X-Ray fluorescence (XRF) scanning of works of art is becoming an increasingly popular non-destructive analytical method. The high quality XRF spectra is necessary to obtain significant information on both major and mi...
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ISBN:
(纸本)9781509019304
X-Ray fluorescence (XRF) scanning of works of art is becoming an increasingly popular non-destructive analytical method. The high quality XRF spectra is necessary to obtain significant information on both major and minor elements used for characterization and provenance analysis. However, there is a trade-off between the spatial resolution of an XRF scan and the Signal-to-Noise Ratio (SNR) of each pixel's spectrum, due to the limited scanning time. In this paper, we propose an XRF image super-resolution method to address this trade-off, thus obtaining a high spatial resolution XRF scan with high SNR. We use a sparse representation of each pixel using a dictionary trained from the spectrum samples of the image, while imposing a spatial smoothness constraint on the sparse coefficients. We then increase the spatial resolution of the sparse coefficient map using a conventional super-resolution method. Finally the high spatial resolution XRF image is reconstructed by the high spatial resolution sparse coefficient map and the trained spectrum dictionary.
There has recently been increasing interests in using system virtualization to improve the dependability of HPC cluster systems. However, it is not cost-free and may come with some performance degradation, uncertain Q...
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There has recently been increasing interests in using system virtualization to improve the dependability of HPC cluster systems. However, it is not cost-free and may come with some performance degradation, uncertain QoS and loss of functionalities. Meanwhile, many virtualization-enabled features such as online maintenance and fault tolerance do not require virtualization being always on. This paper proposes a technique, called self-virtualization, that supports dynamically attaching and detaching a full-fledged virtual machine monitor (VMM) beneath an operating system, without disturbing applications thereon, and rid the system of potential overhead when the virtualization is not needed. This technique enables HPC clusters to reap most benefits from virtualization without sacrificing performance. This paper presents the design and implementation of Mercury, a working prototype based on Linux and Xen VMM. Our performance measurement shows that Mercury incurs very little overhead: about 0.2 ms to complete a mode switch, and negligible performance degradation compared to Linux.
In this paper a different approach to build an uninterruptible power supply (UPS) system is presented. The proposed scheme includes features such as high power factor, low total harmonic distortion and good dynamic re...
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In this paper a different approach to build an uninterruptible power supply (UPS) system is presented. The proposed scheme includes features such as high power factor, low total harmonic distortion and good dynamic response at the output voltage. This scheme has the desirable features of high efficiency, simple circuit and low cost compared to a traditional standalone multiple stages UPS with power factor correction. The circuit operation, analysis and experimental results of the proposed UPS scheme are presented. The UPS approach is a good solution for low power applications (&le 500 W).
Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is c...
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ISBN:
(数字)9798350384727
ISBN:
(纸本)9798350384734
Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.
Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applicati...
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We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces...
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ISBN:
(纸本)1595930515
We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces of the Uniform Resource Locators of such images as predictors. Experimental results tend to confirm that the accuracy of the predictions converges quickly to very high levels.
This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Rough Sets and Intelligent systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was h...
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ISBN:
(数字)9783319087290
ISBN:
(纸本)9783319087283
This book constitutes the refereed proceedings of the 23rd Australasian Joint Conference on Rough Sets and Intelligent systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short papers which were carefully reviewed and selected from 120 submissions and presented in two volumes. This volume contains the papers accepted for the conference RSEISP 2014, as well as the three invited papers presented at the conference. The papers are organized in topical sections on plenary lecture and tutorial papers; foundations of rough set theory; granular computing and covering-based rough sets; applications of rough sets; induction of decision rules - theory and practice; knowledge discovery; spatial data analysis and spatial databases; information extraction from images.
In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner...
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In this paper, we propose an iterative algorithm towards the automatic labeling of Twitter accounts in respect to thematic categories derived from DBpedia properties. We describe the rationale behind the selection of ...
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ISBN:
(纸本)9781509052479
In this paper, we propose an iterative algorithm towards the automatic labeling of Twitter accounts in respect to thematic categories derived from DBpedia properties. We describe the rationale behind the selection of these thematic categories, and discuss their evaluation assessment. Finally, we propose and analyze two generic and adaptable methodologies for discovering the necessary linked data resources for further enhancing the thematic description of Twitter accounts.
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