Semiconductor Laser Dynamics

This is a collection of 18 papers, two of which are reviews and seven are invited feature papers, that together form the Photonics Special Issue “Semiconductor Laser Dynamics: Fundamentals and Applications”, published in 2020. This collection is edited by Daan Lenstra, an internationally recognized specialist in the field for 40 years

Application of Physics Model in prediction of the Hellas Euro election results

In this paper we use chaos theory to predict the Hellenic Euro election results in the form of time series for Hellenic political parties New Democracy (ND), Panhellenic Socialistic Movement (PASOK), Hellenic Communistic Party (KKE) , Coalition of the Radical Left (SYRIZA) and (Popular Orthodox Rally) LAOS, using the properties of the reconstructed strange attrac-tor of the corresponding non linear system, creating a new scientific field called “DemoscopoPhysics”. For this purpose we found the optimal delay time, the correlation and embedding dimension with the method of Grassberger and Procassia. With the help of topological properties of the corresponding strange attractor we achieved up to a 60 time steps out of sample pre-diction of the public survey

18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XII

Physical reservoir computing with dynamical electronics

Since the advent of data-driven society, mass information generated from human activity and the natural environment has been collected, stored, processed, and then dispersed under conventional von Neumann architecture. However, further scaling the computing capability in terms of speed and power efficiency has been significantly slowed down in recent years due to the fundamental limits of transistors. To meet the increasingly demanding requirement for data-intensive computation, neuromorphic computing is a promising field taking the inspiration from the human brain, an extremely efficient biological computer, to develop unconventional computing paradigms for artificial intelligence. Reservoir computing, a recurrent neural network algorithm invented two decades ago, has received wide attention in the field of neuromorphic computing because of its unique recurrent dynamics and hardware-friendly implementation schemes. Under the concept of reservoir computing, hardware’s intrinsic physical behaviours can be explored as computing resources to keep the machine learning within the physical domain to improve processing efficiency, which is also known as physical reservoir computing. This thesis focuses on modelling and implementing physical reservoir computing based on dynamical electronics, along with its applications with sensory signals. First, the fundamental of the reservoir computing algorithm is introduced. Second, based on the reservoir algorithm and its functionalities, two different architectures for physically implementing reservoir computing, delay-based reservoir and parallel devices, are investigated to perform temporal signal processing. Thirdly, an efficient implementation architecture, namely rotating neurons reservoir, is developed. This novel architecture is evaluated in both theoretical analysis and experiments. An electrical prototype of the rotating neurons reservoir exhibits unique advantages such as resource-efficient implementation and low power consumption. More importantly, the theory of rotating neurons reservoir is highly universal, indicating that a rotational object embedded with dynamical elements can act as a reservoir computer

Mixed-Signal Neural Network Implementation with Programmable Neuron

This thesis introduces implementation of mixed-signal building blocks of an artificial neural network; namely the neuron and the synaptic multiplier. This thesis, also, investigates the nonlinear dynamic behavior of a single artificial neuron and presents a Distributed Arithmetic (DA)-based Finite Impulse Response (FIR) filter. All the introduced structures are designed and custom laid out

Microwave resonant sensors

Microwave resonant sensors use the spectral characterisation of a resonator to make high sensitivity measurements of material electromagnetic properties at GHz frequencies. They have been applied to a wide range of industrial and scientific measurements, and used to study a diversity of physical phenomena. Recently, a number of challenging dynamic applications have been developed that require very high speed and high performance, such as kinetic inductance detectors and scanning microwave microscopes. Others, such as sensors for miniaturised fluidic systems and non-invasive blood glucose sensors, also require low system cost and small footprint. This thesis investigates new and improved techniques for implementing microwave resonant sensor systems, aiming to enhance their suitability for such demanding tasks. This was achieved through several original contributions: new insights into coupling, dynamics, and statistical properties of sensors; a hardware implementation of a realtime multitone readout system; and the development of efficient signal processing algorithms for the extraction of sensor measurements from resonator response data. The performance of this improved sensor system was verified through a number of novel measurements, achieving a higher sampling rate than the best available technology yet with equivalent accuracy and precision. At the same time, these experiments revealed unforeseen applications in liquid metrology and precision microwave heating of miniature flow systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo