A data security and validation framework of a SOA based system for management, storage, processing and visualization of data obtained from scientific experiments is proposed in this paper. The framework covers the thr...
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Neurons use electricity in order to communicate to each other. Due to numerous signals sent by neurons, there are oodles of electrical activity in the brain. Sensitive equipment like electroencephalogram (EEG) biosens...
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The purpose of the automatic conversion of scientific data into canonical format is to provide a link between raw representation of data and database schemas. On the basis of these concepts Web services are developed ...
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This paper is concerned with the design procedures of an automated testing tool, developed in Matlab®/Simulink® environment, that performs software verification during runtime on a PLC (Programmable Logic Co...
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The construction sector is one of the sectors that make energy efficiency a must. In almost every field related to the construction sector, there is a need for studies in which high performance can be achieved by incl...
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We show that elementary ACI10 unification is in P, even with constant restrictions. As a corollary, we prove that validity of quantified Horn formulae can be tested in O(n2) time. Solvability of elementary disunificat...
We show that elementary ACI10 unification is in P, even with constant restrictions. As a corollary, we prove that validity of quantified Horn formulae can be tested in O(n2) time. Solvability of elementary disunification problems modulo ACI10 is shown to be NP-hard.
Nowadays, quality is the most important attribute that creates value. It is considered as the competitive advantage of private and public organizations by which they differentiate their products or services. Quality a...
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Structural (manual or automated) testing today often overlooks typical programming faults because of inherent flaws in the simple criteria applied (e.g. branch or all-uses). Dedicated testing strategies that address s...
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Advanced Encryption Standard (AES), which is approved and published by Federal Information Processing Standard (FIPS), is a cryptographic algorithm that can be used to protect electronic data. The AES algorithm can be...
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ISBN:
(纸本)9783319009506
Advanced Encryption Standard (AES), which is approved and published by Federal Information Processing Standard (FIPS), is a cryptographic algorithm that can be used to protect electronic data. The AES algorithm can be programmed in software or hardware. This paper presents encryption time comparison of the AES algorithm on FPGA and computer. In the study, Verilog HDL and C programming language is used on the FPGA and computer, respectively. The AES algorithm with 128-bit input and key length 128-bit (AES-128) was simulated on Xilinx ISE Design Suite 13.3. It was observed that, the AES algorithm runs on the FPGA faster than on a computer. We measured the time of encryption on FPGA and computer. Encryption time is 390ns of AES on FPGA and 11 μs of AES on a computer.
In robust regression we often have to decide how many are the unusualobservations, which should be removed from the sample in order to obtain better fitting for the restof the observations. Generally, we use the basic...
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In robust regression we often have to decide how many are the unusualobservations, which should be removed from the sample in order to obtain better fitting for the restof the observations. Generally, we use the basic principle of LTS, which is to fit the majority ofthe data, identifying as outliers those points that cause the biggest damage to the robust ***, in the LTS regression method the choice of default values for high break down-point affectsseriously the efficiency of the estimator. In the proposed approach we introduce penalty cost fordiscarding an outlier, consequently, the best fit for the majority of the data is obtained bydiscarding only catastrophic observations. This penalty cost is based on robust design weights andhigh break down-point residual scale taken from the LTS estimator. The robust estimation is obtainedby solving a convex quadratic mixed integer programming problem, where in the objective functionthe sum of the squared residuals and penalties for discarding observations is minimized. Theproposed mathematical programming formula is suitable for small-sample data. Moreover, we conduct asimulation study to compare other robust estimators with our approach in terms of their efficiencyand robustness.
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