Real time rendering of three-dimensional scenes in Ray Tracing is a hard problem. However, parallel implementations have been enabling real time performance, as the algorithm can be highly parallelized. Thus, a custom...
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Real time rendering of three-dimensional scenes in Ray Tracing is a hard problem. However, parallel implementations have been enabling real time performance, as the algorithm can be highly parallelized. Thus, a custom parallel design in hardware is likely to achieve a good performance. In this paper, we further improve the GridRT architecture overall performance by embedding the ray-triangle intersection computation into the precessing elements that form the architecture. Low cost and high rendering performance are the main concerns in this novel design. The results show that the execution time of each intersection computation is reduced by at least 50%, while the area cost is practically unchanged or even reduced when compared to the original GridRT implementation.
In this paper, we investigate an opportunistic relaying scheme where the selected relay assists the source-destination (direct) communication. In our study, we consider a regenerative opportunistic relaying scheme in ...
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In this paper, we investigate an opportunistic relaying scheme where the selected relay assists the source-destination (direct) communication. In our study, we consider a regenerative opportunistic relaying scheme in which the direct path may be considered unusable, and the destination may use a selection combining technique. We first derive the exact statistics of each hop, in terms of probability density function (PDF). Then, the PDFs are used to determine accurate closed form expressions for end-to-end outage probability for a transmission rate R. Furthermore, we evaluate the asymptotical performance analysis and the diversity order is deduced. Finally, we validate our analysis by showing that performance simulation results coincide with our analytical results over different network architectures.
Spike detection and sorting is a fundamental step in the analysis of extracellular neural recording. Here, we propose a combined spike detection-feature extraction algorithm that relies on a sparse representation spac...
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Spike detection and sorting is a fundamental step in the analysis of extracellular neural recording. Here, we propose a combined spike detection-feature extraction algorithm that relies on a sparse representation space of the spike waveforms. The proposed method captures the wavelet footprint of the waveform, by calculating the power of the scale space vectors and finding an optimal detection threshold using histogram equalization techniques. Under the proposed scheme, a compact feature set is obtained simultaneously during detection, which eliminates the need for a separate feature extraction step for spike sorting. Our results demonstrate that this method yields improved performance, particularly in low SNRs, while preserving the separability between neuronal clusters in the feature space.
Enhanced rates of oxide growth have been observed on silicon when exposed to high-energy x-ray irradiation. This observed effect could potentially be utilized for remote total ionizing dose-sensing applications.
Enhanced rates of oxide growth have been observed on silicon when exposed to high-energy x-ray irradiation. This observed effect could potentially be utilized for remote total ionizing dose-sensing applications.
Wireless sensor networks can be used in habitat monitoring for detecting fire, in disaster for helping rescue teams and in agriculture for sensing humidity. Node localization is essential for some important sensor net...
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Wireless sensor networks can be used in habitat monitoring for detecting fire, in disaster for helping rescue teams and in agriculture for sensing humidity. Node localization is essential for some important sensor network applications. Despite the high relative accuracy of some localization algorithms, node localization is still an opened research area, due to the physical phenomena such as attenuation, reflection, diffraction, scattering and so forth. The current developed algorithms have different accuracy when are tested under dissimilar environments. We propose to use Smart Beacon Nodes (SBNs) to infer the Obstruction Level Indicator over an occupied area, then, use this indicator for estimating the distance among nodes. In our experimental simulation, SBNs decrease the node localization error of Triangular Centroid Localization and Weighted Centroid Localization up to 18%.
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.
The effect of back biasing on the performance of a planar tunnel field-effect transistor (TFET) implemented on a silicon-on-insulator substrate is investigated. It is found that reverse back biasing reduces the subthr...
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The effect of back biasing on the performance of a planar tunnel field-effect transistor (TFET) implemented on a silicon-on-insulator substrate is investigated. It is found that reverse back biasing reduces the subthreshold swing SS and increases the range of drain current over which SS is less than ( k B T / q ) ln (10); hence, it is effective for improving the TFET on/off current ratio for low operating voltages (≤ 0.5 V).
We report on focus group feedback regarding the services provided by existing education-related Digital Libraries (DL). Participants provided insight into how they seek educational resources online, and what they perc...
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In this work we study the role of fluorine for the selective suppression of porous silicon in localized SOI applications. The formation of porous silicon can be done selectively by controlling the Fermi level in areas...
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This paper focuses on the problem of minimum time trajectory planning for helicopter UAVs. It is formulated as a nonlinear optimal control subject to the dynamics and limitations of helicopter UAVs. The dynamical syst...
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This paper focuses on the problem of minimum time trajectory planning for helicopter UAVs. It is formulated as a nonlinear optimal control subject to the dynamics and limitations of helicopter UAVs. The dynamical system is defined by a set of fifteen states nonlinear differential equations developed for HeLion, a UAV helicopter constructed in National University of Singapore (NUS). The problem is then solved numerically using pseudospectral method for dynamic optimization. The results show that minimum time trajectories are highly nonlinear that require complicated maneuvering.
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