| 1. |
- Iakovidis, Dimitris K., et al.
(författare)
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Roadmap on signal processing for next generation measurement systems
- 2022
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Ingår i: Measurement Science and Technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 33:1
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Forskningsöversikt (refereegranskat)abstract
- Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
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| 2. |
- Wituschek, Andreas, et al.
(författare)
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High-gain harmonic generation with temporally overlapping seed pulses and application to ultrafast spectroscopy
- 2020
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Ingår i: Optics Express. - 1094-4087. ; 28, s. 29976-90
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Tidskriftsartikel (refereegranskat)abstract
- Collinear double-pulse seeding of the High-Gain Harmonic Generation (HGHG) process in a free-electron laser (FEL) is a promising approach to facilitate various coherent nonlinear spectroscopy schemes in the extreme ultraviolet (XUV) spectral range. However, in collinear arrangements using a single nonlinear medium, temporally overlapping seed pulses may introduce nonlinear mixing signals that compromise the experiment at short time delays. Here, we investigate these effects in detail by extending the analysis described in a recent publication (Wituschek et al., Nat. Commun., 11, 883, 2020). High-order fringe-resolved autocorrelation and wave packet interferometry experiments at photon energies > 23 eV are performed, accompanied by numerical simulations. It turns out that both the autocorrelation and the wave-packet interferometry data are very sensitive to saturation effects and can thus be used to characterize saturation in the HGHG process. Our results further imply that time-resolved spectroscopy experiments are feasible even for time delays smaller than the seed pulse duration.
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| 3. |
- Wituschek, Andreas, et al.
(författare)
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Tracking attosecond electronic coherences using phase-manipulated extreme ultraviolet pulses
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The recent development of ultrafast extreme ultraviolet (XUV) coherent light sources bears great potential for a better understanding of the structure and dynamics of matter. Promising routes are advanced coherent control and nonlinear spectroscopy schemes in the XUV energy range, yielding unprecedented spatial and temporal resolution. However, their implementation has been hampered by the experimental challenge of generating XUV pulse sequences with precisely controlled timing and phase properties. In particular, direct control and manipulation of the phase of individual pulses within an XUV pulse sequence opens exciting possibilities for coherent control and multidimensional spectroscopy, but has not been accomplished. Here, we overcome these constraints in a highly time-stabilized and phase-modulated XUV-pump, XUV-probe experiment, which directly probes the evolution and dephasing of an inner subshell electronic coherence. This approach, avoiding any XUV optics for direct pulse manipulation, opens up extensive applications of advanced nonlinear optics and spectroscopy at XUV wavelengths.
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| 4. |
- Hofbauer, Heinz, et al.
(författare)
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A Ground Truth for Iris Segmentation
- 2014
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Ingår i: 2014 22nd International Conference on Pattern Recognition (ICPR). - Los Alamitos : IEEE Computer Society. - 9781479952083 ; , s. 527-532
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Konferensbidrag (refereegranskat)abstract
- Classical iris biometric systems assume ideal environmental conditions and cooperative users for image acquisition. When conditions are less ideal or users are uncooperative or unaware of their biometrics being taken the image acquisition quality suffers. This makes it harder for iris localization and segmentation algorithms to properly segment the acquired image into iris and non-iris parts. Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. In order to streamline the development and assessment of iris segmentation algorithms with the dependence on the whole biometric system we have generated a iris segmentation ground truth database. We will show a method for evaluating iris segmentation performance base on this ground truth database and give examples of how to identify problematic cases in order to further analyse the segmentation algorithms. ©2014 IEEE.
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| 5. |
- Hofbauer, Heinz, et al.
(författare)
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Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate
- 2016
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Ingår i: IET Biometrics. - Stevenage : Institution of Engineering and Technology. - 2047-4938 .- 2047-4946. ; 5:3, s. 200-211
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Tidskriftsartikel (refereegranskat)abstract
- In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance. © The Institution of Engineering and Technology 2016
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| 6. |
- Kaiser, Andreas-Marius, et al.
(författare)
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Comparison of extraction methods for per- and polyfluoroalkyl substances (PFAS) in human serum and placenta samples-insights into extractable organic fluorine (EOF)
- 2021
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer. - 1618-2642 .- 1618-2650. ; 413, s. 865-876
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Tidskriftsartikel (refereegranskat)abstract
- Since the detection of per- and polyfluoroalkyl substances (PFAS) in humans and different environmental media in the last two decades, this substance group has attracted a lot of attention as well as increasing concerns. The fluorine mass balance approach, by comparing the levels of targeted PFAS after conversion to fluorine equivalents with those of extractable organic fluorine (EOF), showed the presence of unidentified organofluorine in different environmental samples. Out of the thousands of PFAS in existence, only a very small fraction is included in routine analysis. In recent years, liquid chromatography coupled with tandem-mass spectrometry (LC-MS/MS) has demonstrated the ability to analytically cover a wide spectrum of PFAS. In contrast, conventional extraction methods developed 10 to 15 years ago were only evaluated for a limited number of PFAS. The aim of the present study was to evaluate the advantages and disadvantages of three different extraction methods, adapted from the literatures without further optimization (ion-pair liquid-liquid extraction, solid-phase extraction (SPE), using hydrophilic-lipophilic (HLB) or weak anion exchange (WAX) sorbents), for human biomonitoring of 61 PFAS in serum and placental tissue samples. In addition, levels of EOF were compared among these extraction methods via spiked samples. Results showed that performance, in terms of recovery, differed between the extraction methods for different PFAS; different extraction methods resulted in different EOF concentrations indicating that the choice of extraction method is important for target PFAS and EOF analysis. Results of maternal serum samples, analyzed in two different laboratories using two different extraction methods, showed an accordance of 107.6% (± 21.3); the detected perfluoroalkyl acids (PFAAs) in maternal and cord serum samples were in the range of 0.076 to 2.9 ng/mL.Graphical abstract.
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| 7. |
- Kaiser, Andreas-Marius, et al.
(författare)
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Extractable Organofluorine Analysis in Pooled Human Serum and Placental Tissue Samples from an Austrian Subpopulation : A Mass Balance Analysis Approach
- 2021
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Ingår i: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 55:13, s. 9033-9042
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Tidskriftsartikel (refereegranskat)abstract
- Embryos and fetuses are of major concern due to their high vulnerability. Previous studies demonstrated that human exposure to per- and polyfluoroalkyl substances (PFAS) may be underestimated because only a limited number of known PFAS can be measured. This investigation studied the total PFAS exposure by measuring the extractable organofluorine (EOF) in pooled maternal serum, placental tissue, and cord serum samples (total number of pooled samples: n = 45). The EOF was analyzed using combustion ion chromatography, and the concentrations of known PFAS were determined using ultraperformance liquid chromatography coupled with a tandem mass spectrometer. Using a mass balance analysis approach, the amount of unknown PFAS was estimated between the levels of known PFAS and EOF. The EOF levels ranged from 2.85 to 7.17 ng F/mL (21 PFAS were quantified) in the maternal serum, from 1.02 to 1.85 ng F/g (23 PFAS were quantified) in the placental tissue, and from 1.2 to 2.10 ng F/mL (18 PFAS were quantified) in the cord serum. An average of 24, 51, and 9% of EOF is unidentified in the maternal serum, placental tissue, and cord serum, respectively. The results show that the levels of unidentified EOF are higher in the placental tissue, suggesting accumulation or potential transformation of precursors in the placenta.
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| 8. |
- Ribeiro, Eduardo, et al.
(författare)
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Exploring Deep Learning Image Super-Resolution for Iris Recognition
- 2017
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Ingår i: 25th European Signal Processing Conference (EUSIPCO 2017). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780992862671 - 9780992862688 - 9781538607510 ; , s. 2176-2180
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Konferensbidrag (refereegranskat)abstract
- In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms. © EURASIP 2017.
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| 9. |
- Ribeiro, Eduardo, et al.
(författare)
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Iris Super-Resolution using CNNs : is Photo-Realism Important to Iris Recognition?
- 2019
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Ingår i: IET Biometrics. - Stevenage : Institution of Engineering and Technology. - 2047-4938 .- 2047-4946. ; 8:1, s. 69-78
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Tidskriftsartikel (refereegranskat)abstract
- The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in Iris Recognition nowadays. Concurrently, a great variety of single image Super-Resolution techniques are emerging, specially with the use of convolutional neural networks. The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimization of an objective function depending basically on the CNN architecture and the training approach. In this work, we explore single image Super-Resolution using CNNs for iris recognition. For this, we test different CNN architectures as well as the use of different training databases, validating our approach on a database of 1.872 near infrared iris images and on a mobile phone image database. We also use quality assessment, visual results and recognition experiments to verify if the photo-realism provided by the CNNs which have already proven to be effective for natural images can reflect in a better recognition rate for Iris Recognition. The results show that using deeper architectures trained with texture databases that provide a balance between edge preservation and the smoothness of the method can lead to good results in the iris recognition process. © The Institution of Engineering and Technology 2015
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| 10. |
- Ribeiro, Eduardo, et al.
(författare)
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Super-Resolution and Image Re-Projection for Iris Recognition
- 2019
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Ingår i: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA). - Piscataway, N.J. : IEEE. - 9781728105321 - 9781728105314 - 9781728105338 ; , s. 1-7
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Konferensbidrag (refereegranskat)abstract
- Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully.
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