Program comprehension is an important task in the software maintenance process. One of the challenges faced by Java developers is the inability to determine the correct number of class dependencies. the ability to rec...
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ISBN:
(纸本)9781467378635
Program comprehension is an important task in the software maintenance process. One of the challenges faced by Java developers is the inability to determine the correct number of class dependencies. the ability to recover class dependencies would help developers to understand the design of an existing system prior to modifying it. Many Java dependency analysis tools for this purpose have been proposed, but few are able to analyze the dependency types associated with Java bytecode. In this paper, we propose a reverse engineering tool to extract the dependencies from a compiled Java program. the tool provides a visualization of the recovered dependencies in a form that facilitates the developer's ability to examine the classes and class relationships in the software system. the resulting dependency extraction capability will also enhance software maintenance and evolution. the results of experiments conducted withthe intent of evaluating the proposed tool demonstrate both its accuracy and a few of its limitations.
the proceedings contain 35 papers. the topics discussed include: incorporating benchmark programming in the teaching of undergraduate computer architecture;a framework for integrating project-based learning into the c...
ISBN:
(纸本)9781479988105
the proceedings contain 35 papers. the topics discussed include: incorporating benchmark programming in the teaching of undergraduate computer architecture;a framework for integrating project-based learning into the curriculum for outcome based education;nuclear human research development in national institutes of technology;the effects of providing a space for students to progress with self-directed free study;development of GUI power system load flow analysis tool based on Newton Raphson method;the application of action research to enhance the ability of students to conduct project-based research;comparing measured performance and perceived learning in group tasks of a civil engineering technology course;educational data mining for prediction and classification of engineering students achievement;high resolution mapping of engineering subjects from multiple perspectives;the bilingual teaching practice of mechanical engineering measurements;overview of capstone project implementation in the faculty of electrical engineering, Universiti Teknologi MARA, Malaysia;and enhancing students' geometrical thinking levels through Van Hiele's phase-based geometer's sketchpad-aided learning.
Image denoising using sparse and redundant representation has drawn a lot of research attentions. For the existing denoising algorithms, the additive noise is always assumed to follow the Gaussian distribution. But in...
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ISBN:
(纸本)9781509000760
Image denoising using sparse and redundant representation has drawn a lot of research attentions. For the existing denoising algorithms, the additive noise is always assumed to follow the Gaussian distribution. But in many application cases, the noise is not Gaussian. In this paper, we address the image Laplace denoising problem, where the additive noise is Laplace. thus, our model is proposed by adopting the Bayesian MAP estimation theory. We operate this model on image patches and show how to solve it with linear programming. Our experimental results have shown good performance of our new method both in terms of peak signal-to-noise ratio (PSNR) and visually.
K-Means has been paid attention to many areas recently, however, it is easy to fall into local optimum and the outliers influence the final results. this paper proposes an improved method for k-means clustering. Diffe...
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ISBN:
(纸本)9781509000760
K-Means has been paid attention to many areas recently, however, it is easy to fall into local optimum and the outliers influence the final results. this paper proposes an improved method for k-means clustering. Different from the traditional k-means algorithms, in our algorithm both intracluster compactness and intercluster separation are considered in our new presented method. A new model is established for hard cluster assignments of k-means clustering. Our new method transforms the problem of clustering to an integer programming problem and genetic algorithm is introduced to update cluster assignments iteratively. Experiment results on UCI data sets have showed the potential performance improvement of our method.
In engineering design processes, there are many cases where feasibility has to be checked. Some applications, such as radiation therapy, can be formulated as linear feasibility problems. In addition, a feasible point ...
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ISBN:
(纸本)9781509000760
In engineering design processes, there are many cases where feasibility has to be checked. Some applications, such as radiation therapy, can be formulated as linear feasibility problems. In addition, a feasible point is required to start many other algorithms. In this paper, we propose an interior point method for linear feasibility problems. this algorithm is suitable for problems that are large and sparse. Convergence of the algorithm is proved. Numerical experiment results on a standard set of linear programming problems are reported. this set includes some large, hard, and highly degenerated cases. the numerical experiment shows that our proposed algorithm is efficient and robust.
Capturing the dynamics of neuronal activity across whole nervous systems at high temporal resolution has been a long-standing dream in neuroscience. While point-scanning microscopy methods provide the necessary 3D res...
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ISBN:
(纸本)9781467363891
Capturing the dynamics of neuronal activity across whole nervous systems at high temporal resolution has been a long-standing dream in neuroscience. While point-scanning microscopy methods provide the necessary 3D resolution, their volume acquisition rates are limited. Widefield microscopes on the other hand do not provide sufficient optical sectioning capability. We recently implemented two complementary fluorescence microscopy methods that allow for simultaneous whole-animal imaging of genetically encoded calcium indicator activity in C. elegans, and whole-brain readout in zebrafish larvae. While Wide-field Temporal Focusing Microscopy "sculpts" the spectral components of femtosecond laser pulses to achieve sectioning, Light Field Deconvolution Microscopy, a tomography related method, uses a microlens array to simultaneously capture spatial and angular information followed by computational reconstruction to acquire volumetric information from a single sensor exposure. Here, we discuss our recent results using both techniques for acquiring whole-brain functional imaging data at speeds up to tens of Hertz and near single cell resolution for small model organisms.
Comparing serially acquired fMRI scans is a typical way to detect functional brain changes in different conditions. However, this approach introduces additional variation on physical and physiological conditions, whic...
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ISBN:
(纸本)9781467363891
Comparing serially acquired fMRI scans is a typical way to detect functional brain changes in different conditions. However, this approach introduces additional variation on physical and physiological conditions, which results in substantial noise. To improve sensitivity and accuracy of signal detection in such highly noisy fMRI data, potentially important information should be incorporated. Here we propose a new significance indicator, the critical regularization value (CR-value), which detects significantly changed voxels by taking boththe magnitude of the voxel-wise signal variation and spatial smoothness into account. the CR-value allows voxels that survive in a stronger sparse constraint to be considered as more significant. We demonstrate our method using a simulation dataset and a real fMRI dataset collected from the previous study. the results show that CR-value more accurately detects the true activation than GLM P-value, Posterior Probability Maps (PPM) and the threshold Free Cluster Enhancement (TFCE) in noisy datasets.
the present article describes simulation results for single queue P/M/1/K model. Simulink has been used to create this model and evaluate its performance. the main purpose of the research made is to optimize costs of ...
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ISBN:
(纸本)9781467370165
the present article describes simulation results for single queue P/M/1/K model. Simulink has been used to create this model and evaluate its performance. the main purpose of the research made is to optimize costs of resources allocation for TCP traffic type at the same time striving to achieve the best packet loss probability. the optimization was made with dynamic programming method by using Bellman algorithm for simulated traffic with different self-similarity (Hurst) parameter and utilization coefficient values. the results of this work indicate that Hurst parameter affects greatly the costs of resources to be allocated. With estimation of Hurst parameter for network traffic these results can be used to estimate buffer memory capacity and ensure the specified value of packet loss probability. the research made also suggests to increase buffer memory capacity rather than channel bandwidth, whenever it is possible.
Wind speed fluctuates heavily and affects a smaller locality than other weather elements. Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of predic...
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Code similarity detection has been studied for several decades, which are prevailing categorized into attributecounting and structure-metric. Due to the one fold validity of attribute-counting for full replication, ma...
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ISBN:
(纸本)9789897581076
Code similarity detection has been studied for several decades, which are prevailing categorized into attributecounting and structure-metric. Due to the one fold validity of attribute-counting for full replication, mature systems usually use the GST string matching algorithm to detect code structure. However, the accuracy of GST is vulnerable to interference in code similarity detection. this paper presents a code similarity detection method combining string matching and sub-graph isomorphism. the similarity is calculated withthe GST algorithm. then according to the similarity, the system determines whether further processing withthe subgraph iIsomorphism algorithm is required. Extensive experimental results illustrate that our method significantly enhances the efficiency of string matching as well as the accuracy of code similarity detecting.
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