An Image Based PCB Fault Detection and Its Classification

The field of electronics is skyrocketing like never before. The habitat for the electronic components is a printed circuit board (PCB). With the advent of newer and finer technologies it has almost become impossible to detect the faults in a printed circuit board manually which consumes lot of manpower and time. This paper proposes a simple and cost effective method of fault diagnosis in a PCB using image processing techniques. In addition to fault detection and its classification this paper addresses various problems faced during the pre-processing phase. This paper overcomes the drawbacks of the previous works such as improper orientations of the image and size variations of the image. Basically image subtraction algorithm is used for fault detection. The most commonly occurring faults are concentrated in this work and the same are implemented using MATLAB tool

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article

Endless Data

Small and Medium Enterprises (SMEs), as well as micro teams, face an uphill task when delivering software to the Cloud. While rapid release methods such as Continuous Delivery can speed up the delivery cycle: software quality, application uptime and information management remain key concerns. This work looks at four aspects of software delivery: crowdsourced testing, Cloud outage modelling, collaborative chat discourse modelling, and collaborative chat discourse segmentation. For each aspect, we consider business related questions around how to improve software quality and gain more significant insights into collaborative data while respecting the rapid release paradigm

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect

Development, Test and Evaluation of Autonomous Unmanned Aerial Systems in A Simulated Wide Area Search Scenario: An Implementation of the Autonomous Systems Reference Architecture

The implementation and testing of autonomous and cooperative unmanned systems is challenging due to the inherent design complexity, infinite test spaces, and lack of autonomy specific measures. These challenges are limiting the USAF\u27s ability to deploy and take advantage of tactical and strategic advantages offered by these systems. This research instantiates an Autonomous System Reference Architecture (ASRA) on a Wide Area Search (WAS) scenario as a test bed for rapid prototyping and evaluation of autonomous and cooperative systems. This research aims to pro- vide a framework to evaluate the system’s ability to achieve mission and autonomy objectives, develop reusable autonomous behaviors, and develop reusable cooperative decision making algorithms. For this research and application to the WAS mission, metrics of autonomy were derived from literature requirements for autonomous systems implementing reactive architectures and control: responsiveness, robustness, and perception accuracy. Autonomous behaviors, to include more complex behaviors combining simple (atomic) behaviors were developed, and a variety of cooperative decision rules were defined. The subsequent evaluation implemented a face centered cubic design of experiments over four scenarios including a single vehicle, and three levels of cooperation between two vehicles. Following a rigorous test plan, the tests were conducted in simulation implementing automated testing and expedited analysis. The test results were used to create a response surface model to characterize the system and conduct multiple response optimization to determine an optimal configuration that maximizes area searched, percent detected, and perception accuracy in a given target density

Integrating genetics with newborn metabolomics in infantile hypertrophic pyloric stenosis

Introduction Infantile hypertrophic pyloric stenosis (IHPS) is caused by hypertrophy of the pyloric sphincter muscle. Objectives: Since previous reports have implicated lipid metabolism, we aimed to (1) investigate associations between IHPS and a wide array of lipid-related metabolites in newborns, and (2) address whether detected differences in metabolite levels were likely to be driven by genetic differences between IHPS cases and controls or by differences in early life feeding patterns. Methods: We used population-based random selection of IHPS cases and controls born in Denmark between 1997 and 2014. We randomly took dried blood spots of newborns from 267 pairs of IHPS cases and controls matched by sex and day of birth. We used a mixed-effects linear regression model to evaluate associations between 148 metabolites and IHPS in a matched case-control design. Results The phosphatidylcholine PC(38:4) showed significantly lower levels in IHPS cases (P = 4.68 x 10(-8)) as did six other correlated metabolites (four phosphatidylcholines, acylcarnitine AC(2:0), and histidine). Associations were driven by 98 case-control pairs born before 2009, when median age at sampling was 6 days. No association was seen in 169 pairs born in 2009 or later, when median age at sampling was 2 days. More IHPS cases than controls had a diagnosis for neonatal difficulty in feeding at breast (P = 6.15 x 10(-3)). Genetic variants known to be associated with PC(38:4) levels did not associate with IHPS. Conclusions: We detected lower levels of certain metabolites in IHPS, possibly reflecting different feeding patterns in the first days of life.Peer reviewe