Desenvolvimento e avaliação de um sistema de cálculo de dose independente para controle de qualidade de IMRT do tipo jaws-only

Dissertação (mestrado)—Universidade de Brasília, Faculdade Gama, Programa de Pós-Graduação em Engenharia Biomédica, 2015.

Aditivação de pastas geopoliméricas com tetraborato de sódio e látex não iônico para cimentação de Poços de Petróleo

The development of activities in the oil and gas sector has been promoting the search for materials more adequate to oilwell cementing operation. In the state of Rio Grande do Norte, the cement sheath integrity tend to fail during steam injection operation which is necessary to increase oil recovery in reservoir with heavy oil. Geopolymer is a material that can be used as alternative cement. It has been used in manufacturing of fireproof compounds, construction of structures and for controlling of toxic or radioactive waste. Latex is widely used in Portland cement slurries and its characteristic is the increase of compressive strength of cement slurries. Sodium Tetraborate is used in dental cement as a retarder. The addition of this additive aim to improve the geopolymeric slurries properties for oilwell cementing operation. The slurries studied are constituted of metakaolinite, potassium silicate, potassium hydroxide, non-ionic latex and sodium tetraborate. The properties evaluated were: viscosity, compressive strength, thickening time, density, fluid loss control, at ambient temperature (27 ºC) and at cement specification temperature. The tests were carried out in accordance to the practical recommendations of the norm API RP 10B. The slurries with sodium tetraborate did not change either their rheological properties or their mechanical properties or their density in relation the slurry with no additive. The increase of the concentration of sodium tetraborate increased the water loss at both temperatures studied. The best result obtained with the addition of sodium tetraborate was thickening time, which was tripled. The addition of latex in the slurries studied diminished their rheological properties and their density, however, at ambient temperature, it increased their compressive strength and it functioned as an accelerator. The increase of latex concentration increased the presence of water and then diminished the density of the slurries and increased the water loss. From the results obtained, it was concluded that sodium tetraborate and non-ionic latex are promising additives for geopolymer slurries to be used in oilwell cementing operation

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.

System-on-a-Chip (SoC) based Hardware Acceleration in Register Transfer Level (RTL) Design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption.

Proton Form Factor Puzzle and the CEBAF Large Acceptance Spectrometer(CLAS) Two-Photon Exchange Experiment

The electromagnetic form factors are the most fundamental observables that encode information about the internal structure of the nucleon. The electric ($G_{E}$) and the magnetic ($G_{M}$) form factors contain information about the spatial distribution of the charge and magnetization inside the nucleon. A significant discrepancy exists between the Rosenbluth and the polarization transfer measurements of the electromagnetic form factors of the proton. One possible explanation for the discrepancy is the contributions of two-photon exchange (TPE) effects. Theoretical calculations estimating the magnitude of the TPE effect are highly model dependent, and limited experimental evidence for such effects exists. Experimentally, the TPE effect can be measured by comparing the ratio of positron-proton elastic scattering cross section to that of the electron-proton $\large(R = \frac{\sigma (e^{+}p)}{\sigma (e^{-}p)}\large)$. The ratio $R$ was measured over a wide range of kinematics, utilizing a 5.6 GeV primary electron beam produced by the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. This dissertation explored dependence of $R$ on kinematic variables such as squared four-momentum transfer ($Q^{2}$) and the virtual photon polarization parameter ($\varepsilon$). A mixed electron-positron beam was produced from the primary electron beam in experimental Hall B. The mixed beam was scattered from a liquid hydrogen (LH$_{2}$) target. Both the scattered lepton and the recoil proton were detected by the CEBAF Large Acceptance Spectrometer (CLAS). The elastic events were then identified by using elastic scattering kinematics. This work extracted the $Q^{2}$ dependence of $R$ at high $\varepsilon$ ($\varepsilon > $ 0.8) and the $\varepsilon$ dependence of $R$ at $\langle Q^{2} \rangle \approx 0.85$ GeV$^{2}$. In these kinematics, our data confirm the validity of the hadronic calculations of the TPE effect by Blunden, Melnitchouk, and Tjon. This hadronic TPE effect, with additional corrections contributed by higher excitations of the intermediate state nucleon, largely reconciles the Rosenbluth and the polarization transfer measurements of the electromagnetic form factors.

Ciclos económicos, capital bancario y nuevas opciones de política económica

El objetivo de este trabajo es utilizar algunos hechos estilizados de la "Gran recesión", específicamente la drástica caída en el nivel de capitalización bancario, para analizar la relación entre los ciclos financieros y los ciclos reales, así como la efectividad de la política monetaria no convencional y las políticas macroprudenciales. Para esto, en el primer capítulo se desarrolla una microfundamentación de la banca a partir de un modelo de Costly State Verification, que es incluido posteriomente en distintas especificaciones de modelos DSGE. Los resultados muestran que: (i) los ciclos financieros y los ciclos económicos pueden relacionarse a partir del deterioro del capital bancario; (ii) Las políticas macroprudenciales y no convencionales son efectivas para moderar los ciclos económicos, pero son costosas en términos de recursos e inflación.

Credit spreads and monetary policy in a small open economy: an analysis of the colombian economy

This paper estimates Bejarano and Charry (2014)’s small open economy with financial frictions model for the Colombian economy using Bayesian estimation techniques. Additionally, I compute the welfare gains of implementing an optimal response to credit spreads into an augmented Taylor rule. The main result is that a reaction to credit spreads does not imply significant welfare gains unless the economic disturbances increases its volatility, like the disruption implied by a financial crisis. Otherwise its impact over the macroeconomic variables is null.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.

Study of the tracking performance of a liquid Argon detector based on a novel optical imaging concept

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline accelerator experiment designed to make a significant contribution to the study of neutrino oscillations with unprecedented sensitivity. The main goal of DUNE is the determination of the neutrino mass ordering and the leptonic CP violation phase, key parameters of the three-neutrino flavor mixing that have yet to be determined. An important component of the DUNE Near Detector complex is the System for on-Axis Neutrino Detection (SAND) apparatus, which will include GRAIN (GRanular Argon for Interactions of Neutrinos), a novel liquid Argon detector aimed at imaging neutrino interactions using only scintillation light. For this purpose, an innovative optical readout system based on Coded Aperture Masks is investigated. This dissertation aims to demonstrate the feasibility of reconstructing particle tracks and the topology of CCQE (Charged Current Quasi Elastic) neutrino events in GRAIN with such a technique. To this end, the development and implementation of a reconstruction algorithm based on Maximum Likelihood Expectation Maximization was carried out to directly obtain a three-dimensional distribution proportional to the energy deposited by charged particles crossing the LAr volume. This study includes the evaluation of the design of several camera configurations and the simulation of a multi-camera optical system in GRAIN.

Design of a wearable platform for PPG analysis with multicore low-power processor

Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

GEANT4 Radioprotection studies of the ERHA system for protontherapy treatment

This work is focused on the radiation protection for a protontherapy facility. The aim is to simulate with the best accuracy the prompt radiation field of the proton accelerator situed in Ruvo di Puglia, owned by Linearbeam s.r.l. company. In order to simulate it, is used Geant4, a software for interaction simulations of particles with matter. Thanks to internship work, thesis speaks about cancer therapy with a new method for particle acceleration, a linear beam. For a complete overview of the therapy, this work starts with a crush course on interactions of particle with matter, goes specifically to biological matter, then is shown a brief introduction to shielding studies for a particle acceleration facility, and then a presentation of Geant4. At the end, the main aspects of the proton accelerator are simulated, from proton hitting material of beam-pipe to detectors used to measure dose.