To Örebro University

Nitrocellulose is the main component in many types of ammunition, propellants and explosives. The principles of production for nitrocellulose have not changed much since the 19th century when it started being industrially produced for this purpose. The character of the nitrocellulose has a large effect on the end products abilities. The aim of this study was to develop a method that would be able to characterise and distinguish between nitrocellulose from different manufacturers to be able to relate the character of the nitrocellulose to the properties of ammunition, propellants and explosives. Samples were dissolved in acetone and analysed by GC/MS and data were then analysed by multivariable statistics. FTIR was also used to characterise the nitrocellulose. Results from both methods showed very small differences when chromatograms and spectra were analysed. This study shows that GC/MS and FTIR are not suitable for this type of characterisation. The differences between the data were not sufficient to be able to separate the samples from each other.

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide - dependent deacetylase that belongs to the sirtuin protein family. The protein has been linked to both cancer through its effect on p53 and age related illnesses through its effect on peroxisome proliferator-activated receptor gamma (PPAR-γ). Recent data have shown a correlation between SIRT1, male fertility and spermatogenesis. Because the mechanism of sex differentiation in zebrafish is still not wellunderstood the sirt1 gene is an attractive target to study in order to improve our understanding of this topic. Zebrafish of different age were exposed to various concentrations of EX-527 toinhibit the SIRT1 protein. This was followed by qRT-PCR analysis of apoptotic and sex-related genes. Both apoptotic and sex-related gene expression levels were affected by the exposure. There were differences in genes that were affected, both between the concentrations of EX-527, and between the ages of the exposed zebrafish. The male- specific gene sexdetermining region Y box 9A (sox9a) was down-regulated at both studied EX-527 concentrations in both zebrafish larvae and juveniles. The exposure of the EX-527 resulted in no significant difference in sex-ratio. Further studies are required to describe the pathway for SIRT1 gene regulation in zebrafish.

As part of the global monitoring plan on persistent organic pollutants (GMP) under the Stockholm Convention passive air samplers equipped with polyurethane foam disks (PUFs) were applied to monitor dioxin-like POPs. For sampling, toluene-pretreated PUFs were exposed for three months during two years. Chemical analysis was performed in one accredited expert laboratory using internationally accepted methods; for comparison, all results were normalized to one PUF and 3 month exposure. Total TEQs, using WHO2005-TEFs, were lowest in the Pacific Islands countries (PAC) and had similar mean values in Africa (16.8 pg TEQ/PUF), Asia (16.9 pg TEQ/PUF), and Latin American and Caribbean countries (GRULAC, 13.3 pg TEQ/PUF). Using median values, Asia (13.4 pg TEQ/PUF) and GRULAC (13.1 pg TEQ/PUF) had higher amounts than Africa (6.1 pg TEQ/PUF) and PAC (2.1 pg TEQ/PUF). The contribution of PCDD/PCDF to the total TEQ was 2-3-times higher than from the dl-PCB. Mono-ortho PCB did not play a role in any of the samples. The previous 40 samples during 2010/2011 and the present 195 samples from 2017/2018 did not show a statistical difference (p value = 0.3), only for GRULAC, a downward trend was identified. It is recommended combining 4 PUFs to ‘annual’ samples.

The first worldwide interlaboratory assesment on persistent organic pollutants (POPs) under the Stockholm Convention was organized in the Asian/Pacific, Latin American and African regions during 2009-11.

A relatively large number of laboratories reported data for the PCDDs/PCDFs and dioxin-like PCBs, especially in the Asian region. Within the Asian region, several participants used high-resolution GC/high-resolution MS systems optimized for dioxin analysis. The availibility of High-resolution mass spectrometer instrumentation is limited in the Latin America and African regions, although recently several new laboratories for dioxins have started in the Latin American region.

Society is highly digitalized and digital learning resources have been given a large space at school in almost all grades. The purpose of using digital learning resources is to support students in their learning and development. Digital learning resources have another purpose, which is to facilitate the teacher's work. In this study, a systematic literature study will be done to investigate what previous research that has focused on digital learning resources in mathematics education will be compiled. digital learning resources in mathematics teaching. The purpose of this literature study is to investigate the use of digital learning resources during mathematics teaching and how it can promote student learning. The study will also considerthe use of digital learning resources based on the socio-cultural perspective. The results of the compiled research showed that digital learning resources of various kinds can promote students' learning in different ways. Among other things, it promotes motivation for learning, commitment, problem-solving skills, algebraic thinking, number calculations and pattern recognition. The results also show that a large part of the underlying aspects that can prevent students' learning from being promoted through digital learning resources is the teachers' lack of knowledge regarding the use of the material

In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F ₁ score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

Reliable autonomous navigation is still a challenging problem for robots with simple and inexpensive hardware. A key difficulty is the need to maintain an internal map of the environment and an accurate estimate of the robot’s position in this map. Recently, a stigmergic approach has been proposed in which a navigation map is stored into the environment, on a grid of RFID tags, and robots use it to optimally reach predefined goal points without the need for internal maps. While effective,this approach is limited to a predefined set of goal points. In this paper, we extend this approach to enable robots to travel to any point on the RFID floor, even if it was not previously identified as a goal location, as well as to keep a safe distance from any given critical location. Our approach produces safe, repeatable and quasi-optimal trajectories without the use of internal maps, self localization, or path planning. We report experiments run in a real apartment equipped with an RFID floor, in which a service robot either reaches or avoids a user who wears slippers equipped with an RFID tag reader.

Management of solitons in media with competing quadratic and cubic nonlinearities is investigated. Two schemes, using rapid modulations of a mismatch parameter, and of the Kerr nonlinearity parameter are studied. For both cases, the averaged in time wave equations are derived. In the case of mismatch management, the region of the parameters where stabilization is possible is found. In the case of Kerr nonlinearity management, it is shown that the effective chi(2) nonlinearity depends on the intensity imbalance between fundamental (FH) and second (SH) harmonics. Predictions obtained from the averaged equations are confirmed by numerical simulations of the full PDE’s.

The evolution of vector solitons under nonlinearity management is studied. The averaged over strong and rapid modulations in time of the inter-species interactions vector Gross-Pitaevskii equation (GPE) is derived. The averaging gives the appearance of the effective nonlinear quantum pressure depending on the population of the other component. Using this system of equations, the existence and stability of the vector solitons under the action of the strong nonlinearity management (NM) is investigated. Using a variational approach the parameters of NM vector solitons are found. The numerical simulations of the full time-dependent coupled GPE confirms the theoretical predictions. 

Dynamics of matter waves in the atomic to molecular condensate transition with a time modulated atomic scattering length is investigated. The conditions for dynamical suppression of association of atoms into the molecular field are obtained.

The Zeno effect is investigated for soliton type pulses in a nonlinear directional coupler with dissipation. The effect consists in increase of the coupler transparency with increase of the dissipative losses in one of the arms. It is shown that localized dissipation can lead to switching of solitons between the arms. Power losses accompanying the switching can be fully compensated by using a combination of dissipative and active (in particular, parity-time-symmetric) segments.

The generation of Faraday waves in superfluid Fermi-Bose mixtures in elongated traps is investigated. The generation of waves is achieved by periodically changing a parameter of the system in time. Two types of modulations of parameters are considered: a variation of the fermion-boson scattering length and the boson-boson scattering length. We predict the properties of the generated Faraday patterns and study the parameter regions where they can be excited.

Collective oscillations of superfluid mixtures of ultra cold fermionic and bosonic atoms are investigated while varying the fermion-boson scattering length. We study the dynamics with respect to excited center of mass modes and breathing modes in the mixture. Parametric resonances are also analyzed when the scattering length varies periodically in time, by comparing partial differential equation (PDE) models and ordinary differential equation (ODE) models for the dynamics. An application to the recent experiment with fermionic Li-6 and bosonic Li-7 atoms, which approximately have the same masses, is discussed.

The dynamics of matter waves in the atomic to molecular condensate transition with a time-modulated atomic scattering length is investigated. Both the cases of rapid and slow modulations are studied. In the case of rapid modulations, the average over oscillations for the system is derived. The corresponding conditions for dynamical suppression of the association of atoms into the molecular field, or of second-harmonic generation in nonlinear optical systems, are obtained. For the case of slow modulations, we find resonant enhancement in the molecular field. We then illustrate chaos in the atomic-molecular BEC system. We suggest a sequential application of the two types of modulations, slow and rapid, when producing molecules.

We have previously shown that gadolinium oxide (Gd2O3) nanoparticles are promising candidates to be used as contrast agents in magnetic resonance (MR) imaging applications. In this study, these nanoparticles were investigated in a cellular system, as possible probes for visualization and targeting intended for bioimaging applications. We evaluated the impact of the presence of Gd2O3 nanoparticles on the production of reactive oxygen species (ROS) from human neutrophils, by means of luminol-dependent chemiluminescence. Three sets of Gd2O3 nanoparticles were studied, i.e. as synthesized, dialyzed and both PEG-functionalized and dialyzed Gd2O3 nanoparticles. In addition, neutrophil morphology was evaluated by fluorescent staining of the actin cytoskeleton and fluorescence microscopy. We show that surface modification of these nanoparticles with polyethylene glycol (PEG) is essential in order to increase their biocompatibility. We observed that the as synthesized nanoparticles markedly decreased the ROS production from neutrophils challenged with prey (opsonized yeast particles) compared to controls without nanoparticles. After functionalization and dialysis, more moderate inhibitory effects were observed at a corresponding concentration of gadolinium. At lower gadolinium concentration the response was similar to that of the control cells. We suggest that the diethylene glycol (DEG) present in the as synthesized nanoparticle preparation is responsible for the inhibitory effects on the neutrophil oxidative burst. Indeed, in the present study we also show that even a low concentration of DEG, 0.3%, severely inhibits neutrophil function. In summary, the low cellular response upon PEG-functionalized Gd2O3 nanoparticle exposure indicates that these nanoparticles are promising candidates for MR-imaging purposes.

We here report the genetic basis for susceptibility and resistance to carcinogen-mediated [7,12-dimethylbenz[a] anthracene (DMBA)] mammary tumorigenesis using the full panel of SS/BN consomic rat strains, in which substitutions of individual chromosomes from the resistant BN strain onto the genomic background of the susceptible SS strain were made. Analysis of 252 consomic females identified rat mammary Quantitative Trait Loci (QTLs) affecting tumor incidence on chromosomes 3 and 5, latency on chromosomes 3, 9, 14, and 19, and multiplicity on chromosomes 13, 16, and 19. In addition, we unexpectedly identified a novel QTL on chromosome 6 controlling a lethal toxic phenotype in response to DMBA. Upon further investigation with chromosomes 6 and 13 congenic lines, in which an additional 114 rats were investigated, we mapped (1) a novel mammary tumor QTL to a region of 27.1 Mbp in the distal part of RNO6, a region that is entirely separated from the toxicity phenotype, and (2) a novel and powerful mammary tumor susceptibility locus of 4.5 Mbp that mapped to the proximal q-arm of RNO13. Comparison of genetic strain differences using existing rat genome databases enabled us to further construct priority lists containing single breast cancer candidate genes within the defined QTLs, serving as potential functional variants for future testing.

Cellular uptake of inorganic divalent mercury (Hg(II)) is a key step in microbial formation of neurotoxic methylmercury (MeHg), but the mechanisms remain largely unidentified. We show that the iron reducing bacterium Geobacter sulfurreducens produces and exports appreciable amounts of low molecular mass thiol (LMM-RSH) compounds reaching concentrations of about 100 nM in the assay medium. These compounds largely control the chemical speciation and bioavailability of Hg(II) by the formation of Hg(LMM-RS)<INF><INF><INF>2</INF></INF> </INF>complexes (primarily with cysteine) in assays without added thiols. By characterizing these effects, we show that the thermodynamic stability of Hg(II)-complexes is a principal controlling factor for Hg(II) methylation by this bacterium such that less stable complexes with mixed ligation involving LMM-RSH, OH<SUP>-</SUP>, and Cl<SUP>-</SUP> are methylated at higher rates than the more stable Hg(LMM-RS)<INF>2</INF> complexes. The Hg(II) methylation rate across different Hg(LMM-RS)<INF>2</INF> compounds is also influenced by the chemical structure of the complexes. In contrast to the current perception of microbial uptake of Hg, our results adhere to generalized theories for metal biouptake based on metal complexation with cell surface ligands and refine the mechanistic understanding of Hg(II) availability for microbial methylation.

The cell division cycle in eukaryotic cells is a series of highly coordinated molecular interactions that ensure that cell growth, duplication of genetic material, and actual cell division are precisely orchestrated to give rise to two viable progeny cells. Moreover, the cell cycle machinery is responsible for incorporating information about external cues or internal processes that the cell must keep track of to ensure a coordinated, timely progression of all related processes. This is most pronounced in multicellular organisms, but also a cardinal feature in model organisms such as baker's yeast. The complex and integrative behavior is difficult to grasp and requires mathematical modeling to fully understand the quantitative interplay of the single components within the entire system. Here, we present a self-oscillating mathematical model of the yeast cell cycle that comprises all major cyclins and their main regulators. Furthermore, it accounts for the regulation of the cell cycle machinery by a series of external stimuli such as mating pheromones and changes in osmotic pressure or nutrient quality. We demonstrate how the external perturbations modify the dynamics of cell cycle components and how the cell cycle resumes after adaptation to or relief from stress.

I denna studie har undersökningarna Kortperiodisk Sysselsättningsstatistiks (KS) och Konjunkturstatistik över Vakansers (KV) befintliga granskningssystem utvärderats med avseende på hur effektivt det är. Processdata har framställts och analyserats. Resultaten tyder på att många av de inkomna blanketterna med misstänkt felaktiga uppgifter inte rättas upp, utan tvingas igenom trots att granskningssystemet ej accepterade uppgifterna. Det befintliga granskningssystemet har en högre träffsäkerhet avseende KS-undersökningen, men både KS och KV skulle kunnas granskas mer effektivt.

För att utvärdera det befintliga granskningssystemet ytterligare användes en poängfunktion. Till studien fanns tillgång till både helt ogranskat material och helt granskat material och dessa material användes i poängfunktionen. Det uppräknade ogranskade värdet för varje objekt jämfördes med det uppräknade granskade värdet och ställdes i relation till respektive skattade branschtotal. De poängsatta blanketterna rangordnades sedan. Därefter analyserades materialet för att försöka finna var det skulle vara lämpligt att sätta det tröskelvärde som skulle skilja det material som ”egentligen” skulle ha behövts granskas från det som kunde ha lämnats orört. Att sätta tröskelvärdet är svårt. Här gjordes det godtyckligt utifrån kriterierna att det fel som införs i skattningarna för att allt material inte granskas skulle hållas så lågt som möjligt samt att antalet blanketter som skulle behöva granskas manuellt av produktionsgruppen också skulle hållas så lågt som möjligt. Även här visade det sig att det befintliga granskningssystemet inte är så effektivt som önskas. När resultaten från denna del av utvärderingen analyserades upptäcktes problem som beror på blankettutformningen. Skulle blanketterna ses över och åtgärdas skulle det fel som införs för att allt material inte granskas kunna minskas avsevärt. Genom att minska det införda felet kan tröskelvärdet förmodligen sättas på en ny nivå vilket medför att omfattningen av granskningen skulle minska ytterligare.

Hur skulle då ett mer effektivt granskningssystem kunna se ut? I den här studien har valet fallit på att testa ”significance editing” på KS-undersökningen, det som på svenska kallas för effektgranskning. En poängfunktion användes även här, denna tilldelar de inkomna blanketterna varsin poäng och dessa poäng rangordnas därefter. Efter att poängen rangordnats bestäms en gräns, ett tröskelvärde, och de blanketter med en poäng som överstiger tröskelvärdet granskas och rättas upp av produktionsgruppen. De blanketter med en poäng som understiger det satta tröskelvärdet rättas inte upp, utan behåller sina originalvärden. Poängfunktionen jämför det inkomna ogranskade, uppräknade, värdet med ett uppräknat ”förväntat” värde och ställer denna differens i relation till den skattade branschtotalen. Svårigheten ligger ofta i att hitta ett bra förväntat värde och detta problem uppstår ideligen i urvalsundersökningar. Tanken med effektgranskning är att omfattningen av granskningen ska minska och den granskning som utförs ska ha effekt på slutresultatet.

Det var inte lätt att hitta ett bra förväntat värde på den tid som stod till förfogande. Två problem som snabbt upptäcktes var dels att i KS-undersökningen finns inte uträknade säsongs- eller trendfaktorer per variabel. Dessutom byttes en mycket stor del av urvalet ut till kvartal 2 (som denna studie har avgränsats till att behandla). Detta har fått till följd att cirka hälften av objekten i urvalet inte går att följa bakåt i tiden eftersom de inte ingått i urvalet tidigare. I studien har respektive stratums medelvärde använts som förväntat värde. Resultaten visar att det valda förväntade värdet inte skulle ha använts i praktiken, men det fungerar bra i syfte att illustrera hur det i praktiken skulle kunna gå till att införa en mer effektiv granskning.

Det har pågått ett intensivt utvecklingsarbete på Statistiska Centralbyrån (SCB) under de senaste åren i syfte att standardisera och effektivisera statistikproduktionsprocessen. I detta utvecklingsarbete har fokus främst riktats mot processerna insamling och granskning. Ett flertal studier har visat att det finns potential att reducera granskningens omfattning samtidigt som den övergripande kvaliteten i undersökningarna bibehålls. För att uppnå detta krävs att nya arbetssätt, metoder och verktyg utvecklas och implementeras.

Den traditionella ansatsen på SCB har varit att i granskningsprocessen försöka hitta och rätta alla databearbetnings- och mätfel. Ingen skillnad har gjorts mellan stora och små fel eller om felen har någon effekt på statistiken eller inte. Detta är en ineffektiv ansats där stora resurser åtgår till att rätta fel som inte påverkar den statistiska redovisningen nämnvärt. I mer moderna ansatser betonas vikten av att hitta betydelsefulla fel som har stor påverkan på parameterskattningarna och att fel som inte ger någon påverkan bör lämnas som de är eller åtgärdas via imputering. Detta, att inte granska allt, kallas för selektiv granskning.

SCB har beslutat att införa metoden selektiv granskning med poängfunktioner. Metoden fordrar att poängberäkningar görs, dessa utförs i verktyget Selekt. Verktyget ingår i den framtida verktygslådan för granskning som är under utveckling vid SCB och är uppbyggt av ett stort antal parametrar. För att uppnå så effektiv granskning som möjligt måste de mest lämpliga parametervärdena sökas för att sedan implementeras i Selekt.

I denna studie har ett datamaterial från undersökningen Kortperiodisk Sysselsättningsstatistik, privat sektor (KSP) använts för att studera sambanden mellan statistikens kvalitet och valet av parametrar i Selekt.  Valet av datamaterial motiveras främst av att Selekt ska implementeras i KSP under år 2010. De parametrar som har behandlats i studien kallas för KAPPA, TAU och LAMBDA samt variablerna RPB_20 och Kostnad.

Logistisk regression har använts för att undersöka vilken påverkan parametrarna har på den bias (kallad RPB) som införs i skattningarna vid selektiv granskning. En ansats valdes där sambandet mellan responsvariabeln RPB_20 och förklaringsvariablerna KAPPA, TAU och Kostnad studerades separat för olika värden på LAMBDA.

Vid resultatframställningen indikerades tidigt att valet av värde på LAMBDA inte verkade ha någon nämnvärd betydelse för modellen och i de fortsatta analyserna stärktes denna misstanke och kom att omfatta även KAPPA och TAU. Det var redan från början känt att Kostnad är en viktig variabel att ta hänsyn till och för att undersöka detta närmare konstruerades en modell bestående av ett fjärdegradspolynom med enbart variabeln Kostnad. Modellen lyckades fånga upp huvuddragen av variationen i RPB_20.

Det går inte att dra generella slutsatser från den studie som här har genomförts. Resultaten visar dock att en modell utan KAPPA, TAU och LAMBDA fungerar för att beskriva variationen i RPB_20.  Valet av värden på KAPPA, TAU och LAMBDA i Selekt är av mindre betydelse. I implementeringsarbetet av Selekt i KSP rekommenderas därför att, förutom RPB, fokusera på variabeln Kostnad för att hitta den mest lämpliga kombinationen av parameterinställningar.

The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.

This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:

How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?

While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.

The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public

This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training. 

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

We developed an efficient approach to diagnostic copy number analysis of targeted gene panel or whole exome sequence (WES) data. Here we present CNV-Z as a new tool for detection of copy number variants (CNVs). Deletions and duplications of chromosomal regions are widely implicated in both genomic evolution and genetic disorders. However, calling CNVs from targeted or exome sequence data is challenging. In most cases, the copy number of a chromosomal region is estimated as the depth of reads mapping to a certain bin or sliding window divided by the expected number of reads derived from a set of reference samples. This approach will inevitably miss smaller CNVs on an irregular basis, and quite frequently results in a disturbing number of false positive CNVs. We developed an alternative approach to detect CNVs based on deviation from expected read depth per position, instead of region. Cautiously used, the cohort of samples in the same run will do as a reference. With appropriate filtering, given high quality DNA and a set of suitable reference samples, CNV-Z detects CNVs ranging in length from one nucleotide to an entire chromosome, with few false positives. Performance is proved by benchmarking using both in-house targeted gene panel NGS data and a publicly available NGS dataset, both sets with multiplex ligation-dependent amplification probe (MLPA) validated CNVs. The outcome shows that CNV-Z detects single- and multi-exonic CNVs with high specificity and sensitivity using different kind of NGS data. On gene level, CNV-Z shows both excellent sensitivity and specificity. Compared to competing CNV callers, CNV-Z shows higher specificity and positive predictive value for detecting exonic CNVs.

Semi-partitioned scheduling has become the subject of recent interest for multiprocessors due to better utilization results, compared to conventional global and partitioned scheduling algorithms. Under semi-partitioned scheduling, a major group of tasks are assigned to fixed processors while a low number of tasks are allocated to more than one processor. Various task assigning techniques have recently been proposed in a semi-partitioned environment. However, a synchronization mechanism for resource sharing among tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose and evaluate two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge addressed in this paper is to serve the resource requests of tasks that are assigned to different processors.

Semi-partitioned scheduling has been the subject of recent interest, compared with conventional global and partitioned scheduling algorithms for multiprocessors, due to better utilization results. In semi-partitioned scheduling most tasks are assigned to fixed processors while a low number of tasks are split up and allocated to different processors. Various techniques have recently been proposed to assign tasks in a semi-partitioned environment. However, an appropriate resource sharing mechanism for handling the resource requests between tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge is to handle the resource requests of tasks that are split over multiple processors.

Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

Objective: The aim of this work is to evaluate clinimetric properties of a method for measuring Parkinson’s disease (PD) upper limb temporal irregularities during spiral drawing tasks.

Background: Basal ganglia fluctuations of PD patients are associated with motor symptoms and relating them to objective sensor-based measures may facilitate the assessment of temporal irregularities, which could be difficult to be assessed visually. The present study investigated the upper limb temporal irregularity of patients at different stages of PD and medication time points.

Methods: Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated the videos of patients' performance according to six items of UPDRS-III, dyskinesia (Dys), and Treatment Response Scale (TRS). A temporal irregularity score (TIS) was developed using approximate entropy (ApEn) method. Differences in mean TIS between two groups of patients and healthy subjects, and also across four subject groups: early, intermediate, advanced patients and, healthy subjects were assessed. The relative ability of TIS to detect changes from baseline (no medication) to later time points when patients were on medication was assessed. Correlations between TIS and clinical rating scales were assessed by Pearson correlation coefficients and test-retest reliability of TIS was measured by intra-class correlation coefficients (ICC).

Results: The mean TIS was significantly different between healthy subjects and patients (P<0.0001). When assessing the changes in relation to treatment, clinical-based scores (TRS and Dys) had better responsiveness than TIS. However, the TIS was able to capture changes from Off to On, and the wearing off effects. Correlations between TIS and clinical scales were low indicating poor validity. Test-retest reliability correlation coefficient of the mean TIS was good (ICC=0.67).

Conclusions: Our study found that TIS was able to differentiate spiral drawings drawn by patients from those drawn by healthy subjects. In addition, TIS could capture changes throughout the levodopa cycle.TIS was weakly correlated to clinical ratings indicating that TIS measures high frequency upper limb temporal irregularities that could be difficult to be detected during clinical observations.

Objectives

The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD.

Methods

Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables.

Results

There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls.

Conclusions

Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC=0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R=0.84) and gait (R=0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.