AODVCS,a new bio-inspired routing protocol based on cuckoo search algorithm for mobile ad hoc networks

Mobile ad hoc networks (MANETs) are becoming an emerging technology that offer several advantages to users in terms of cost and ease of use. A MANET is a collection of mobile nodes connected by wireless links that form a temporary network topology that operates without a base station and centralized administration. Routing is a method through which information is forwarded from a transmitter to a specific recipient. Routing is a strategy that guarantees, at any time, the connection between any two nodes in a network. In this work, we propose a novel routing protocol inspired by the cuckoo search method. Our routing protocol is implemented using Network simulator 2. We chose Random WayPoint model as our mobility model. To validate our work, we opted for the comparison with the routing protocol ad hoc on-demand distance vector, destination sequence distance vector and the bio-inspired routing protocol AntHocNet in terms of the quality of service parameters: packet delivery ratio and end-to-end delay (E2ED).     

A single-photon counter for long-haul telecom

Quite a few groups have turned their attention to using commercially available InGaAs/InP APDs, originally developed for optical communication applications, as SPADs for photon counting at 1300 nm and 1550 nm. This research has turned out to be quite fruitful, and there are many applications emerging in optical metrology (optical time-domain reflectometry) in eye-safe range finding and in future quantum technologies, where databits are encoded on individual photons. This article describes the status of these commercially available InGaAs/InP APDs used as single-photon counters in the telecom wavelength region of 1550 nm     

Design and evaluation of a device worn for fall detection and localization: Application for the continuous monitoring of risks incurred by dependents in an Alzheimer’s care unit

The Homecare project, which is part of a research project funded by the French National Research Agency (ANR), aims to define a new multi-sensor monitoring system for the elderly with cognitive disabilities in a care unit. Two subjects were recruited to participate to experimental trials. The main objective of this project is to design and test a complete monitoring system at a real site. It is a new clinical and technical approach which is complex to implement: Homecare is intended to propose a possible technical solution, demonstrate its feasibility and illustrate its use working at a protected site. The system consists of a motion sensor network deployed on the ceiling to monitor motion and an electronic patch worn by the subjects to identify them and detect falls. In order to locate tagged subjects inside the care unit, a network of anchor points is used. From these positions and movement data, an analysis algorithm detects an abnormal situation and alerts the nursing staff in real time. A Web application allows the medical staff to access movements and alarms. The complete monitoring system has been functioning for several months and continuously monitors two patients around the clock. In this paper, we present the implementation of the system, the method of localization inside the care unit, and the characterization of the fall detector, and we show certain results relating to activity data.     

Three-photon W-state

Abstract

Multiphoton entanglement is the basis of many quantum communication schemes, quantum cryptographic protocols, and fundamental tests of quantum theory. For entangled three-qubit states it has been shown that there are two inequivalent classes of states, under stochastic local operations and classical communications. The classes are represented by the GHZ- and W-state. The GHZ-state has been used to prove Bell's theorem without inequality. Contrary to the GHZ-state, the W-state shows high robustness of entanglement against photon loss. Here we show the first experimental results on the observation of the polarization entangled three-photon W-state from spontaneous parametric down-conversion.     

Retinal vessel segmentation using a probabilistic tracking method

Vessel structures such as retinal vasculature are important features for computer-aided diagnosis. In this paper, a probabilistic tracking method is proposed to detect blood vessels in retinal images. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. At a given step, a statistic sampling scheme is adopted to select a number of vessel edge points candidates in a local studying area. Local vessel's sectional intensity profiles are estimated by a Gaussian shaped curve. A Bayesian method with the Maximum a posteriori (MAP) probability criterion is then used to identify local vessel's structure and find out the edge points from these candidates. Evaluation is performed on both simulated vascular and real retinal images. Different geometric shapes and noise levels are used for computer simulated images, whereas real retinal images from the REVIEW database are tested. Evaluation performance is done using the Segmentation Matching Factor (SMF) as a quality parameter. Our approach performed better when comparing it with Sun's and Chaudhuri's methods. ROC curves are also plotted, showing effective detection of retinal blood vessels (true positive rate) with less false detection (false positive rate) than Sun's method.     

Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy

In this paper, we consider the problem of multichannel restoration. Current multichannel least squares restoration filters assume the separability of the signal covariance, which describes the between‐channel and within‐channel relationships. We propose a new solution for a multichannel restoration scheme, the Adaptive Linear Minimum Mean Square Error (ALMMSE), based on a local signal model, without the hypothesis of spectral and spatial separability. The proposed filter is developed to be used as a preprocessing step for detection in hyperspectral imagery. Tests on real data show that the proposed filter enables us to enhance detection performance in target detection and anomaly detection applications with the well‐known hyperspectral imagery detection algorithms AMF and RX. The comparison with detection results, after classical restoration methods, shows the superiority of the proposed approach for hyperspectral images.     

Noise Removal From Hyperspectral Images by Multidimensional Filtering

A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). Commonly, multidimensional data filtering is based on data vectorization or matricization. Few new approaches have been proposed to deal with multidimensional data. Multidimensional Wiener filtering (MWF) is one of these techniques. It considers a multidimensional data set as a third-order tensor. It also relies on the separability between a signal subspace and a noise subspace. Using multilinear algebra, MWF needs to flatten the tensor. However, flattening is always orthogonally performed, which may not be adapted to data. In fact, as a Tucker-based filtering, MWF only considers the useful signal subspace. When the signal subspace and the noise subspace are very close, it is difficult to extract all the useful information. This may lead to artifacts and loss of spatial resolution in the restored HSI. Our proposed method estimates the relevant directions of tensor flattening that may not be parallel either to rows or columns. When rearranging data so that flattening can be performed in the estimated directions, the signal subspace dimension is reduced, and the signal-to-noise ratio is improved. We adapt the bidimensional straight-line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree partitioning to tensors in order to adapt the filtering to the image discontinuities. Comparative studies with MWF, wavelet thresholding, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs.     

Wideband array processing : Focusing operators

Since many years, many methods have been proposed to wideband signals. These methods use the coherent signal subspace. Several focusing operators have been developed. In this paper we study the spectral matrix eigenvector operators. Improved operators are proposed which reduce the computer time. In order to do that, we use the focalisation operators obtained from the signal subspaces estimated at each analysis frequency. A new focusing operator is presented which uses directly the received data. Results of computer simulations are included to support our analysis.