks (MANETs), made by vehicles communicating among themselves, and by vehicles communicating to devices located in the margins of roads and highways. The main characteristic of a VANET is the high speed of network nodes - that can go up to 200 km/h -, and that impacts directly on the ability the network has to deliver data, given we might have a network formed for just a small amount of time. It has been shown in several works that ant-based routing can be successfully applied to both wired and wireless networks. This work proposes Ant Colony Optimization (ACO) procedures that take advantage of information available in vehicular networks - such as the vehicles' position and speed -, in order to design an ant-based algorithm that performs well in the dynamics of such networks. The authors have also adapted the Dynamic MANET On-demand (DYMO) routing protocol to make use of the ACO procedures proposed in this paper, and the resulting bio-inspired protocol, MAR-DYMO, had its performance evaluated in an urban scenario and compared against a few other routing protocols. The obtained results suggest that making use of environmental information can make ACO algorithms more suitable for routing in vehicular ad hoc networks.
In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they ar...
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In parallel programs, the tasks of a given application must cooperate in order to accomplish the required computation. However, the communication time between the tasks may be different depending on which core they are executing and how the memory hierarchy and interconnection are used. The problem is even more important in multi-core machines with NUMA characteristics, since the remote access imposes high overhead, making them more sensitive to thread and data mapping. In this context, process mapping is a technique that provides performance gains by improving the use of resources such as interconnections, main memory and cache memory. The problem of detecting the best mapping is considered NP-Hard. Furthermore, in shared memory environments, there is an additional difficulty of finding the communication pattern, which is implicit and occurs through memory accesses. This work aims to provide a method for static mapping for NUMA architectures which does not require any prior knowledge of the application. Different metrics were adopted and an heuristic method based on the Edmonds matching algorithm was used to obtain the mapping. In order to evaluate our proposal, we use the NAS Parallel Benchmarks (NPB) and two modern multi-core NUMA machines. Results show performance gains of up to 75% compared to the native scheduler and memory allocator of the operating system.
We demonstrate the resonance wavelength and quality factor dependence of 50nm defect-hole placement within photonic crystal L3 microcavities. Proper placement of defect-holes leads to a 12% increase in photonic crysta...
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ISBN:
(纸本)9781557529107
We demonstrate the resonance wavelength and quality factor dependence of 50nm defect-hole placement within photonic crystal L3 microcavities. Proper placement of defect-holes leads to a 12% increase in photonic crystal sensor detection sensitivity.
Poly(Ε-caprolactone) (PCL) films have been applied for vascular tissue engineering. However, few studies studied the effects of fabrication process of PCL film on vascular cell proliferation. In this study, we used d...
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ISBN:
(纸本)9781424492763
Poly(Ε-caprolactone) (PCL) films have been applied for vascular tissue engineering. However, few studies studied the effects of fabrication process of PCL film on vascular cell proliferation. In this study, we used different processing methods, incorporating stretching, to fabricate various PCL films. Thickness of films before and after stretch and proliferation ability of human fetal mesenchymal stem cells (hfMSCs) on these films were investigated. Our results showed that stretching significantly reduces the thickness of solvent cast, heat press and cast stretch films (0.22, 0.59, 0.60, p
Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power...
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Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power consumption and portability must be accomplished. This paper proposes a hardware implementation of the Particle Swarm Optimization algorithm with passive congregation (HPPSOpc), which was developed using several floating-point arithmetic libraries. The passive congregation is a biological behavior which allows the swarm to preserve its integrity, balancing between global and local search. The HPPSOpc architecture was implemented on a Virtex5 FPGA device and validated using two multimodal benchmark problems. Synthesis, simulation and execution time results demonstrates that the passive congregation approach is a low cost solution for solving embedded optimization problems with a high performance.
Soft and high magnetic moment Co37Fe63 films were electro-deposited with variable additives on Cu/Ti/Si substrates. The correlation between structure and magnetic properties has been investigated. TEM showed the cryst...
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ISBN:
(纸本)9781118029473
Soft and high magnetic moment Co37Fe63 films were electro-deposited with variable additives on Cu/Ti/Si substrates. The correlation between structure and magnetic properties has been investigated. TEM showed the crystal structure of the films to be BCC with a 〈111〉 texture, and a grain size in the range of 10-20 nm. Oxygen in the deposited films has been identified by EDS and EELS using HAADF STEM. SIMS analysis revealed the presence of hydrogen and oxygen in the deposited CoFe films. Electron microscopy results showed that the oxygen was mainly distributed along the grain boundaries in the CoFe film. In regions where oxygen was present in the films, the Fe content was enhanced relative to Co. The magnetic properties of the deposits have been measured by Vibrating Sample Magnetometer (VSM), quantifying the impact of incorporated oxygen in the film on the saturation magnetization and the coercivity.
Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfac...
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Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfaces and a process of trial and error for creating musical sounds similar to a target sound. This drawback can be overcome by adjusting automatically the synthesizer parameters using optimization algorithms. In this paper a hybrid particle swarm optimization (PSO) algorithm is proposed to solve the frequency modulation (FM) matching synthesis problem. The proposed algorithm takes advantage of a shuffle process for exchanging information between particles and applies the selective passive congregation and the opposition-based learning approaches to preserve swarm diversity. Both approaches for injecting diversity are based on simple operators, preserving the easy implementation philosophy of the particle swarm optimization. The proposed hybrid particle swarm optimization algorithm was validated for a three-nested FM synthesizer, which represents a 6-dimensional multimodal optimization problem with strong epistasis. Simulation results revealed that the proposed algorithm presented promising results in terms of quality of solutions.
Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-tempora...
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Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular computing. This paper outlines a fuzzy set based granular evolving modeling FBeM approach for learning from imprecise data. Granulation arises because modeling uncertain data dispenses attention to details. The evolving aspect is fundamental to account endless flows of nonstationary data and structural adaptation of models. Experiments with classic Box-Jenkins and Mackey-Glass benchmarks as well as with actual Global40 bond data suggest that the FBeM approach outperforms alternative approaches.
This paper presents a fully complex-valued functional link network (CFLN). The CFLN is a single-layered neural network, which introduces nonlinearity in the input layer using nonlinear functions of the original input ...
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This paper presents a fully complex-valued functional link network (CFLN). The CFLN is a single-layered neural network, which introduces nonlinearity in the input layer using nonlinear functions of the original input variables. In this study, we consider multivariate polynomials as the nonlinear functions. Unlike multilayer neural networks, the CFLN is free from local minima problem, and it offers very fast learning in parameters because of its linear structure. In the complex domain, polynomial based CFLN has an additional advantage of not requiring activation functions, which is a major concern in the complex-valued neural networks. However, it is important to select a smaller subset of polynomial terms (monomials) for faster and better performance, since the number of all possible monomials may be quite large. In this paper, we use the orthogonal least squares method in a constructive fashion (starting from lower degree to higher) for the selection of a parsimonious subset of monomials. Simulation results demonstrate that computing CFLN in purely complex domain is advantageous than in double-dimensional real domain, in terms of number of connection parameters, faster design, and possibly generalization performance. Moreover, our proposed CFLN compares favorably with several other multilayer networks in the complex domain.
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