Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.     

Dynamic denoising studies in wideband radio channel measurement and modeling

The fixed level and dynamic denoising method was studied based on indoor-to-outdoor measured channel impulse responses ORs） at 5.25 GHz with radio frequency （RF） 100 MHz bandwidth. It is found that the dynamic ranges, peak powers and noise floors of the IRs are with close correlations. The comparisons with different denoising methods are given by deriving the power delay profiles （PDPs）, root mean square （RMS） delay spread （RMS DS）, number of paths （NOPs） and Ricean K-factors. It is concluded that the traditional fixed level noise cut is under estimate of DS and NOPs. The Ricean K-factors are of little sensitive to noise cut irrespective of what kind of methods applied. The PDPs are not very sensitive to the fixed level noise cut, however, obvious changes can be found by dynamic noise cut. The dynamic noise cut is preferred when clear noise floors is observed and decided from the measured IRs, it＇s of importance in data post processing for wideband radio channel measurements as well as the relevant modeling work.     

ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks

Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

     

Simulation d’un filtre en technologie microruban

The pedagogical product presented below is a practical work intended to the electrical engineering students, fourth year, of the National Institut of Applied Sciences of Rennes. This work is a part of filter realisation programme either in microstrip technology or lumped element. The aim of this practical work is restricted to calculate by optimizing all the basic parameters of the filter realization. Thus, an optimization and simulation work is performed on computer, by using a Touchstone software product of the Electronic Engineering Software society, to obtain parameter values close to the wished ones. This practical work is performed by groups of two students during three hours.     

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Recently multiserver queues with setup times have been extensively studied because they have applications in power-saving data centers. A challenging model is the M/M/c/Setup queue where a server is turned off when it is idle and is turned on if there are some waiting jobs. Recently, Gandhi et al. (in: Proceedings of the ACM SIGMETRICS, pp. 153–166, ACM, 2013; Queueing Syst. 77(2):177–209, 2014) obtain the generating function of the number of jobs in the system, as well as the Laplace transform of the response time using the recursive renewal reward approach and the distributional Little’s law (Keilson and Servi in Oper Res Lett 7(5):223– 227, 1988). In this paper, we derive exact solutions for the joint stationary queue length distribution of the same model using two alternative methodologies: generating function approach and matrix analytic method. The generating function approach yields exact closed form expressions for the joint stationary queue length distribution and the conditional decomposition formula. On the other hand, the matrix analytic approach leads to an exact recursive algorithm to calculate the joint stationary distribution and performance measures so as to provide some application insights.     

Introduction of affinity set and its application in data-mining example of delayed diagnosis

At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as best as they can. It requires the rapid evaluation and prioritisation of life threatening injuries in the primary survey followed by a detailed secondary survey in the emergency room. However, time is always valuable and limited such that some important vital signs may be delayed and ignored. This research explores delayed diagnosis problem and uses the affinity set by Topology concept to classify/focus on key attributes causing delayed diagnosis (missed injury) in order to reduce error risk. Results interestingly indicate that when a patient can breathe normally, but his (or her) blood-pressure or pulse is abnormal, a high probability of delayed diagnosis exists. This affinity work also compares the performance with the model of rough set (Rosetta), neural network, support vector machine and logistic regression. And our affinity model shows its advantage by prediction accuracy and explanation power.     

Properties of InAs grown on misoriented GaAs substrates by atmospheric pressure metal–organic vapor phase epitaxy

InAs epilayers were grown by atmospheric pressure metal–organic vapor phase epitaxy on GaAs (1 0 0) exactly oriented substrates and misoriented by 2° and 10° toward [1 1 1]A. The layers had varying thicknesses and were deposited under the same growth conditions. Atomic force microscopy analysis show that surface morphology depends on surface misorientation and presents a low root mean square. High resolution X-ray diffraction analysis and Hall effect measurements were preformed to check the substrate misorientation effect on the crystalline quality and electrical properties respectively.     

Effects of Surfactants on the Properties of Styrene/Methacrylate–Type Superplasticizer in Cement Paste

Copolymer emulsion lattices based on styrene (St) with methacrylate monomers, were synthesized with composition ratio (5/5) in the presence of a co-surfactants that consists of dodecyl benzene sodium sulfonate with polyvinyl alcohol (DBSS/PVA) and DBBS with polyoxyethylene glycol monomethyl ether (DBBS/POE). The obtained latices were characterized by ¹H NMR, rheological and morphological techniques. The effect of latices on the physicomechanical properties of ordinary Portland cement (OPC) pastes was investigated. The results showed that the addition of water mixed to the cement with copolymer improves most of the specific characteristics of OPC.