Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.

Identification and characterisation of a novel adhesin of Streptococcus suis and its use as a target of adhesion inhibition and bacterial detection

Streptococcus suis is an important pig pathogen but it is also zoonotic, i.e. capable of causing diseases in humans. Human S. suis infections are quite uncommon but potentially life-threatening and the pathogen is an emerging public health concern. This Gram-positive bacterium possesses a galabiose-specific (Galalpha1−4Gal) adhesion activity, which has been studied for over 20 years. P-fimbriated Escherichia coli−bacteria also possess a similar adhesin activity targeting the same disaccharide. The galabiose-specific adhesin of S. suis was identified by an affinity proteomics method. No function of the protein identified was formerly known and it was designated streptococcal adhesin P (SadP). The peptide sequence of SadP contains an LPXTG-motif and the protein was proven to be cell wall−anchored. SadP may be multimeric since in SDS-PAGE gel it formed a protein ladder starting from about 200 kDa. The identification was confirmed by producing knockout strains lacking functional adhesin, which had lost their ability to bind to galabiose. The adhesin gene was cloned in a bacterial expression host and properties of the recombinant adhesin were studied. The galabiose-binding properties of the recombinant protein were found to be consistent with previous results obtained studying whole bacterial cells. A live-bacteria application of surface plasmon resonance was set up, and various carbohydrate inhibitors of the galabiose-specific adhesins were studied with this assay. The potencies of the inhibitors were highly dependent on multivalency. Compared with P-fimbriated E. coli, lower concentrations of galabiose derivatives were needed to inhibit the adhesion of S. suis. Multivalent inhibitors of S. suis adhesion were found to be effective at low nanomolar concentrations. To specifically detect galabiose adhesin−expressing S. suis bacteria, a technique utilising magnetic glycoparticles and an ATP bioluminescence bacterial detection system was also developed. The identification and characterisation of the SadP adhesin give valuable information on the adhesion mechanisms of S. suis, and the results of this study may be helpful for the development of novel inhibitors and specific detection methods of this pathogen.

Imaging techniques applied for detection of secretion, transgene delivery and G proteincoupled receptors in reproductive and endocrine cells in vivo and in vitro

Post-testicular sperm maturation occurs in the epididymis. The ion concentration and proteins secreted into the epididymal lumen, together with testicular factors, are believed to be responsible for the maturation of spermatozoa. Disruption of the maturation of spermatozoa in the epididymis provides a promising strategy for generating a male contraceptive. However, little is known about the proteins involved. For drug development, it is also essential to have tools to study the function of these proteins in vitro. One approach for screening novel targets is to study the secretory products of the epididymis or the G protein-coupled receptors (GPCRs) that are involved in the maturation process of the spermatozoa. The modified Ca2+ imaging technique to monitor release from PC12 pheochromocytoma cells can also be applied to monitor secretory products involved in the maturational processes of spermatozoa. PC12 pheochromocytoma cells were chosen for evaluation of this technique as they release catecholamines from their cell body, thus behaving like endocrine secretory cells. The results of the study demonstrate that depolarisation of nerve growth factor -differentiated PC12 cells releases factors which activate nearby randomly distributed HEL erythroleukemia cells. Thus, during the release process, the ligands reach concentrations high enough to activate receptors even in cells some distance from the release site. This suggests that communication between randomly dispersed cells is possible even if the actual quantities of transmitter released are extremely small. The development of a novel method to analyse GPCR-dependent Ca2+ signalling in living slices of mouse caput epididymis is an additional tool for screening for drug targets. By this technique it was possible to analyse functional GPCRs in the epithelial cells of the ductus epididymis. The results revealed that, both P2X- and P2Y-type purinergic receptors are responsible for the rapid and transient Ca2+ signal detected in the epithelial cells of caput epididymides. Immunohistochemical and reverse transcriptase-polymerase chain reaction (RTPCR) analyses showed the expression of at least P2X1, P2X2, P2X4 and P2X7, and P2Y1 and P2Y2 receptors in the epididymis. Searching for epididymis-specific promoters for transgene delivery into the epididymis is of key importance for the development of specific models for drug development. We used EGFP as the reporter gene to identify proper promoters to deliver transgenes into the epithelial cells of the mouse epididymis in vivo. Our results revealed that the 5.0 kb murine Glutathione peroxidase 5 (GPX5) promoter can be used to target transgene expression into the epididymis while the 3.8 kb Cysteine-rich secretory protein-1 (CRISP-1) promoter can be used to target transgene expression into the testis. Although the visualisation of EGFP in living cells in culture usually poses few problems, the detection of EGFP in tissue sections can be more difficult because soluble EGFP molecules can be lost if the cell membrane is damaged by freezing, sectioning, or permeabilisation. Furthermore, the fluorescence of EGFP is dependent on its conformation. Therefore, fixation protocols that immobilise EGFP may also destroy its usefulness as a fluorescent reporter. We therefore developed a novel tissue preparation and preservation techniques for EGFP. In addition, fluorescence spectrophotometry with epididymal epithelial cells in suspension revealed the expression of functional purinergic, adrenergic, cholinergic and bradykinin receptors in these cell lines (mE-Cap27 and mE-Cap28). In conclusion, we developed new tools for studying the role of the epididymis in sperm maturation. We developed a new technique to analyse GPCR dependent Ca2+ signalling in living slices of mouse caput epididymis. In addition, we improved the method of detecting reporter gene expression. Furthermore, we characterised two epididymis-specific gene promoters, analysed the expression of GPCRs in epididymal epithelial cells and developed a novel technique for measurement of secretion from cells.

Design and production of protein nanostructures for biomolecular detection

Particulate nanostructures are increasingly used for analytical purposes. Such particles are often generated by chemical synthesis from non-renewable raw materials. Generation of uniform nanoscale particles is challenging and particle surfaces must be modified to make the particles biocompatible and water-soluble. Usually nanoparticles are functionalized with binding molecules (e.g., antibodies or their fragments) and a label substance (if needed). Overall, producing nanoparticles for use in bioaffinity assays is a multistep process requiring several manufacturing and purification steps. This study describes a biological method of generating functionalized protein-based nanoparticles with specific binding activity on the particle surface and label activity inside the particles. Traditional chemical bioconjugation of the particle and specific binding molecules is replaced with genetic fusion of the binding molecule gene and particle backbone gene. The entity of the particle shell and binding moieties are synthesized from generic raw materials by bacteria, and fermentation is combined with a simple purification method based on inclusion bodies. The label activity is introduced during the purification. The process results in particles that are ready-to-use as reagents in bioaffinity. Apoferritin was used as particle body and the system was demonstrated using three different binding moieties: a small protein, a peptide and a single chain Fv antibody fragment that represents a complex protein including disulfide bridge.If needed, Eu3+ was used as label substance. The results showed that production system resulted in pure protein preparations, and the particles were of homogeneous size when visualized with transmission electron microscopy. Passively introduced label was stably associated with the particles, and binding molecules genetically fused to the particle specifically bound target molecules. Functionality of the particles in bioaffinity assays were successfully demonstrated with two types of assays; as labels and in particle-enhanced agglutination assay. This biological production procedure features many advantages that make the process especially suited for applications that have frequent and recurring requirements for homogeneous functional particles. The production process of ready, functional and watersoluble particles follows principles of “green chemistry”, is upscalable, fast and cost-effective.

Automatic meter reading – benefits and applications

In liberalized electricity markets, which have taken place in many countries over the world, the electricity distribution companies operate in the competitive conditions. Therefore, accurate information about the customers’ energy consumption plays an essential role for the budget keeping of the distribution company and for correct planning and operation of the distribution network. This master’s thesis is focused on the description of the possible benefits for the electric utilities and residential customers from the automatic meter reading system usage. Major benefits of the AMR, illustrated in the thesis, are distribution network management, power quality monitoring, load modelling, and detection of the illegal usage of the electricity. By the example of the power system state estimation, it was illustrated that even the partial installation of the AMR in the customer side leads to more accurate data about the voltage and power levels in the whole network. The thesis also contains the description of the present situation of the AMR integration in Russia.

Determination of sugars and organic acids in pulp samples by capillary electrophoresis with UV detection

This MSc work was done in the project of BIOMECON financed by Tekes. The prime target of the research was, to develop methods for separation and determination of carbohydrates (sugars), sugar acids and alcohols, and some other organic acids in hydrolyzed pulp samples by capillary electrophoresis (CE) using UV detection. Aspen, spruce, and birch pulps are commonly used for production of papers in Finland. Feedstock components in pulp predominantly consist of carbohydrates, organic acids, lignin, extractives, and proteins. Here in this study, pulps have been hydrolyzed in analytical chemistry laboratories of UPM Company and Lappeenranta University in order to convert them into sugars, acids, alcohols, and organic acids. Foremost objective of this study was to quantify and identify the main and by-products in the pulp samples. For the method development and optimization, increased precision in capillary electrophoresis was accomplished by calculating calibration data of 16 analytes such as D-(-)-fructose, D(+)-xylose, D(+)-mannose, D(+)-cellobiose, D-(+)-glucose, D-(+)-raffinose, D(-)-mannitol, sorbitol, rhamnose, sucrose, xylitol, galactose, maltose, arabinose, ribose, and, α-lactose monohydratesugars and 16 organic acids such as D-glucuronic, oxalic, acetic, propionic, formic, glycolic, malonic, maleic, citric, L-glutamic, tartaric, succinic, adipic, ascorbic, galacturonic, and glyoxylic acid. In carbohydrate and polyalcohol analyses, the experiments with CE coupled to direct UV detection and positive separation polarity was performed in 36 mM disodium hydrogen phosphate electrolyte solution. For acid analyses, CE coupled indirect UV detection, using negative polarity, and electrolyte solution made of 2,3 pyridinedicarboxylic acid, Ca2+ salt, Mg2+ salts, and myristyltrimethylammonium hydroxide in water was used. Under optimized conditions, limits of detection, relative standard deviations and correlation coefficients of each compound were measured. The optimized conditions were used for the identification and quantification of carbohydrates and acids produced by hydrolyses of pulp. The concentrations of the analytes varied between 1 mg – 0.138 g in liter hydrolysate.

Regulation of self-renewal and detection of karyotypic changes of pluripotent human embryonic stem cells

Human embryonic stem cells are pluripotent cells capable of renewing themselves and differentiating to specialized cell types. Because of their unique regenerative potential, pluripotent cells offer new opportunities for disease modeling, development of regenerative therapies, and treating diseases. Before pluripotent cells can be used in any therapeutic applications, there are numerous challenges to overcome. For instance, the key regulators of pluripotency need to be clarified. In addition, long term culture of pluripotent cells is associated with the accumulation of karyotypic abnormalities, which is a concern regarding the safe use of the cells for therapeutic purposes. The goal of the work presented in this thesis was to identify new factors involved in the maintenance of pluripotency, and to further characterize molecular mechanisms of selected candidate genes. Furthermore, we aimed to set up a new method for analyzing genomic integrity of pluripotent cells. The experimental design applied in this study involved a wide range of molecular biology, genome-wide, and computational techniques to study the pluripotency of stem cells and the functions of the target genes. In collaboration with instrument and reagent company Perkin Elmer, KaryoliteTM BoBsTM was implemented for detecting karyotypic changes of pluripotent cells. Novel genes were identified that are highly and specifically expressed in hES cells. Of these genes, L1TD1 and POLR3G were chosen for further investigation. The results revealed that both of these factors are vital for the maintenance of pluripotency and self-renewal of the hESCs. KaryoliteTM BoBsTM was validated as a novel method to detect karyotypic abnormalities in pluripotent stem cells. The results presented in this thesis offer significant new information on the regulatory networks associated with pluripotency. The results will facilitate in understanding developmental and cancer biology, as well as creating stem cell based applications. KaryoliteTM BoBsTM provides rapid, high-throughput, and cost-efficient tool for screening of human pluripotent cell cultures.

Switchable Lanthanide Luminescence for Detection of Biomolecules

Binary probes are oligonucleotide probe pairs that hybridize adjacently to a complementary target nucleic acid. In order to detect this hybridization, the two probes can be modified with, for example, fluorescent molecules, chemically reactive groups or nucleic acid enzymes. The benefit of this kind of binary probe based approach is that the hybridization elicits a detectable signal which is distinguishable from background noise even though unbound probes are not removed by washing before measurement. In addition, the requirement of two simultaneous binding events increases specificity. Similarly to binary oligonucleotide probes, also certain enzymes and fluorescent proteins can be divided into two parts and used in separation-free assays. Split enzyme and fluorescent protein reporters have practical applications among others as tools to investigate protein-protein interactions within living cells. In this study, a novel label technology, switchable lanthanide luminescence, was introduced and used successfully in model assays for nucleic acid and protein detection. This label technology is based on a luminescent lanthanide chelate divided into two inherently non-luminescent moieties, an ion carrier chelate and a light harvesting antenna ligand. These form a highly luminescent complex when brought into close proximity; i.e., the label moieties switch from a dark state to a luminescent state. This kind of mixed lanthanide complex has the same beneficial photophysical properties as the more typical lanthanide chelates and cryptates - sharp emission peaks, long emission lifetime enabling time-resolved measurement, and large Stokes’ shift, which minimize the background signal. Furthermore, the switchable lanthanide luminescence technique enables a homogeneous assay set-up. Here, switchable lanthanide luminescence label technology was first applied to sensitive, homogeneous, single-target nucleic acid and protein assays with picomolar detection limits and high signal to background ratios. Thereafter, a homogeneous four-plex nucleic acid array-based assay was developed. Finally, the label technology was shown to be effective in discrimination of single nucleotide mismatched targets from fully matched targets and the luminescent complex formation was analyzed more thoroughly. In conclusion, this study demonstrates that the switchable lanthanide luminescencebased label technology can be used in various homogeneous bioanalytical assays.

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

BotCloud Detection System

Leveraging cloud services, companies and organizations can significantly improve their efficiency, as well as building novel business opportunities. Cloud computing offers various advantages to companies while having some risks for them too. Advantages offered by service providers are mostly about efficiency and reliability while risks of cloud computing are mostly about security problems. Problems with security of the cloud still demand significant attention in order to tackle the potential problems. Security problems in the cloud as security problems in any area of computing, can not be fully tackled. However creating novel and new solutions can be used by service providers to mitigate the potential threats to a large extent. Looking at the security problem from a very high perspective, there are two focus directions. Security problems that threaten service user’s security and privacy are at one side. On the other hand, security problems that threaten service provider’s security and privacy are on the other side. Both kinds of threats should mostly be detected and mitigated by service providers. Looking a bit closer to the problem, mitigating security problems that target providers can protect both service provider and the user. However, the focus of research community mostly is to provide solutions to protect cloud users. A significant research effort has been put in protecting cloud tenants against external attacks. However, attacks that are originated from elastic, on-demand and legitimate cloud resources should still be considered seriously. The cloud-based botnet or botcloud is one of the prevalent cases of cloud resource misuses. Unfortunately, some of the cloud’s essential characteristics enable criminals to form reliable and low cost botclouds in a short time. In this paper, we present a system that helps to detect distributed infected Virtual Machines (VMs) acting as elements of botclouds. Based on a set of botnet related system level symptoms, our system groups VMs. Grouping VMs helps to separate infected VMs from others and narrows down the target group under inspection. Our system takes advantages of Virtual Machine Introspection (VMI) and data mining techniques.

Detection of Epilepsy with a Commercial EEG Headband

Epilepsy is a chronic brain disorder, characterized by reoccurring seizures. Automatic sei-zure detector, incorporated into a mobile closed-loop system, can improve the quality of life for the people with epilepsy. Commercial EEG headbands, such as Emotiv Epoc, have a potential to be used as the data acquisition devices for such a system. In order to estimate that potential, epileptic EEG signals from the commercial devices were emulated in this work based on the EEG data from a clinical dataset. The emulated characteristics include the referencing scheme, the set of electrodes used, the sampling rate, the sample resolution and the noise level. Performance of the existing algorithm for detection of epileptic seizures, developed in the context of clinical data, has been evaluated on the emulated commercial data. The results show, that after the transformation of the data towards the characteristics of Emotiv Epoc, the detection capabilities of the algorithm are mostly preserved. The ranges of acceptable changes in the signal parameters are also estimated.