Pascal Libuschewski

Application of surface plasmon resonance imaging technique for the detection of single spherical biological submicrometer particles

Recent proof-of-principle studies demonstrated the suitability of the surface plasmon resonance imaging (SPRi) technique for the detection of individual submicrometer and nanoparticles in solutions. In the current study, we used the SPRi technique for visualization of the binding of round-shaped viruses (inactivated influenza A virus) and virus-like particles (human immunodeficiency virus (HIV)-based virus-like particles) to the functionalized sensor surface. We show the applicability of the SPRi technique for the detection of individual virus-like particles in buffers without serum as well as in buffers containing different concentrations of serum. Furthermore, we prove the specificity of visualized binding events using two different pseudotypes of HIV virus-like particles. We also demonstrate the applicability of the SPRi technique for the determination of relative particle concentrations in solutions. Moreover, we suggest a technical approach, which allows enhancing the magnitude of binding signals. Our studies indicate that the SPRi technique represents an efficient research tool for quantification and characterization of biological submicrometer objects such as viruses or virus-like particles, for example.

Plasmon-based Virus Detection on Heterogeneous Embedded Systems

Embedded systems, e.g. in computer vision applications, are expected to provide significant amounts of computing power to process large data volumes. Many of these systems, such as used in medical diagnosis, are mobile devices and face significant challenges to provide sufficient performance while operating on a constrained energy budget. Modern embedded MPSoC platforms use heterogeneous CPU and GPU cores providing a large number of optimization parameters. This allows to find useful trade-offs between energy consumption and performance for a given application. In this paper, we describe how the complex data processing required for PAMONO, a novel type of biosensor for the detection of biological viruses, can efficiently be implemented on a state-of-the-art heterogeneous MPSoC platform. An additional optimization dimension explored is the achieved quality of service. Reducing the virus detection accuracy enables additional optimizations not achievable by modifying hardware or software parameters alone. Instead of relying on often inaccurate simulation models, our design space exploration employs a hardware-in-the-loop approach to evaluate the performance and energy consumption on the embedded target platform. Trade-offs between performance, energy and accuracy are controlled by a genetic algorithm running on a PC control system which deploys the evaluation tasks to a number of connected embedded boards. Using our optimization approach, we are able to achieve frame rates meeting the requirements without losing accuracy. Further, our approach is able to reduce the energy consumption by 93% with a still reasonable detection quality.

Energy-Aware Design Space Exploration for GPGPUs

This work presents a novel approach for automatically determining the most power- or energy-efficient Graphics Processing Units (GPUs) with respect to given parallel computation problems.

Modellierung und Optimierung eines Biosensors zur Detektion viraler Strukturen

Die echtzeitfahige Detektion mannigfaltiger viraler Strukturen gewinnt zunehmend an Bedeutung. Hier setzt die vorliegende Arbeit an, welche die adaptive Modellierung und Optimierung eines Biosensors vorstellt und zur automatischen Synthese von segmentierten Trainingsdaten nutzt, was den manuellen Aufwand zur Adaption an unterschiedliche Virustypen nachhaltig reduziert. Im vorliegenden Anwendungsfall des PAMONO-Sensors werden uber diesen Ansatz die Parameter eines GPGPU-basierten Objekt-Detektors genetisch optimiert. Die Gute des Ansatzes zeigt sich bei der Ubertragung der optimierten Parameter auf reale Eingabedaten: Die Qualitatsmase Precision und Recall erreichen Werte groser als 0.92.

Multi-objective, Energy-Aware GPGPU Design Space Exploration for Medical or Industrial Applications

This work presents a multi-objective design space exploration for Graphics Processing Units (GPUs). For any given GPGPU application, a Pareto front of best suited GPUs can be calculated. The objectives can be chosen according to the demands of the system, for example energy efficiency, run time and real-time capability. The simulated GPUs can be desktop, high performance or mobile versions. Also GPUs that do not yet exist can be modeled and simulated. The main application area for the presented approach is the identification of suitable GPU hardware for given medical or industrial applications, e.g. For real-time process control or in healthcare sensor environments. As use case a real-time capable medical biosensor program for an automatic detection of pathogens and a wide variety of industrial, biological and physical applications were evaluated.

Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks.

In this work, we improve several steps of our PLASMON ASSISTED MICROSCOPY OF NANO-SIZED OBJECTS (PAMONO) sensor data processing pipeline through application of deep neural networks. The PAMONObiosensor is a mobile nanoparticle sensor utilizing SURFACE PLASMON RESONANCE (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low SIGNAL-TO-NOISE RATIO (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables our data processing pipeline to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop GRAPHICS PROCESSING UNITs (GPUs).

Multi-objective computation offloading for mobile biosensors via LTE

For a rapid identification of viral epidemics a mobile virus detection is needed, which can process samples without a laboratory. The application of medical biosensors, at key positions with a high passenger volume (e.g. airports) became increasingly important as epidemic early warning systems. As mobile biosensors have to fulfill various demands, like a rapid analysis and a long battery lifetime, in this study we present a multi-objective computation offloading for mobile sensors. The decision whether it is beneficial to offload work to a server, using the Long Term Evolution (LTE) wireless network, can be made automatically and dynamically on the basis of conflicting objectives and several constraints.

Signal/Background Classification of Time Series for Biological Virus Detection

This work proposes translation-invariant features based on a wavelet transform that are used to classify time series as containing either relevant signals or noisy background. Due to the translation-invariant property, signals appearing at arbitrary locations in time have similar representations in feature space. Classification is carried out by a condensed \(k\)-Nearest-Neighbors classifier trained on these features, i.e. the training set is reduced for faster classification. This reduction is conducted by a \(k\)-means clustering of the original training set and using the obtained cluster centers as a new training set. The coreset-technique BICO is employed to accelerate this initial clustering for big datasets. The resulting feature extraction and classification pipeline is applied successfully in the context of biological virus detection. Data from Plasmon Assisted Microscopy of Nano-size Objects (PAMONO) is classified, achieving accuracy \(0.999\) for the most important classification task.

Mobile Detektion viraler Pathogene durch echtzeitfähige GPGPU-Fuzzy-Segmentierung

Die vorliegende Arbeit stellt einen neuartigen Fuzzy-Logik-basierten Segmentierungsalgorithmus zur Detektion von biologischen Viren in stark Artefakt-behafteten Bildsequenzen vor, der konform ist zu den differenzierten Ressourcenbeschrankungen mobiler Endgerate. Als Sensor kommt der neuartige PAMONO-Biosensor zum indirekten Nachweis von Viren mittels optischer Mikroskopie zum Einsatz. Die Segmentierungen weisen eine hohe positive Ubereinstimmung bei idealisierten synthetischen Segmentierungen auf, die durch den Fuzzy-Ansatz insbesondere bei kleinen Viren/schlechtem Signal-Rausch-Verhaltnis nochmals verbessert wird. Ferner wird gezeigt, dass eine GPU-gestutzte Datenanalyse die Detektion viraler Strukturen in Echtzeit auf mobilen Endgeraten ermoglicht, und im Vergleich zur CPU den Energieverbrauch im Durchschnitt um Faktor 3.7 senkt.

Parameteroptimierte und GPGPU-basierte Detektion viraler Strukturen innerhalb Plasmonen-unterstützter Mikroskopiedaten

Die lokale Verfugbarkeit von effizienten und leistungsf ahigen Biosensoren, z.B. an Flughafen, gewinnt durch die zunehmende Verbreitung viraler Infektionen zunehmend an Bedeutung. Die zentralen Herausforderungen fur entsprechende in situ Virusdetektionssysteme sind eine schnelle und sichere Erkennung der Viren respektive die Adaptivitat an unterschiedliche Auspragungen von Erregern. Optische Verfahren, wie die neuartige Plasmonen-unterstutzte Mikroskopie von Nanoobjekten erlauben es, diesen Anforderungen zu entsprechen. Aufgrund starker multipler Artefaktbelastung des Signals und hohen Datenmengen (zeitlichen und ortlichen), werden nachhaltige Anforderungen an die Bildrestauration und -analyse gestellt. Hier setzt die vorliegende Arbeit an, welche eine GPGPU-basierte Bildrestaurations- und Bildanalysepipeline vorstellt. Uber eine Kombination aus lokaler und globaler Parameteroptimierung mittels Genetischer Algorithmen kann eine hohere Effektivitat der einzelnen Stufen der Verarbeitung erzielt werden, aber auch im ubergreifenden Verbund – dies zeigt sich nachhaltig in der Erkennungsrate des Biosensors fur Viren.