We present an efficient method for the reduction of model equations in the linearized diffuse optical tomography (DOT) problem. We first implement the maximum a posteriori (MAP) estimator and Tikhonov regularization, which are based on applying preconditioners to linear perturbation equations. For model reduction, the precondition is split into two parts: the principal components are consid...

A chaotic noise is one of the most important implements for information processing such as neural networks. It has been suggested that chaotic neural networks have high performance ability for information processing. In this paper, we report two designs of a compact chaotic noise generator for large integration circuits using CMOS technology. The chaotic noise is generated using map chaos. We d...

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geolocalised measurements and further personal annotation to pro...

The annealed random map model is one of the simplest models of statistical mechanics with stochastic dynamics. For this model, we define valleys by saying that two configurations submitted to the same stochastic forces belong to the same valley at time I iftheir trajectories have met before time t . We compute in the long-time limit the probability distribution of the number and the sizes of th...

Depth maps generated by Kinect cameras often contain a significant amount of missing pixels and strong noise, limiting their usability in many computer vision applications. We present a new energy minimization method to fill the missing regions and remove noise in a depth map, by exploiting the strong correlation between color and depth values in local image neighborhoods. To preserve sharp edg...

This paper analyses the properties of the full covariance simultaneous map building problem (SLAM). We prove that, for the special case of a stationary vehicle (with no process noise) which uses a range-bearing sensor and has non-zero angular uncertainty, the fullcovariance SLAM algorithm always yields an inconsistent map. We also show, through simulations, that these conclusions appear to exte...

We present in this article two different ways to make CMB maps in practice, from large timelines. One is to make a simple destriping, fitting the data and using the scan intercepts to remove the low frequency noise (stripes). The second, optimal, is to resolve linearly the map-making problem, which in case of big timelines must be simplified and changed from matrices to vectors for the calculat...

Sound as a non-visual component of landscape, has a significant impact on individuals perception of space. Because unlike the landscape that seeing and looking is totally an optional action, soundscape regardless of people comes to be heard and understood. Lack of attention to quality of emitted sounds in the environment may leads to problems such as noise pollution, lack of concentration, nois...

We have compared cosmic microwave background (CMB) maps made from one-year time ordered data (TOD) streams that simulated observations of the originally planned 100 GHz P Low Frequency Instrument (LFI). The maps were made with the ROMA, MapCUMBA and destriping algorithms. We produced angular power spectrum estimates from the ROMA and destriping maps and compared them as well. ROMA and MapC...

The effect of noise is studied in one-dimensional maps undergoing transcritical, tangent, and pitchfork bifurcations. The attractors of the noiseless map become metastable states in the presence of noise. In the weak-noise limit, a symplectic two-dimensional map is associated with the original one-dimensional map. The consequences of their noninvertibility on the phase-space structures are disc...