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This paper discusses a neural network development approach based on an exponential smoothing method which aims at enhancing previously used neural networks for traffic flow forecasting. The approach uses the exponential smoothing method to pre-process traffic flow data before implementing on neural networks for training purpose. The pre-processed traffic flow data, which is lesser non-smooth, discontinuous...
In this paper we discuss Gauss-Markov Random Field (GMRF) based on multiple sub-aperture decomposition method for the analysis of targets in complex-valued high-resolution SAR data. Gauss-Markov Random Field (GMRF) model with a quadratic energy function as a parametric analysis parameterizes the spectogram of the signal, whereas sub-aperture decomposition method exploits the holographic property of...
In this paper we compare parametric and non-parametric method for the analysis of complex valued high-resolution SAR data. Gauss-Markov Random Field (GMRF) model with a quadratic energy function as a parametric analysis parameterizes the spectogram of the signal, whereas nonlinear short time Fourier transform (STFT) method, the method based on time frequency analysis (TFA) as a non-parametric approach...
Healthcare is a highly collaborative environment, where the active sharing of information is central to the care process. Due to the sensitive nature of medical information, care providers are responsible for protecting data, controlling the circumstances in which it is released to others. The publish/subscribe (pub/sub) communication paradigm is useful for data dissemination, as it allows parties...
In systems that support disconnected operations, shared objects must be cached at client sites to ensure that the objects are accessible while the client is disconnected. It is desirable that objects cached at a client site are mutually consistent with respect to the level of consistency provided by the system. We present a new technique for detecting mutual consistency of objects and show how it...
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