Controller of a new pulsed source for linac 4 (MEGADISCAP)

This document presents the implementation of a control system for a new multiple-stage pulsed current source converter. A new topology that has been proposed to overcome some limitations inherent to capacitor discharge converter is presented in detail and explained. Its implementation is described and the design considerations adopted are accounted for. Besides, a control strategy is proposed, which has been implemented using an existing control board with some modifications on the acquisition system. A prototype whose current and voltage are scaled down with respect to those required for the converters that will be used for CERN Booster injection with LINAC 4 has been built. This reduced scale system has been simulated taking into account the control system implementation. Finally, the topology operating principle has been validated, the results obtained with the scaled down prototype have been compared with simulations and the need for more hardware resources for the control system implementation has been demonstrated

GaN LIGHT EMISSION FOR CONTROL SYSTEM FEEDBACK

This work explores previous research showing the correlation of light emission to current and temperature in a gallium nitride (GaN) vertical diode to predict current within a power converter circuit. Use of light emissions to measure current would offer an improvement over present sensors, since light would not be affected by the EMI found in most switched power converters. Matrices of the light emissions at 370, 380, 390, 400, 440, and 550 nm wavelengths over a range of 0.2 A to 4 A and 20 °C to 110 °C were used to develop best-fit polynomials for each matrix. Two of these polynomials can then be utilized to derive a unique solution of current and temperature based on the light output at the distinct wavelengths. Lock-in amplifiers allowed the amplification of weak light signals without gain bandwidth product limitations. Future efforts will be to duplicate the lock-in amplifier as well as the current and temperature prediction with use of a microcontroller.Lieutenant, United States NavyApproved for public release; distribution is unlimited

Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Synthesis of Biological and Mathematical Methods for Gene Network Control

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Recursive algorithm for the control of output remnant of Preisach hysteresis operator

We study in this letter the control of hysteresis-based actuator systems where its remanence behavior (e.g., the remaining memory when the actuation signal is set to zero) must follow a desired reference point. We present a recursive algorithm for the output regulation of the hysteresis remnant behavior described by Preisach operators. Under some mild conditions, we prove that our proposed algorithm guarantees that the output remnant converges to a desired value. Simulation result shows the efficacy of our proposed algorithm

A geographically distributed bio-hybrid neural network with memristive plasticity

Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired and bio-hybrid architectures are just beginning to materialise. The use of memristive technologies in brain-inspired architectures for computing or for sensing spiking activity of biological neurons8 are only recent examples, however interlinking brain and electronic neurons through plasticity-driven synaptic elements has remained so far in the realm of the imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where memristors work as "synaptors" between rat neural circuits and VLSI neurons. The two fundamental synaptors, from artificial-to-biological (ABsyn) and from biological-to- artificial (BAsyn), are interconnected over the Internet. The bNN extends across Europe, collapsing spatial boundaries existing in natural brain networks and laying the foundations of a new geographically distributed and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure

Derivation of Power System Module Metamodels for Early Shipboard Design Explorations

The U.S. Navy is currently challenged to develop new ship designs under compressed schedules. These ship designs must necessarily incorporate emerging technologies for high power energy conversion in order to enable smaller ship designs with a high degree of electrification and next generation electrified weapons. One way this challenge is being addressed is through development of collaborative concurrent design environment that allows for design space exploration across a wide range of implementation options. The most significant challenge is assurance of a dependable power and energy service via the shipboard Integrated Power and Energy System (IPES). The IPES is largely made up of interconnected power conversion and distribution equipment with allocated functionalities in order to meet demanding Quality of Power, Quality of Service and Survivability requirements. Feasible IPES implementations must fit within the ship hull constraints and must not violate limitations on ship displacement. This Thesis applies the theory of dependability to the use of scalable metamodels for power conversion and distribution equipment within a collaborative concurrent design environment to enable total ship set-based design outcomes that result implementable design specifications for procurement of equipment to be used in the final ship implementation