Cyberbroker: A lightweight web object collector

Centralized heavyweight collectors have been used to collect text-based web objects for building search engines. However, these collectors do not meet the need of collecting big web objects such as images and videos. We propose a lightweight mobile on-call object collector for the world wide web. Using this approach, we can deploy a collector easily on the site being indexed. We present a prototype system consisting of mobile Java collectors and search engines for collecting HTML/image objects. We provide analysis to find a lower bound in terms of total data transfer time required. This idealized scenario is complemented with another model based on implementation of the HTTP over TCP. The lower bound and the upper bound of the gains accrued are then validated using experiments. This revised version was published online in August 2006 with corrections to the Cover Date.     

Experiments on Surface Reconstruction for Partially Submerged Marine Structures

Over the past 10 years, significant scientific effort has been dedicated to the problem of three‐dimensional (3‐D) surface reconstruction for structural systems. However, the critical area of marine structures remains insufficiently studied. The research presented here focuses on the problem of 3‐D surface reconstruction in the marine environment. This paper summarizes our hardware, software, and experimental contributions on surface reconstruction over the past few years (2008–2011). We propose the use of off‐the‐shelf sensors and a robotic platform to scan marine structures both above and below the waterline, and we develop a method and software system that uses the Ball Pivoting Algorithm (BPA) and the Poisson reconstruction algorithm to reconstruct 3‐D surface models of marine structures from the scanned data. We have tested our hardware and software systems extensively in Singapore waters, including operating in rough waters, where water currents are around 1–2 m/s. We present results on construction of various 3‐D models of marine structures, including slowly moving structures such as floating platforms, moving boats, and stationary jetties. Furthermore, the proposed surface reconstruction algorithm makes no use of any navigation sensor such as GPS, a Doppler velocity log, or an inertial navigation system.     

Narrow passage sampling for probabilistic roadmap planning

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.     

A pseudo‐steady state model of the kinetics of laccase‐catalysed oxidation of aqueous phenol

A model has been developed to describe the kinetics of the oxidation of phenol by oxygen using laccase (EC 1.10.3.2) from Trametes versicolor as catalyst. The model incorporates an assumption, referred to as the pseudo‐steady state assumption, that at any instant during the reaction the enzyme achieves an approximate steady state distribution of its various forms around the catalytic cycle. The model was validated by comparing model predictions with experimental observations of phenol transformation and oxygen consumption over time. Excellent agreement was found between experimental data and model predictions. The pseudo‐steady state assumption had the advantage of reducing the complexity of the model equations to be solved while simultaneously maintaining the model's predictive ability. The pseudo‐steady state model was further simplified in accordance with specific initial conditions to provide a simplified method for the estimation of the model constants. Copyright © 2006 Society of Chemical Industry     

On the spectral density of a class of chaotic time series

The purpose of this paper is to show explicitly the spectral density function of the stationary stochastic process determined by a certain class of two-dimensional maps F_α defined below (α is a parameter in (0, 1)), the random variable φ(x, y) = x and the invariant probability described below. We first define the transformation T_α: [0, 1]←[0, 1] given by T _α(x) = {x/α if 0 ≤x < α and (α(x?α)/1 ?α) if α≤x≤ 1 where α∈ (0, 1) is a constant. The map T_α describes a model for a particle (or the probability of a certain kind of element in a given population) that moves around, in discrete time, in the interval [0, 1]. The results presented here can be stated either for T_α or for F_α but we prefer the latter. The results for T_α can be obtained from the more general setting described by F_α. The map F_α is defined from K = ([0, 1]× (0, α)) ∨ ([0, α]×[α, 1]) ?;R;² to itself and is given by F_α(x, y) = (T_α(x), G_α(x, y)) for (x, y) ∈K, where G _α(x, y) = {αy if 0 ≤x < α and α + ((1 ?α)/α)y if α≤x < 1. The spectral density function of the stationary process with probability ν (invariant for F_α and absolutely continuous with respect to the Lebesgue measure) Z_t = X_t + ξ_t = φ{F^t_α(X₀, Y₀)} + ξ_t for t∈Z where (X₀, Y₀) ∈R² and ξ_t}_t∈Z is a white noise process, is given explicitly (Theorem 1) by f _Z (λ) = f_X(λ) + (σ²_ξ/2π) = (1/2πvar(X_t))[γ{exp(iλ)}?C(0)] + (σ²/2π) for all λ∈[0, 2π), where var(X_t) = (α²?α + 1)(α²? 5α + 5){12(2 ?α)²}^?1, γ is given by Equation (2.10) of Proposition 5 and C(0) = (1 + α²?α³){3(2 ?α)}^?1. We also estimate the parameter α based on a time series.     

Liquid phase equilibrium of phenol extraction from bio-oil produced by biomass pyrolysis using thermodynamic models

Utilization of biomass as a new and renewable energy source is being actively conducted by various parties. One of the technologies for utilizing or converting biomass as an energy source is pyrolysis, to convert biomass into a more valuable product which is bio-oil. Bio-oil is a condensed liquid from the vapor phase of biomass pyrolysis such as coconut shells and coffee shells. Biomass composition consisting of hemicellulose, cellulose, and lignin will oxidize to phenol which is the main content in bio-oil. The total phenolic compounds contained in bio-oil are 47.03% (coconut shell) and 45% (coffee shell). The content of phenol compounds in corrosive bio-oils still quite high, the use of this bio-oil directly will cause various difficulties in the combustion system due to high viscosity, low calorific value, corrosivity, and instability. Phenol compounds have some benefits as one of the compounds for floor cleaners and disinfectants which are contained in bio-oil.The correlation between experimental data and calculations shows that the UNIQUAC Functional-group Activity Coefficients (UNIFAC) equilibrium model can be used to predict the liquid–liquid equilibrium in the phenol extraction process of the coconut shell pyrolysis bio-oil. While the Non-Random Two Liquid (NRTL) equilibrium model can be used to predict liquid–liquid equilibrium in the extraction process of phenol from bio-oil pyrolysis of coffee shells.     

Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics

Meatball is one of the favorite foods in Indonesia. For the economic reason (due to the price difference), the substitution of beef meat with pork can occur. In this study, FTIR spectroscopy in combination with chemometrics of partial least square (PLS) and principal component analysis (PCA) was used for analysis of pork fat (lard) in meatball broth. Lard in meatball broth was quantitatively determined at wavenumber region of 1018–1284 cm^− 1. The coefficient of determination (R²) and root mean square error of calibration (RMSEC) values obtained were 0.9975 and 1.34% (v/v), respectively. Furthermore, the classification of lard and beef fat in meatball broth as well as in commercial samples was performed at wavenumber region of 1200–1000 cm^− 1. The results showed that FTIR spectroscopy coupled with chemometrics can be used for quantitative analysis and classification of lard in meatball broth for Halal verification studies. The developed method is simple in operation, rapid and not involving extensive sample preparation.     

Global motion planning under uncertain motion, sensing, and environment map

Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space?B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.