An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Computer designing and programming Final report

Computer designing and programmin

Neuromorphic Systems for Pattern Recognition and Uav Trajectory Planning

Detection and control are two essential components in an intelligent system. This thesis investigates novel techniques in both areas with a focus on the applications of handwritten text recognition and UAV flight control. Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. The difficulty is greatly increased if the detection algorithms is solely based on pattern matching without information of dynamics of handwriting trajectories. Motivated by the aforementioned challenges, this thesis first investigates the pattern recognition problem. We use offline handwritten texts recognition as a case study to explore the performance of a recurrent belief propagation model. We first develop a probabilistic inference network to post process the recognition results of deep Convolutional Neural Network (CNN) (e.g. LeNet) and collect individual characters to form words. The output of the inference network is a set of words and their probability. A series of post processing and improvement techniques are then introduced to further increase the recognition accuracy. We study the performance of proposed model through various comparisons. The results show that it significantly improves the accuracy by correcting deletion, insertion and replacement errors, which are the main sources of invalid candidate words. Deep Reinforcement Learning (DRL) has widely been applied to control the autonomous systems because it provides solutions for various complex decision-making tasks that previously could not be solved solely with deep learning. To enable autonomous Unmanned Aerial Vehicles (UAV), this thesis presents a two-level trajectory planning framework for UAVs in an indoor environment. A sequence of waypoints is selected at the higher-level, which leads the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated analytically between each pair of adjacent waypoints. The goal of trajectory generation is to maintain the stability of the UAV, and the goal of the waypoints planning is to select waypoints with the lowest control thrust throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using DRL, which learns the highly complicated and nonlinear interaction between those two levels, and the impact from the environment. Given the pre-planned trajectory, this thesis further presents an actor-critic reinforcement learning framework that realizes continuous trajectory control of the UAV through a set of desired waypoints. We construct a deep neural network and develop reinforcement learning for better trajectory tracking. In addition, Field Programmable Gate Arrays (FPGA) based hardware acceleration is designed for energy efficient real-time control. If we are to integrate the trajectory planning model onto a UAV system for real-time on-board planning, a key challenge is how to deliver required performance under strict memory and computational constraints. Techniques that compress Deep Neural Network (DNN) models attract our attention because they allow optimized neural network models to be efficiently deployed on platforms with limited energy and storage capacity. However, conventional model compression techniques prune the DNN after it is fully trained, which is very time-consuming especially when the model is trained using DRL. To overcome the limitation, we present an early phase integrated neural network weight compression system for DRL based waypoints planning. By applying pruning at an early phase, the compression of the DRL model can be realized without significant overhead in training. By tightly integrating pruning and retraining at the early phase, we achieve a higher model compression rate, reduce more memory and computing complexity, and improve the success rate compared to the original work

Ocular attention-sensing interface system

The purpose of the research was to develop an innovative human-computer interface based on eye movement and voice control. By eliminating a manual interface (keyboard, joystick, etc.), OASIS provides a control mechanism that is natural, efficient, accurate, and low in workload

Securing critical utility systems & network infrastructures

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009As infra-estruturas críticas de TI para serviços públicos são apoiadas por inúmeros sistemas complexos. Estes sistemas permitem a gestão e recolha de informação em tempo-real, constituindo a base para a gestão eficiente das operações. A utilização, cada vez mais frequente, de software e hardware (Commercial Off-The-Shelf, COTS) em sistemas SCADA permitiu grandes beneficios financeiros na aquisição e desenvolvimento de soluções técnicas que suportam os serviços públicos. O uso de hardware e software COTS em sistemas SCADA transferiu para as infra-estruturas críticas os problemas de segurança de uma infraestrutura de TI empresarial. Neste contexto, um desafio para as equipas de gestão operacional dos sistemas de TI é a gestão eficaz dos sistemas e redes que compõem as infra-estruturas críticas dos serviços públicos. Apesar de estas organizações adoptarem, cada vez mais, normas e melhores práticas que visam melhorar a gestão, operações e processos de configuração. Este projecto de investigação propõe-se a desenvolver um estudo comparativo de plataformas de gestão integrada no contexto dos sistemas SCADA que suportam serviços públicos. Adicionalmente, este projecto de investigação irá desenvolver estudos acerca de perfis operacionais dos Sistemas Operativos que suportam a infra-estrutura IT dos serviços públicos críticos. Este projecto de investigação irá descrever como as decisões estratégicas de gestão têm impacto nas operações de gestão de uma infra-estrutura TI.Modern critical utility IT infrastructures are supported by numerous complex systems. These systems allow real-time management and information collection, which is the basis of efficient service management operations. The usage of commercial off-the-shelf (COTS) hardware and software in SCADA systems allowed for major financial advantages in purchasing and developing technical solutions. On the other hand, this COTS hardware and software generalized usage in SCADA systems, exposed critical infrastructures to the security problems of a corporate IT infrastructure. A significant challenge for IT teams is managing critical utility IT infrastructures even upon adopting security best practices that help management, operations and configuration of the systems and network components that comprise those infrastructures. This research project proposes to survey integrated management software that can address the specific security constraints of a SCADA infrastructure supported by COTS software. Additionally, this research project proposes to investigate techniques that will allow the creation of operational profiles of Operating Systems supporting critical utility IT infrastructures. This research project will describe how the strategic management decisions impact tactical operations management of an IT environment. We will investigate desirable technical management elements in support of the operational management

System configuration and executive requirements specifications for reusable shuttle and space station/base

System configuration and executive requirements specifications for reusable shuttle and space station/bas

Use of the Smartphone Camera to Monitor Adherence to Inhaled Therapy

Self-management strategies can lead to improved health outcomes, fewer unscheduled treatments, and improved disease control. Compliance with inhaled control drugs is essential to achieve good clinical outcomes in patients with chronic respiratory diseases. However, compliance assessments suffer from the difficulty of achieving a high degree of trustworthiness, as patients often self-report high compliance rates and are considered unreliable. This thesis aims to enable reliable adhesion measurement by developing a mobile application module to objectively verify inhalation usage using image snapshots of the inhalation counter. To achieve this, a mobile application module featuring pre and post processing techniques and a default machine learning framework was built, for inhaler and dosage counter numbers detection. In addition, in an effort to improve the app’s capabilities of text recognition on a worst-performing inhaler, a machine learning model was trained on an inhaler image dataset. Some of the features worked on during this project were incorporated on the current version of the app InspirerMundi, a medication management mobile application, planned to be made available at the PlayStore by the end of 2021. The proposed approach was validated through a series of different inhaler image datasets. The carried-out tests with the default machine learning configuration showed correct detection of dosage counters for 70% of inhaler registration events and 93% for three commonly used inhalers in Portugal. On the other hand, the trained model had an average accuracy of 88 % in recognizing the digits on the dose counter of one of the worst-performing inhaler models. These results show the potential to explore mobile and embedded capabilities to gain additional evidence for inhaler compliance. These systems can help bridge the gap between patients and healthcare professionals. By empowering patients with disease selfmanagement and drug adherence tools and providing additional relevant data, these systems pave the way for informed disease management decisions

Voyager spacecraft system. Volume C - Design for operational support equipment Final technical report

Operational support equipment needed to support Voyager spacecraft missio