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Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM
(2023)
The economic use of wood is a growing sector, not only because of the significant advantage of wood to retain CO 2 . It is crucial to increase the material recycling of wood in several lifecycles, but currently, there is no reliable post-consumer wood sorting technique in line. This research measures 365 post-consumer wood samples of classes A1-4 four times with the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) subset method rapid fluorescence lifetime imaging microscopy (Rapid-FLIM). The data is analyzed on their statistical features. Four neural networks based on Multilayer perceptron are then trained and tested with twelve statistical features extracted from the Rapid-FLIM images. The best model for this application contains the optimizer RMSprop, the activation function SELU and the loss function binary crossentropy. The best model of this structure could achieve a false positive ratio of 4.79 % over the ten folds.
Environmental pollution by plastics is an increasing problem. However, state-of-the-art methods have significant disadvantages in detecting and identifying plastics directly in an environmental matrix. In this study, we propose a blob detection algorithm in combination with a neural network for fast and automated identification of plastics and non-plastics in a single fluorescence lifetime image. Therefore an artificial environmental matrix is prepared that contains soil, grass, spruce and HDPE (high density polyethylene) particles. Several FD-FLIM (frequency domain fluorescence lifetime imaging microscopy) images are taken, and the detection algorithm and the neural network are applied. We successfully demonstrated the suitability of the thresholding algorithm and the binary classification of the HDPE particles directly in the environmental matrix.
The FD-FLIM technique has a high potential for automated post-consumer wood sorting. A problem of analysing post-consumer wood fluorescence properties is the uncertainty of the post-consumer wood category as the origin of the samples are unknown. In this study, the fluorescence properties of actual post-consumer wood is compared with prepared wood samples. The post-consumer wood samples display slightly different fluorescence intensities and lifetimes due to environmental influences and a higher sample diversity. For improved training of evaluation algorithms for post-consumer wood sorting, the prepared sample set should be extended or the post-consumer wood should be additionally analysed in the laboratory.
Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.
Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples.
Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.
Texas Instruments' MSP430 Launch Pad is used as a key component for remote labs that complement an introduction course to microcomputer technology in the bachelor program Mechatronic at the University of Applied Sciences in Rosenheim. The remote labs use breakout boards and include a timer experiment with generation of pulse width modulation signals, I2C communication and analog to digital conversion.
The System HAILsens implements a device for Real-Time sensor data processing, running on a Texas Instruments Concerto M3 core. An I2C interface is used for sensor communications. Ethernet with UDP on a custom developed three layered frame organisation of sensor data serves as a link to the main system on board of an aeroplane. For backing up the acquired data, all frames are locally stored on an SD Card. To have the possibility of easily checking the current system status, an LCD module and a push-button provide a minimal user interface.
Comprehensive guide to the process and test methods used for electronic device failure analysis. Contains dozens of articles—contributed by industry experts—covering a wide range of topics involving the failure analysis of microelectronics. It places the most important and up-to-date information on this subject at your fingertips.
Topic coverage includes:
Failure Analysis Process Flow
Failure Verification
Failure Modes and Failure Classification
Special Devices (MEMS, Optoelectronics, Passives)
Fault Localization Techniques: Package Level (NDT)
Die Level (Depackaging, Photon Emission, Microthermography, Laser-Based Methods, Particle Beam Methods)
Deprocessing & Imaging Techniques: Deprocessing
General Imaging Techniques
Local Deprocessing & Imaging
Circuit Edit and Design Modification
Material Analysis Techniques
Reference Information: Important Topics for Semiconductor Devices
Failure Analysis Techniques Roadmap
Failure Analysis Operations and Management
Appendices: Failure Analysis Terms, Definitions, and Acronyms
Industry Standards
A DDR2 SDRAM test setup implemented on the Griffin III ATE test system from HILEVEL Technologies is used to analyse the row hammer bug. Row hammer pattern experiments are compared to standard retention tests.
The analysis confirms that the row hammer effect is caused by a charge excitation process depending on the number of stress activation cycles. The stress has to occur in the local neighborhood of the cells under test.
Shallow impurity levels support the responsible charge carrier transport process in the used DDR2 SDRAM technology
Reference design projects are implemented to the Xilinx Zynq on the Zedboard which is extended by user defined electronic hardware connected through the Zedboard's FMC (FPGA Mezzanine Card) connector. The implementation method with HDL Workflow Advisor includes constraint definitions for modified reference design projects for use with digital input and outputs and Zynq's XADC (Analog-Digital converter) which is characterized and compared to its data sheet. The bandwidth of the Zynq's internal AXI (Advanced eXtensible Interface) bus connected to the Simulink host PC is determined.
A DDR2 DRAM test setup is developed and implemented on the Griffin III ATE test system from HILEVEL Technologies. The test system provides a raw platform for performing various mixed signal and digital tests. In order to configure patterns easily in a vector format, a software platform is developed to manage test patterns according to the user's analysis needs. As examples, retention test patterns with disabled self-refresh are applied to 2 Gbit DDR2 SDRAM of two different DRAM vendors. The devices are characterized in respect to their intrinsic leakage and data retention behavior under the influence of stress conditions such as temperature or access algorithm. The tests are automated and test data is logged for an off-line data analysis. Data is recorded before and after solder simulation steps in order to observe a retention time degradation.
Für eine Einführung in die fertigungstechnischen Grundlagen der Aufbau- und Verbindungstechnik sind im Wahlfach „Leiterplattentechnik Praktikum“ an der Technischen Hochschule (TH) Rosenheim Inhalte der Prozesstechnik und des Leiterplattendesigns miteinander kombiniert. Zu Beginn der Veranstaltung zeichnen die Praktikumsteilnehmer ein Layout für eine Leiterbahn-Teststruktur, welche anschließend im Haus gefertigt wird. Nach der Fertigung folgt eine elektrische und optische Charakterisierung zur Kontrolle der Prozessqualität. Die praxisnahe Erstellung eines einlagigen Layouts für eine LED-Taschenlampe auf Basis eines vorgegebenen Schaltplans und innerhalb einer begrenzten Platinenfläche sowie dessen Fertigung befähigen die Kursteilnehmer später, eigene Layouts für elektronische Schaltungen selbstständig zu entwickeln. Die anspruchsvolle, teilautomatische Bestückung einer zweilagigen, vorgegebenen SMT(Surface Mount Technology)-Leiterplatte, die ein FM-Radio mit Capacitive Touch-Steuerung bildet, rundet die erfolgreiche Qualifizierung in der Leiterplattentechnik ab.
Ein zweistufiger Operationsverstärker wird parallel in einer 180nm CMOS Technologie in der Entwicklungsumgebung von Cadence ADE und in MATLAB/Simulink in einem modellbasierten Ansatz entworfen und simuliert. Für den Entwurf in MATLAB/Simulink wird das Kleinsignalersatzschaltbild der physikalischen Schaltung verwendet. Mit Hilfe der Co-Simulation zwischen Cadence und MATLAB/Simulink wird das Verhalten simuliert und ausgewertet. Die Ergebnisse aus beiden Modellen stimmen überein, wodurch festgestellt werden kann, dass die Co-Simulation erfolgreich war.
Plastic waste is one of the biggest growing factors contributing to environmental pollution. So far there has been no established method to detect and identify plastics in environmental matrices. Thus, a method based on their characteristic fluorescence behavior is used to investigate whether plastics can be detected and identified in tap water under laboratory conditions. The experiments show that the identification of plastics as a function of water depth is possible. As the identification becomes more difficult with higher water depths, investigations with a highly sensitive imaging method were carried out to obtain an areal integration of the fluorescent light and thus better results.
A DDR3 SDRAM test setup implemented on the Griffin III test system from HILEVEL Technologies is used to analyse the row hammer bug. Row hammer pattern experiments are compared to standard retention tests for different manufacturing technologies. The row hammer effect is depending on the number of stress activation cycles. The analysis is extended to an avoidance scheme with refreshes similar to the Target Row Refresh scheme for the DDR4 SDRAM technology.
A Novel Approach to Identify Wood Species Optically using Fluorescence Lifetime Imaging Microscopy
(2021)
This contribution presents the results obtained with fluorescence lifetime imaging microscopy (FLIM)within the optical identification and differentiation of the four wood species walnut, beech, spruce, and maple. The experimental setup as well as the evaluation algorithm, with which the experiments were carried out, is explained briefly.
Forschungsbericht 2015
(2016)
Die wohl größte aktuelle Umweltverschmutzung mit Kunststoffen findet in Ozeanen, Seen und Flüssen statt. Bis jetzt gibt es kein etabliertes Verfahren, mit dem Kunststoffe in Umwelt- bzw. Wassermatrizen detektiert werden können. Deshalb wird mit einem Verfahren, welches auf dem Prinzip der Fluoreszenz basiert, untersucht, ob unter Laborbedingungen Kunststoffe in Wasser detektiert und identifiziert werden können. Anhand eines mathematischen Modells, einer Simulation und mit Hilfe von optischen Experimenten wird gezeigt, dass der Nachweis und auch eine Identifikation von Kunststoffen in Abhängigkeit der Wassertiefe, mit diesem Verfahren möglich ist.
Nowadays, fluorescence lifetime measurements and the method of fluorescence lifetime imaging are already state of the art in biomedical research. Also first investigations show that polymers could be identified due to their endogenous fluorescent lifetime. Thus an approach of identifying polymers with fluorescence lifetime imaging is done in this contribution. Therefore, four different polymers are examined and evaluated with statistical methods in order to determine their specific fluorescence lifetimes.
A parallel design concept of Delta-Sigma modulators that optimizes the resolution and the bandwidth for a highly dynamic position control in industrial applications. The idea is realized on a printed circuit board and tested by using a comprehensive measurement setup. The effective number of bits is increased by 2.5 bits at a fixed frequency. For a constant resolution, the design approach allows smaller filter lengths and a decrease of the delay by 25%.
A novel approach to optically distinguish plastics based on fluorescence lifetime measurements
(2020)
In medical and biological research, fluorescence lifetime measurements and fluorescence lifetime imaging is already a part of the standardized analysis procedures. As first investigations have shown, polymers can be identified using fluorescence lifetime imaging and an evaluation algorithm. Thus, this contribution pursues a novel approach for the direct differentiation of four polymers with fluorescence lifetime imaging. Therefore, the evaluation algorithm is extended to compare several fluorescence lifetime images to prove that a distinction is possible.
The fluorescence decay time allows to identify and distinguish polymers from each other. Three differently treated biaxially-oriented polyethylene terephthalate films are examined with two excitation wavelengths of 445 and 488nm. The fluorescence decay time is dependent of the treatment method of the films and is a means for identification.
Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er für den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung für die Weiterverarbeitung von Altholz spielt für einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung für die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsansätze auf Basis der Programmiersprache Python gewählt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 % noch ausbaufähig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 % ist dagegen vielversprechend.
A novel approach to optically distinguish plastics based on fluorescence lifetime measurements
(2020)
Today, the automatic separation of polymers from each other in an industrial scale is an unsolved problem. In laboratory environments, two methods are known whereby plastic is sorted either by color or by fluorescence decay time measurements that require fast synchronization and thus expensive equipment. A simple and pragmatic process is proposed to separate plastics from each other: all fluorescent photons are counted in relation to the absorbed photons. A theoretical model and an experimental setup are built in order to determine an apparatus specific quantum efficiency.
Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy
(2022)
Plastics have become a major part of human’s daily life. An uncontrolled usage of plastic leads to an accumulation in the environment posing a threat to flora and fauna, if not recycled correctly. The correct sorting and recycling of the most commonly available plastic types and an identification of plastic in the environment are important. Fluorescence lifetime imaging microscopy shows a high potential in sorting and identifying plastic types. A data-based and an image-based classification are investigated using python programming language to demonstrate the potential of a neural network based on fluorescence lifetime images to identify plastic types. The results indicate that the data-based classification has a higher identification accuracy compared to the image-based classification.
In this contribution the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) technique is evaluated for post-consumer wood sorting. The fluorescence characteristics of several wood samples were determined, whereby two excitation wavelengths (405 and 488 nm) were used. The measured data were processed using algorithmic methods to identify the wood species and post-consumer wood category. With the excitation wavelength of 405 nm, 16 out of 19 samples could be correctly assigned to the corresponding post-consumer wood category by means of the fluorescence lifetimes. Thus, the experimental results revealed the high potential of the FD-FLIM technique for automated post-consumer wood sorting.
For sustainable handling of wood and high material utilization, post-consumer wood has to be sorted by post-consumer wood categories in Germany. The non-destructive FD-FLIM method could improve the wood recycling process by automatic classification of waste wood into post-consumer wood classes. Thus, the phase-dependent fluorescence decay time of treated wood is analysed in this study to obtain the impact of the treatment on the fluorescence decay time. It is shown that treated and untreated wood can be differentiated using the fluorescence decay time, although the state-of-the-art theoretical fluorescence decay time differs from the experimentally determined fluorescence decay time.
Holz ist eine der wichtigsten erneuerbaren Ressourcen und kann recycelt werden, wobei derzeit nur unbehandeltes Holz in mehreren Lebenszyklen verwendet werden kann. Allerdings werden in Altholzsortieranlagen meist behandeltes und unbehandeltes Altholz gemischt angeliefert und es gibt keine automatische Klassifizierung zur sortenreinen Sortierung. Frühere Studien zeigen, dass behandelte und unbehandelte Holzproben mit Hilfe von bildgebender Fluoreszenzabklingzeit Mikroskopie unterschieden werden können. Um den Prozess der Klassifizierung von Altholz anhand von Fluoreszenzabklingzeitbildern zu automatisieren, werden zwei neuronale Netzwerke (NN) evaluiert. Das erste NN ist ein MLP (Multi Layer Perceptron), das das Altholz anhand von Erwartungswert und Standardabweichung eines Fluoreszenzabklingzeitbildes klassifiziert. Das zweite untersuchte NN ist ein CNN (Convolutional Neural Network), das das Holz direkt anhand der Fluoreszenzabklingzeitbilder klassifiziert. Beide Netzwerke sind in Python implementiert, um die spätere Verwendung in einem automatischen Klassifizierungs- und Sortierprozess zu erleichtern. Die Evaluation der NN zeigt vielversprechende Ergebnisse zur automatisierten Klassifikation von Altholz.
The fast classification and identification of plastics presents a significant challenge. The study assesses the suitability of a Multilayer Perceptron to classify and identify commonly found plastic types using Rapid-FLIM, achieving an accuracy of 88.33%. @inproceedingsSchwarz:23, Increasing demand for wood products requires methods to determine its harvest origin and ensure sustainable and legal sourcing. In 15 out of 21 cases, the origin of Quercus robur was differentiable in FD-FLIM studies.
Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.
Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er für den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung für die Weiterverarbeitung von Altholz spielt für einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung für die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsansätze auf Basis der Programmiersprache Python gewählt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 % noch ausbaufähig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 % ist dagegen vielversprechend.
The Proceedings of the 12th PTEE conference 2024 in Rosenheim present 15 articles from a wide range of actual topics in physics teaching – from artificial intelligence, innovative teaching and examination methods, lab experiments, tutorials to thoughts on what would be desirable to know, when you start teaching (activating) physics.
A written exam in measurement technologies usually includes problems for operational amplifier circuits, error propagation, signal analysis and analog to digital conversion. During corona, teaching changed from presence lectures to remote lectures and examination formats changed, too. Instead of a written exam, student project exams were proposed. The projects included a computer aided temperature measurement with operational amplifier circuits, a calibration procedure, error propagation and a written report. The student project exams were successful and the students’ feedback was so positive that the student project exams are going to be continued.