Spectral fundus autofluorescence peak emission wavelength in ageing and AMD. - In: Acta ophthalmologica, ISSN 1755-3768, Bd. 100 (2022), 6, S. e1223-e1231
Purpose To investigate the spectral characteristics of fundus autofluorescence (FAF) in AMD patients and controls. Methods Fundus autofluorescence spectral characteristics was described by the peak emission wavelength (PEW) of the spectra. Peak emission wavelength (PEW) was derived from the ratio of FAF recordings in two spectral channels at 500-560 nm and 560-720 nm by fluorescence lifetime imaging ophthalmoscopy. The ratio of FAF intensity in both channels was related to PEW by a calibration procedure. Peak emission wavelength (PEW) measurements were done in 44 young (mean age: 24.0 ± 3.8 years) and 18 elderly (mean age: 67.5 ± 10.2 years) healthy subjects as well as 63 patients with AMD (mean age: 74.0 ± 7.3 years) in each pixel of a 30˚ imaging field. The values were averaged over the central area, the inner and the outer ring of the ETDRS grid. Results There was no significant difference between PEW in young and elderly controls. However, PEW was significantly shorter in AMD patients (ETDRS grid centre: 571 ± 26 nm versus 599 ± 17 nm for elderly controls, inner ring: 596 ± 17 nm versus 611 ± 11 nm, outer ring: 602 ± 16 nm versus 614 ± 11 nm). After a mean follow-up time of 50.8 ± 10.8 months, the PEW in the patients decreased significantly by 9 ± 19 nm in the inner ring of the grid. Patients, showing progression to atrophic AMD in the follow up, had significantly (p ≤ 0.018) shorter PEW at baseline than non-progressing patients. Conclusions Peak emission wavelength (PEW) is related to AMD pathology and might be a diagnostic marker in AMD. Possibly, a short PEW can predict progression to retinal and/or pigment epithelium atrophy.
https://doi.org/https://doi.org/10.1111/aos.15070
Graph based mining of code change patterns from version control commits. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 48 (2022), 3, S. 848-863
Detailed knowledge of frequently recurring code changes can be beneficial for a variety of software engineering activities. For example, it is a key step to understand the process of software evolution, but is also necessary when developing more sophisticated code completion features predicting likely changes. Previous attempts on automatically finding such code change patterns were mainly based on frequent itemset mining, which essentially finds sets of edits occurring in close proximity. However, these approaches do not analyze the interplay among code elements, e.g., two code objects being named similarly, and thereby neglect great potential in identifying a number of meaningful patterns. We present a novel method for the automated mining of code change patterns from Git repositories that captures these context relations between individual edits. Our approach relies on a transformation of source code into a graph representation, while keeping relevant relations present. We then apply graph mining techniques to extract frequent subgraphs, which can be used for further analysis of development projects. We suggest multiple usage scenarios for the resulting pattern type. Additionally, we propose a transformation into complex event processing (CEP) rules which allows for easier application, especially for event-based auto-completion recommenders or similar tools. For evaluation, we mined seven open-source code repositories. We present 25 frequent change patterns occurring across these projects. We found these patterns to be meaningful, easy to interpret and mostly persistent across project borders. On average, a pattern from our set appeared in 45 percent of the analyzed code changes.
https://doi.org/10.1109/TSE.2020.3004892
Robust continuous finite-time tracking control with finite-time observer for a Stewart platform. - In: 2022 IEEE 17th International Conference on Advanced Motion Control (AMC), (2022), S. 306-310
In this paper, a third-order sliding mode control (SMC) with a super-twisting finite time observer is proposed for the position control of a Stewart platform. The Stewart platform is a parallel robotic manipulator with a fixed base platform connected to a movable top platform with the help of six actuator legs. The proposed control makes the movable platform track a reference trajectory in finite time while rejecting matched disturbances. Conventional SMC offers robustness towards matched disturbances, but will induce chattering in the system. The proposed third-order SMC can ease out the chattering problem and achieve finite-time tracking. Furthermore, a robust super-twisting observer is designed, which converges to the actual states in finite time, such that the proposed control can be computed from the system output using the observer. A simulation study of the Stewart platform is done for the proposed robust continuous finite-time control with the finite-time observer. The results using the proposed control are compared with conventional SMC, PID control and twisting control.
https://doi.org/10.1109/AMC51637.2022.9729266
Control of an overactuated nanopositioning system with hysteresis by means of control allocation. - In: 2022 IEEE 17th International Conference on Advanced Motion Control (AMC), (2022), S. 280-287
This paper is devoted to the analysis, modeling and controller design of the overactuated nanopositioning lifting and actuating unit (LAU) applying dynamic control allocation. A detailed model is provided and its parameters are identified from experimental data. Since the LAU system is subject to magnetic hysteresis, we apply Preisach's operator to handle this phenomenon. The control objective is the vertical displacement in the millimeter range with nanometer precision under a control effort distribution. For this, a dynamic control allocation is proposed in which a frequency distribution of the control effort is considered. Low frequency components are assigned to the slow pneumatic actuator while higher frequencies are handled by the voice coil drive. A Kalman filter is used to compensate the significant actuator dynamics. The position controller is based on a feedback linearization framework with a disturbance observer for enhanced robustness. Finally, the validity and feasibility of the proposed method is demonstrated by simulation and experiment.
https://doi.org/10.1109/AMC51637.2022.9729294
Scavenging of bacteria or bacterial products by magnetic particles functionalized with a broad-spectrum pathogen recognition receptor motif offers diagnostic and therapeutic applications. - In: Acta biomaterialia, ISSN 1878-7568, Bd. 141 (2022), S. 418-428
Sepsis is a dysregulated host response of severe bloodstream infections, and given its frequency of occurrence and high mortality rate, therapeutic improvements are imperative. A reliable biomimetic strategy for the targeting and separation of bacterial pathogens in bloodstream infections involves the use of the broad-spectrum binding motif of human GP-340, a pattern-recognition receptor of the scavenger receptor cysteine rich (SRCR) superfamily that is expressed on epithelial surfaces but not found in blood. Here we show that these peptides, when conjugated to superparamagnetic iron oxide nanoparticles (SPIONs), can separate various bacterial endotoxins and intact microbes (E. coli, S. aureus, P. aeruginosa and S. marcescens) with high efficiency, especially at low and thus clinically relevant concentrations. This is accompanied by a subsequent strong depletion in cytokine release (TNF, IL-6, IL-1β, Il-10 and IFN-γ), which could have a direct therapeutic impact since escalating immune responses complicates severe bloodstream infections and sepsis courses. SPIONs are coated with aminoalkylsilane and capture peptides are orthogonally ligated to this surface. The particles behave fully cyto- and hemocompatible and do not interfere with host structures. Thus, this approach additionally aims to dramatically reduce diagnostic times for patients with suspected bloodstream infections and accelerate targeted antibiotic therapy. - Statement of significance - Sepsis is often associated with excessive release of cytokines. This aspect and slow diagnostic procedures are the major therapeutic obstacles. The use of magnetic particles conjugated with small peptides derived from the binding motif of a broad-spectrum mucosal pathogen recognition protein GP-340 provides a highly efficient scavenging platform. These peptides are not found in blood and therefore are not subject to inhibitory mechanisms like in other concepts (mannose binding lectine, aptamers, antibodies). In this work, data are shown on the broad bacterial binding spectrum, highly efficient toxin depletion, which directly reduces the release of cytokines. Host cells are not affected and antibiotics not adsorbed. The particle bound microbes can be recultured without restriction and thus be used directly for diagnostics.
https://doi.org/10.1016/j.actbio.2022.01.001
Robust distributed resource allocation for cellular vehicle-to-vehicle communication. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (xiii, 170 Seiten)
Technische Universität Ilmenau, Dissertation 2022
Mit Release 14 des LTE Standards unterstützt dieser die direkte Fahrzeug-zu-Fahrzeug-Kommunikation über den Sidelink. Diese Dissertation beschäftigt sich mit dem Scheduling Modus 4, einem verteilten MAC-Protokoll ohne Involvierung der Basisstation, das auf periodischer Wiederverwendung von Funkressourcen aufbaut. Der Stand der Technik und eine eigene Analyse des Protokolls decken verschiedene Probleme auf. So wiederholen sich Kollisionen von Paketen, wodurch manche Fahrzeuge für längere Zeit keine sicherheitskritischen Informationen verbreiten können. Kollisionen entstehen vermehrt auch dadurch, dass Hidden-Terminal-Probleme in Kauf genommen werden oder veränderliche Paketgrößen und -raten schlecht unterstützt werden. Deshalb wird ein Ansatz namens "Scheduling based on Acknowledgement Feedback Exchange" vorgeschlagen. Zunächst wird eine Funkreservierung in mehrere ineinander verschachtelte Unter-Reservierungen mit verschiedenen Funkressourcen unterteilt, was die Robustheit gegenüber wiederholenden Kollisionen erhöht. Dies ist die Grundlage für eine verteilte Staukontrolle, die die Periodizitätseigenschaft nicht verletzt. Außerdem können so veränderliche Paketgrößen oder -raten besser abgebildet werden. Durch die periodische Wiederverwendung können Acknowledgements für Funkressourcen statt für Pakete ausgesendet werden. Diese können in einer Bitmap in den Padding-Bits übertragen werden. Mittels der Einbeziehung dieser Informationen bei der Auswahl von Funkressourcen können Hidden-Terminal-Probleme effizient vermieden werden, da die Acknowledgements auch eine Verwendung dieser Funkressource ankündigen. Kollisionen können nun entdeckt und eine Wiederholung vermieden werden. Die Evaluierung des neuen MAC-Protokolls wurde zum großen Teil mittels diskreter-Event-Simulationen durchgeführt, wobei die Bewegung jedes einzelnen Fahrzeuges simuliert wurde. Der vorgeschlagene Ansatz führt zu einer deutlich erhöhten Paketzustellrate. Die Verwendung einer anwendungsbezogenen Awareness-Metrik zeigt, dass die Zuverlässigkeit der Kommunikation durch den Ansatz deutlich verbessert werden kann. Somit zeigt sich der präsentierte Ansatz als vielversprechende Lösung für die erheblichen Probleme, die der LTE Modus 4 mit sich bringt.
https://doi.org/10.22032/dbt.51540
Quality-driven regularization for deep learning networks and its application to industrial soft sensors. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-2388, (2022), S. 1-11
Early access
The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.
https://doi.org/10.1109/TNNLS.2022.3144162
StickyLocalization: robust end-to-end relocalization on point clouds using graph neural networks. - In: 2022 IEEE Winter Conference on Applications of Computer Vision, (2022), S. 307-316
Relocalization inside pre-built maps provides a big benefit in the course of today’s autonomous driving tasks where the map can be considered as an additional sensor for refining the estimated current pose of the vehicle. Due to potentially large drifts in the initial pose guess as well as maps containing unfiltered dynamic and temporal static objects (e.g. parking cars), traditional methods like ICP tend to fail and show high computation times. We propose a novel and fast relocalization method for accurate pose estimation inside a pre-built map based on 3D point clouds. The method is robust against inaccurate initialization caused by low performance GPS systems and tolerates the presence of unfiltered objects by specifically learning to extract significant features from current scans and adjacent map sections. More specifically, we introduce a novel distance-based matching loss enabling us to simultaneously extract important information from raw point clouds and aggregating inner- and inter-cloud context by utilizing self- and cross-attention inside a Graph Neural Network. We evaluate StickyLocalization’s (SL) performance through an extensive series of experiments using two benchmark datasets in terms of Relocalization on NuScenes and Loop Closing using KITTI’s Odometry dataset. We found that SL outperforms state-of-the art point cloud registration and relocalization methods in terms of transformation errors and runtime.
https://doi.org/10.1109/WACV51458.2022.00038
PRECODE - a generic model extension to prevent deep gradient leakage. - In: 2022 IEEE Winter Conference on Applications of Computer Vision, (2022), S. 3605-3614
Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. Although training data entirely resides with the clients, recent work shows that training data can be reconstructed from such exchanged gradient information. To enhance privacy, gradient perturbation techniques have been proposed. However, they come at the cost of reduced model performance, increased convergence time, or increased data demand. In this paper, we introduce PRECODE, a PRivacy EnhanCing mODulE that can be used as generic extension for arbitrary model architectures. We propose a simple yet effective realization of PRECODE using variational modeling. The stochastic sampling induced by variational modeling effectively prevents privacy leakage from gradients and in turn preserves privacy of data owners. We evaluate PRECODE using state of the art gradient inversion attacks on two different model architectures trained on three datasets. In contrast to commonly used defense mechanisms, we find that our proposed modification consistently reduces the attack success rate to 0% while having almost no negative impact on model training and final performance. As a result, PRECODE reveals a promising path towards privacy enhancing model extensions.
https://doi.org/10.1109/WACV51458.2022.00366
Image-based automated recognition of 31 Poaceae species: the most relevant perspectives. - In: Frontiers in plant science, ISSN 1664-462X, Bd. 12 (2022), 804140, S. 1-12
Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.
https://doi.org/10.3389/fpls.2021.804140