Mathematical modelling of downflow floating medium filter (DFF) with in-line flocculation arrangement

A laboratory-scale set-up consisting of rapid mixing device and floating medium filter was used to study the use of a downflow floating medium filter (DFF) with an in-line flocculation arrangement as a static flocculator and a prefilter. The semi-empirical mathematical model formulated incorporates flocculation within the filter, particle/floc attachment onto the filter and the detachment of flocs from the medium. The mathematical model for filtration takes into account the expansion of the filter bed. The removal efficiency of DFF and headless development were successfully simulated for different conditions of filtration velocity, filter depth and influent suspended solids (SS). The values of attachment coefficient a(p)β and headless coefficient β1 were found to be independent of filtration velocity, filter depth and influent SS concentration.

Surface coverage of filter medium in deep bed filtration : mathematical modelling and experiments

This paper highlights the importance of surface coverage in modeling the removal of particles in deep bed filtration. A model that considers the saturation of sites on which particle deposition occurs is used. Experimental results obtained with monodispersed suspensions of 0.46 and 0.816 μm latex particles at different influent concentrations and ionic strengths were used to calculate the fraction of filter grain surface (β1) on which actual particle deposition occurs. This will be useful in evaluating the filter performance in terms of the utilization of available surface area of the filter medium. Further, the level of dendrite formation of particles on filter grains during filtration is expressed in terms of β1 and the specific surface coverage, θT (the fraction of a filter grain surface that is covered by particles at time T, assuming that the filter grain is covered by a monolayer of particles). This can be used to compare the contribution of deposited particles in the removal efficiency of deep bed filtration for suspensions with different physical and chemical characteristics.

Local energy, the pre-envelope, and filter resolution

We examine the construction of new filters for computing local energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh [l]. Further, we demonstrate that the effect of convolution with complex Gabor filters is to band-pass (with some differentiating effect) and compute the local energy of the result. The magnitude of the resulting local energy is then used to detect features [2], [3] (step features, texture etc.), and the phase is used to classify the detected features [l], [4] or provide disparity information for stereo [5] and motion work [6], [7]. Each of these types of information can be obtained at multiple resolutions, enabling the use of course to fine strategies for computing disparity, and allowing the discrimination of image textures on the basis of which parts of the Fourier domain they dominate [8], [9].

Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.

Parameter estimation of the BOLD fMRI model within a general particle filter framework

This work demonstrates a novel Bayesian learning approach for model based analysis of Functional Magnetic Resonance (fMRI) data. We use a physiologically inspired hemodynamic model and investigate a method to simultaneously infer the neural activity together with hidden state and the physiological parameter of the model. This joint estimation problem is still an open topic. In our work we use a Particle Filter accompanied with a kernel smoothing approach to address this problem within a general filtering framework. Simulation results show that the proposed method is a consistent approach and has a good potential to be enhanced for further fMRI data analysis.

Generation of total runoff hydrographs using a method derived from a digital filter algorithm

A reliable prediction of the total runoff hydrograph is necessary for water resources management. This study investigates two approaches to generate total runoff hydrograph by adding baseflow to direct runoff hydrographs. The first approach uses a method, derived from a digital filter algorithm for hydrograph separation, to generate baseflow hydrographs from direct runoff hydrographs. The method appears to perform well in producing the overall shape of the total runoff hydrographs and the acceptable mass balance errors for a year of water cycle. For application, the recession baseflow constant needs to be estimated reliably and the initial baseflow could be approximated to the long-term mean dry weather flow. The second approach assumes a constant baseflow rate. Although this approach is still capable of producing the overall hydrograph shape, it yields high mass balance errors in the total runoff hydrographs for both monthly and long-term periods. Further analysis shows that two-third of the mass balance errors are contributed from periods with direct runoff, implying that the constant baseflow assumption could introduce significant errors into the computations of total runoff hydrograph

Bloom filter based secure and anonymous DSR protocol in wireless ad hoc networks

Wireless ad hoc networks, especially in the hostile environment, are vulnerable to traffic analysis which allows the adversary to trace the routing messages and the sensitive data packets. Anonymity mechanism in ad hoc networks is a critical securing measure method employed to mitigate these problems. In this paper, we propose a novel secure and anonymous source routing protocol, called SADSR, based on Dynamic Source Routing (DSR) for wireless ad hoc networks. In the proposed scheme, we use the pseudonym, pseudonym based cryptography and the bloom filter to establish secure and anonymous routing in wireless ad hoc networks. Compared to other anonymous routing protocol, SADSR is not only anonymous but also the secure in the routing discover process and data transmission process.