Performance analysis of incremental LMS over flat fading channels

We study the effect of fading in the communication channels between sensor nodes on the performance of the incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE). We obtain conditions for mean convergence of the ILMS algorithm, and show that in the presence of fading channels, the ILMS algorithm is asymptotically biased. Furthermore, the dynamic range for mean stability depends only on the mean channel gain, and under simplifying technical assumptions, we show that the MSD, EMSE and MSE are non-decreasing functions of the channel gain variances, with mean-square convergence to the steady states possible only if the channel gain variances are limited. We derive sufficient conditions to ensure mean-square convergence, and verify our results through simulations

Robust search-free car number plate localization incorporating hierarchical saliency

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost

Incorporating negentropy in saliency-based search free car number plate localization

License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications

Analysis of incremental augmented affine projection algorithm for distributed estimation of complex-valued signals

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations

Seroepidemiology of rubella, measles, HBV, HCV and B19 virus within women in child bearing ages (Saravan City of Sistan and Bloochastan Province)

Present survey basically focused on women between 15-45 years of age resident in a town of Sistan and Baluchistan province named as Saravan city located in border of Pakistan-Iran in order to find out the seropositivity against the viruses in child bearing ages in the above stated under study community. This descriptive cross-sectional study was carried-out from 2001 up to 2002. Saravan town was divided into 4 geographical areas and each area was further sub-divided into 10 blocks and in each block 10 families were chosen randomly. In the next step by referring to each family from the chosen married women with specified age i.e., 15-45 years, 5 mL blood was collected. Serum was then separated and stored at -20°C before the assay. ELISA kit was employed to detect anti B19, anti rubella, anti measles, anti HBV and anti HCV antibody. Furthermore during samples collection a questionnaire filled for each woman under study. This study showed that 89.6% of women understudy were seropositive against measles, rubella (96.2%), B19 (59.2%), HCV (0.8%) and HBV (19.8%), respectively. According to the results of no serious problem with rubella in this area; But, about measles, the present immunity against measles in this area is insufficient. It seems that incidence of B19 infection in this region is same as other places in Iran. The rate of seropositivity against HBV and HCV indicated of these viruses circulating in the population in this area. © 2007 Academic Journals

A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure

Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise

In this paper the steady-state tracking performance of minimum kernel risk-sensitive loss (MKRSL) in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations