Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

A novel isolation method of Brucella species and molecular tracking of Brucella suis biovar 2 in domestic and wild animals

Brucella suis biovar 2 is the most common aetiological agent of porcine brucellosis in Europe. B. suis biovar 2 is considered to have low zoonotic potential, but is a causative agent of reproductive losses in pigs, and it is thus economically important. The multilocus variable-number of tandem repeats genotyping analysis of 16 loci (MLVA-16) has proven to be highly discriminatory and is the most suitable assay for simultaneously identifying B. suis and tracking infections. The aim of this study was to investigate the relatedness between isolates of B. suis biovar 2 obtained during a brucellosis outbreak in domestic pigs and isolates from wild boars and hares collected from proximal or remote geographical areas by MLVA-16. A cluster analysis of the MLVA-16 data revealed that most of the isolates obtained from Switzerland clustered together, with the exception of one isolate. The outbreak isolates constituted a unique subcluster (with a genetic similarity >93.8%) distinct from that of the isolates obtained from wild animals, suggesting that direct transmission of the bacterium from wild boars to domestic pigs did not occur in this outbreak. To obtain a representative number of isolates for MLVA-16, alternative methods of Brucella spp. isolation from tissue samples were compared with conventional direct cultivation on a Brucella-selective agar. We observed an enhanced sensitivity when mechanical homogenisation was followed by host cell lysis prior to cultivation on the Brucella-selective agar. This work demonstrates that MLVA-16 is an excellent tool for both monitoring brucellosis and investigating outbreaks. Additionally, we present efficient alternatives for the isolation of Brucella spp.

Comparison of real-time PCR assays for detection, quantification, and differentiation of campylobacter jejuni and campylobacter coli in broiler neck skin samples

We tested the use of multiplex real-time PCR for detection and quantification of Campylobacter jejuni and Campylobacter coli on broiler carcass neck skin samples collected during 2008 from slaughterhouses in Switzerland. Results from an established TaqMan assay based on two different targets (hipO and ceuE for C. jejuni and C. coli, respectively) were corroborated with data from a newly developed assay based on a single-nucleotide polymorphism in the fusA gene, which allows differentiation between C. jejuni and C. coli. Both multiplex real-time PCRs were applied simultaneously for direct detection, differentiation, and quantification of Campylobacter from 351 neck skin samples and compared with culture methods. There was good correlation in detection and enumeration between real-time PCR results and quantitative culture, with real-time PCR being more sensitive. Overall, 251 (71.5%) of the samples were PCR positive for Campylobacter, with 211 (60.1%) in the hipO-ceuE assays, 244 (69.5%) in the fusA assay, and 204 (58.1%) of them being positive in both PCR assays. Thus, the fusA assay was similarly sensitive to the enrichment culture (72.4% positive); however, it is faster and allows for quantification. In addition, real-time PCR allowed for species differentiation; roughly 60% of positive samples contained C. jejuni, less than 10% C. coli, and more than 30% contained both species. Real-time PCR proved to be a suitable method for direct detection, quantification, and differentiation of Campylobacter from carcasses, and could permit time-efficient surveillance of these zoonotic agents.

[First detection of "Candidatus Mycoplasma haemolamae" in South American Camelids of Switzerland and evaluation of prevalence]

Haemotrophic mycoplasmas (also known as haemoplasmas), small bacterias which parasite the surface of erythrocytes, have been described in several species. Recently, molecular methods were developed for the diagnosis of haemoplasma infection. The presented study describes the first detection and the investigation of prevalence of "Candidatus Mycoplasma haemolamae" in South American Camelids in Switzerland. A random sample of the latter population was tested for haemoplasma infections using real-time PCR. The infection was detected in 18.6% of the animals and was found both in indigenous and in imported camelids. Of the tested herds 39,1% harboured at least one animal positive for haemoplasmas in PCR. There was no difference in prevalence between male and female animals and llamas and alpacas, respectively. Furthermore, the prevalence of infection was not significantly different in diseased animals compared to healthy camelids. From the latter observation and the fact that the high prevalence was accompanied by an undetectable incidence, we concluded that the pathogenicity of "Candidatus Mycoplasma haemolamae" may be low.

Challenges in the early detection of psychosis in children and adolescents

The early detection and treatment of persons at-risk for psychosis is currently regarded a promising strategy in fighting the devastating consequences of psychotic disorders. The two current at-risk approaches, i.e., the "ultra high risk" and the "basic symptom" criteria, were mainly developed on adult samples. Initial evidence suggests, however, that they cannot simply be applied to children and adolescents. For ultra high risk criteria, there is indication of some attenuated psychotic symptoms being potentially non-specific in adolescents and of brief limited intermittent symptoms being difficult to clinically classify in children when observable behavioral correlates are missing. For basic symptoms, too, only preliminary indication of their usefulness in children and adolescents exists. Since developmental peculiarities in the assessment of basic symptoms should be considered, a child and youth version of the Schizophrenia Proneness Instrument (SPI-CY) was developed. In conclusion, research on the clinical-prognostic validity of the at-risk criteria and their potential adoption to the special needs of children and adolescents is needed. If a Prodromal Risk Syndrome for Psychosis or Attenuated Psychotic Symptoms Syndrome will be included into DSM-V, it has to be highlighted that its suitability for children and adolescents is only insufficiently known.

Tracking the subprocesses of decision-based action in the human frontal lobes

Situationally adaptive behavior relies on the identification of relevant target stimuli, the evaluation of these with respect to the current context and the selection of an appropriate action. We used functional magnetic resonance imaging (fMRI) to disentangle the neural networks underlying these processes within a single task. Our results show that activation of mid-ventrolateral prefrontal cortex (PFC) reflects the perceived presence of a target stimulus regardless of context, whereas context-appropriate evaluation is subserved by mid-dorsolateral PFC. Enhancing demands on response selection by means of response conflict activated a network of regions, all of which are directly connected to motor areas. On the midline, rostral anterior paracingulate cortex was found to link target detection and response selection by monitoring for the presence of behaviorally significant conditions. In summary, we provide new evidence for process-specific functional dissociations in the frontal lobes. In target-centered processing, target detection in the VLPFC is separable from contextual evaluation in the DLPFC. Response-centered processing in motor-associated regions occurs partly in parallel to these processes, which may enhance behavioral efficiency, but it may also lead to reaction time increases when an irrelevant response tendency is elicited.

HIV-1 coreceptor usage and CXCR4-specific viral load predict clinical disease progression during combination antiretroviral therapy

BACKGROUND: Although combination antiretroviral therapy (cART) dramatically reduces rates of AIDS and death, a minority of patients experience clinical disease progression during treatment. OBJECTIVE: To investigate whether detection of CXCR4(X4)-specific strains or quantification of X4-specific HIV-1 load predict clinical outcome. METHODS: From the Swiss HIV Cohort Study, 96 participants who initiated cART yet subsequently progressed to AIDS or death were compared with 84 contemporaneous, treated nonprogressors. A sensitive heteroduplex tracking assay was developed to quantify plasma X4 and CCR5 variants and resolve HIV-1 load into coreceptor-specific components. Measurements were analyzed as cofactors of progression in multivariable Cox models adjusted for concurrent CD4 cell count and total viral load, applying inverse probability weights to adjust for sampling bias. RESULTS: Patients with X4 variants at baseline displayed reduced CD4 cell responses compared with those without X4 strains (40 versus 82 cells/microl; P = 0.012). The adjusted multivariable hazard ratio (HR) for clinical progression was 4.8 [95% confidence interval (CI) 2.3-10.0] for those demonstrating X4 strains at baseline. The X4-specific HIV-1 load was a similarly independent predictor, with HR values of 3.7 (95% CI, 1.2-11.3) and 5.9 (95% CI, 2.2-15.0) for baseline loads of 2.2-4.3 and > 4.3 log10 copies/ml, respectively, compared with < 2.2 log10 copies/ml. CONCLUSIONS: HIV-1 coreceptor usage and X4-specific viral loads strongly predicted disease progression during cART, independent of and in addition to CD4 cell count or total viral load. Detection and quantification of X4 strains promise to be clinically useful biomarkers to guide patient management and study HIV-1 pathogenesis.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Reproducibility and accuracy of the ICDAS-II for occlusal caries detection

OBJECTIVES: The aim of this in vitro study was to assess the inter- and intra-examiner reproducibility and the accuracy of the International Caries Detection and Assessment System-II (ICDAS-II) in detecting occlusal caries. METHODS: One hundred and sixty-three molars were independently assessed twice by two experienced dentists using the 0- to 6-graded ICDAS-II. The teeth were histologically prepared and classified using two different histological systems [Ekstrand et al. (1997) Caries Research vol. 31, pp. 224-231; Lussi et al. (1999) Caries Research vol. 33, pp. 261-266] and assessed for caries extension. Sensitivity, specificity, accuracy and area under the ROC curve (A(z)) were obtained at D(2) and D(3) thresholds. Unweighted kappa coefficient was used to assess inter- and intra-examiner reproducibility. RESULTS: For the Ekstrand et al. histological classification the sensitivity was 0.99 and 1.00, specificity 1.00 and 0.69 and accuracy 0.99 and 0.76 at D(2) and D(3), respectively. For the Lussi et al. histological classification the sensitivity was 0.91 and 0.75, specificity 0.47 and 0.62 and accuracy 0.86 and 0.68 at D(2) and D(3), respectively. The A(z) varied from 0.54 to 0.73. The inter- and intra-examiner kappa values were 0.51 and 0.58, respectively. CONCLUSIONS: ICDAS-II presented good reproducibility and accuracy in detecting occlusal caries, especially caries lesions in the outer half of the enamel.

IS711-based real-time PCR assay as a tool for detection of Brucella spp. in wild boars and comparison with bacterial isolation and serology

BACKGROUND: Control of brucellosis in livestock, wildlife and humans depends on the reliability of the methods used for detection and identification of bacteria. In the present study, we describe the evaluation of the recently established real-time PCR assay based on the Brucella-specific insertion sequence IS711 with blood samples from 199 wild boars (first group of animals) and tissue samples from 53 wild boars (second group of animals) collected in Switzerland. Results from IS711 real-time PCR were compared to those obtained by bacterial isolation, Rose Bengal Test (RBT), competitive ELISA (c-ELISA) and indirect ELISA (i-ELISA). RESULTS: In the first group of animals, IS711 real-time PCR detected infection in 11.1% (16/144) of wild boars that were serologically negative. Serological tests showed different sensitivities [RBT 15.6%, c-ELISA 7.5% and i-ELISA 5.5%] and only 2% of blood samples were positive with all three tests, which makes interpretation of the serological results very difficult. Regarding the second group of animals, the IS711 real-time PCR detected infection in 26% of animals, while Brucella spp. could be isolated from tissues of only 9.4% of the animals. CONCLUSION: The results presented here indicate that IS711 real-time PCR assay is a specific and sensitive tool for detection of Brucella spp. infections in wild boars. For this reason, we propose the employment of IS711 real-time PCR as a complementary tool in brucellosis screening programs and for confirmation of diagnosis in doubtful cases.

Fluorescent In Situ hybridization: a new tool for the direct identification and detection of F. psychrophilum

F. psychrophilum is the causative agent of Bacterial Cold Water Disease (BCW) and Rainbow Trout Fry Syndrome (RTFS). To date, diagnosis relies mainly on direct microscopy or cultural methods. Direct microscopy is fast but not very reliable, whereas cultural methods are reliable but time-consuming and labor-intensive. So far fluorescent in situ hybridization (FISH) has not been used in the diagnosis of flavobacteriosis but it has the potential to rapidly and specifically detect F. psychrophilum in infected tissues. Outbreaks in fish farms, caused by pathogenic strains of Flavobacterium species, are increasingly frequent and there is a need for reliable and cost-effective techniques to rapidly diagnose flavobacterioses. This study is aimed at developing a FISH that could be used for the diagnosis of F. psychrophilum infections in fish. We constructed a generic probe for the genus Flavobacterium ("Pan-Flavo") and two specific probes targeting F. psychrophilum based on 16S rRNA gene sequences. We tested their specificity and sensitivity on pure cultures of different Flavobacterium and other aquatic bacterial species. After assessing their sensitivity and specificity, we established their limit of detection and tested the probes on infected fresh tissues (spleen and skin) and on paraffin-embedded tissues. The results showed high sensitivity and specificity of the probes (100% and 91% for the Pan-Flavo probe and 100% and 97% for the F. psychrophilum probe, respectively). FISH was able to detect F. psychrophilum in infected fish tissues, thus the findings from this study indicate this technique is suitable as a fast and reliable method for the detection of Flavobacterium spp. and F. psychrophilum.

Traditional and novel methods for occlusal caries detection: performance on primary teeth

This study aimed to assess the performance of International Caries Detection and Assessment System (ICDAS), radiographic examination, and fluorescence-based methods for detecting occlusal caries in primary teeth. One occlusal site on each of 79 primary molars was assessed twice by two examiners using ICDAS, bitewing radiography (BW), DIAGNOdent 2095 (LF), DIAGNOdent 2190 (LFpen), and VistaProof fluorescence camera (FC). The teeth were histologically prepared and assessed for caries extent. Optimal cutoff limits were calculated for LF, LFpen, and FC. At the D (1) threshold (enamel and dentin lesions), ICDAS and FC presented higher sensitivity values (0.75 and 0.73, respectively), while BW showed higher specificity (1.00). At the D (2) threshold (inner enamel and dentin lesions), ICDAS presented higher sensitivity (0.83) and statistically significantly lower specificity (0.70). At the D(3) threshold (dentin lesions), LFpen and FC showed higher sensitivity (1.00 and 0.91, respectively), while higher specificity was presented by FC (0.95), ICDAS (0.94), BW (0.94), and LF (0.92). The area under the receiver operating characteristic (ROC) curve (Az) varied from 0.780 (BW) to 0.941 (LF). Spearman correlation coefficients with histology were 0.72 (ICDAS), 0.64 (BW), 0.71 (LF), 0.65 (LFpen), and 0.74 (FC). Inter- and intraexaminer intraclass correlation values varied from 0.772 to 0.963 and unweighted kappa values ranged from 0.462 to 0.750. In conclusion, ICDAS and FC exhibited better accuracy in detecting enamel and dentin caries lesions, whereas ICDAS, LF, LFpen, and FC were more appropriate for detecting dentin lesions on occlusal surfaces in primary teeth, with no statistically significant difference among them. All methods presented good to excellent reproducibility.

Hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence

Ecology and conservation require reliable data on the occurrence of animals and plants. A major source of bias is imperfect detection, which, however, can be corrected for by estimation of detectability. In traditional occupancy models, this requires repeat or multi-observer surveys. Recently, time-to-detection models have been developed as a cost-effective alternative, which requires no repeat surveys and hence costs could be halved. We compared the efficiency and reliability of time-to-detection and traditional occupancy models under varying survey effort. Two observers independently searched for 17 plant species in 44100m(2) Swiss grassland quadrats and recorded the time-to-detection for each species, enabling detectability to be estimated with both time-to-detection and traditional occupancy models. In addition, we gauged the relative influence on detectability of species, observer, plant height and two measures of abundance (cover and frequency). Estimates of detectability and occupancy under both models were very similar. Rare species were more likely to be overlooked; detectability was strongly affected by abundance. As a measure of abundance, frequency outperformed cover in its predictive power. The two observers differed significantly in their detection ability. Time-to-detection models were as accurate as traditional occupancy models, but their data easier to obtain; thus they provide a cost-effective alternative to traditional occupancy models for detection-corrected estimation of occurrence.

Differentiation between Staphylococcus aureus and coagulase-negative Staphylococcus species by real-time PCR including detection of methicillin resistants in comparison to conventional microbiology testing.

BACKGROUND Staphylococcus aureus has long been recognized as a major pathogen. Methicillin-resistant strains of S. aureus (MRSA) and methicillin-resistant strains of S. epidermidis (MRSE) are among the most prevalent multiresistant pathogens worldwide, frequently causing nosocomial and community-acquired infections. METHODS In the present pilot study, we tested a polymerase chain reaction (PCR) method to quickly differentiate Staphylococci and identify the mecA gene in a clinical setting. RESULTS Compared to the conventional microbiology testing the real-time PCR assay had a higher detection rate for both S. aureus and coagulase-negative Staphylococci (CoNS; 55 vs. 32 for S. aureus and 63 vs. 24 for CoNS). Hands-on time preparing DNA, carrying out the PCR, and evaluating results was less than 5 h. CONCLUSIONS The assay is largely automated, easy to adapt, and has been shown to be rapid and reliable. Fast detection and differentiation of S. aureus, CoNS, and the mecA gene by means of this real-time PCR protocol may help expedite therapeutic decision-making and enable earlier adequate antibiotic treatment.