The transmission of constant-envelope orthogonal frequency division multiplexing (CE-OFDM) signals, based on electrical phase modulation, was shown to improve the tolerance to noise and the nonlinearity introduced by light-emitting diodes (LEDs) in visible light communication (VLC) systems. This allows the application of larger signal amplitudes despite the LED-nonlinearities and, thus, data transmission over larger distances. The performance of a 9.51 Mb/s CE-OFDM based system, with 16-QAM subcarrier mapping in a bandwidth of 5 MHz, was compared to the efficiency of a conventional OFDM system. The error vector magnitude (EVM) was reduced from 17.5% to 10% (which is below the FEC limit), an improvement around 43%, when the CE-OFDM scheme was applied in the VLC link of 6 m. A good performance was achieved by the CE-OFDM based VLC system in a link of 8 m, when 4-QAM was used as subcarrier mapping.
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The nonlinearity induced by light-emitting diodes in visible light communication (VLC) systems presents a challenge to the parametrization of orthogonal frequency division multiplexing (OFDM). The goal of the multi-objective optimization problem presented in this study is to maximize the transmitted power (superimposed LED bias-current and signal amplification) for both conventional and constant envelope (CE) OFDM while also maximizing spectral efficiency. The bit error rate (BER) metric is used to evaluate the optimization using the non-dominated sorting genetic algorithm II. Simulation results show that for a BER of 1×10 −3 , the signal-to-noise ratio (SNR) required decreases with the guard band due to intermodulation distortions. In contrast to SNR values of approximately 13 and 25 dB achieved by traditional OFDM-based systems, the VLC system with CE signals achieves a guard band of 6% of the signal bandwidth with required SNR values of approximately 10.8 and 24 dB for 4-quadrature amplitude modulation (QAM) and 16-QAM modulation orders, respectively.
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In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
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With a market demand for low cost, easy to produce, flexible and portable applications in healthcare, energy, biomedical or electronics markets, large research programs are initiated to develop new technologies to provide this demand with new innovative ideas. One of these fast developing technologies is organic printed electronics. As the term printed electronics implies, functional materials are printed via, e.g. inkjet, flexo or gravure printing techniques, on to a substrate material. Applications are, among others, organic light emitting diodes (OLED), sensors and Lab-on-a-chip devices. For all these applications, in some way, the interaction of fluids with the substrate is of great importance. The most used substrate materials for these low-cost devices are (coated) paper or plastic. Plastic substrates have a relatively low surface energy which frequently leads to poor wetting and/or poor adhesion of the fluids on the substrates during printing and/ or post-processing. Plasma technology has had a long history in treating materials in order to improve wetting or promote adhesion. The µPlasma patterning tool described in this thesis combines a digital inkjet printing platform with an atmospheric dielectric barrier discharge plasma tool. Thus enabling selective and local plasma treatment, at atmospheric pressure, of substrates without the use of any masking materials. In this thesis, we show that dependent on the gas composition the substrate surface can either be functionalized, thus increasing its surface energy, or material can be deposited on the surface, lowering its surface energy. Through XPS and ATR-FTIR analysis of the treated (polymer) substrate surfaces, chemical modification of the surface structure was confirmed. The chemical modification and wetting properties of the treated substrates remained present for at least one month after storage. Localized changes in wettability through µPlasma patterning were obtained with a resolution of 300µm. Next to the control of wettability of an ink on a substrate in printed electronics is the interaction of ink droplets with themselves of importance. In printing applications, coalescence of droplets is standard practice as consecutive droplets are printed onto, or close to each other. Understanding the behaviour of these droplets upon coalescence is therefore important, especially when the ink droplets are of different composition and/or volume. For droplets of equal volume, it was found that dye transport across the coalescence bridge could be fully described by diffusion only. This is as expected, as due to the droplet symmetry on either side of the bridge, the convective flows towards the bridge are of equal size but opposite in direction. For droplets of unequal volume, the symmetry across the bridge is no longer present. Experimental analysis of these merging droplets show that in the early stages of coalescence a convective flow from the small to large droplet is present. Also, a smaller convective flow of shorter duration from the large into the small droplet was identified. The origin of this flow might be due to the presence of vortices along the interface of the bridge, due to the strong transverse flow to open the bridge. To conclude, three potential applications were showcased. In the first application we used µPlasma patterning to create hydrophilic patterns on hydrophobic dodecyl-trichlorosilane (DTS) covered glass. Capillaries for a Lab-on-a-chip device were successfully created by placing two µPlasma patterned glass slides on top of each other separated by scotch tape. In the second application we showcased the production of a RFID tag via inkjet printing. Functional RFID-tags on paper were created via inkjet printing of silver nanoparticle ink connected to an integrated circuit. The optimal operating frequency of the produced tags is in the range of 860-865 MHz, making them usable for the European market, although the small working range of 1 m needs further improvement. Lastly, we showed the production of a chemresistor based gas sensor. In house synthesised polyemeraldine salt (PANi) was coated by hand on top of inkjet printed silver electrodes. The sensor proved to be equally sensitive to ethanol and water vapour, reducing its selectivity in detecting changes in gas composition.
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A modified genetic algorithm (MGA) optimization procedure, alongside time series machine learning (ML) classifiers, is proposed to minimize handovers in a digital twin-based visible light communication (VLC) system. Frequent handovers have a direct impact on the overall performance of the VLC system due to the inherent connection downtime of a handover process. The handover scheme proposed in this article considers the receiver trajectory information to minimize handovers, maintaining the system performance below the forward error correction limit. Simulation results indicate that the proposed scheme outperforms a power-based handover scheme, achieving handover reductions of 42.47%. Therefore, the MGA combined to the ML models approach is an effective means of minimizing handovers, as well as improving overall VLC system performance.
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The age estimation of biological traces is one of the holy grails in forensic investigations. We developed a method for the age estimation of semen stains using fluorescence spectroscopy in conjunction with a stoichiometric ageing model. The model describes the degradation and generation rate of proteins and fluorescent oxidation products (FOX) over time. The previously used fluorimeter is a large benchtop device and requires system optimization for forensic applications. In situ applications have the advantage that measurements can be performed directly at the crime scene, without additional sampling or storage steps. Therefore, a portable fiber-based fluorimeter was developed, consisting of two optimized light-emitting diodes (LEDs) and two spectrometers to allow the fluorescence protein and FOX measurements. The handheld fiber can be used without touching the traces, avoiding the destruction or contamination of the trace. In this study, we have measured the ageing kinetics of semen stains over time using both our portable fluorimeter and a laboratory benchtop fluorimeter and compared their accuracies for the age estimation of semen stains. Successful age estimation was possible up to 11 days, with a mean absolute error of 1.0 days and 0.9 days for the portable and the benchtop fluorimeters, respectively. These results demonstrate the potential of using the portable fluorimeter for in situ applications.
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Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m−2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm–1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging.
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De missie van het lectoraat Fotonica is om een bijdrage te leveren aan een gezonde wereld en een duurzame economie door het toepasbaar maken van fotonicatechnologie in de praktijk. Ook draagt het lectoraat bij aan het opleiden van professionals op het gebied van fotonica, wat een voorwaarde is om de ambities van deze groeisector waar te kunnen maken. Het fotonica-onderzoek richt zich op de toepassingsgebieden Hightech Industrie, Agri & Food, Energie & Klimaat, Gezondheid en Mobiliteit. Digitale technologie speelt in de ontwikkeling van deze gebieden een grote rol, waarbij fotonica op grote schaal wordt ingezet voor het verkrijgen van digitale data. Sleutelwoorden voor het onderzoek zijn spectroscopie, metrologie en afbeelding. Het toepassen van optische sensoren, zoals spectrometers of glasvezel-gebaseerde sensoren, speelt hierbij een centrale rol. De lijfspreuk van de natuurkundige Heike Kamerlingh Onnes ‘door meten tot weten’, aangevuld met ‘door weten tot handelen’, is dan ook een leidraad voor het lectoraat.
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In this paper, artificial intelligence tools are implemented in order to predict trajectory positions, as well as channel performance of an optical wireless communications link. Case studies for industrial scenarios are considered to this aim. In a first stage, system parameters are optimized using a hybrid multi-objective optimization (HMO) procedure based on the grey wolf optimizer and the non-sorting genetic algorithm III with the goal of simultaneously maximizing power and spectral efficiency. In a second stage, we demonstrate that a long short-term memory neural network (LSTM) is able to predict positions, as well as channel gain. In this way, the VLC links can be configured with the optimal parameters provided by the HMO. The success of the proposed LSTM architectures was validated by training and test root-mean square error evaluations below 1%.
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Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
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