Table of Contents

Abstract: In this paper we consider a basic clustering problem that has uses in bioinformatics. A structural fragment is a sequence of l points in a 3D space, where l is a fixed natural number. Two structural fragments f₁ and f₂ are equivalent if and only if f₁ = f₂ x R + τ under some rotation R and translation τ . We consider the distance between two structural fragments to be the sum of the squared Euclidean distance between all corresponding points of the structural fragments. Given a set of n structural fragments, we consider the problem of finding k (or fewer) structural fragments g₁, g₂, ... , g_k, so as to minimize the sum of the distances between each of f₁, f₂, ... , f_n to its nearest structural fragment in g₁, ... , g_k. In this paper we show a polynomial-time approximation scheme (PTAS) for the problem through a simple sampling strategy.

Abstract: The main objective of this study is to present an improved modeling technique called Vegetation ET (VegET) that integrates commonly used water balance algorithms with remotely sensed Land Surface Phenology (LSP) parameter to conduct operational vegetation water balance modeling of rainfed systems at the LSP’s spatial scale using readily available global data sets. Evaluation of the VegET model was conducted using Flux Tower data and two-year simulation for the conterminous US. The VegET model is capable of estimating actual evapotranspiration (ETa) of rainfed crops and other vegetation types at the spatial resolution of the LSP on a daily basis, replacing the need to estimate crop- and region-specific crop coefficients.

Abstract: Traditional local active noise control systems minimise the measured acoustic pressure to generate a zone of quiet at the physical error sensor location. The resulting zone of quiet is generally limited in size and this requires the physical error sensor be placed at the desired location of attenuation, which is often inconvenient. To overcome this, a number of virtual sensing algorithms have been developed for active noise control. Using the physical error signal, the control signal and knowledge of the system, these virtual sensing algorithms estimate the error signal at a location that is remote from the physical error sensor, referred to as the virtual location. Instead of minimising the physical error signal, the estimated error signal is minimised with the active noise control system to generate a zone of quiet at the virtual location. This paper will review a number of virtual sensing algorithms developed for active noise control. Additionally, the performance of these virtual sensing algorithms in numerical simulations and in experiments is discussed and compared.

Abstract: The field of computational biology has experienced a tremendous growth in the past 15 years. In this bibliography, we survey some of the most significant contributions that were made to the field and which employ mathematical programming techniques, while giving a broad overview of application areas of modern computational molecular biology. The areas include sequence analysis, microarrays, protein structure and function, haplotyping and evolutionary distances.

Abstract: Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

Abstract: This paper surveys the developments of last 10 years in the area of vision based target tracking for autonomous vehicles navigation. First, the motivations and applications of using vision based target tracking for autonomous vehicles navigation are presented in the introduction section. It can be concluded that it is very necessary to develop robust visual target tracking based navigation algorithms for the broad applications of autonomous vehicles. Then this paper reviews the recent techniques in three different categories: vision based target tracking for the applications of land, underwater and aerial vehicles navigation. Next, the increasing trends of using data fusion for visual target tracking based autonomous vehicles navigation are discussed. Through data fusion the tracking performance is improved and becomes more robust. Based on the review, the remaining research challenges are summarized and future research directions are investigated.

Abstract: A new algorithm for divisive hierarchical clustering of chemical compounds based on 2D structural fragments is suggested. The algorithm is deterministic, and given a random ordering of the input, will always give the same clustering and can process a database up to 2 million records on a standard PC. The algorithm was used for classification of 1,183 antibiotics mixed with 999,994 random chemical structures. Similarity threshold, at which best separation of active and non active compounds took place, was estimated as 0.6. 85.7% of the antibiotics were successfully classified at this threshold with 0.4% of inaccurate compounds. A .sdf file was created with the probe molecules for clustering of external databases.