Ön Gerilmeli Piezoelektrik Örtü Tabakası Ve Ön Gerilmeli Elastik Yarı Düzlemden Oluşan Sistemde Genelleştirilmiş Rayleigh Dalgalarının Dispersiyonu

Konferans Bildirisi -- Teorik ve Uygulamalı Mekanik Türk Milli Komitesi, 2013Conference Paper -- Theoretical and Applied Mechanical Turkish National Committee, 2013Bu çalışmada doğrusallaştırılmış dalga teorisi kullanılarak ön gerilmeli piezoelektrik tabaka ile örtülü ön gerilmeli yarı düzlem ortamında genelleştirilmiş Rayleigh dalgalarının dispersiyonu incelenmiştir.In this paper, generalized Rayleigh wave dispersion in a pre-stressed half-plane covered with a pre-stressed piezoelectric layer is studied using linearized wave theory

Optimum Power Allocation for Average Power Constrained Jammers in the Presense of Non-Gaussian Noise

Cataloged from PDF version of article.We study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman-Pearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results

Android malware detection: An eigenspace analysis approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method

Android Malware Detection Using Parallel Machine Learning Classifiers

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers

High Accuracy Android Malware Detection Using Ensemble Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3–99% detection accuracy with very low false positive rates

Mapping geographical inequalities in oral rehydration therapy coverage in low-income and middle-income countries, 2000–17

Background Oral rehydration solution (ORS) is a form of oral rehydration therapy (ORT) for diarrhoea that has the potential to drastically reduce child mortality; yet, according to UNICEF estimates, less than half of children younger than 5 years with diarrhoea in low-income and middle-income countries (LMICs) received ORS in 2016. A variety of recommended home fluids (RHF) exist as alternative forms of ORT; however, it is unclear whether RHF prevent child mortality. Previous studies have shown considerable variation between countries in ORS and RHF use, but subnational variation is unknown. This study aims to produce high-resolution geospatial estimates of relative and absolute coverage of ORS, RHF, and ORT (use of either ORS or RHF) in LMICs. Methods We used a Bayesian geostatistical model including 15 spatial covariates and data from 385 household surveys across 94 LMICs to estimate annual proportions of children younger than 5 years of age with diarrhoea who received ORS or RHF (or both) on continuous continent-wide surfaces in 2000–17, and aggregated results to policy-relevant administrative units. Additionally, we analysed geographical inequality in coverage across administrative units and estimated the number of diarrhoeal deaths averted by increased coverage over the study period. Uncertainty in the mean coverage estimates was calculated by taking 250 draws from the posterior joint distribution of the model and creating uncertainty intervals (UIs) with the 2·5th and 97·5th percentiles of those 250 draws. Findings While ORS use among children with diarrhoea increased in some countries from 2000 to 2017, coverage remained below 50% in the majority (62·6%; 12 417 of 19 823) of second administrative-level units and an estimated 6519000 children (95% UI 5 254000–7733 000) with diarrhoea were not treated with any form of ORT in 2017. Increases in ORS use corresponded with declines in RHF in many locations, resulting in relatively constant overall ORT coverage from 2000 to 2017. Although ORS was uniformly distributed subnationally in some countries, withincountry geographical inequalities persisted in others; 11 countries had at least a 50% difference in one of their units compared with the country mean. Increases in ORS use over time were correlated with declines in RHF use and in diarrhoeal mortality in many locations, and an estimated 52230 diarrhoeal deaths (36910–68860) were averted by scaling up of ORS coverage between 2000 and 2017. Finally, we identified key subnational areas in Colombia, Nigeria, and Sudan as examples of where diarrhoeal mortality remains higher than average, while ORS coverage remains lower than average. Interpretation To our knowledge, this study is the first to produce and map subnational estimates of ORS, RHF, and ORT coverage and attributable child diarrhoeal deaths across LMICs from 2000 to 2017, allowing for tracking progress over time. Our novel results, combined with detailed subnational estimates of diarrhoeal morbidity and mortality, can support subnational needs assessments aimed at furthering policy makers’ understanding of within-country disparities. Over 50 years after the discovery that led to this simple, cheap, and life-saving therapy, large gains in reducing mortality could still be made by reducing geographical inequalities in ORS coverage.publishedVersio

A New Android Malware Detection Approach Using Bayesian Classification

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach

Spectrally-efficient 168 Gb/s/λ WDM 64-QAM single-sideband Nyquist-subcarrier modulation with Kramers-Kronig direct-detection receivers

Due to their simple and cost-effective transceiver architecture, single-polarization and single-photodiode based direct-detection (DD) systems offer advantages for metropolitan area network and data-center interconnect applications. Single-sideband subcarrier modulation (SSB SCM) signaling with direct detection has the potential to achieve high information spectral density (ISD) but its performance can be significantly degraded by signal-signal beat interference (SSBI). The recently proposed Kramers-Kronig (KK) digital signal processing (DSP) scheme is effective in eliminating the SSBI penalty. Through the use of the KK scheme, we achieved 4 × 168 Gb/s wavelength division multiplexing DD SSB 64-QAM Nyquist-SCM signal transmission over 80 km of uncompensated standard single-mode fiber at a net ISD of up to 4.61 (b/s)/Hz. The joint optimization of the optical carrier-to-signal power ratio (CSPR) and the KK algorithm sampling rate is described