2D SLAM Quality Evaluation Methods

SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. The choice of the algorithm that might be used in every particular problem requires prior knowledge about advantages and disadvantages of each algorithm. This paper presents the approach for comparison of SLAM algorithms that allows to find the most accurate one. The accent of research is made on 2D SLAM algorithms and the focus of analysis is 2D map that is built after algorithm performance. Three metrics for evaluation of maps are presented in this pape

Methods for innovation projects risk evaluation

Starting an innovation project assumes to state some competitive objectives referring to the allocated budget, time limit for project’s ending and also to the quality and performance parameters of the new obtained product. Referring to the innovation project development, the risk of unfulfilling the stated competitive objectives referrers to the exceeding the project’s budget and terms, and also to unfitting in the quality and performance parameters established in the innovation project planning stage. The large diversity of risk sources can be expressed by the possibility of appearance of some unexpected variations of the cost, time and quality of the new products. The innovation projects risk is settled by the variations of the cost, time and quality objectives effective values comparing to the planned values. Those variations are determined by purely random factors. The innovation projects characterized by uniform variations of the cost, time and quality objectives effective values around the mean are considered to be under statistic control. Those projects’ risk may be quantified and the risk impact over the project can be limited. The innovation projects characterized by fluctuant variations of the cost, time and quality objectives effective values around the mean are considered to be out of statistic control. The aim of this paper is to present two categories of statistic methods for innovation projects risk quantifying. The first statistic methods that quantify the risk of unfitting the quantitative objectives referrers to the time risk, cost risk and the risk of unfitting established performance parameters. The second category of methods represents statistic methods that quantify the risk of unfitting the qualitative objectives of the projects – the risk of appearance major quality deficiencies.innovation project, risk evaluation, cost, time and quality objectives

Performance evaluation of DNA copy number segmentation methods

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. We have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling real SNP microarray data from genomic regions with known copy-number state. The original real data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. In this paper, we describe this framework and illustrate some of the benefits of the proposed data generation approach on a practical use case: a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons for the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. Availability: R package jointSeg: http://r-forge.r-project.org/R/?group\_id=156