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Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.
This work aimed to determine the influence of two hydrogels (alginate, alginate-di-aldehyde (ADA)/gelatin) on the mechanical strength of microporous ceramics, which have been loaded with these hydrogels. For this purpose, the compressive strength was determined using a Zwick Z005 universal testing machine. In addition, the degradation behavior according to ISO EN 10993-14 in TRIS buffer pH 5.0 and pH 7.4 over 60 days was determined, and its effects on the compressive strength were investigated. The loading was carried out by means of a flow-chamber. The weight of the samples (manufacturer: Robert Mathys Foundation (RMS) and Curasan) in TRIS solutions pH 5 and pH 7 increased within 4 h (mean 48 ± 32 mg) and then remained constant over the experimental period of 60 days. The determination surface roughness showed a decrease in the value for the ceramics incubated in TRIS compared to the untreated ceramics. In addition, an increase in protein concentration in solution was determined for ADA gelatin-loaded ceramics. The macroporous Curasan ceramic exhibited a maximum failure load of 29 ± 9.0 N, whereas the value for the microporous RMS ceramic was 931 ± 223 N. Filling the RMS ceramic with ADA gelatin increased the maximum failure load to 1114 ± 300 N. The Curasan ceramics were too fragile for loading. The maximum failure load decreased for the RMS ceramics to 686.55 ± 170 N by incubation in TRIS pH 7.4 and 651 ± 287 N at pH 5.0.
Introduction: The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue engineering. The in vivo environment of the cells exerts complex stimuli on the cells, thereby directly influencing cell behavior, including proliferation and differentiation. Therefore, to create suitable replacement or regeneration procedures for human tissues, the conditions of the cells’ natural environment should be well mimicked. Therefore, current research is trying to develop 3-dimensional scaffolds (scaffolds) that can elicit appropriate cellular responses and thus help the body regenerate or replace tissues. In this work, scaffolds were printed from the biomaterial polycaprolactone (PCL) on a 3D bioplotter. Biocompatibility testing was used to determine whether the printed scaffolds were suitable for use in tissue engineering.
Material and Methods: An Envisiontec 3D bioplotter was used to fabricate the scaffolds. For better cell-scaffold interaction, the printed polycaprolactone scaffolds were coated with type-I collagen. Three different cell types were then cultured on the scaffolds and various tests were used to investigate the biocompatibility of the scaffolds.
Results: Reproducible scaffolds could be printed from polycaprolactone. In addition, a coating process with collagen was developed, which significantly improved the cell-scaffold interaction. Biocompatibility tests showed that the PCL-collagen scaffolds are suitable for use with cells. The cells adhered to the surface of the scaffolds and as a result extensive cell growth was observed on the scaffolds. The inner part of the scaffolds, however, remained largely uninhabited. In the cytotoxicity studies, it was found that toxicity below 20% was present in some experimental runs. The determination of the compressive strength by means of the universal testing machine Z005 by ZWICK according to DIN EN ISO 604 of the scaffolds resulted in a value of 68.49 ± 0.47 MPa.
Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
This article presents a study of cultural differences affecting the acceptance and design preferences of social robots. Based on a survey with 794 participants from Germany and the three Arab countries of Egypt, Jordan, and Saudi Arabia, we discuss how culture influences the preferences for certain attributes. We look at social roles, abilities and appearance, emotional awareness and interactivity of social robots, as well as the attitude toward automation. Preferences were found to differ not only across cultures, but also within countries with similar cultural backgrounds. Our findings also show a nuanced picture of the impact of previously identified culturally variable factors, such as attitudes toward traditions and innovations. While the participants’ perspectives toward traditions and innovations varied, these factors did not fully account for the cultural variations in their perceptions of social robots. In conclusion, we believe that more real-life practices emerging from the situated use of robots should be investigated. Besides focusing on the impact of broader cultural values such as those associated with religion and traditions, future studies should examine how users interact, or avoid interaction, with robots within specific contexts of use.
Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
(2021)
This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.
A Review on Kinetic Energy Harvesting with Focus on 3D Printed Electromagnetic Vibration Harvesters
(2021)
The increasing amount of Internet of Things (IoT) devices and wearables require a reliable energy source. Energy harvesting can power these devices without changing batteries. Three-dimensional printing allows us to manufacture tailored harvesting devices in an easy and fast way. This paper presents the development of hybrid and non-hybrid 3D printed electromagnetic vibration energy harvesters. Various harvesting approaches, their utilised geometry, functional principle, power output and the applied printing processes are shown. The gathered harvesters are analysed, challenges examined and research gaps in the field identified. The advantages and challenges of 3D printing harvesters are discussed. Reported applications and strategies to improve the performance of printed harvesting devices are presented.
A strong heat load in buildings and cities during the summer is not a new phenomenon. However, prolonged heat waves and increasing urbanization are intensifying the heat island effect in our cities; hence, the heat exposure in residential buildings. The thermophysiological load in the interior and exterior environments can be reduced in the medium and long term, through urban planning and building physics measures. In the short term, an increasingly vulnerable population must be effectively informed of an impending heat wave. Building simulation models can be favorably used to evaluate indoor heat stress. This study presents a generic simulation model, developed from monitoring data in urban multi-unit residential buildings during a summer period and using statistical methods. The model determines both the average room temperature and its deviations and, thus, consists of three sub-models: cool, average, and warm building types. The simulation model is based on the same mathematical algorithm, whereas each building type is described by a specific data set, concerning its building physical parameters and user behavior, respectively. The generic building model may be used in urban climate analyses with many individual buildings distributed across the city or in heat–health warning systems, with different building and user types distributed across a region. An urban climate analysis (with weather data from a database) may evaluate local differences in urban and indoor climate, whereas heat–health warning systems (driven by a weather forecast) obtain additional information on indoor heat stress and its expected deviations.