NachhaltigF: Elektrotechnik und MedientechnikS: TC Freyung
A. Dudkiewicz, A. Lehner, Q. Chaudhry, K. Molhave, G. Allmaier, K. Tiede, Boxall, A. B. A., Peter Hofmann, J. Lewis
Development of a sample preparation approach to measure the size of nanoparticle aggregates by electron microscopy
[Available online 29 November 2018]
Electron microscopy (EM) is widely used for nanoparticle (NP) sizing. Following an initial assessment of two sample preparation protocols described in the current literature as “unperturbed”, we found that neither could accurately measure the size of NPs featuring a broad size distribution, e.g., aggregates. Because many real-world NP samples consist of aggregates, this finding was of considerable concern. The data showed that the protocols introduced errors into the measurement by either inducing agglomeration artefacts or providing a skewed size distribution towards small particles (skewing artefact). The focus of this work was to develop and apply a mathematical refinement to correct the skewing artefact. This refinement provided a much improved agreement between EM and a reference methodology, when applied to the measurement of synthetic amorphous silica NPs. Further investigation, highlighted the influence of NP chemistry on the refinement. This study emphasised the urgent need for greater and more detailed consideration regarding the sample preparation of NP aggregates to routinely achieve accurate measurements by EM. This study also provided a novel refinement solution applicable to the size characterisation of silica and citrate-coated gold NPs featuring broad size distributions. With further research, this approach could be extended to other NP types
DigitalF: Maschinenbau und MechatronikI: Fraunhofer AWZ CTMT
A. Detterbeck, M. Hofmeister, D. Haddad, D. Weber, M. Schmid, A. Hölzing, S. Zabler, E. Hofmann, K.-H. Hiller, P. Jakob, J. Engel, Jochen Hiller, U. Hirschenfelder
Determination of the mesio-distal tooth width via 3D imaging techniques with and without ionizing radiation: CBCT, MSCT, and μCT versus MRI
European Journal of Orthodontics, vol. 39, no. 3, pp. 310-319
The purpose of this study was to estimate the feasibility and accuracy of mesio-distal width measurements with magnetic resonance imaging (MRI) in comparison to conventional 3D imaging techniques [multi-slice CT (MSCT), cone-beam CT (CBCT), and µCT]. The measured values of the tooth widths were compared to each other to estimate the amount of radiation necessary to enable orthodontic diagnostics.
Material and Methods:
Two pig skulls were measured with MSCT, CBCT, µCT, and MRI. Three different judges were asked to determine the mesio-distal tooth width of 14 teeth in 2D tomographic images and in 3D segmented images via a virtual ruler in every imaging dataset.
Approximately 19% (27/140) of all test points in 2D tomographic slice images and 12% (17/140) of the test points in 3D segmented images showed a significant difference (P ≤ 0.05). The largest significant difference was 1.6mm (P < 0.001). There were fewer significant differences in the measurement of the tooth germs than in erupted teeth.
Measurement of tooth width by MRI seems to be clinically equivalent to the conventional techniques (CBCT and MSCT). Tooth germs are better illustrated than erupted teeth on MRI. Three-dimensional segmented images offer only a slight advantage over 2D tomographic slice images. MRI, which avoids radiation, is particularly appealing in adolescents if these data can be corroborated in further studies.
NachhaltigS: TC Freyung
A. Dudkiewicz, Boxall, A. B. A., Q. Chaudhry, K. Mølhave, K. Tiede, Peter Hofmann, Linsinger, T. P. J.
Uncertainties of size measurements in electron microscopy characterization of nanomaterials in foods
Food Chemistry, vol. 176, no. June 1, pp. 472-479
Electron microscopy is a recognized standard tool for nanomaterial characterization, and recommended by the European Food Safety Authority for the size measurement of nanomaterials in food. Despite this, little data have been published assessing the reliability of the method, especially for size measurement of nanomaterials characterized by a broad size distribution and/or added to food matrices. This study is a thorough investigation of the measurement uncertainty when applying electron microscopy for size measurement of engineered nanomaterials in foods. Our results show that the number of measured particles was only a minor source of measurement uncertainty for nanomaterials in food, compared to the combined influence of sampling, sample preparation prior to imaging and the image analysis. The main conclusion is that to improve the measurement reliability, care should be taken to consider replications and matrix removal prior to sample preparation.
S: TC Freyung
R. Marschallinger, S. Golaszewski, A. Kunz, M. Kronbichler, G. Ladurner, Peter Hofmann, E. Trinka, M. McCoy, J. Kraus
Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns
Journal of Neuroimaging, vol. 24, no. 3, pp. 278-286
F: Elektrotechnik und Medientechnik
Beitrag (Sammelband oder Tagungsband)
J. Benze, Andreas Berl, K. Daniel, G. Eibl, D. Engel, Andreas Fischer, U. Hofmann, A. Kießling, S. Köpsell, L. Langer, H. Meer, C. Neureiter, M. Niedermeier, T. Pfeiffenberger, M. Pietsch, A. Veichtlbauer
VDE-Positionspapier Energieinformationsnetze und -systeme (Smart Grid Security)
VDE-Kongress 2014 Smart Cities (Intelligente Lösungen für das Leben in der Zukunft, Kongressbeiträge 20./21.10.2014, Frankfurt/Main, Messe), Berlin
F: Maschinenbau und Mechatronik
A. Hofmann, Bernhard Sick
On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling
IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 2, pp. 282-294
Alert aggregation is an important subtask of intrusion detection. The goal is to identify and to cluster different alerts—produced by low-level intrusion detection systems, firewalls, etc.—belonging to a specific attack instance which has been initiated by an attacker at a certain point in time. Thus, meta-alerts can be generated for the clusters that contain all the relevant information whereas the amount of data (i.e., alerts) can be reduced substantially. Meta-alerts may then be the basis for reporting to security experts or for communication within a distributed intrusion detection system. We propose a novel technique for online alert aggregation which is based on a dynamic, probabilistic model of the current attack situation. Basically, it can be regarded as a data stream version of a maximum likelihood approach for the estimation of the model parameters. With three benchmark data sets, we demonstrate that it is possible to achieve reduction rates of up to 99.96 percent while the number of missing meta-alerts is extremely low. In addition, meta-alerts are generated with a delay of typically only a few seconds after observing the first alert belonging to a new attack instance.
S: TC Freyung
Beitrag (Sammelband oder Tagungsband)
A. Nazarkulova, J. Strobl, Peter Hofmann
Green Spaces in Bishkek - A Satellite Perspective
Proceedings of the Fourth Central Asia GIS Conference GISCA'10 (Water: Life, Risk, Energy and Landuse) [Bishkek, Kyrgyzstan; May 27-28, 2010]
F: Maschinenbau und Mechatronik
D. Fisch, A. Hofmann, Bernhard Sick
On the Versatility of Radial Basis Function Neural Networks: A Case Study in the Field of Intrusion Detection
Information Sciences, vol. 180, no. 12, pp. 2421-2439
Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.