Linear Imperfections

This lecture gives an overview of the impacts on linear machine optics of machine imperfections due to incorrect field settings and misalignments. The effects of imperfections in dipole, quadrupole, and sextupole magnets are presented, along with beam observables and correction techniques that may be used to restore the nominal machine parameters. The main concepts of orbit correction are discussed in detail, because the principles underlying those techniques can be used for other corrections.Comment: 30 pages, contribution to the CAS - CERN Accelerator School: Beam Instrumentation, 2-15 June 2018, Tuusula, Finlan

Process Parameter Optimization with Numerical modelling and Experimentation design of Binder Jet Additive Manufacturing

Binder jetting technology is an additive manufacturing technology in which powder materials are binded together layer by layer forming the product from input CAD model. The process involves printing the product layer by layer, curing and sintering. The mechanical properties of 3D printed samples varies based on process parameters, hence there is a need to tune the process parameters for optimal characteristics. Three main parameters namely layer thickness, sintering time and sintering temperature were identified and the study focuses on the effect of parameters on dimensional accuracy and compressive strength of the samples. Full factorial experimental approach was used to conduct the experiments and analysis of variance was performed to determine the significance of parameters. Along with parameters optimization, feed forward back propagation artificial neural network model is developed to quantify the relationship between three parameters and compressive strength, the model is developed based on experimental data and validated with known data. Also, Compressive behavior of four lattice designs considered in the study were simulated by finite element analysis and numerical results were compared with experimental data in order to validate the finite element model. FE models of different lattice designs were developed from experimental test data using ANSYS and the simulated compressive behavior is compared to that experimental compression test results

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Reinforcement Learning for Photonic Component Design

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers (PhCGC) fabricated on a 220nm silicon on insulator (SOI) single etch platform. This fab-in-the-loop algorithm improves the insertion loss from 8.8 dB to 3.24 dB. The widest bandwidth designs produced using our fab-in-the-loop algorithm are able to cover a 150nm bandwidth with less than 10.2 dB of loss at their lowest point