BFO optimized RLS algorithm for power system harmonics estimation

In this paper a new estimation approach combining both Recursive Least Square (RLS) and Bacterial Foraging Optimization (BFO) is developed for accurate estimation of harmonics in distorted power system signals. The proposed RLS–BFO hybrid technique has been employed for estimating the fundamental as well as harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive by using RLS that sequentially updates the unknown parameters of the signal. Simulation and experimental studies are included justifying the improvement in performance of this new estimation algorithm.     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

A class of new transforms tailored for the hypergeometric series

A new class of transforms, tailored for the hypergeometric series, is proposed and tested on a number of series. From a knowledge of a given number of terms of a sequence, these transforms reproduce the functions with a better accuracy than the Levin-like transforms. Though there exists a correlation between the approximative power of a rational approximant and its ability to predict the leading terms of a series, there are exceptions to this, especially in the case of divergent series. The new transforms can, in most cases, predict a number of extra terms not used in the construction of the approximants.     

Differential Evolution and Levenberg Marquardt Trained Neural Network Scheme for Nonlinear System Identification

This paper proposes a new nonlinear system identification scheme using differential evolution (DE), neural network and Levenberg Marquardt algorithm (LM). Here, DE and LM in a combined framework are used to train a neural network for achieving better convergence of neural network weight optimization. A number of examples including a practical case-study have been considered for implementation of different system identification methods namely, only NN, DE+NN and DE+LM+NN. After, a series of simulation studies of these methods on the different nonlinear systems it has been confirmed that the proposed DE and LM trained NN approach to nonlinear system identification has yielded better identification results in terms of time of convergence and less identification error.     

Prevalence,characterization, and genetic diversity of Listeria monocytogenes isolated from foods of animal origin in North East India

Listeria monocytogenes is a pathogenic microorganism infects man mostly through food. A total of 1615 samples of foods of animal origin and water were collected from retail meat shops of North-Eastern India and processed. Sixty-three (3.9%) samples were positive for L. monocytogenes. Animal origin foods showing the highest prevalence was chevon (9.8%) followed by beef (8.9%), chicken (8.5%), pork (2.8%) and milk (1.8%). The prevalence rate in water from retail meat shops was 10%. Recovered L. monocytogenes were distributed into 3 serogroups, of which 74.6% fit in to 1/2a, 3a serogroup, 17.5% to 1/2b, 3b and 7.9 % to 4b, 4d, 4e serogroups. Thirty-five isolates out of 63 possessed all the tested four virulence genes. RAPD- and ERIC -PCR based analyses jointly revealed a discriminative genetic profile for the L. monocytogenes. On the whole, the occurrence of L. monocytogenes in foods of animal origin of North Eastern India displays public health hazard.     

Design and evaluation of mucoadhesive microemulsion for neuroprotective effect of ibuprofen following intranasal route in the MPTP mice model

Background: The present study is to investigate the neuroprotective effect of ibuprofen by intranasal administration of mucoadhesive microemulsion (MMEI) against inflammation-mediated by dopaminergic neurodegeneration in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of Parkinson’s disease (PD).

Methods: Ibuprofen-loaded polycarbophil-based MMEI was developed by using response surface methodology (RSM). Ibuprofen with dose of 2.86 mg/kg/day was administered intranasally to male C57BL/6 mice for two consecutive weeks which were pre-treated with four intraperitoneal injections of MPTP (20?mg/kg of body weight) at 2?h intervals. Immunohistochemistry was performed.

Results: Optimal MMEI was stable and non-ciliotoxic with 66.29?±?4.15?nm as average globule size and??20.9?±?3.98?mV as zeta potential. PDI value and transmission electron microscopy result showed the narrow globule size distribution of MMEI. The result showed that all three independent variables had a significant effect (p?<?0.05) on the responses. Rota-rod and open-field test findings revealed the significant improvement in motor performance and gross behavioral activity of the mice. The results from in vivo study and immunohistochemistry showed that nasal administration of Ibuprofen significantly reduced the MPTP-mediated dopamine depletion. Furthermore TH neurons count in the substantia nigra and the density of striatal dopaminergic nerve terminals were found to be significant higher for ibuprofen treated groups.

Conclusion: Findings of the investigation revealed that Ibuprofen through developed MMEI was shown to protect neurons against MPTP-induced injury in the Substantia nigra pars compacta (SNpc) and striatum and hence, could be a promising approach for brain targeting of Ibuprofen through intranasal route to treat PD.     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.     

A Robust Extended Complex Kalman Filter and Sliding-mode Control Based Shunt Active Power Filter

Abstract—This article presents the design of a new shunt active power filter that employs a modified robust extended complex Kalman filter approach with an exponential robust term embedded for reference current estimation together with a current controller based on the sliding-mode control concept. The robust extended complex Kalman filter exploits a new weighted exponential function to handle these grid perturbations to estimate the reference signal in shunt active power filter system. The current controller in the proposed shunt active power filter has been designed using a sliding-mode control strategy because of its ability to handle parameter uncertainties and ease in implementation. To test the effectiveness of the proposed shunt active power filter, extensive simulations were performed using MATLAB/Simulink (The MathWorks, Natick, Massachusetts, USA), and real-time studies were made using OPAL-RT (Montreal, Quebec, Canada). Results obtained from the above studies using the proposed shunt active power filter together with the different variants of Kalman filter (Kalman filter, extended Kalman filter, extended complex Kalman filter) are analyzed, and it is observed that the proposed robust extended complex Kalman filter-sliding-mode control based shunt active power filter provides accurate and improved harmonics mitigation and reactive power compensation.