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This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).
Sensing is nowadays an accepted application of phononic crystals with chemical and biosensors for liquid analytes being the perhaps most promising ones. A liquid-filled defect is introduced that turns into a resonant cavity. Similar to the well-known resonant sensors sensitivity can be increased with higher operating frequencies. We introduce a Sandwiched Phononic Crystal (SPC), which applies a resonant cavity located between two stacked layers on top of a SAW device. Key challenges are coupling of surface acoustic wave into the liquid cavities and avoiding scattering of the guided waves into the bulk of the sensor chip.
Phononic crystals (PnC) with a specifically designed liquid-filled defect have been recently introduced as a novel sensor platform (Lucklum et al. in Sens Actuators B Chem 171–172:271–277, 2012). Sensors based on this principle feature a band gap covering the typical input span of the measurand as well as a narrow transmission peak within the band gap where the frequency of maximum transmission is governed by the measurand. This approach has been applied for determination of volumetric properties of liquids (Lucklum et al. in Sens Actuators B Chem 171–172:271–277, 2012; Oseev et al. in Sens Actuators B Chem 189:208–212, 2013; Lucklum and Li in Meas Sci Technol 20(12):124014, 2009) and has demonstrated attractive sensitivity. One way to improve sensitivity requires higher probing frequencies in the range of 100 MHz and above. In this range surface acoustic wave (SAW) devices are an established basis for sensors. We have performed first tests towards a PnC microsensors (Lucklum et al. in Towards a SAW based phononic crystal sensor platform. In: 2013 Joint European frequency and time forum and international frequency control symposium (EFTF/IFC), pp 69–72, 2013). The respective feature size of the PnC SAW sensor has dimensions in the range of 10 µm and below. Whereas those dimensions are state of the art for common MEMS materials, etching of holes and cavities in piezoelectric materials that have an aspect ratio diameter/depth is still challenging. In this contribution we describe an improved technological process able to realize considerably deep and uniform holes in a SAW substrate.
The study focuses on high-performance combined electro-spark alloying of titanium and titanium alloy (VT1-0, VT16) surface and porous matrix structure oxidation. The metal-oxide coatings morphology is the result of melt drop transfer, heat treatment, and oxidation. The study establishes the influence of technological regimes of alloying and oxidation on morphological heterogeneity of biocompatible layered metal-oxide system Ti-Ta-(Ti,Ta)xOy. It was found that during electro-spark alloying the concentration of tantalum on the titanium surface ranges from 0.1 to 3.2 at.%. Morphology of the deposited splats is represented by uniformly grown crystals of titanium and tantalum oxides, which increase from nano- to submicron size.
Accounting-Compliance
(2016)
Tracking und Matchmoving
(2016)
Compositing
(2016)
Multi-level reference architecture development for digital public infrastructures based on the cloud
(2016)
Estimation of the Average Gait Velocity based on Statistical Stride Parameters of Foot Sensor Data
(2016)
Investigation of adaptive local threshold segmentation in context of 3D-handwriting forensics
(2016)
Informationssuche im Internet – Suchverhalten von an Brustkrebs erkrankten und Nicht-Betroffenen
(2016)
Game of colors: moderne Bewegtbildproduktion : Theorie und Praxis für Film, Video und Fernsehen
(2016)
Innovation - für mehr Bahn!
(2016)
Mehr Güter auf die Schiene
(2016)
Baustelle Digitalisierung
(2016)
Bahn und Bürger
(2016)
MultiMo: A Multimodal University Evaluation Software Designed for High Response Rates and Usability
(2016)
Resistance spot welding is the dominant process in the present mass production of steel constructions without sealing requirements with single sheet thicknesses up to 3 mm. Two of the main applications of resistance spot welding are the automobile and the railway vehicle manufacturing industry. The majority of these connections has safety-related character and therefore they must not fall below a certain weld diameter. Since resistance spot welding has been established, this weld diameter has been usually used as the gold standard. Despite intensive efforts, there has not been found yet a reliable method to detect this connection quality non-destructively. Considerable amounts of money and steel sheets are wasted on making sure that the process does not result in faulty joints. The indication of the weld diameter by in-process monitoring in a reliable way would allow the quality documentation of joints during the welding process and additionally lead through demand-actuated milling cycles to a substantial decrease of electrode consumption. An annual, estimated reduction in the seven- to nine-figure range could be achieved. It has an important impact, because the economics of the process is essentially characterized by the electrode caps (Klages 24). We propose a simple and straightforward approach using data mining techniques to accurately predict the weld diameter from recorded data during the welding process. In this paper, we describe the methods used during data preprocessing and segmentation, feature extraction and selection, and model creation and validation. We achieve promising results during an analysis of more than 3000 classified welds using a model tree as a predictor with a success rate of 93 %. In the future, we hope to validate our model with unseen welding data and implement it in a real world application.
Ultraschnelle Chips
arbeiten mit zehnfacher Geschwindigkeit im Vergleich zu aktuell
verwendeten CMOS Computerchips und sollen bis zur Marktreife gebracht werden.
Für spezielle Anwendungen wird ein „Baukasten“ in Form einer
wiederverwendbaren Bibliothek geschaffen. Anwender können mit den
Bauelementen spezielle Mikrochips (ASICs) entwerfen, die digitale Daten mit
Taktfrequenzen bis zu 35 GHz verarbeiten können.