Machine learning-based Raman amplifier design

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.Comment: conferenc

Active rectifier circuits with sequential charging of storage capacitors (SCSC) for energy harvesting in autonomous sensors

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Learning Semantic Part-Based Models from Google Images

We propose a technique to train semantic part-based models of object classes from Google Images. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. We learn these rich models by collecting training instances for both parts and objects, and automatically connecting the two levels. Our framework works incrementally, by learning from easy examples first, and then gradually adapting to harder ones. A key benefit of this approach is that it requires no manual part location annotations. We evaluate our models on the challenging PASCAL-Part dataset [1] and show how their performance increases at every step of the learning, with the final models more than doubling the performance of directly training from images retrieved by querying for part names (from 12.9 to 27.2 AP). Moreover, we show that our part models can help object detection performance by enriching the R-CNN detector with parts

Nonlinear Multi-Frequency Converter Array for Vibration Energy Harvesting in Autonomous Sensors☆

Abstract This work proposes and experimentally validates a vibration energy harvester which combines the multi-frequency and nonlinear approaches into a converter array. The converter array consists of four piezoelectric cantilevers composed of ferromagnetic substrates with screen-printed lead zirconate titanate (PZT) layers coupled with a single permanent magnet elastically suspended on the array base in order to create a nonlinear behaviour. The presence of a moving magnet and the possibility to realize cantilevers with different potential curves can be useful to obtain a collective nonlinear behaviour due to strong coupling irrespective of the amplitude of the mechanical excitation, therefore increasing the overall effectiveness of the converter array. The experimental results confirm that combining cantilevers with different potential curves can be useful to obtain a collective bistable behaviour, therefore increasing the overall effectiveness of the converter array

A single-magnet nonlinear piezoelectric converter for enhanced energy harvesting from random vibrations

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Monitoring Wound Healing with Contactless Measurements and Augmented Reality

Objective: This work presents a device for non-invasive wound parameters assessment, designed to overcome the drawbacks of traditional methods, which are mostly rough, inaccurate, and painful for the patient. The device estimates the morphological parameters of the wound and provides augmented reality (AR) visual feedback on the wound healing status by projecting the wound border acquired during the last examination, thus improving doctor-patient communication. Methods: An accurate 3D model of the wound is created by stereophotogrammetry and refined through self-organizing maps. The 3D model is used to estimate physical parameters for wound healing assessment and integrates AR functionalities based on a miniaturized projector. The physical parameter estimation functionalities are evaluated in terms of precision, accuracy, inter-operator variability, and repeatability, whereas AR wound border projection is evaluated in terms of accuracy on the same phantom. Results: The accuracy and precision of the device are respectively 2% and 1.2% for linear parameters, and 1.7% and 1.3% for area and volume. The AR projection shows an error distance <1 mm. No statistical difference was found between the measurements of different operators. Conclusion: The device has proven to be an objective and non-operator-dependent tool for assessing the morphological parameters of the wound. Comparison with non-contact devices shows improved accuracy, offering reliable and rigorous measurements. Clinical Impact: Chronic wounds represent a significant health problem with high recurrence rates due to the ageing of the population and diseases such as diabetes and obesity. The device presented in this work provides an easy-to-use non-invasive tool to obtain useful information for treatment

Teukolsky-Starobinsky Identities - a Novel Derivation and Generalizations

We present a novel derivation of the Teukolsky-Starobinsky identities, based on properties of the confluent Heun functions. These functions define analytically all exact solutions to the Teukolsky master equation, as well as to the Regge-Wheeler and Zerilli ones. The class of solutions, subject to Teukolsky-Starobinsky type of identities is studied. Our generalization of the Teukolsky-Starobinsky identities is valid for the already studied linear perturbations to the Kerr and Schwarzschild metrics, as well as for large new classes of of such perturbations which are explicitly described in the present article. Symmetry of parameters of confluent Heun's functions is shown to stay behind the behavior of the known solutions under the change of the sign of their spin weights. A new efficient recurrent method for calculation of Starobinsky's constant is described.Comment: 8 pages, LaTeX file, no figures, final versio

Leggere le Indicazioni. Riflessioni e proposte per la scuola dell'infanzia

Il volume presenta il testo delle indicazioni nazionali per il curricolo della scuola dell'infanzia e del primo ciclo d'istruzione, pubblicato in forma ufficiale dal Ministero dell'Istruzione nel 2013, e ne propone un commento per quanto riguarda la parte relativa alla scuola dell'infanzia, mettendo a fuoco alcune tematiche cruciali: l'idea di scuola, di relazione educativa, di contesto, di continuità, di attività professional

Characterization of multilayer stack parameters from X-ray reflectivity data using the PPM program: measurements and comparison with TEM results

Future hard (10 -100 keV) X-ray telescopes (SIMBOL-X, Con-X, HEXIT-SAT, XEUS) will implement focusing optics with multilayer coatings: in view of the production of these optics we are exploring several deposition techniques for the reflective coatings. In order to evaluate the achievable optical performance X-Ray Reflectivity (XRR) measurements are performed, which are powerful tools for the in-depth characterization of multilayer properties (roughness, thickness and density distribution). An exact extraction of the stack parameters is however difficult because the XRR scans depend on them in a complex way. The PPM code, developed at ERSF in the past years, is able to derive the layer-by-layer properties of multilayer structures from semi-automatic XRR scan fittings by means of a global minimization procedure in the parameters space. In this work we will present the PPM modeling of some multilayer stacks (Pt/C and Ni/C) deposited by simple e-beam evaporation. Moreover, in order to verify the predictions of PPM, the obtained results are compared with TEM profiles taken on the same set of samples. As we will show, PPM results are in good agreement with the TEM findings. In addition, we show that the accurate fitting returns a physically correct evaluation of the variation of layers thickness through the stack, whereas the thickness trend derived from TEM profiles can be altered by the superposition of roughness profiles in the sample image