Remote photoplethysmography (rPPG) enables contact-free monitoring of the blood volume pulse using a color camera. Essentially, it detects the minute optical absorption changes caused by blood volume variations in the skin. In this paper, we show that the different absorption spectra of arterial blood and bloodless skin cause the variations to occur along a very specific vector in a normalized RGB-space. The exact vector can be determined for a given light spectrum and for given transfer characteristics of the optical filters in the camera. We show that this 'signature' can be used to design an rPPG algorithm with a much better motion robustness than the recent methods based on blind source separation, and even better than the chrominance-based methods we published earlier. Using six videos recorded in a gym, with four subjects exercising on a range of fitness devices, we confirm the superior motion robustness of our newly proposed rPPG methods. A simple peak detector in the frequency domain returns the correct pulse-rate for 68% of total measurements compared to 60% for the best previous method, while the SNR of the pulse-signal improves from  - 5 dB to  - 4 dB. For a large population of 117 stationary subjects we prove that the accuracy is comparable to the best previous method, although the SNR of the pulse-signal drops from  + 8.4 dB to  + 7.6 dB. We expect the improved motion robustness to significantly widen the application scope of the rPPG-technique.

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SummaryIn traditional cancer diagnosis, (histo)pathological images of biopsy samples are visually analysed by pathologists. However, this judgment is subjective and leads to variability among pathologists. Digital scanners may enable automated objective assessment, improved quality and reduced throughput time. Nucleus detection is seen as the corner stone for a range of applications in automated assessment of (histo)pathological images.In this paper, we propose an efficient nucleus detector designed with machine learning. We applied colour deconvolution to reconstruct each applied stain. Next, we constructed a large feature set and modified AdaBoost to create two detectors, focused on different characteristics in appearance of nuclei. The proposed modification of AdaBoost enables inclusion of the computational cost of each feature during selection, thus improving the computational efficiency of the resulting detectors. The outputs of the two detectors are merged by a globally optimal active contour algorithm to refine the border of the detected nuclei. With a detection rate of 95% (on average 58 incorrectly found objects per field-of-view) based on 51 field-of-view images of Her2 immunohistochemistry stained breast tissue and a complete analysis in 1 s per field-of-view, our nucleus detector shows good performance and could enable a range of applications in automated assessment of (histo)pathological images.

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In Part I of this study a theoretical model was recommended describing the hydraulic characteristics, being Sauter drop diameter, hold-up, operating regimes, and operational window, of caprolactam extraction in a pulsed disc and doughnut column. In order to confirm the theoretical model pilot plant experiments for the caprolactam forward and back-extraction were performed to determine the hydraulic characteristics as a function of the operating conditions. The experimental conditions covered the industrial operating range. All hydraulic experiments were performed at equilibrium conditions in order to avoid the influence of mass transfer.In the determination of the operational window flooding because of too low pulsation was qualitatively observed, while it was found that at high pulsation phase inversion was limiting for the back-extraction process and flooding for the forward extraction process. Application of the in Part I recommended theoretical model for the description of the obtained hydraulic data resulted in an accurate description after fitting the drop diameter, hold-up, and phase inversion data.

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