While some physics educators have included computing in courses or have developed specialized courses for over 50 years, computational physics education has only slowly made inroads into the broader physics education ...
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While some physics educators have included computing in courses or have developed specialized courses for over 50 years, computational physics education has only slowly made inroads into the broader physics education community. Even now, when computation is arguably more important than ever in physics research and applications, it’s rare that a physics department offers more than a single course in the topic to its undergraduate students. There have been several times over the years when interest in a more global approach to computational physics education has surged, only to subside without attaining the goal that computing finally take an essential role in the education of undergraduate physicists. The author reviews some of the history of computational physics education, briefly discusses his experience with the program at Illinois State University, and suggests some direction for the future.
Approximate computing is claimed to be a powerful knob for alleviating the peak power and energy-efficiency issues. However, providing a consistent benchmark suit with diverse applications amenable to approximate comp...
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Approximate computing is claimed to be a powerful knob for alleviating the peak power and energy-efficiency issues. However, providing a consistent benchmark suit with diverse applications amenable to approximate computing is crucial to ensure fair and reproducible comparisons. This article makes an important attempt toward it in the form of the AxBench suite, which contains applications for CPUs, GPUs, and hardware design with necessary annotations to mark the approximable regions and output quality metrics. -Muhammad Shafique, Vienna University of Technology.
Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number o...
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Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast. In view of the steadfast growth of GEP and its importance to both the academia and industry, here a review on GEP is considered. In particular, this paper presents a comprehensive review on the recent progress of GEP. The state-of-the-art approaches of GEP, with enhanced designs from six aspects, i.e., encoding design, evolutionary mechanism design, adaptation design, cooperative coevolutionary design, constant creation design, and parallel design, are presented. The theoretical studies and intriguing representative applications of GEP are given. Finally, a discussion of potential future research directions of GEP is also provided.
An Integration of image processing and soft computing techniques for the image analysis plays a significant contribution in the field of image processing. One of the important class of image analysis techniques is ima...
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ISBN:
(纸本)9781467394178
An Integration of image processing and soft computing techniques for the image analysis plays a significant contribution in the field of image processing. One of the important class of image analysis techniques is image segmentation. There are applications in various fields some of them are sorting product in the industry, surveillance system in the security zones, biomedical processing, medical imaging, environmental predictions etc. In this paper the study of fuzzy C-means clustering method, watershed based image segmentation and Simulink design based image segmentation with some enhancement designalgorithm for the improvement of the overall performance of image segmentation is used. The main objective of this paper is to bring the conviction of utility of watershed, Simulink and FCM based segmentation for the applications in medical imaging, and other images where lots of spatial & inherent information is available, by considering their performance analysis metrics. This performance analysis is used to justify and decide which method is appropriate and more convincing for the effective analysis of more complex imaging system.
This paper investigates the feasibility of the distance measuring equipment (DME)-based alternative position, navigation, and timing architecture using the recent advances on DME/N (normal) signal processing technique...
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This paper investigates the feasibility of the distance measuring equipment (DME)-based alternative position, navigation, and timing architecture using the recent advances on DME/N (normal) signal processing techniques;an improved DME/N pulse waveform and learning-based multipath mitigation algorithm. This paper evaluates the achievable DMErange accuracy by using the advanced signal processing techniques, and provides the analysis on the required augmentation of ground DME stations in a selected test region in contiguous United States.
In the past few years, researchers have introduced several sorting algorithms to enhance time complexity, space complexity, and stability. A double hashing methodology first collects statistics about element distribut...
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In the past few years, researchers have introduced several sorting algorithms to enhance time complexity, space complexity, and stability. A double hashing methodology first collects statistics about element distribution and then maps between elements of the array and indexes based on the knowledge collected during the first hashing.
References to NP-completeness and NP-hardness are common in the computer science literature, but unfortunately they are often in error or misguided. This article explains the most widespread misconceptions, why they a...
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References to NP-completeness and NP-hardness are common in the computer science literature, but unfortunately they are often in error or misguided. This article explains the most widespread misconceptions, why they are wrong, and why we should care.
Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains w...
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Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.
Processing large complex networks like social networks or web graphs has attracted considerable interest. To do this in parallel, we need to partition them into pieces of roughly equal size. Unfortunately, previous pa...
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Processing large complex networks like social networks or web graphs has attracted considerable interest. To do this in parallel, we need to partition them into pieces of roughly equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for complex networks. Here we address this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. This way we exploit the hierarchical cluster structure present in many complex networks. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsest graph. The resulting system is both more scalable and achieves higher quality than state-of-theart systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. As an example, our algorithm partitions a web graph with 3.3 G edges in 16 seconds using 512 cores of a high-performance cluster while producing a high quality partition-none of the competing systems can handle this graph on our system.
The discrete Fourier transform (DFT) is the standard tool for spectral analysis in digital signal processing, typically computed using the fast Fourier transform (FFT). However, for real-time applications that require...
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The discrete Fourier transform (DFT) is the standard tool for spectral analysis in digital signal processing, typically computed using the fast Fourier transform (FFT). However, for real-time applications that require recalculating the DFT at each sample or over only a subset of the N center frequencies of the DFT, the FFT is far from optimal.
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