Blackbox macro-modeling of the nonlinearity based on Volterra series representation of X-parameters

Volterra series representation is a powerful mathematical model for nonlinear devices. However, the difficulties in determining higher-order Volterra kernels limited its broader applications. This paper proposed a systematic approach that enables a convenient extraction of Volterra kernels from X-parameters for the first time. Then the Vandermonde method is employed to separate different orders of Volterra kernels at the same frequency, which leads to a highly efficient extraction process. The proposed Volterra series representation based on X-parameters is further benchmarked for verification. The proposed new algorithm is very useful for the blackbox macro-modeling of nonlinear devices and systems. © 2014 IEEE.postprin

Secure Autonomous UAVs Fleets by Using New Specific Embedded Secure Elements

International audienc

Delay-Aware Resource Management Frameworks for Intelligent Vehicular Networks

Autonomous vehicles bring great societal benefits but also potential impact and disruption to road safety, traffic congestion, and driving behaviors. One important technology that is indispensable to the success of such systems is vehicular networks. Vehicular networks provide the backbone for ensuring communication and connectivity among vehicles, all crucial to ensuring road safety and traffic control efficiency. One key requirement of such networks is low latency and high reliability, both of which needed for supporting vehicular applications that are often real-time and QoS-sensitive in nature. In this study, we designed a framework that uses integrated emerging network technologies, namely Software-Defined Networking (SDN), Deep Neural Networks (DNN), and Disruption Tolerant Network (DTN), to improve vehicular network latency and reliability. To evaluate the performance of our framework, we used two simulations. In the first simulation, we relied on different scenarios to assess the performance of continuous transmission with and without DNN. We found that while using DNN, the continuous transmissions performed more efficiently. In the second simulation, we show that DTN improves data transmissions in networks with disconnections occurring due to physical barriers. We found that DTN preserves and improves the accuracy of the DNN results. Our approach has made the infrastructure intelligent enough to handle congestion in vehicular networks, thereby improving the latency and reliability of such networks

Classification of microarray gene expression cancer data by using artificial intelligence methods

Günümüzde bilgisayar teknolojilerinin gelişmesi ile birçok alanda yapılan çalışmaları etkilemiştir. Moleküler biyoloji ve bilgisayar teknolojilerinde meydana gelen gelişmeler biyoinformatik adlı bilimi ortaya çıkarmıştır. Biyoinformatik alanında meydana gelen hızlı gelişmeler, bu alanda çözülmeyi bekleyen birçok probleme çözüm olma yolunda büyük katkılar sağlamıştır. DNA mikroarray gen ekspresyonlarının sınıflandırılması da bu problemlerden birisidir. DNA mikroarray çalışmaları, biyoinformatik alanında kullanılan bir teknolojidir. DNA mikroarray veri analizi, kanser gibi genlerle alakalı hastalıkların teşhisinde çok etkin bir rol oynamaktadır. Hastalık türüne bağlı gen ifadeleri belirlenerek, herhangi bir bireyin hastalıklı gene sahip olup olmadığı büyük bir başarı oranı ile tespit edilebilir. Bireyin sağlıklı olup olmadığının tespiti için, mikroarray gen ekspresyonları üzerinde yüksek performanslı sınıflandırma tekniklerinin kullanılması büyük öneme sahiptir. DNA mikroarray’lerini sınıflandırmak için birçok yöntem bulunmaktadır. Destek Vektör Makinaları, Naive Bayes, k-En yakın Komşu, Karar Ağaçları gibi birçok istatistiksel yöntemler yaygın olarak kullanlmaktadır. Fakat bu yöntemler tek başına kullanıldığında, mikroarray verilerini sınıflandırmada her zaman yüksek başarı oranları vermemektedir. Bu yüzden mikroarray verilerini sınıflandırmada yüksek başarı oranları elde etmek için yapay zekâ tabanlı yöntemlerin de kullanılması yapılan çalışmalarda görülmektedir. Bu çalışmada, bu istatistiksel yöntemlere ek olarak yapay zekâ tabanlı ANFIS gibi bir yöntemi kullanarak daha yüksek başarı oranları elde etmek amaçlanmıştır. İstatistiksel sınıflandırma yöntemleri olarak K-En Yakın Komşuluk, Naive Bayes ve Destek Vektör Makineleri kullanılmıştır. Burada Göğüs ve Merkezi Sinir Sistemi kanseri olmak üzere iki farklı kanser veri seti üzerinde çalışmalar yapılmıştır. Sonuçlardan elde edilen bilgilere göre, genel olarak yapay zekâ tabanlı ANFIS tekniğinin, istatistiksel yöntemlere göre daha başarılı olduğu tespit edilmiştir

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates

A Performance Assessment Framework for Mobile Biometrics

This project aims to develop and explore a robust framework for assessing biometric systems on mobile platforms, where data is often collected in non-constrained, potentially challenging environments. The framework enables the performance assessment given a particular platform, biometric modality, usage environment, user base and required security level. The ubiquity of mobile devices such as smartphones and tablets has increased access to Internet-based services across various scenarios and environments. Citizens use mobile platforms for an ever-expanding set of services and interactions, often transferring personal information, and conducting financial transactions. Accurate identity authentication for physical access to the device and service is, therefore, critical to ensure the security of the individual, information, and transaction. Biometrics provides an established alternative to conventional authentication methods. Mobile devices offer considerable opportunities to utilise biometric data from an enhanced range of sensors alongside temporal information on the use of the device itself. For example, cameras and dedicated fingerprint devices can capture front-line physiological biometric samples (already used for device log-on applications and payment authorisation schemes such as Apple Pay) alongside voice capture using conventional microphones. Understanding the performance of these biometric modalities is critical to assessing suitability for deployment. Providing a robust performance and security assessment given a set of deployment variables is critical to ensure appropriate security and accuracy. Conventional biometrics testing is typically performed in controlled, constrained environments that fail to encapsulate mobile systems' daily (and developing) use. This thesis aims to develop an understanding of biometric performance on mobile devices. The impact of different mobile platforms, and the range of environmental conditions in use, on biometrics' accuracy, usability, security, and utility is poorly understood. This project will also examine the application and performance of mobile biometrics when in motion

Advanced Remote Attestation Protocols for Embedded Systems

Small integrated computers, so-called embedded systems, have become a ubiquitous and indispensable part of our lives. Every day, we interact with a multitude of embedded systems. They are, for instance, integrated in home appliances, cars, planes, medical devices, or industrial systems. In many of these applications, embedded systems process privacy-sensitive data or perform safety-critical operations. Therefore, it is of high importance to ensure their secure and safe operation. However, recent attacks and security evaluations have shown that embedded systems frequently lack security and can often be compromised and misused with little effort. A promising technique to face the increasing amount of attacks on embedded systems is remote attestation. It enables a third party to verify the integrity of a remote device. Using remote attestation, attacks can be effectively detected, which allows to quickly respond to them and thus minimize potential damage. Today, almost all servers, desktop PCs, and notebooks have the required hardware and software to perform remote attestation. By contrast, a secure and efficient attestation of embedded systems is considerably harder to achieve, as embedded systems have to encounter several additional challenges. In this thesis, we tackle three main challenges in the attestation of embedded systems. First, we address the issue that low-end embedded devices typically lack the required hardware to perform a secure remote attestation. We present an attestation protocol that requires only minimal secure hardware, which makes our protocol applicable to many existing low-end embedded devices while providing high security guarantees. We demonstrate the practicality of our protocol in two applications, namely, verifying code updates in mesh networks and ensuring the safety and security of embedded systems in road vehicles. Second, we target the efficient attestation of multiple embedded devices that are connected in challenging network conditions. Previous attestation protocols are inefficient or even inapplicable when devices are mobile or lack continuous connectivity. We propose an attestation protocol that particularly targets the efficient attestation of many devices in highly dynamic and disruptive networks. Third, we consider a more powerful adversary who is able to physically tamper with the hardware of embedded systems. Existing attestation protocols that address physical attacks suffer from limited scalability and robustness. We present two protocols that are capable of verifying the software integrity as well as the hardware integrity of embedded devices in an efficient and robust way. Whereas the first protocol is optimized towards scalability, the second protocol aims at robustness and is additionally suited to be applied in autonomous networks. In summary, this thesis contributes to enhancing the security, efficiency, robustness, and applicability of remote attestation for embedded systems