37507961
A new model-based approach for industrial visual inspection
Abstract Two critical steps in industrial visual inspection are to design a flexible representation scheme and to match it with an effective inspection algorithm. Good representation and inspection algorithms can drastically speed up the subsequent matching process, and strengthen the feasibility of complicated on-line inspection. A novel pictorial representation called pattern attributed hypergraph (PAHG) and a new structural inspection algorithm are presented. The PAHG is used to describe the pictorial information of an input pattern, which includes attributed nodes, connectivity between nodes (intra-relations), regional primitives, and neighborhood relations between regions (inter-relations). The new structural inspection algorithm based on PAHG is proposed to match the inspected scene with the model efficiently. Consequently, the amount of matching computation is significantly reduced to roughly 1/ k 3 of the currently available method (Darwish and Jain, IEEE Trans. Pattern Analysis Mach. Intell. 10 , 56–68 (1988)), where k is the number of partitioned regions. The theoretical analysis and experimental results have shown the superiority of the proposed method.
34749384
Endocardial boundary detection using a neural network
Abstract Echocardiography has been widely used as a real-time non-invasive clinical tool to diagnose cardiac functions. Due to the poor quality and inherent ambiguity in echocardiograms, it is difficult to detect the myocardial boundaries of the left ventricle. Many existing methods are semi-automatic and detect cardial boundaries by serial computation which is too slow to be practical in real applications. In this paper, a new method for detecting the endocardial boundary by using a Hopfield neural network is proposed. Taking advantage of parallel computation and energy convergence capability in the Hopfield network, this method is faster and more stable for the detection of the endocardial border. Moreover, neither manual operations nor a priori assumptions are needed in this method. Experiments on several LV echocardiograms and clinical validation have shown the effectiveness of our method in these patient studies.
2128704
Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using micorarray GeneChip®, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.
14465308
Online Web Cluster Capacity Estimation and Its Application to Energy Conservation
Designers of data centers and Web servers aim to make on-demand allocation of resources to clients in order to lower the deployment cost of hosted services. Moreover, they must also minimize operating costs, such as energy consumption, by matching service-capacity demand with resource supply. However, since the term "capacity" is typically defined vaguely or inadequately, it is difficult to assess resource needs and, hence, servers, which are several times larger than needed at runtime, are usually deployed. The time-varying nature of the workload model further complicates the problem and necessitates an online capacity-estimation solution. To address this overprovisioning problem, we first define the capacity of a server cluster as the sustainable throughput subject to a request retransmission ratio constraint and then analyze different approaches to capacity estimation in a running system. Various capacity-estimation mechanisms, such as offline benchmarking and CPU-utilization evaluation, are discussed and compared with our queue-monitoring method. We employ several different data-collection methods (application instrumentation, user-space tools, simple network management protocol (SNMP), and kernel modules) to compare their effects on estimation accuracy. Of these, queue monitoring is found to provide a good and stable estimate of server capacity. To validate this finding, we propose a simple cluster- resizing mechanism and evaluate the energy-conservation performance. A good combination of data collection and online capacity estimation is found to make significantly more energy savings than traditional approaches (that is, static estimation and scheduled capacity). Our experimental results show that more than 40 percent of energy can be saved for regular daily usage patterns without any prior knowledge of the workload and that long start-up and shutdown delays affect energy savings considerably.
11904027
Automated fault localization with statistically suspicious program states
A number of fault localization techniques have been developed to reduce the time in manually debugging a faulty program. The technique of code coverage [8] has been recognized by its effectiveness in identifying suspicious statements that may contain the fault. However, a programmer still needs to manually examine each variable referenced in a suspicious statement and such a process can become extremely time-consuming if this suspicious statement is part of a loop. In this paper, we propose a novel technique called state coverage to significantly reduce the time in examining variables. We first insert a set of checkpoints to record the state of each variable referenced in a branching statement. We next execute the faulty program by a test suite consisting of both passed and failed cases. A state is statistically considered to be more suspicious if it appears more in failed cases and less in passed cases. We implemented both code coverage and state coverage in a debugging tool and used a commonly-used benchmark consisting of 58 faulty programs to evaluate their performance. For 34% of these programs, their faults are within 20 statement steps of the most suspicious statement identified by code coverage. By adding state coverage and breaking at the most suspicious state, we increase this ratio to 64%, an 88% performance improvement. Finally, we also explain a few cases in which both state coverage and code coverage cannot perform well.
13950166
Disease-Oriented Evaluation of Dual-Bootstrap Retinal Image Registration
This paper presents a disease-oriented evaluation of two recent retinal image registration algorithms, one for aligning pairs of retinal images and one for simultaneously aligning all images in a set. Medical conditions studied include diabetic retinopathy, vein occlusion, and both dry and wet age-related macular degeneration. The multi-image alignment worked virtually flawlessly, missing only 2 of 855 images. Pairwise registration, the Dual-Bootstrap ICP algorithm, worked nearly as well, successfully aligning 99.5% of the image pairs having a sufficient set of common features and 78.5% overall. Images of retinas having an edema and pairs of images taken before and after laser treatment proved the most difficult to register.