A 3D indoor positioning system based on common visible LEDs

We propose a realistic 3D positioning system for indoor navigation that exploits visible Light EmittingDiodes (LEDs), placed on the ceiling. A unique frequency tone is assigned to each lamp and modulatesits intensity in periodic time slots. The Time Difference of Arrival (TDOA) is measured without theneed of a synchronization system between the sources and the receiver, then it is used to accuratelyestimate the receiver position. We first describe the theoretical approach, then propose the modeland characterize the possible sources of noise. Finally, we demonstrate the proof-of-concept of theproposed system by simulation of lightwave propagation. Namely, we assess its performance by usingMontecarlo simulations in a common room and estimate the impact of the different implementationparameters on the accuracy of the proposed solution. We find that, in realistic conditions, thetechnique allows for centimeter precision. Pushing the device requirements, the precision can befurther increased to a sub-centimeter accuracy

Modelization and characterization of a CMOS camera as an optical real-time oscilloscope

Complementary Metal-Oxide Semiconductor (CMOS) camera sensors are embedded in many consumer electronics products: Thanks to the Rolling Shutter (RS) readout mode, they can detect a time-varying light intensity, which is the key to realize Optical Camera Communication (OCC). To this aim, we introduce here a model describing the camera as a Real-Time Oscilloscope (RTO) detecting optical signals; by means of this approach, we can now characterize the Complementary Metal-Oxide Semiconductor (CMOS) camera by means of parameters that correspond to common oscilloscope specifications, such as the frequency response, the noise, the Signal to Noise Ratio (SNR), the total harmonic distortion (THD), etc.; all of these are introduced and measured in terms of the camera parameters. This approach provides for the first time a set of quantitative tools that should be used to maximize the OCC transmission performance by allowing the optimal selection of the camera settings

Integrating Optical Wireless Communication Into an Optical Bifocal Metrology for Aerospace

Recently an innovative bifocal optical metrology method was proposed for space applications (e.g., rendez-vous and docking), based on unmodulated white LEDs. Here we design, realize, and test a solution that upgrades the metrology to include a digital communication feature, with no modification of the optical elements of the original system: indeed, the scheme exploits the same optical sources that are needed for metrology, which are now also working as optical antennas as their intensity is now modulated. At the receiver side, the conventional camera is now sided by a common photodiode. The system provides unidirectional data communication at 10 kbit/s speed. It is designed to support manoeuvres up to 400 m distance. The lab tests confirm the effectiveness of the proposed solutions, showing correct data transfer without any noticeable degradation of the metrology system

A SPIRED\texttt{SPIRED} code for the reconstruction of spin distribution

In Nuclear Magnetic Resonance (NMR), it is of crucial importance to have an accurate knowledge of the sample probability distribution corresponding to inhomogeneities of the magnetic fields. An accurate identification of the sample distribution requires a set of experimental data that is sufficiently rich to extract all fundamental information. These data depend strongly on the control fields (and their number) used experimentally. In this work, we present and analyze a greedy reconstruction algorithm, and provide the corresponding $\texttt{SPIRED}$ code, for the computation of a set of control functions allowing the generation of data that are appropriate for the accurate reconstruction of a sample distribution. In particular, the focus is on NMR and the Bloch system with inhomogeneities in the magnetic fields in all spatial directions. Numerical examples illustrate this general study.Comment: 31 pages, 6 figure

Molecular Lego of Human Cytochrome P450: The Key Role of Heme Domain Flexibility for the Activity of the Chimeric Proteins

The cytochrome P450 superfamily are heme-thiolate enzymes able to carry out monooxygenase reactions. Several studies have demonstrated the feasibility of using a soluble bacterial reductase from Bacillus megaterium, BMR, as an artificial electron transfer partner fused to the human P450 domain in a single polypeptide chain in an approach known as ‘molecular Lego’. The 3A4-BMR chimera has been deeply characterized biochemically for its activity, coupling efficiency, and flexibility by many different biophysical techniques leading to the conclusion that an extension of five glycines in the loop that connects the two domains improves all the catalytic parameters due to improved flexibility of the system. In this work, we extend the characterization of 3A4-BMR chimeras using differential scanning calorimetry to evaluate stabilizing role of BMR. We apply the ‘molecular Lego’ approach also to CYP19A1 (aromatase) and the data show that the activity of the chimeras is very low (<0.003 min−1) for all the constructs tested with a different linker loop length: ARO-BMR, ARO-BMR-3GLY, and ARO-BMR-5GLY. Nevertheless, the fusion to BMR shows a remarkable effect on thermal stability studied by differential scanning calorimetry as indicated by the increase in Tonset by 10 °C and the presence of a cooperative unfolding process driven by the BMR protein domain. Previously characterized 3A4-BMR constructs show the same behavior of ARO-BMR constructs in terms of thermal stabilization but a higher activity as a function of the loop length. A comparison of the ARO-BMR system to 3A4-BMR indicates that the design of each P450-BMR chimera should be carefully evaluated not only in terms of electron transfer, but also for the biophysical constraints that cannot always be overcome by chimerization

Spectral Analysis of Stellar Light Curves by Means of Neural Networks

Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound