Cowpea sorter: An alternative to the manual cowpea sorting process

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, April 2019Cowpea traders find the manual sorting process of cowpeas laborious and time-consuming. Based on the volume of cowpeas and the proportion of damaged cowpeas, the process can span a time interval of about 6 hours. This project integrates the power of computer vision and convolutional neural networks to develop a solution for the cowpea trader to effectively segregate good cowpeas from the damaged ones.Ashesi Universit

Automated Microraft Array Platform for Immune Cell Assays and Cell Sorting

Immunology research and cell therapies have provided great advancements in recent years and have highlighted the need to understand the effects of single cell heterogeneity within immune cell populations. Single cell platforms currently used in immune cell analysis often include tedious manual work, are not able to measure immune cell function in a time-dependent manner, or are not selective. The following work describes the development of an automated microraft array platform for analyzing the function of individual immune cells, including helper T cells and chimeric antigen receptor T cells (CAR-T cells), and for the sorting of cells of interest. In this dissertation, an automated microraft array platform was developed and adapted to address the challenges seen in immune cell research. In Chapter 2, an automated microraft array platform was developed to assay thousands of single cells in parallel and sort individual cells of interest. The platform was used to assay single CD4+ T cells, isolate cells displaying proliferation in response to allogeneic cell stimulation, and sequence their T cell receptor genes. In Chapter 3, a next-generation magnetic microbead-based microraft array was developed as an alternative to nanoparticle-based microraft arrays. The microbead-based arrays were shown to substantially reduce fabrication time compared to nanoparticle-based microraft arrays and improve performance in imaging of fluorescently labeled cells. Chapter 4 focused on the development and application of the automated microraft array platform to assay CD19 CAR-T cells for cell-mediated cytotoxicity and isolate T cells of interest for gene expression analysis. CAR-T cells were shown to participate in serial-killing of target cells and T cells demonstrating high cytotoxicity were isolated for future gene expression analysis using single-cell multiplex qPCR. The findings presented in this dissertation demonstrate the capabilities of an automated microraft array platform and its uses in immunology research. The studies described in each chapter provided valuable insight into the behavior and phenotype of immune cells at the single cell level.Doctor of Philosoph

CMS level-1 trigger muon momentum assignment with machine learning

With the advent of the High-Luminosity phase of the LHC (HL-LHC), the instantaneous luminosity of the Large Hadron Collider at CERN will increase up to 7,5 10^34 cm^-2s^-1. Therefore, new algorithmic techniques for data acquisition and processing will be necessary, in preparation for a high pile-up environment that would eventually make the current electronics and trigger devices obsolete. Nowadays, Machine Learning techniques represent a promising alternative to this problem, as they make possible the selection of multiple information - collected by the detector - and build from them different models, able to predict with a certain efficiency fundamental physical quantities, including the transverse momentum pT. The analysis presented in this Master Thesis consists in the production of such models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the Barrel region of the CMS muon chambers, and compare the results with the pT assigned by the current CMS Level 1 Barrel Muon Track Finder (BMTF) trigger system

Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting

In optical belt sorting, accurate predictions of the bulk material particles’ motions are required for high-quality results. By implementing a multitarget tracker tailored to the scenario and deriving novel motion models, the predictions are greatly enhanced. The tracker’s reliability is improved by also considering the particles’ orientations. To this end, new estimators for directional quantities based on orthogonal basis functions are presented and shown to outperform the state of the art

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion

Bildfolgenbasierte Gewinnung und Nutzung partikelindividueller Bewegungsinformation in der optischen Schüttgutsortierung

Die sensorgestützte Sortierung ermöglicht die Trennung einzelner Partikel aus einem Materialstrom. In dieser Arbeit wird eine neue Gattung eines Schüttgutsortiersystems mit Flächenkamera erforscht. Der Einsatz von Hochgeschwindigkeitskameras als Inspektionssensorik wirft aus Sicht der Informatik spannende Forschungsfragen hinsichtlich der Gewinnung und Nutzung weitergehender Merkmale, insbesondere von Bewegungsinformation über zu sortierende Materialien, auf

Searches for Nonresonant Higgs Boson Pair Production and Long-Lived Particles at the LHC and Machine-Learning Solutions for the High-Luminosity LHC Era

This thesis presents two physics analyses using 137 fb−1 proton-proton collision data collected by the CMS experiment at √s = 13 TeV, along with a series of machine-learning solutions to extend the physics program at the LHC and to address the computational challenges in the High-Luminosity LHC era. The first analysis searches for nonresonant Higgs boson pair production in final states with two photons and two bottom quarks, with no significant deviation from the background-only hypothesis observed. The observed (expected) upper limit on the product of the Higgs boson pair production cross section and branching fraction into bb&#773;γγ is 0.67 (0.45) fb, corresponding to 7.7 (5.2) times the Standard Model prediction. The modifier of the Higgs trilinear self-coupling is constrained within the range -3.3 &lt; κλ &lt; 8.5. The modifier for coupling between a pair of Higgs bosons and a pair of vector bosons, along with the 2-dimensional constraint of the modifiers of Higgs self-coupling and Yukawa coupling, are also reported. A graph-based algorithm to identify boosted H → bb&#773; jets to improve future Higgs search is presented. The second analysis searches for long-lived supersymmetry particles decaying to photons and gravitinos in the context of gauge-mediated supersymmetry breaking model. Results are presented in terms of 95% confidence level expected exclusion limits on the masses and proper decay lengths of the neutralino, which exceed the limits from the previous searches by up to 100 GeV for the neutralino mass and by five times for the neutralino proper decay length. A strategy for model-independent new physics searches is presented with an anomaly trigger based on unsupervised learning algorithms that can be deployed in both the high-level trigger and the Level-1 trigger in CMS. Three other machine-learning solutions are presented to address the computational challenges in the HL-LHC era: a layer based on multi-modal deep neural networks that can reduce the false-positive events selected by the trigger by over one order of magnitude while retaining 99% of signal events, a full-event simulation algorithm based on recurrent generative adversarial networks that has potential to replace traditional simulation method while being five orders of magnitude faster, and a fast simulation algorithm for specific analyses based on encoder-decoder architecture that would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow.</p