Smart innovative engineering for smart agriculture modernization. - In: Online engineering and society 4.0, (2022), S. 155-163
Smart engineering, as it is taught in many engineering disciplines at universities becomes more and more important to the agricultural sector as well. In the paper there are considered such approaches as remote engineering, digital twins and online teaching tools, which could be effectively implemented for the tasks of Smart Agriculture. The authors discuss three examples of IoT-A related solutions in remote engineering to enrich the education in the agricultural field.
On the development of a unified online laboratory framework. - In: Online engineering and society 4.0, (2022), S. 10-22
This work focuses on the requirements analysis of a modular framework to simplify developing and integrating new or existing remote experiments and laboratories. Besides this technical view, this paper also gives an organizational view on developing such a framework and managing the corresponding modules, which can also be developed by a third party. On a technical side, we provide the requirements to ultimately define the interface between different modules, enabling easy integration on different abstraction levels.The work's basis is the review of individual remote laboratories and existing systems of the past two decades and the author's collective experience. In the spirit of uniting the remote laboratory community, we will follow the IEEE 1876 standard wherever possible and extend it to make our vision of an easy to use, integrable, and extendable framework possible.
GOLDi 2.0: beyond raw digital signals - electrical interface emulation. - In: Online engineering and society 4.0, (2022), S. 23-34
The interactive hybrid remote lab Grid of online lab devices Ilmenau (GOLDi) has been successfully used internationally for many years as a cloud-based system for controlling electromechanical hardware models with various specification techniques. However, the complexity of digital systems is constantly increasing. To keep up with this trend, the didactic concept of remote labs is also constantly evolving. GOLDi is currently reaching its limitations on complexity and variability because all sensors and actuators are required to communicate via a few binary, not time-sensitive signals. This prevents the usage of sensors and actuators which require specific protocols, modulated or specifically timed signals. We propose a new way of transmitting complex signals including timing information by introducing interface hardware between the control unit and the physical system to emulate the output signals of one as the input signals of the other. This in turn allows for new experiment designs tailored towards more realistic setups of sensors and actuators which provide a much richer learning experience.
An investigation on the heat dissipation in Zn-substituted magnetite nanoparticles, coated with citric acid and pluronic F127 for hyperthermia application. - In: Physica, ISSN 1873-2135, Bd. 625 (2022), 413468
Zinc substituted spinel ferrite nanoparticles are appropriate for magnetic fluid hyperthermia. Stable suspensions of Zn2+ substituted magnetite (ZnxFe3-xO4, 0 ≤ x ≤ 0.20) nanoparticles in aqueous solutions (pH 5.5) were synthesized by means of co-precipitation approach, using citric acid (CA) and pluronic F127 as surfactants for hyperthermia application. The specimens were characterized by different methods. XRD patterns of the precipitates confirmed that all specimens have single phase cubic spinel structures and their lattice parameters increased as Zn2+ content increased. Mean crystallite sizes of the uncoated specimens were determined to be around 28 nm, using Scherrer's formula. By increasing the Zn2+ content, Curie temperature of the uncoated specimens reduced from 545 to 410 ˚C monotonically caused by reduction in super-exchange interactions. Room temperature saturation magnetizations of the uncoated specimens increased to 98.8 emu/g for x = 0.10 initially, and then decreased to 79.6 emu/g for x = 0.20. It is attributed to the replacement of paramagnetic Fe3+ ions by diamagnetic Zn2+ ones and spin canting. FTIR spectra reconfirmed formation of pure magnetite and Zn2+ substituted magnetite nanoparticles and also proved the presence of ligands on the surface of the nanoparticles. TEM investigation showed that mean particle sizes of the coated nanoparticles were in the range of 35-40 nm. The obtained ferrofluids showed a good stability in aqueous medium (pH 5.5) and according to the room temperature magnetic measurements, heating efficiency is scarcely released due to relaxation processes. Maximum obtained specific loss power (SLP) was 539 W/g and that of intrinsic loss power (ILP) was 7.26 nHm2/kg for x = 0.05 (f = 290 kHz, H = 16 kA/m) with a nanoparticle concentration as low as 1.2 mg/ml, which is a promising candidate for magnetic hyperthermia applications potentially.
Deep security analysis of program code : a systematic literature review. - In: Empirical software engineering, ISSN 1573-7616, Bd. 27 (2022), 1, 2, S. 1-39
Due to the continuous digitalization of our society, distributed and web-based applications become omnipresent and making them more secure gains paramount relevance. Deep learning (DL) and its representation learning approach are increasingly been proposed for program code analysis potentially providing a powerful means in making software systems less vulnerable. This systematic literature review (SLR) is aiming for a thorough analysis and comparison of 32 primary studies on DL-based vulnerability analysis of program code. We found a rich variety of proposed analysis approaches, code embeddings and network topologies. We discuss these techniques and alternatives in detail. By compiling commonalities and differences in the approaches, we identify the current state of research in this area and discuss future directions. We also provide an overview of publicly available datasets in order to foster a stronger benchmarking of approaches. This SLR provides an overview and starting point for researchers interested in deep vulnerability analysis on program code.
Multi-task near-field perception for autonomous driving using surround-view fisheye cameras. - Ilmenau : Universitätsbibliothek, 2021. - 1 Online-Ressource (xxv, 219 Seiten)
Technische Universität Ilmenau, Dissertation 2021
Literaturverzeichnis: Seite 183-219
Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0-10 Metern und 360˚ Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.
Kraftmessung von Elektroden an einem menschlichen Kopfmodell :
Force measurement of electrodes on a human head model. - In: Technisches Messen, ISSN 2196-7113, Bd. 88 (2021), 11, S. 724-730
Electroencephalography (EEG) and transcranial electric stimulation (TES) require caps for holding the respective electrodes in place. To support the optimal design of such caps, knowledge of the force-displacement curves for each electrode position is desirable. We propose a calibrated setup to traceably measure force-displacement curves which consists of a human head model, a force sensor, a linear guide, a stepper motor, and a multiplexing multimeter. Repeated measures of a textile EEG-cap and a TES-cap show significant non-linearity and hysteresis effects for the force-displacement curves. Our setup will allow for the assessment of the fit of EEG and TES-caps for various head shapes and sizes.
3D retinal imaging and measurement using light field technology. - In: Journal of biomedical optics, ISSN 1560-2281, Bd. 26 (2021), 12, S. 126002-1-126002-19
Significance: Light-field fundus photography has the potential to be a new milestone in ophthalmology. Up-to-date publications show only unsatisfactory image quality, preventing the use of depth measurements. We show that good image quality and, consequently, reliable depth measurements are possible, and we investigate the current challenges of this novel technology. Aim: We investigated whether light field (LF) imaging of the retina provides depth information, on which structures the depth is estimated, which illumination wavelength should be used, whether deeper layers are measurable, and what kinds of artifacts occur. Approach: The technical setup, a mydriatic fundus camera with an LF imager, and depth estimation were validated by an eye model and in vivo measurements of three healthy subjects and three subjects with suspected glaucoma. Comparisons between subjects and the corresponding optical coherence tomography (OCT) measurements were used for verification of the depth estimation. Results: This LF setup allowed for three-dimensional one-shot imaging and depth estimation of the optic disc with green light. In addition, a linear relationship was found between the depth estimates of the OCT and those of the setup developed here. This result is supported by the eye model study. Deeper layers were not measurable. Conclusions: If image artifacts can be handled, LF technology has the potential to help diagnose and monitor glaucoma risk at an early stage through a rapid, cost-effective one-shot technology.
Me-doped Ti-Me intermetallic thin films used for dry biopotential electrodes: a comparative case study. - In: Sensors, ISSN 1424-8220, Bd. 21 (2021), 23, 8143, S. 1-17
In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and overcoming several of the limitations of conventional gel-based electrodes such as reusability, durability, biocompatibility, and comfort. The thin films were deposited on stainless steel (SS) discs and polyurethane (PU) substrates to be used as dry electrodes, for non-invasive monitoring of body surface biopotentials. Four different Ti-Me (Me = Al, Cu, Ag, or Au) metallic binary systems were prepared by magnetron sputtering. The morphology of the resulting Ti-Me systems was found to be dependent on the chemical composition of the films, specifically on the type and amount of Me. The existence of crystalline intermetallic phases or glassy amorphous structures also revealed a strong influence on the morphological features developed by the different systems. The electrodes were tested in an in-vivo study on 20 volunteers during sports activity, allowing study of the application-specific characteristics of the dry electrodes, based on Ti-Me intermetallic thin films, and evaluation of the impact of the electrode-skin impedance on biopotential sensing. The electrode-skin impedance results support the reusability and the high degree of reliability of the Ti-Me dry electrodes. The Ti-Al films revealed the least performance as biopotential electrodes, while the Ti-Au system provided excellent results very close to the Ag/AgCl reference electrodes.
Big Data Analytics APIs architecture for formative assessors. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), insges. 9 S.
This Research to Practice Full Paper is driven by the question: Within limited time resources available to trainers in projects for Big Data Analytics (BDA) problems, how can they define project requirements for Formative Assessment (FA) actions? The paper suggests BDA APIs architecture as helping tool for formative assessors. It helps them effectively produce and adapt visual diagnostic reports for FA-actions in agile based requirements (i.e. features) definition. The paper presents two core architectures: Architecture for a parametrized feature-descriptor-system to define/refine a BDA API feature and its visual diagnostic reports, and an initial resources architecture for BDA API to initialize an analytics algorithm with its input big data sets. Clarifying visually the trainee's challenges (i.e. incremental features in a BDA API) is our main FA action. The FA action is designed based on Csikszentmihalyi's flow model to support a trainee in matching balance between his/her challenges and his/her skills. To test the architecture's functions, the paper has test setups for two formal projects (each has 1 to 6 trainees) and two informal projects (each has 1 to 3 trainees). The projects are to attack BDA problems in learning analytics and in image automatic classification. The test results show that the visual diagnostic reports produced by the trainers are very effective in clarifying visually incremental BDA API features not only for simple classifiers (i.e. classical data mining algorithms) but also for complex classifiers (i.e. deep learning algorithms). The results show also how visual diagnostic reports are easily produced for comparing the algorithm performances using different input big data sets, whereas other reports are produced for comparing performances between different algorithms, using one input data set. Related works are also discussed to show the architecture's differences and advantages. Its main advantages are: 1) it enables the trainers to use deep learning algorithms beside classical data mining algorithms in its BDA API parameterizable feature descriptors for visual diagnostic reports. 2) The descriptors can be extended, reused, shared, and scaled out to help trainers in other universities providing flow model based FA actions. 3) Finally, it has extensions to integrate other theoretical frameworks like Buckingham Shum and Deakin Crick's framework for dispositional learning analytics instead of the used flow model.