FB Technik
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Institut
- FB Technik (14)
Organic semiconductor distributed feedback laser fabricated by direct laser interference ablation
(2007)
We use a pulsed, frequency tripled picosecond Nd:YAG laser for holographic ablation to pattern a surface relief grating into an organic semiconductor guest-host system. The resulting second order distributed feedback lasers exhibit laser action with laser thresholds being comparable to those obtained with resonators structured by standard lithographic techniques. The details of the interference ablation of tris-(8-hydroxyquinoline) aluminum (Alq(3)) doped with the laser dye 4- dicyanomethylene-2-methyl-6-(p-dimethylaminostyryl)-4H-pyran (DCM) are presented and discussed. Lasing action is demonstrated at a wavelength of 646.6 nm, exploiting second order Bragg reflection in a relief grating with a period of 399 nm.
Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCc). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.
Electrical stimulation is used for example to treat neuronal disorders and depression with deep brain stimulation or transcranial electrical stimulation. Depending on the application, different electrodes are used and thus different electrical characteristics exist, which have to be handled by the stimulator. Without a measuring device the user would have to rely on the stimulator being able to deliver the needed stimulation signal. Therefore, the objective of this paper is to present a method to increase the level of confidence with characterization and modelling of the electrical behavior by using the example of one channel of our stimulation device for experimental use. In several simulation studies with an electrode model with values in a typical range for cortical applications the influence of the load onto the stimulator and the possibility to pre-estimate measuring signals in complex networks are shown.
Understanding and modulating CNS function in physiological as well as pathophysiological contexts remains a significant ambition in research and clinical applications. The investigation of the multifaceted CNS cell types including their interactions and contributions to neural function requires a combination of the state-of-the-art in vivo electrophysiology and imaging techniques. We developed a novel type of liquid crystal polymer (LCP) surface micro-electrode manufactured in three customized designs with up to 16 channels for recording and stimulation of brain activity. All designs include spare central spaces for simultaneous 2P-imaging. Nanoporous platinum-plated contact sites ensure a low impedance and high current transfer. The epidural implantation of the LCP micro-electrodes could be combined with standard cranial window surgery. The epidurally positioned electrodes did not only display long-term biocompatibility, but we also observed an additional stabilization of the underlying CNS tissue. We demonstrate the electrode’s versatility in combination with in vivo 2P-imaging by monitoring anesthesia-awake cycles of transgenic mice with GCaMP3 expression in neurons or astrocytes. Cortical stimulation and simultaneous 2P Ca2+ imaging in neurons or astrocytes highlighted the astrocytes’ integrative character in neuronal activity processing. Furthermore, we confirmed that spontaneous astroglial Ca2+ signals are dampened under anesthesia, while evoked signals in neurons and astrocytes showed stronger dependency on stimulation intensity rather than on various levels of anesthesia. Finally, we show that the electrodes provide recordings of the electrocorticogram (ECoG) with a high signal-to noise ratio and spatial signal differences which help to decipher brain activity states during experimental procedures. Summarizing, the novel LCP surface micro-electrode is a versatile, convenient, and reliable tool to investigate brain function in vivo.
In this paper, the radio frequency (RF) behavior of mechanically stressed coaxial and for the first time also twisted-pair transmission lines is investigated over their service life. The main goal is to enable predictive maintenance for cables in moving applications and avoid preventive replacement. This also reduces the use of high-cost resources. For this purpose, stranded and solid-core variants of coaxial and twisted-pair type cables are mechanically loaded on the two-pulley apparatus according to EN 50396. Their RF transmission (S21) behavior is measured using a vector network analyzer and presented over bending cycles. For the first time, the phase response of mechanically loaded transmission lines is evaluated with respect to their service life. Two significant causes for the increasing attenuation and altered phase response are identified: breakage in foil screen and increasing surface roughness on the copper conductors. The identified causes are supported with literature evidence. Through measurements and theoretical calculations, it is proven that the phase is much more suitable for an assessment of the remaining service life than the amplitude. The findings can be used to implement a cable monitoring system in industrial environments which monitors the lines in-situ and reminds the user to replace them, whenever a certain wear-level is reached.
Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.
For the assessment of human reaction time, a test environment was developed. This system consists of an embedded device with organic light-emitting diode (OLED) displays with push buttons for the combined presentation of visual stimulation and registration of the haptic human reaction. The test leader can define the test sequence with the aid of a graphical user interface (GUI) on a personal computer (PC). The validation of the system was proved by measuring the latency times of the whole system, which are conditioned by the specific hard- and software constellation. Through the investigation of the display’s light radiation by a photodiode and the recorded current consumption, latency times and their variance were specified. In the fastest mode the system can reach an error limit of 60 μs.
The photo-Dember effect is a source of impulsive THz emission following femtosecond pulsed optical excitation. This emission results from the ultrafast spatial separation of electron-hole pairs in strong carrier gradients due to their different diffusion coefficients. The associated time dependent polarization is oriented perpendicular to the excited surface which is inaptly for efficient out coupling of THz radiation. We propose a scheme for generating strong carrier gradients parallel to the excited surface. The resulting photo-Dember currents are oriented in the same direction and emit THz radiation into the favorable direction perpendicular to the surface. This effect is demonstrated for GaAs and In(0.53)Ga(0.47)As. Surprisingly the photo-Dember THz emitters provide higher bandwidth than photoconductive emitters. Multiplexing of phase coherent photo-Dember currents by periodically tailoring the photoexcited spatial carrier distribution gives rise to a strongly enhanced THz emission, which reaches electric field amplitudes comparable to a high-efficiency externally biased photoconductive emitter.
Abstract: This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.