Structural characterization of classical and memristive circuits with purely imaginary eigenvalues

The hyperbolicity problem in circuit theory concerns the existence of purely imaginary eigenvalues (PIEs) in the linearization of the time-domain description of the circuit dynamics. In this paper we characterize the circuit configurations which, in a strictly passive setting, yield purely imaginary eigenvalues for all values of the capacitances and inductances. Our framework is based on branch-oriented, semistate (differential-algebraic) circuit models which capture explicitly the circuit topology, and uses several notions and results from digraph theory. So-called P-structures arising in the analysis turn out to be the key element supporting our results. The analysis is shown to hold not only for classical (RLC) circuits but also for nonlinear circuits including memristors and other mem-devices

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Kriptolojik uygulamalar için FPGA tabanlı yeni kaotik osilatörlerin ve gerçek rasgele sayı üreteçlerinin tasarımı ve gerçeklenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu tez çalışmasında, gerçek zamanlı, yüksek çalışma frekansı ve bit üretim hızına sahip Gerçek Rasgele Sayı Üreteçleri (GRSÜ), FPGA tabanlı kaotik osilatörler kullanılarak tasarlanmış ve gerçekleştirilmiştir. Bu amaçla tezin ilk aşamasında, çeşitli sistem parametrelerinin karşılaştırılması ve değerlendirilmesi amacıyla iki farklı kaotik sistem dört farklı nümerik diferansiyel denklem çözüm metodu ile modellenerek sistemlerin dinamik davranışları incelenmiş ve kaos analizleri yapılmıştır. İkinci aşamada, seçilen kaotik sistemler bir ECAD programında şematik giriş yapılarak analog devre elemanları ile modellenmiştir. Nümerik benzetim sonuçları ile ECAD benzetim sonuçları karşılaştırılmıştır. Elde edilen sonuçlara göre analog elemanlar kullanılarak yapılan ECAD benzetimi ile Matlab destekli nümerik model sonuçları birbiri ile uyumlu değerler üretmiştir. Sonraki aşamada, kaotik sistemler dört farklı diferansiyel denklem çözüm metotlarından yararlanılarak, 32-bit IEEE 754-1985 kayan noktalı sayı standardında VHDL programlama dili ile FPGA üzerinde modellenmiştir. Tasarımlar Virtex–6 ailesi XC6VLX550T-2FF1759 çipi için Xilinx ISE Design Tools 14.2 benzetim programı kullanılarak sentezlenmiştir. Elde edilen sonuçlara göre FPGA-tabanlı kaotik osilatörlerin maksimum çalışma frekansları yaklaşık olarak 390-464 MHz arasında değişmektedir. Buna göre kaotik osilatör ünitesi 1 milyon veriyi 46 ms gibi çok kısa bir sürede hesaplayabilmektedir. Bu aşamada, FPGA tabanlı ünitelerin ürettiği sonuçların doğruluğunu test etmek amacıyla RMSE yöntemi kullanılarak hassasiyet analizleri de yapılmıştır. Dördüncü aşamada, FPGA-tabanlı örnek kaotik sistemler kullanılarak GRSÜ tasarımı gerçekleştirilmiştir. Genel olarak iki farklı kaotik sistem, kaotik osilatör tasarımında dört ayrı algoritma ve kuantalama için üç değişik yöntem sunularak toplamda 24 farklı gerçek rasgele sayı üreteci ünitesi tasarlanmıştır. Tasarımlardan elde edilen sonuçlara göre, ünitelerin maksimum çalışma frekansları 339-401 MHz ve bit üretim hızları 53-132 Mbit/s arasında değişmektedir. Son aşamada, FPGA tabanlı GRSÜ'den elde edilen sayı dizileri FIPS-140-1 ve NIST-800-22 istatistiksel rasgelelik testleri kullanılarak test edilmiş ve tüm testlerden başarılı olmuştur.In this thesis, real-time True Random Number Generators (TRNGs) with high operating frequency and bit generation rate have been designed and implemented using FPGA-based chaotic oscillators. In the first stage, two separate chaotic systems have been determined and their dynamical behavioral and chaotic analysis have been investigated to compare various system parameters using by four diverse numerical differential equation solution methods. In the second stage, the chaotic systems have been modelled using analog components in an ECAD program. After that numerical and ECAD simulation results have been compared and the results obtained from each simulation proves that both approaches have produced compatible outcomes. In the next stage, the chaotic systems have been modelled in VHDL in 32-bit IEEE 754-1985 floating point number standard using by four diverse numerical differential equation solution methods. The designs have been synthesized for Virtex–6 using Xilinx ISE Design Tools 14.2. According to the syntheses results, the maximum operating frequency of the FPGA-based chaotic oscillators varies between 390 MHz and 464 MHz. Accordingly, the chaotic oscillator unit has been able to calculate 1 million data sets in 46 ms. In this stage, in order to test accuracy of results produced by FPGA-based units, the sensitivity analysis have been also performed by employing RMSE method. In the fourth stage, TRNG designs have been implemented using FPGA-based chaotic systems. 24 different TRNG units have been designed and implemented by employing two distinct chaotic systems, four different algorithms in the design of the chaotic oscillators and three diverse quantification methods. According to the results, operating frequency of the units varies between 339 MHz and 401 MHz and the bit-rates varies between 53 Mbit/s and 132 Mbit/s

Theoretical Approaches in Non-Linear Dynamical Systems

From Preface: The 15th International Conference „Dynamical Systems - Theory and Applications” (DSTA 2019, 2-5 December, 2019, Lodz, Poland) gathered a numerous group of outstanding scientists and engineers who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without great effort of the staff of the Department of Automation, Biomechanics and Mechatronics of the Lodz University of Technology. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our event was attended by over 180 researchers from 35 countries all over the world, who decided to share the results of their research and experience in different fields related to dynamical systems. This year, the DSTA Conference Proceedings were split into two volumes entitled „Theoretical Approaches in Non-Linear Dynamical Systems” and „Applicable Solutions in Non-Linear Dynamical Systems”. In addition, DSTA 2019 resulted in three volumes of Springer Proceedings in Mathematics and Statistics entitled „Control and Stability of Dynamical Systems”, „Mathematical and Numerical Approaches in Dynamical Systems” and „Dynamical Systems in Mechatronics and Life Sciences”. Also, many outstanding papers will be recommended to special issues of renowned scientific journals.Cover design: Kaźmierczak, MarekTechnical editor: Kaźmierczak, Mare

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis