Olli Nevalainen

Optimization of the movements of a component placement machine

Abstract The efficient operation of an automatic electronic component placement machine ( Panasert RH) is considered. The machine inserts electronic components into predefined places on a printed circuit board. The components are supplied from a set of feeders each containing a sequence of identical components. We treat the problem as two different sequencing problems. The optimal insertion sequence for a fixed feeder setting is obtained by considering the problem as a three-dimensional asymmetric traveling salesman problem. The optimal permutation of the components in the feeders for a fixed insertion sequence can be formulated as a quadratic assignment problem. The overall problem is solved heuristically and this approach brings suboptimal, but in practice good enough component insertion sequences and feeder permutations.

Compression of Digital Images by Block Truncation Coding: A Survey

Block truncation coding (BTC) is a lossy moment preserving quantization method for compressing digital gray-level images. Its advantages are simplicity, fault tolerance, the relatively high compression efficiency and good image quality of the decoded image. Several improvements of the basic method have been recently proposed in the literature. In this survey we will study the basic algorithm and its improvements by dividing it into three separate tasks; performing quantization, coding the quantization data and cling the bit plane. Each phase of the algorithm will be analyzed separately. On the basis of the analysis, a combined BTC algorithm will be proposed and the comparisons to the standard JPEG algoritbm will be made

Identification of Novel IL-4/Stat6-Regulated Genes in T Lymphocytes

IL-4, primarily produced by T cells, mast cells, and basophiles, is a cytokine which has pleiotropic effects on the immune system. IL-4 induces T cells to differentiate to Th2 cells and activated B lymphocytes to proliferate and to synthesize IgE and IgG1. IL-4 is particularly important for the development and perpetuation of asthma and allergy. Stat6 is the protein activated by signal transduction through the IL-4R, and studies with knockout mice demonstrate that Stat6 is critical for a number of IL-4-mediated functions including Th2 development and production of IgE. In the present study, novel IL-4- and Stat6-regulated genes were discovered by using Stat6−/− mice and Affymetrix oligonucleotide arrays. Genes regulated by IL-4 were identified by comparing the gene expression profile of the wild-type T cells induced to polarize to the Th2 direction (CD3/CD28 activation + IL-4) to gene expression profile of the cells induced to proliferate (CD3/CD28 activation alone). Stat6-regulated genes were identified by comparing the cells isolated from the wild-type and Stat6−/− mice; in this experiment the cells were induced to differentiate to the Th2 direction (CD3/CD28 activation + IL-4). Our study demonstrates that a number a novel genes are regulated by IL-4 through Stat6-dependent and -independent pathways. Moreover, elucidation of kinetics of gene expression at early stages of cell differentiation reveals several genes regulated rapidly during the process, suggesting their importance for the differentiation process.

Genetic Algorithms for Large-Scale Clustering Problems

We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced here. The hybridization of the genetic algorithm and k-means algorithm is discussed.

Identification of Novel Genes Regulated by IL-12, IL-4, or TGF-β during the Early Polarization of CD4+ Lymphocytes

Th1 and Th2 cells arise from a common precursor cell in response to triggering through the TCR and cytokine receptors for IL-12 or IL-4. This leads to activation of complex signaling pathways, which are not known in detail. Disturbances in the balance between type 1 and type 2 responses can lead to certain immune-mediated diseases. Thus, it is important to understand how Th1 and Th2 cells are generated. To clarify the mechanisms as to how IL-12 and IL-4 induce Th1 and Th2 differentiation and how TGF-β can inhibit this process, we have used oligonucleotide arrays to examine the early polarization of Th1 and Th2 cells in the presence and absence of TGF-β. In addition to genes previously implicated in the process, we have identified 20 genes with various known and unknown functions not previously associated with Th1/2 polarization. We have also further determined which genes are targets of IL-12, IL-4, and TGF-β regulation in the cells induced to polarize to Th1 and Th2 directions. Interestingly, a subset of the genes was coregulated by IL-12 or IL-4 and TGF-β. Among these genes are candidates that may modulate the balance between Th1 and Th2 responses.

Accurate segmentation of the breast region from digitized mammograms

The segmentation of a digital mammogram into the breast region and the background is a necessary prerequisite in computer-assisted diagnosis of mammograms. By the exclusion of the background region, the accuracy of the analysis is increased and the running-time is decreased. The algorithm which segments the breast region from the background should be fully automated and give correct results for all kinds of digitized mammograms, including low-quality images. In this paper we present such an algorithm based on histogram thresholding, morphological filtering and contour modeling. Quantitative test results indicate that the computed boundary follows the estimated boundary accurately.

A fast exact GLA based on code vector activity detection

This paper introduces a new method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA), which is a widely used method to construct a codebook in vector quantization. Reduced comparison search detects the activity of the code vectors and utilizes it on the classification of the training vectors. For training vectors whose current code vector has not been modified, we calculate distances only to the active code vectors. A large proportion of the distance calculations can be omitted without sacrificing the optimality of the partition. The new method is included in several fast GLA variants reducing their running times over 50% on average.

A comparison of lossless compression methods for medical images

In this work, lossless grayscale image compression methods are compared on a medical image database. The database contains 10 different types of images with bit rates varying from 8 to 16 bits per pixel. The total number of test images was about 3000, originating from 125 different patient studies. Methods used for compressing the images include seven methods designed for grayscale images and 18 ordinary general-purpose compression programs. Furthermore, four compressed image file formats were used. The results show that the compression ratios strongly depend on the type of the image. The best methods turned out to be TMW, CALIC and JPEG-LS. The analysis step in TMW is very time-consuming. CALIC gives high compression ratios in a reasonable time, whereas JPEG-LS is nearly as effective and very fast.

Quality classification of tandem mass spectrometry data

UNLABELLED Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.

Self-Adaptive Genetic Algorithm for Clustering

Clustering is a hard combinatorial problem which has many applications in science and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the optimal selection of which depends on the problem instance. We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the algorithm. In this way, the algorithm is able to find the most suitable combination of the available components. The method is robust and achieves results comparable to or better than a carefully fine-tuned non-adaptive GA.