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Algorithms, Volume 1, Issue 2 (December 2008), Pages 43-200

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Research

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Open AccessArticle A PTAS For The k-Consensus Structures Problem Under Squared Euclidean Distance
Algorithms 2008, 1(2), 43-51; doi:10.3390/a1020043
Received: 5 September 2008 / Revised: 1 September 2008 / Accepted: 9 October 2008 / Published: 9 October 2008
Cited by 1 | PDF Full-text (192 KB) | HTML Full-text | XML Full-text
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
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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, ... , gk, so as to minimize the sum of the distances between each of f1, f2, ... , fn to its nearest structural fragment in g1, ... , gk. In this paper we show a polynomial-time approximation scheme (PTAS) for the problem through a simple sampling strategy. Full article
Open AccessArticle Modeling Landscape Evapotranspiration by Integrating Land Surface Phenology and a Water Balance Algorithm
Algorithms 2008, 1(2), 52-68; doi:10.3390/a1020052
Received: 19 September 2008 / Accepted: 23 October 2008 / Published: 30 October 2008
Cited by 20 | PDF Full-text (863 KB) | HTML Full-text | XML Full-text
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
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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. Full article
(This article belongs to the Special Issue Sensor Algorithms)
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Open AccessArticle Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels
Algorithms 2008, 1(2), 183-200; doi:10.3390/a1020183
Received: 23 October 2008 / Revised: 23 November 2008 / Accepted: 5 December 2008 / Published: 18 December 2008
Cited by 5 | PDF Full-text (312 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
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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. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
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Review

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Open AccessReview A Review of Virtual Sensing Algorithms for Active Noise Control
Algorithms 2008, 1(2), 69-99; doi:10.3390/a1020069
Received: 29 September 2008 / Accepted: 29 October 2008 / Published: 3 November 2008
Cited by 29 | PDF Full-text (636 KB) | HTML Full-text | XML Full-text
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
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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. Full article
(This article belongs to the Special Issue Sensor Algorithms)
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Open AccessReview Mathematical Programming in Computational Biology: an Annotated Bibliography
Algorithms 2008, 1(2), 100-129; doi:10.3390/a1020100
Received: 16 September 2008 / Revised: 3 October 2008 / Accepted: 20 November 2008 / Published: 24 November 2008
Cited by 3 | PDF Full-text (287 KB) | HTML Full-text | XML Full-text
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
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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. Full article
Open AccessReview Machine Learning: A Crucial Tool for Sensor Design
Algorithms 2008, 1(2), 130-152; doi:10.3390/a1020130
Received: 31 October 2008 / Revised: 21 November 2008 / Accepted: 29 November 2008 / Published: 3 December 2008
Cited by 13 | PDF Full-text (354 KB) | HTML Full-text | XML Full-text
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
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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. Full article
(This article belongs to the Special Issue Sensor Algorithms)
Open AccessReview Autonomous Vehicles Navigation with Visual Target Tracking: Technical Approaches
Algorithms 2008, 1(2), 153-182; doi:10.3390/a1020153
Received: 31 August 2008 / Revised: 22 November 2008 / Accepted: 28 November 2008 / Published: 15 December 2008
Cited by 10 | PDF Full-text (1113 KB) | HTML Full-text | XML Full-text
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
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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. Full article
(This article belongs to the Special Issue Sensor Algorithms)

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