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Neophytou S.N.,University of Nicosia | Neophytou S.N.,Research Center for Intelligent Systems and Networks | Michael M.K.,Research Center for Intelligent Systems and Networks | Michael M.K.,University of Cyprus
IEEE Transactions on Very Large Scale Integration (VLSI) Systems | Year: 2012

While defect oriented testing in digital circuits is a hard process, detecting a modeled fault more than one time has been shown to result in high defect coverage. Previous work shows that such test sets, known as multiple detect or n-detect test sets, are of increased quality for a number of common defects in deep sub-micrometer technologies. Method for multiple detect test generation usually produce fully specified test patterns. This limits their usage in a number of important applications such as low power test and test compression. This work proposes a systematic methodology for identifying a large number of bits that can be unspecified in a multiple detect (n-detect) test set, while preserving the original fault coverage. The experimental results demonstrate that the number of specified bits in, even compact, n-detect test sets can be significantly reduced without any impact on the n-detect property. Additionally, in many cases, the size of the test set is reduced. © 2011 IEEE. Source


Kyriakides A.,University of Cyprus | Kyriakides A.,Research Center for Intelligent Systems and Networks | Kastanos E.,University of Nicosia | Hadjigeorgiou K.,University of Cyprus | And 2 more authors.
Optics InfoBase Conference Papers | Year: 2013

Bacterial identification is one of the applications for which classification using Raman spectra has proved to be successful. In this paper, we propose the use of Rank Order Kernels to classify Raman spectra in order to identify bacterial samples. Rank Order Kernels are two-dimensional image functions. The first step in the process transforms each Raman spectrum to a two-dimensional image. This is achieved by splitting the spectra into segments and calculating the ratio between the mean value of each and every other segment. The resulting two-dimensional matrix of ratios for each Raman spectrum is the image processed by the Rank Order Kernels. A similarity metric is used with a nearest neighbor algorithm for classification. The metric is based on rank order kernels. Our results show that the rank order kernel method is comparable in accuracy to other previously-used methods. © 2013 OSA-SPIE. Source


Kyriakides A.,University of Cyprus | Kyriakides A.,Research Center for Intelligent Systems and Networks | Kastanos E.,University of Nicosia | Hadjigeorgiou K.,University of Cyprus | And 2 more authors.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2011

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. The first stage in the classification of Raman spectra is commonly some form of preprocessing. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose the use of Support Vector Machines with a novel correlation kernel. Results, obtained from the analysis of Raman spectra of bacteria, illustrate that the correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing, even on highly-noisy data obtained using inexpensive equipment. In addition, the performance does not degrade when applied to distinct test sets, a key feature of a clinically viable diagnostic application of Raman Spectroscopy. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). Source


Lambrou T.P.,Research Center for Intelligent Systems and Networks | Panayiotou C.G.,Research Center for Intelligent Systems and Networks
International Journal of Robotics Research | Year: 2013

Mixed Wireless Sensor Network (WSN) is a network that consists of static and mobile sensor nodes. This article presents a collaborative framework where a team of autonomous mobile sensor nodes navigate through a sparse network with static sensors to improve the overall area coverage and search for events that may have occurred in areas not monitored by the static network. The mobile sensor nodes have limited communication and sensing ranges and collaborate to autonomously and dynamically decide their trajectories to enhance the area coverage, avoiding obstacles and collisions and adapting to new information such as failures of static nodes. In the context of the proposed framework, one can address various trade-offs. Examples include the trade off between the area coverage and the energy cost in terms of traveled distance and the one between the area coverage and information exchange among the mobile nodes. Furthermore, the proposed framework can be used to address spatially adaptive sampling. Finally, the proposed framework has been evaluated under different scenarios and has been shown to perform very well. © The Author(s) 2013. Source


Kyriakides A.,University of Cyprus | Kyriakides A.,Research Center for Intelligent Systems and Networks | Kastanos E.,University of Nicosia | Hadjigeorgiou K.,University of Cyprus | And 2 more authors.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2013

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. In this paper, we propose the use of Rank Order Kernels to classify bacterial samples. Rank Order Kernels are two-dimensional image functions which operate on two-dimensional images. The first step in the classification therefore, is to transform the Raman spectra to two-dimensional images. This is achieved by splitting the spectra into segments and calculating the ratio between the mean value of each and every other segment. This creates a two-dimensional matrix of ratios for each Raman spectrum. A similarity metric based on rank order kernels operating on the two-dimensional matrices is then used with a nearest neighbor algorithm for classification. Our results show that this method is comparable in accuracy to other methods which were used previously for the same data set. © 2013 Copyright SPIE. Source

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