Pupillometry examinations of the human eye with the eye diagnostic device PEP-2000 - first results. - In: Current directions in biomedical engineering, ISSN 2364-5504, Bd. 8 (2022), 2, S. 648-651
Pupillometry forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the pupillometry measurements. A study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the minimal pupil diameter is 40 % higher than the literature values. The main reason for the differences is the low light intensity of 15 cd/m2. However, the maximum pupil diameter is within the range of the researched values. The results of the pupillary reaction measurements show that the values obtained (amplitude, contraction time and peak time) are within the range of literature values. The latency time of 690 ms is 40 % too high. The reason for this could be the moderate pupil detection rate of 50-70 %. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further work will look at improving pupil detection rates.
https://doi.org/10.1515/cdbme-2022-1165
Feature-based differentiation of malignant melanomas, lesions and healthy skin in multiphoton tomography skin images. - In: Current directions in biomedical engineering, ISSN 2364-5504, Bd. 8 (2022), 2, S. 45-48
https://doi.org/10.1515/cdbme-2022-1013
Evaluation of spatio-temporal electrocutaneous warning signals. - In: Current directions in biomedical engineering, ISSN 2364-5504, Bd. 8 (2022), 2, S. 313-316
Acoustic or visual warning signals for workers in hazardous situations might fail under loud and/or lowvisibility work situations. A warning system that uses electrocutaneous stimulation can overcome this problem. The aim of this pilot study was to find spatio-temporal stimulation patterns for appropriate electrical warning. Eight electrode pairs were attached to the upper right arm of 16 participants. The stimulation was conducted with bi-phasic rectangular pulses of 150 μs and an amplitude of up to 25 mA. Pulse intervals that generate a single pulse, pulsating, vibrating, and continuous perception as well as varying spatial patterns (e.g. alternating between electrode pairs or circumferentially around the arm) were investigated and evaluated with regard to alertness, discomfort, and urgency. The pilot study revealed that a stimulation signal that generates a vibrating perception and is applied as a circumferential signal around the arm showed the highest values of alertness and is therefore considered a potential warning pattern for future studies with larger study groups.
https://doi.org/10.1515/cdbme-2022-1080
Bad channel detection in EEG recordings. - In: Current directions in biomedical engineering, ISSN 2364-5504, Bd. 8 (2022), 2, S. 257-260
Electroencephalography (EEG) is widely used in clinical applications and basic research. Dry EEG opened the application area to new fields like self-application during gaming and neurofeedback. While recording, the signals are always affected by artefacts. Manual detection of bad channels is the gold standard in both gel-based and dry EEG but is timeconsuming. We propose a simple and robust method for automatic bad channel detection in EEG. Our method is based on the iterative calculation of standard deviations for each channel. Statistical measures of these standard deviations serve as indications for bad channel detection. We compare the new method to the results obtained from the manually identified bad channels for EEG recordings. We analysed EEG signals during resting state with eyes closed and datasets with head movement. The results showed an accuracy of 99.69 % for both gel-based and dry EEG for resting state EEG. The accuracy of our new method is 99.38 % for datasets with the head movement for both setups. There was no significant difference between the manual gold standard of bad channel identification and our iterative standard deviation method. Therefore, the proposed iterative standard deviation method can be used for bad channel detection in resting state and movement EEG recordings.
https://doi.org/10.1515/cdbme-2022-1066
Novel replaceable EEG electrode system. - In: Current directions in biomedical engineering, ISSN 2364-5504, Bd. 8 (2022), 2, S. 249-252
Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements.
https://doi.org/10.1515/cdbme-2022-1064
SpikiLi: a spiking simulation of LiDAR based real-time object detection for autonomous driving. - In: 2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP), (2022), insges. 5 S.
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency. They do so by using asynchronous spike-based data flow, event-based signal generation, processing, and modifying the neuron model to resemble biological neurons closely. While some initial works have shown significant initial evidence of applicability to common deep learning tasks, their applications in complex real-world tasks have been relatively low. In this work, we first illustrate the applicability of spiking neural networks to a complex deep learning task, namely LiDAR based 3D object detection for automated driving. Secondly, we make a step-by-step demonstration of simulating spiking behavior using a pre-trained Convolutional Neural Network. We closely model essential aspects of spiking neural networks in simulation and achieve equivalent run-time and accuracy on a GPU. We expect significant improvements in power efficiency when the model is implemented on neuromorphic hardware.
https://doi.org/10.1109/EBCCSP56922.2022.9845647
Simple model for the shortest medium-voltage cable to supply sustainable loads. - In: Conference Proceedings 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), (2022), insges. 6 S.
Passive loads in power systems are well-known, but sustainable loads (SL's), due to distributed renewable energy sources and possible reverse active power flow (RAPF) from a low voltage level through a medium-voltage (MV) cable towards a high voltage level, introduce new challenges. In this paper, a simple model for the shortest MV cable required to supply SL's is introduced. The concept of unconstrained and constrained domains to design the shortest cable, at which the limiting constraint for the thermal capacity and the limiting constraint for the voltage drop are equal, is proposed. In this case, the cable can be fully used, that is, using the whole feasible region at the shortest length. A sample 2-node/1-cable network shows that the power-voltage relationship can theoretically be nonlinear within the unconstrained domains, but it can be well linearized within the proposed constrained domains. Another 3-node/2-cable network is developed to show the effects when using different cables types (copper and aluminium) and power factors. It is shown that the developed model can simply be used for estimating the active power domains of new SL's while considering the shortest length of MV cables.
https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854705
Analysis of safety-critical cloud architectures with multi-trajectory simulation. - In: 2022 Annual Reliability and Maintainability Symposium (RAMS), (2022), insges. 7 S.
Dynamic safety-critical systems require model-based techniques and tools for their systems design. The paper presents a stochastic Petri net model of an industrial safetycritical cloud server architecture for train control. Its reliability has to be evaluated to assess tradeoffs in architecture and level of fault tolerance. Simulation methods are too slow for such rare-event problems, while numerical analysis techniques suffer from the state-space explosion problem. The paper extends a recently developed multi-trajectory simulation algorithm combining elements of simulation and numerical analysis such that it increases the accuracy of rare-event simulations within a given computation time budget. Simulation experiments have been carried out with a prototype tool.
https://doi.org/10.1109/RAMS51457.2022.9893923
Dynamic reference programming-based model predictive control for optimal robust tracking. - In: 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), (2022), S. 126-131
In this paper, a new idea of dynamic reference programming (DRP) is proposed and applied to the design of robust tube-based model predictive control (RTMPC) to realize tracking of the system under additive bounded uncertainty and robust constraints. The cost function of the resulting DRP-based RTMPC simultaneously penalizes: i) the weighted Euclidean distance (WED) between the nominal state trajectory and the reference-determined steady states; and ii) the WED between the last-step-ahead-reference-determined steady state and the setpoint, which causes the nominal state to converge to the optimal tracking point. As the decision variables for online RTMPC optimization, the multi-step-ahead references also take on the responsibilities of constraint satisfaction and recursive feasibility. Consequently, the proposed strategy can drive the system state to converge to the disturbance invariant set centered on the optimal tracking point, and thus, realize the optimal robust tracking. A numerical example verifies that the proposed DRP-based RTMPC can, not only realize the optimal robust tracking of the system, but also effectively reduce the controller burden and avoid drastic state changes because the multistep references can make the degree of freedom for online optimization flexible.
https://doi.org/10.1109/AdCONIP55568.2022.9894181
Mutual information induced slow-feature analysis of nonlinear dynamic systems and the application in soft sensors. - In: 2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP), (2022), S. 319-324
Slow-feature analysis (SFA) seeks to extract the most slowly varying components of dynamic systems. However, the original definition of SFA implies a linear relationship of system states between adjacent time instants. In this paper, a new approach to SFA, which is called EVOLVE.INFOMAX, is defined, based on which the mutual-information-based SFA (MI-based SFA) is proposed. The optimisation problem can be solved by joint diagonalisation of the mutual-information (MI) matrices. The MI matrices are approximated by quantities related to Rényi entropy that can be calculated using the kernel trick. The case studies show MI-based SFA is better for slow feature extraction, especially for nonlinear systems. This allows a better soft sensor to be developed.
https://doi.org/10.1109/AdCONIP55568.2022.9894163