RTOS Framework for Real-Time Control System

This thesis is done as a complementary part for the active magnet bearing (AMB) control software development project in Lappeenranta University of Technology. The main focus of the thesis is to examine an idea of a real-time operating system (RTOS) framework that operates in a dedicated digital signal processor (DSP) environment. General use real-time operating systems do not necessarily provide sufficient platform for periodic control algorithm utilisation. In addition, application program interfaces found in real-time operating systems are commonly non-existent or provided as chip-support libraries, thus hindering platform independent software development. Hence, two divergent real-time operating systems and additional periodic extension software with the framework design are examined to find solutions for the research problems. The research is discharged by; tracing the selected real-time operating system, formulating requirements for the system, and designing the real-time operating system framework (OSFW). The OSFW is formed by programming the framework and conjoining the outcome with the RTOS and the periodic extension. The system is tested and functionality of the software is evaluated in theoretical context of the Rate Monotonic Scheduling (RMS) theory. The performance of the OSFW and substance of the approach are discussed in contrast to the research theme. The findings of the thesis demonstrates that the forged real-time operating system framework is a viable groundwork solution for periodic control applications.

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.

Emerging animal viruses: real threats or simple bystanders?

The list of animal viruses has been frequently added of new members raising permanent concerns to virologists and veterinarians. The pathogenic potential and association with disease have been clearly demonstrated for some, but not for all of these emerging viruses. This review describes recent discoveries of animal viruses and their potential relevance for veterinary practice. Dogs were considered refractory to influenza viruses until 2004, when an influenza A virus subtype H3N8 was transmitted from horses and produced severe respiratory disease in racing greyhounds in Florida/USA. The novel virus, named canine influenza virus (CIV), is considered now a separate virus lineage and has spread among urban canine population in the USA. A new pestivirus (Flaviviridae), tentatively called HoBi-like pestivirus, was identified in 2004 in commercial fetal bovine serum from Brazil. Hobi-like viruses are genetically and antigenically related to bovine viral diarrhea virus (BVDV) and induce similar clinical manifestations. These novel viruses seem to be widespread in Brazilian herds and have also been detected in Southeast Asia and Europe. In 2011, a novel mosquito-borne orthobunyavirus, named Schmallenberg virus (SBV), was associated with fever, drop in milk production, abortion and newborn malformation in cattle and sheep in Germany. Subsequently, the virus disseminated over several European countries and currently represents a real treat for animal health. The origin of SBV is still a matter of debate but it may be a reassortant from previous known bunyaviruses Shamonda and Satuperi. Hepatitis E virus (HEV, family Hepeviridae) is a long known agent of human acute hepatitis and in 1997 was first identified in pigs. Current data indicates that swine HEV is spread worldwide, mainly associated with subclinical infection. Two of the four HEV genotypes are zoonotic and may be transmitted between swine and human by contaminated water and undercooked pork meat. The current distribution and impact of HEV infection in swine production are largely unknown. Avian gyrovirus type 2 (AGV2) is a newly described Gyrovirus, family Circoviridae, which was unexpectedly found in sera of poultry suspected to be infected with chicken anemia virus (CAV). AGV2 is closely related to CAV but displays sufficient genomic differences to be classified as a distinct species. AGV2 seems to be distributed in Brazil and also in other countries but its pathogenic role for chickens is still under investigation. Finally, the long time and intensive search for animal relatives of human hepatitis C virus (HCV) has led to the identification of novel hepaciviruses in dogs (canine hepacivirus [CHV]), horses (non-primate hepaciviruses [NPHV] or Theiler's disease associated virus [TDAV]) and rodents. For these, a clear and definitive association with disease is still lacking and only time and investigation will tell whether they are real disease agents or simple spectators.

Development of a Real-time PCR test for porcine group A rotavirus diagnosis

Group A Rotavirus (RVA) is one of the most common causes of diarrhea in humans and several animal species. A SYBR-Green Real-Time polymerase chain reaction (PCR) was developed to diagnose RVA from porcine fecal samples, targeting amplification of a 137-bp fragment of nonstructural protein 5 (NSP5) gene using mRNA of bovine NADH-desidrogenase-5 as exogenous internal control. Sixty-five samples were tested (25 tested positive for conventional PCR and genetic sequencing). The overall agreement (kappa) was 0.843, indicating 'very good' concordance between tests, presenting 100% of relative sensitivity (25+ Real Time PCR/25+ Conventional PCR) and 87.5% of relative sensitivity (35- Real Time PCR/40- Conventional PCR). The results also demonstrated high intra- and inter-assay reproducibility (coefficient of variation ≤1.42%); thus, this method proved to be a fast and sensitive approach for the diagnosis of RVA in pigs.

Comparison between dSPACE and NI systems based on real-time intelligent control of a teleoperated hydraulic servo system

The Laboratory of Intelligent Machine researches and develops energy-efficient power transmissions and automation for mobile construction machines and industrial processes. The laboratory's particular areas of expertise include mechatronic machine design using virtual technologies and simulators and demanding industrial robotics. The laboratory has collaborated extensively with industrial actors and it has participated in significant international research projects, particularly in the field of robotics. For years, dSPACE tools were the lonely hardware which was used in the lab to develop different control algorithms in real-time. dSPACE's hardware systems are in widespread use in the automotive industry and are also employed in drives, aerospace, and industrial automation. But new competitors are developing new sophisticated systems and their features convinced the laboratory to test new products. One of these competitors is National Instrument (NI). In order to get to know the specifications and capabilities of NI tools, an agreement was made to test a NI evolutionary system. This system is used to control a 1-D hydraulic slider. The objective of this research project is to develop a control scheme for the teleoperation of a hydraulically driven manipulator, and to implement a control algorithm between human and machine interaction, and machine and task environment interaction both on NI and dSPACE systems simultaneously and to compare the results.

Genetic variability of vascular endothelial growth factor and prognosis of head and neck cancer in a Brazilian population

Vascular endothelial growth factor (VEGF) is one of the most potent endothelial cell mitogens and plays a critical role in angiogenesis. Polymorphisms in this gene have been evaluated in patients with several types of cancer. The objectives of this study were to determine if there was an association of the -1154G/A polymorphism of the VEGF gene with head and neck cancer and the interaction of this polymorphism with lifestyle and demographic factors. Additionally, the distribution of the VEGF genotype was investigated with respect to the clinicopathological features of head and neck cancer patients. The study included 100 patients with histopathological diagnosis of head and neck squamous cell carcinoma. Patients with treated tumors were excluded. A total of 176 individuals 40 years or older were included in the control group and individuals with a family history of neoplasias were excluded. Analysis was performed after extraction of genomic DNA using the real-time PCR technique. No statistically significant differences between allelic and genotype frequencies of -1154G/A VEGF polymorphism were identified between healthy individuals and patients. The real-time PCR analyses showed a G allele frequency of 0.72 and 0.74 for patients and the control group, respectively. The A allele showed a frequency of 0.28 for head and neck cancer patients and 0.26 for the control group. However, analysis of the clinicopathological features showed a decreased frequency of the A allele polymorphism in patients with advanced (T3 and T4) tumors (OR = 0.36; 95%CI = 0.14-0.93; P = 0.0345). The -1154A allele of the VEGF gene may decrease the risk of tumor growth and be a possible biomarker for head and neck cancer. This polymorphism is associated with increased VEGF production and may have a prognostic importance.

Real-time monitoring of laser scribing process utilizing high-speed camera

Currently, laser scribing is growing material processing method in the industry. Benefits of laser scribing technology are studied for example for improving an efficiency of solar cells. Due high-quality requirement of the fast scribing process, it is important to monitor the process in real time for detecting possible defects during the process. However, there is a lack of studies of laser scribing real time monitoring. Commonly used monitoring methods developed for other laser processes such a laser welding, are sufficient slow and existed applications cannot be implemented in fast laser scribing monitoring. The aim of this thesis is to find a method for laser scribing monitoring with a high-speed camera and evaluate reliability and performance of the developed monitoring system with experiments. The laser used in experiments is an IPG ytterbium pulsed fiber laser with 20 W maximum average power and Scan head optics used in the laser is Scanlab’s Hurryscan 14 II with an f100 tele-centric lens. The camera was connected to laser scanner using camera adapter to follow the laser process. A powerful fully programmable industrial computer was chosen for executing image processing and analysis. Algorithms for defect analysis, which are based on particle analysis, were developed using LabVIEW system design software. The performance of the algorithms was analyzed by analyzing a non-moving image from the scribing line with resolution 960x20 pixel. As a result, the maximum analysis speed was 560 frames per second. Reliability of the algorithm was evaluated by imaging scribing path with a variable number of defects 2000 mm/s when the laser was turned off and image analysis speed was 430 frames per second. The experiment was successful and as a result, the algorithms detected all defects from the scribing path. The final monitoring experiment was performed during a laser process. However, it was challenging to get active laser illumination work with the laser scanner due physical dimensions of the laser lens and the scanner. For reliable error detection, the illumination system is needed to be replaced.

Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Genetic and Electrophysiological Correlates of Self-Regulation in Adolescence

Self-regulation is considered a powerful predictor of behavioral and mental health outcomes during adolescence and emerging adulthood. In this dissertation I address some electrophysiological and genetic correlates of this important skill set in a series of four studies. Across all studies event-related potentials (ERPs) were recorded as participants responded to tones presented in attended and unattended channels in an auditory selective attention task. In Study 1, examining these ERPs in relation to parental reports on the Behavior Rating Inventory of Executive Function (BRIEF) revealed that an early frontal positivity (EFP) elicited by to-be-ignored/unattended tones was larger in those with poorer self-regulation. As is traditionally found, N1 amplitudes were more negative for the to-be-attended rather than unattended tones. Additionally, N1 latencies to unattended tones correlated with parent-ratings on the BRIEF, where shorter latencies predicted better self-regulation. In Study 2 I tested a model of the associations between selfregulation scores and allelic variations in monoamine neurotransmitter genes, and their concurrent links to ERP markers of attentional control. Allelic variations in dopaminerelated genes predicted both my ERP markers and self-regulatory variables, and played a moderating role in the association between the two. In Study 3 I examined whether training in Integra Mindfulness Martial Arts, an intervention program which trains elements of self-regulation, would lead to improvement in ERP markers of attentional control and parent-report BRIEF scores in a group of adolescents with self-regulatory difficulties. I found that those in the treatment group amplified their processing of attended relative to unattended stimuli over time, and reduced their levels of problematic behaviour whereas those in the waitlist control group showed little to no change on both of these metrics. In Study 4 I examined potential associations between self-regulation and attentional control in a group of emerging adults. Both event-related spectral perturbations (ERSPs) and intertrial coherence (ITC) in the alpha and theta range predicted individual differences in self-regulation. Across the four studies I was able to conclude that real-world self-regulation is indeed associated with the neural markers of attentional control. Targeted interventions focusing on attentional control may improve self-regulation in those experiencing difficulties in this regard.

Genetic diversity of Mycoplasma hyopneumoniae isolates of abattoir pigs.

Mycoplasma hyopneumoniae, the causative agent of porcine enzootic pneumonia, is present in swine herds worldwide. However, there is little information on strains infecting herds in Canada. A total of 160 swine lungs with lesions suggestive of enzootic pneumonia originating from 48 different farms were recovered from two slaughterhouses and submitted for gross pathology. The pneumonic lesion scores ranged from 2% to 84%. Eighty nine percent of the lungs (143/160) were positive for M. hyopneumoniae by real-time PCR whereas 10% (16/160) and 8.8% (14/160) were positive by PCR for M. hyorhinis and M. flocculare, respectively. By culture, only 6% of the samples were positive for M. hyopneumoniae (10/160). Among the selected M. hyopneumoniae-positive lungs (n = 25), 9 lungs were co-infected with M. hyorhinis, 9 lungs with PCV2, 2 lungs with PRRSV, 12 lungs with S. suis and 10 lungs with P. multocida. MLVA and PCR-RFLP clustering of M. hyopneumoniae revealed that analyzed strains were distributed among three and five clusters respectively, regardless of severity of lesions, indicating that no cluster is associated with virulence. However, strains missing a specific MLVA locus showed significantly less severe lesions and lower numbers of bacteria. MLVA and PCR-RFLP analyses also showed a high diversity among field isolates of M. hyopneumoniae with a greater homogeneity within the same herd. Almost half of the field isolates presented less than 55% homology with selected vaccine and reference strains.