940 research outputs found
Analysis of Different Filters for Image Despeckling : A Review
Digital image acquisition and processing in clinical diagnosis plays a significant part. Medical images at the time of acquisition can be corrupted via noise. Removal of this noise from images is a challenging problem. The presence of signal dependent noise is referred as speckle which degrades the actual quality of an image. Considering, several techniques have been developed focused on speckle noise reduction. The primary purpose of these techniques was to improve visualization of an image followed by preprocessing step for segmentation, feature extraction and registration. The scope of this paper is to provide an overview of despeckling techniques
Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales
With the increasing interplay between experimental and computational
approaches at multiple length scales, new research directions are emerging in
materials science and computational mechanics. Such cooperative interactions
find many applications in the development, characterization and design of
complex material systems. This manuscript provides a broad and comprehensive
overview of recent trends where predictive modeling capabilities are developed
in conjunction with experiments and advanced characterization to gain a greater
insight into structure-properties relationships and study various physical
phenomena and mechanisms. The focus of this review is on the intersections of
multiscale materials experiments and modeling relevant to the materials
mechanics community. After a general discussion on the perspective from various
communities, the article focuses on the latest experimental and theoretical
opportunities. Emphasis is given to the role of experiments in multiscale
models, including insights into how computations can be used as discovery tools
for materials engineering, rather than to "simply" support experimental work.
This is illustrated by examples from several application areas on structural
materials. This manuscript ends with a discussion on some problems and open
scientific questions that are being explored in order to advance this
relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J.
Mater. Sc
An Analysis of Variation Between Cores For Intel Xeon Phi Knights Corner And Xeon Phi Knights Landing
As we move towards exascale computing, the efficiency of application performance and energy utilization, must be optimized by redefining architectural features and application performance analysis. This research analyzes the performance per core of 8 applications on Intel Xeon Phi Knights Corner (KNC) and Knights Landing (KNL) to determine if performance variation within cores can lead to performance and energy improvements. Our results showed that KNC architecture\u27s core vary in performance, leading to faster inner core performance as a result of memory characteristics and core utilization. It also shows that cores 17, 34, and 51 on the KNL architectures performs consistently slower than other cores, with core 0 performing either faster, slower or within the average performance time all the cores. A power performance study was then done utilizing different core configurations on the KNC. The results show that by targeting inner cores for applications that exhibit better inner core performance, a maximum energy reduction of 16.4% compared to a con- figuration using all cores was possible with its optimal thread configuration. Energy reduction was achieved with along with a 2% reduction in the fastest execution time of the same application. Our results also show how application characteristics lead to different core variation performances on KNC and KNL Xeon Phi architectures
SAR Image Edge Detection: Review and Benchmark Experiments
Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art
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