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Since the invention of scanning probe microscopy, researchers have desired to use this technique to monitor sub-second surface dynamics with atomic spatial resolution. A recently presented add-on electronics module enables the speed-up of existing, conventional scanning probe microscopes without any modification of the actual instrument. The resulting one-dimensional (1D) data stream, recorded while the tip oscillates in a sinusoidal motion, has to be reconstructed into a layered rectangular matrix in order to visualize the movie. The Python-based pyfastspm package performs this conversion, while also correcting for sample tilt, noise frequencies, piezo creep, and thermal drift. Quick automatic conversion even of considerable batches of data is achieved by efficient algorithms that bundle time-expensive steps, such as interpolation based on Delaunay triangulation.

Here, we report on the conceptual design, the hardware realization, and the first experimental results of a novel and compact x-ray polarimeter capable of a single-pulse linear polarization angle detection in the extreme ultraviolet photon energy range. The polarimeter is tested by performing time resolved pump–probe experiments on a Ni80Fe20 Permalloy film at the M2,3 Ni edge at an externally seeded free-electron laser source. Comparison with similar experiments reported in the literature shows the advantages of our approach also in view of future experiments.

Charge transfer at molecule-metal interfaces affects the overall physical and magnetic properties of organic-based devices, and ultimately their performance. Here, the authors report evidence of a pronounced charge transfer involving nickel tetraphenyl porphyrin molecules adsorbed on copper.

The original version of this Article contained an error in the spelling of the author Claus Michael Schneider, which was incorrectly given as Claus Michael Schneidery. This has now been corrected in both the PDF and HTML versions of the Article.

High-speed video recording of the vocal folds during sustained phonation has become a widespread diagnostic tool, and the development of imaging techniques able to perform automated tracking and analysis of relevant glottal cues, such as folds edge position or glottal area, is an active research field. In this paper, a vocal folds vibration analysis method based on the processing of visual data through a biomechanical model of the layngeal dynamics is proposed. The procedure relies on a Bayesian non-stationary estimation of the biomechanical model parameters and state, to fit the folds edge position extracted from the high-speed video endoscopic data. This finely tuned dynamical model is then used as a state transition model in a Bayesian setting, and it allows to obtain a physiologically motivated estimation of upper and lower vocal folds edge position. Based on model prediction, an hypothesis on the lower fold position can be made even in complete fold occlusion conditions occurring during the end of the closed phase and the beginning of the open phase of the glottal cycle. To demonstrate the suitability of the procedure, the method is assessed on a set of audiovisual recordings featuring high-speed video endoscopic data from healthy subjects producing sustained voiced phonation with different laryngeal settings.

In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD) approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.