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Printed systems spark immense interest in industry, and for several parts such as solar cells or radio frequency identification antennas, printed products are already available on the market. This has led to intense research; however, printed field-effect transistors (FETs) and logics derived thereof still have not been sufficiently developed to be adapted by industry. Among others, one of the reasons for this is the lack of control of the threshold voltage during production. In this work, we show an approach to adjust the threshold voltage (Vth) in printed electrolyte-gated FETs (EGFETs) with high accuracy by doping indium-oxide semiconducting channels with chromium. Despite high doping concentrations achieved by a wet chemical process during precursor ink preparation, good on/off-ratios of more than five orders of magnitude could be demonstrated. The synthesis process is simple, inexpensive, and easily scalable and leads to depletion-mode EGFETs, which are fully functional at operation potentials below 2 V and allows us to increase Vth by approximately 0.5 V.
Printed electrolyte-gated oxide electronics is an emerging electronic technology in the low voltage regime (≤1 V). Whereas in the past mainly dielectrics have been used for gating the transistors, many recent approaches employ the advantages of solution processable, solid polymer electrolytes, or ion gels that provide high gate capacitances produced by a Helmholtz double layer, allowing for low-voltage operation. Herein, with special focus on work performed at KIT recent advances in building electronic circuits based on indium oxide, n-type electrolyte-gated field-effect transistors (EGFETs) are reviewed. When integrated into ring oscillator circuits a digital performance ranging from 250 Hz at 1 V up to 1 kHz is achieved. Sequential circuits such as memory cells are also demonstrated. More complex circuits are feasible but remain challenging also because of the high variability of the printed devices. However, the device inherent variability can be even exploited in security circuits such as physically unclonable functions (PUFs), which output a reliable and unique, device specific, digital response signal. As an overall advantage of the technology all the presented circuits can operate at very low supply voltages (0.6 V), which is crucial for low-power printed electronics applications.
Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
In this paper pathophysiological interrelated deactivation/activation phenomena are set out in the example of whiplash injury. These phenomena could have been underestimated in previous positron emission tomography studies as their focus was on hypoperfusion rather than hyperperfusion. In addition, statistical parametric mapping analysis of cerebral studies is normally not fine-tuned to special interesting areas rather than to obvious clusters of difference.
In users of a cochlear implant (CI) together with a contralateral hearing aid (HA), so-called bimodal listeners, differences in processing latencies between digital HA and CI up to 9 ms constantly superimpose interaural time differences. In the present study, the effect of this device delay mismatch on sound localization accuracy was investigated. For this purpose, localization accuracy in the frontal horizontal plane was measured with the original and minimized device delay mismatch. The reduction was achieved by delaying the CI stimulation according to the delay of the individually worn HA. For this, a portable, programmable, battery-powered delay line based on a ring buffer running on a microcontroller was designed and assembled. After an acclimatization period to the delayed CI stimulation of 1 hr, the nine bimodal study participants showed a highly significant improvement in localization accuracy of 11.6% compared with the everyday situation without the delay line (p < .01). Concluding, delaying CI stimulation to minimize the device delay mismatch seems to be a promising method to increase sound localization accuracy in bimodal listeners.
Spinal cord stimulation (SCS) is the most commonly used technique of neurostimulation. It involves the stimulation of the spinal cord and is therefore used to treat chronic pain. The existing esophageal catheters are used for temperature monitoring during an electrophysiology study with ablation and transesophageal echocardiography. The aim of the study was to model the spine and new esophageal electrodes for the transesophageal electrical pacing of the spinal cord, and to integrate them in the Offenburg heart rhythm model for the static and dynamic simulation of transesophageal neurostimulation. The modeling and simulation were both performed with the electromagnetic and thermal simulation software CST (Computer Simulation Technology, Darmstadt). Two new esophageal catheters were modelled as well as a thoracic spine based on the dimensions of a human skeleton. The simulation of directed transesophageal neurostimulation is performed using the esophageal balloon catheter with an electric pacing potential of 5 V and a trapezoidal signal. A potential of 4.33 V can be measured directly at the electrode, 3.71 V in the myocardium at a depth of 2 mm, 2.68 V in the thoracic vertebra at a depth of 10 mm, 2.1 V in the thoracic vertebra at a depth of 50 mm and 2.09 V in the spinal cord at a depth of 70 mm. The relation between the voltage delivered to the electrodes and the voltage applied to the spinal cord is linear. Virtual heart rhythm and catheter models as well as the simulation of electrical pacing fields and electrical sensing fields allow the static and dynamic simulation of directed transesophageal electrical pacing of the spinal cord. The 3D simulation of the electrical sensing and pacing fields may be used to optimize transesophageal neurostimulation.
The visualization of heart rhythm disturbance and atrial fibrillation therapy allows the optimization of new cardiac catheter ablations. With the simulation software CST (Computer Simulation Technology, Darmstadt) electromagnetic and thermal simulations can be carried out to analyze and optimize different heart rhythm disturbance and cardiac catheters for pulmonary vein isolation. Another form of visualization is provided by haptic, three-dimensional print models. These models can be produced using an additive manufacturing method, such as a 3d printer. The aim of the study was to produce a 3d print of the Offenburg heart rhythm model with a representation of an atrial fibrillation ablation procedure to improve the visualization of simulation of cardiac catheter ablation. The basis of 3d printing was the Offenburg heart rhythm model and the associated simulation of cryoablation of the pulmonary vein. The thermal simulation shows the pulmonary vein isolation of the left inferior pulmonary vein with the cryoballoon catheter Arctic Front Advance™ from Medtronic. After running through the simulation, the thermal propagation during the procedure was shown in the form of different colors. The three-dimensional print models were constructed on the base of the described simulation in a CAD program. Four different 3d printers are available for this purpose in a rapid prototyping laboratory at the University of Applied Science Offenburg. Two different printing processes were used and a final print model with additional representation of the esophagus and internal esophagus catheter was also prepared for printing. With the help of the thermal simulation results and the subsequent evaluation, it was possible to draw a conclusion about the propagation of the cold emanating from the catheter in the myocardium and the surrounding tissue. It was measured that just 3 mm from the balloon surface into the myocardium the temperature dropped to 25 °C. The simulation model was printed using two 3d printing methods. Both methods, as well as the different printing materials offer different advantages and disadvantages. All relevant parts, especially the balloon catheter and the conduction, are realistically represented. Only the thermal propagation in the form of different colors is not shown on this model. Three-dimensional heart rhythm models as well as virtual simulations allow very clear visualization of complex cardiac rhythm therapy and atrial fibrillation treatment methods. The printed models can be used for optimization and demonstration of cryoballoon catheter ablation in patients with atrial fibrillation.
A disturbed synchronization of the ventricular contraction can cause a highly developed systolic heart failure in affected patients with reduction of the left ventricular ejection fraction, which can often be explained by a diseased left bundle branch block (LBBB). If medication remains unresponsive, the concerned patients will be treated with a cardiac resynchronization therapy (CRT) system. The aim of this study was to integrate His-bundle pacing into the Offenburg heart rhythm model in order to visualize the electrical pacing field generated by His-Bundle-Pacing. Modelling and electrical field simulation activities were performed with the software CST (Computer Simulation Technology) from Dessault Systèms. CRT with biventricular pacing is to be achieved by an apical right ventricular electrode and an additional left ventricular electrode, which is floated into the coronary vein sinus. The non-responder rate of the CRT therapy is about one third of the CRT patients. His- Bundle-Pacing represents a physiological alternative to conventional cardiac pacing and cardiac resynchronization. An electrode implanted in the His-bundle emits a stronger electrical pacing field than the electrical pacing field of conventional cardiac pacemakers. The pacing of the Hisbundle was performed by the Medtronic Select Secure 3830 electrode with pacing voltage amplitudes of 3 V, 2 V and 1,5 V in combination with a pacing pulse duration of 1 ms. Compared to conventional pacemaker pacing, His-bundle pacing is capable of bridging LBBB conduction disorders in the left ventricle. The His-bundle pacing electrical field is able to spread via the physiological pathway in the right and left ventricles for CRT with a narrow QRS-complex in the surface ECG.
Diffracted waves carry high‐resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high‐accuracy and high‐resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.
Fully Printed Inverters using Metal‐Oxide Semiconductor and Graphene Passives on Flexible Substrates
(2020)
Printed and flexible metal‐oxide transistor technology has recently demonstrated great promise due to its high performance and robust mechanical stability. Herein, fully printed inverter structures using electrolyte‐gated oxide transistors on a flexible polyimide (PI) substrate are discussed in detail. Conductive graphene ink is printed as the passive structures and interconnects. The additive printed transistors on PI substrates show an on/off ratio of 106 and show mobilities similar to the state‐of‐the‐art printed transistors on rigid substrates. Printed meander structures of graphene are used as pull‐up resistances in a transistor–resistor logic to create fully printed inverters. The printed and flexible inverters show a signal gain of 3.5 and a propagation delay of 30 ms. These printed inverters are able to withstand a tensile strain of 1.5% following more than 200 cycles of mechanical bending. The stability of the electrical direct current (DC) properties has been observed over a period of 5 weeks. These oxide transistor‐based fully printed inverters are relevant for digital printing methods which could be implemented into roll‐to‐roll processes.
In this study, a facile method to fabricate a cohesive ion‐gel based gate insulator for electrolyte‐gated transistors is introduced. The adhesive and flexible ion‐gel can be laminated easily on the semiconducting channel and electrode manually by hand. The ion‐gel is synthesized by a straightforward technique without complex procedures and shows a remarkable ionic conductivity of 4.8 mS cm−1 at room temperature. When used as a gate insulator in electrolyte‐gated transistors (EGTs), an on/off current ratio of 2.24×104 and a subthreshold swing of 117 mV dec−1 can be achieved. This performance is roughly equivalent to that of ink drop‐casted ion‐gels in electrolyte‐gated transistors, indicating that the film‐attachment method might represent a valuable alternative to ink drop‐casting for the fabrication of gate insulators.
Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.
A Hybrid Optoelectronic Sensor Platform with an Integrated Solution‐Processed Organic Photodiode
(2021)
Hybrid systems, unifying printed electronics with silicon‐based technology, can be seen as a driving force for future sensor development. Especially interesting are sensing elements based on printed devices in combination with silicon‐based high‐performance electronics for data acquisition and communication. In this work, a hybrid system integrating a solution‐processed organic photodiode in a silicon‐based system environment, which enables flexible device measurement and application‐driven development, is presented. For performance evaluation of the integrated organic photodiode, the measurements are compared to a silicon‐based counterpart. Therefore, the steady state response of the hybrid system is presented. Promising application scenarios are described, where a solution‐processed organic photodiode is fully integrated in a silicon system.
Bei bimodaler Cochlea-Implantat-/Hörgerät-Versorgung kann es aufgrund seitenverschiedener Signalverarbeitung zu einer zeitlich versetzten Stimulation der beiden Modalitäten kommen. Jüngste Studien haben gezeigt, dass durch zeitlichen Abgleich der Modalitäten die Schalllokalisation bei bimodaler Versorgung verbessert werden kann. Um solch einen Abgleich vornehmen zu können, ist die messtechnische Bestimmung der Durchlaufzeit von Hörgeräten erforderlich. Kommerziell verfügbare Hörgerätemessboxen können diese Werte häufig liefern. Die dazu verwendete Signalverarbeitung wird dabei aber oft nicht vollständig offengelegt. In dieser Arbeit wird ein alternativer und nachvollziehbarer Ansatz zum Design eines simplen Messaufbaus basierend auf einem Arduino DUE Mikrocontroller-Board vorgestellt. Hierzu wurde ein Messtisch im 3D-Druck gefertigt, auf welchem Hörgeräte über einen 2-ccm-Kuppler an ein Messmikrofon angeschlossen werden können. Über einen Latenzvergleich mit dem simultan erfassten Signal eines Referenzmikrofons kann die Durchlaufzeit von Hörgeräten bestimmt werden. Frequenzspezifische Durchlaufzeiten werden mittels einer Kreuzkorrelation zwischen Ziel- und Referenzsignal errechnet. Aufnahme, Ausgabe und Speicherung der Signale erfolgt über einen ATMEL SAM3X8E Mikrocontroller, welcher auf dem Arduino DUE-Board verbaut ist. Über eigens entworfene elektronische Schaltungen werden die Mikrofone und der verwendete Lautsprecher angesteuert. Nach Abschluss einer Messung (Messdauer ca. 5 s) werden die Messdaten seriell an einen PC übertragen, auf dem die Datenauswertung mittels MATLAB erfolgt. Erste Validierungen zeigten eine hohe Stabilität der Messergebnisse mit sehr geringen Standardabweichungen im Bereich weniger Mikrosekunden für Pegel zwischen 50 und 75 dB (A). Der Messaufbau wird in laufenden Studien zur Quantifizierung der Durchlaufzeit von Hörgeräten verwendet.
Objective: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on EEG recordings from patients in non-convulsive status epilepticus (NCSE).
Methods: Five children of mixed aetiology in NCSE were given high flow of inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum 120s. EEG was recorded concurrently in all patients. The effects of inhaled carbogen on patient EEG recordings were investigated using band-power, functional connectivity and graph theory measures. Carbogen effect was quantified by measuring effect size (Cohen's d) between "before", "during" and "after" carbogen delivery states.
Results: Carbogen's apparent effect on EEG band-power and network metrics across all patients for "before-during" and "before-after" inhalation comparisons was inconsistent across the five patients.
Conclusion: The changes in different measures suggest a potentially non-homogeneous effect of carbogen on the patients' EEG. Different aetiology and duration of the inhalation may underlie these non-homogeneous effects. Tuning the carbogen parameters (such as ratio between CO2 and O2, duration of inhalation) on a personalised basis may improve seizure suppression in future.
Significant progress in the development and commercialization of electrically conductive adhesives has been made. This makes shingling a very attractive approach for solar cell interconnection. In this study, we investigate the shading tolerance of two types of solar modules based on shingle interconnection: first, the already commercialized string approach, and second, the matrix technology where solar cells are intrinsically interconnected in parallel and in series. An experimentally validated LTspice model predicts major advantages for the power output of the matrix layout under partial shading. Diagonal as well as random shading of a 1.6-m2 solar module is examined. Power gains of up to 73.8 % for diagonal shading and up to 96.5 % for random shading are found for the matrix technology compared to the standard string approach. The key factor is an increased current extraction due to lateral current flows. Especially under minor shading, the matrix technology benefits from an increased fill factor as well. Under diagonal shading, we find the probability of parts of the matrix module being bypassed to be reduced by 40 % in comparison to the string module. In consequence, the overall risk of hotspot occurrence in matrix modules is decreased significantly.
A versatile liquid metal (LM) printing process enabling the fabrication of various fully printed devices such as intra- and interconnect wires, resistors, diodes, transistors, and basic circuit elements such as inverters which are process compatible with other digital printing and thin film structuring methods for integration is presented. For this, a glass capillary-based direct-write method for printing LMs such as eutectic gallium alloys, exploring the potential for fully printed LM-enabled devices is demonstrated. Examples for successful device fabrication include resistors, p–n diodes, and field effect transistors. The device functionality and easiness of one integrated fabrication flow shows that the potential of LM printing is far exceeding the use of interconnecting conventional electronic devices in printed electronics.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input–output pairs to train a neural network (NN) on it. We are able to learn the variables’ tendencies achieving an average R² score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model.
Die Corona-Semester erforderten die Übertragung der Brückenkurse Mathematik in ein digitales Lehr-format. Gerade beim Studieneinstieg spielen persönliche Unterstützung und soziale Eingebundenheit für Studierende eine besonders wichtige Rolle. Deshalb lag die besondere Herausforderung bei der Übertragung in ein digitales Format darin, die wegfallenden üblichen Kennenlern- und Kommunika-tionsmöglichkeiten, die sich in Präsenzformaten beispielsweise in den Pausen oder im Gespräch mit den Sitznachbarn ergeben, zu kompensieren. Vorliegender Beitrag stellt vor, inwieweit der Transfer in ein digitales Format gelungen ist. Das digitale Brückenkurskonzept wurde in ein didaktisches Entwurfsmuster übertragen, um durch die strukturierte und nachvollziehbare Darstellung den Transfer und die Vergleichbarkeit der Ergebnisse zu erleichtern.
Neural networks tend to overfit the training distribution and perform poorly on out-ofdistribution data. A conceptually simple solution lies in adversarial training, which introduces worst-case perturbations into the training data and thus improves model generalization to some extent. However, it is only one ingredient towards generally more robust models and requires knowledge about the potential attacks or inference time data corruptions during model training. This paper focuses on the native robustness of models that can learn robust behavior directly from conventional training data without out-of-distribution examples. To this end, we study the frequencies in learned convolution filters. Clean-trained models often prioritize high-frequency information, whereas adversarial training enforces models to shift the focus to low-frequency details during training. By mimicking this behavior through frequency regularization in learned convolution weights, we achieve improved native robustness to adversarial attacks, common corruptions, and other out-of-distribution tests. Additionally, this method leads to more favorable shifts in decision-making towards low-frequency information, such as shapes, which inherently aligns more closely with human vision.