Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm

Tracking moving objects is a very difficult task for many computers. The task of tracking is a moving object. There are many virtual applications such as video surveillance and object recognition. Many ideas have been suggested for moving objects tracking and detection. One of the main problems solving tracking is finding identical objects in different frames. In this article, this application mainly focuses on the detection and tracking of moving objects using adaptive Background subtraction with adaptive probability evolutionary algorithm (AREA) method. Normal probability evolutionary algorithm will track only human motion but our algorithm tracks all the moving objects very accurately. Our proposed system shows the tracked objects with the red mark border.&nbsp

A comparison of different fuzzy inference systems for prediction of catch per unit effort (CPUE) of fish

60-69Present work was aimed to design Mamdani- Fuzzy Inference System (FIS), Sugeno -FIS and Sugeno-Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the prediction of CPUE of fish. The system was implemented using MATLAB fuzzy toolbox. A prediction of CPUE was made using the models trained. The accuracy of fuzzy inference system models was compared using mean square error (MSE) and average error percentage. Comparative study of all the three systems provided that the results of Sugeno-ANFIS model (MSE =0.05 & Average error percentage=11.02%) are better than the two other Fuzzy Inference Systems. This ANFIS was tested with independent 28 dataset points. The results obtained were closer to training data (MSE=0.08 and Average error percentage=13.45%)

Validation of chlorophyll-a and sea surface temperature concentration and their relationship with the parameters—diffuse attenuation coefficient and photosynthetically active radiation using MODIS data: A case study of Gujarat coastal region

1370-1376In-situ data of chlorophyll-a concentrations (Chl-a) and sea surface temperature (SST) of the Gujarat region for the period, 2002-2009 were obtained from Indian National Centre for Ocean Information Services (INCOIS), Hyderabad. Out of nearly 100 sampling points, 22 and 67 points qualified for comparison with the satellite measurements of Chl-a and SST, respectively. Chl-a concentrations were estimated from the MODIS satellite data (4 km resolution) with the existing global ocean color algorithms, namely, OC2V4, OC4V4, and OC3M. The SST was calculated with the help of bands 31 and 32 using MODIS-Aqua sensor long wave SST algorithm and European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation SST retrieval model (split window method). The satellite images were processed using global Sea WiFS Data Analysis System (SeaDAS) software v.7.3.1. Chl-a retrieved from OC3M algorithm had high coefficient of determination (R2=0.74) and less root mean square error (RMSE=1.24) as compared to OC2V4 and OC4V4 (R2=0.541 & 0.542 and RMSE=1.94 and 1.84, respectively) with in-situ data. The SST retrieved from MODIS-Aqua sensor long wave SST algorithm had a high coefficient of correlation as compared to ECMWF assimilation model (0.798 & 0.32 respectively) with in-situ data and RMSE were 0.80 and 2.65, respectively. SST and Chl-a showed an inverse correlation, with a coefficient of correlation (R) =0.530. Daily retrieval of Chl-a and SST value had very high degree of correlation with remote sensed eight days composite and monthly composite value (0.958 & 0.876, respectively). Retrieval of the value of diffuse attenuation coefficient at 490 nm wavelength (Kd or Kd_490), photosynthetically active radiation (PAR) and vertical attenuation coefficient of PAR (Kd(PAR)) were done and found that Kd and Kd(PAR) had very high degree of positive correlation (R=0.994). In addition, it was found that PAR had a positive correlation with SST(R=0.512) and negative correlation with Chl-a (R=-0.446). The range of this parameter values supports the case-I water and fish assemblage area

Electron transfer reactions in sub-porphyrin-naphthyldiimide dyads

A series of donor-acceptor compounds based on a sub-porphyrin (SubP) as an electron donor and naphthyldiimide (NDI) as an acceptor has been designed, synthesized and investigated by time-resolved emission and transient absorption measurements. The donor and acceptor are separated by a single phenyl spacer substituted by methyl groups in order to systematically vary the electronic coupling. The electron transfer reactions in toluene are found to be quite fast; charge separation is quantitative and occurs within 5-10 ps and charge recombination occurs in 1-10 ns, depending on the substitution pattern. As expected, when steric bulk is introduced on the adjoining phenyl group, electron transfer rates slow down because of smaller electronic coupling. Quantum mechanical modelling of the potential energy for twisting the dihedral angles combined with a simplified model of the electronic coupling semi-quantitatively explains the observed variation of the electron transfer rates. Investigating the temperature variation of the charge separation in 2-methyltetrahydrofuran (2-MTHF) and analyzing using the Marcus model allow experimental estimation of the electronic coupling and reorganization energies. At low temperature, relatively strong phosphorescence is observed from the donor-acceptor compounds with onset at 660 nm signaling that charge recombination occurs, at least partially, through the sub-porphyrin localized triplet excited state. Finally, it is noted that charge separation in all SubP-NDI dyads is efficient even at cryogenic temperatures (85 K) in 2-MTHF glass

Forecasting quarterly landings of total fish and major pelagic fishes and modelling the impacts of climate change on Bombay duck along India’s north-western Gujarat coast

557-565Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 – 2025) and a long term (2030 – 2050) with the last two years’ (2012 – 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term

User Plan Recommendation using Mobile call Log Analysis

ABSTRACT: In recent times, We can observe that there is a tremendous increase in mobile users with the usage of smart phones ,i-pads and tablet PCs for efficient communication with respect to both individual and official communication strata. Studies on mobile users statistics showing that there is also an emerging research area of analysing this new type of user data. In this paper, we perform call log analysis on mobile user data, peak time load of the operator, offers proving by the operator and recommended a suitable plan for the customer. We also investigate how customers are using proposed plan and rating the operators

Comparison between different modeling techniques for assessing the role of environmental variables in predicting the catches of major pelagic fishes off India’s north-west coast

The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM

Use of different modeling approach for sensitivity analysis in predicting the Catch per Unit Effort (CPUE) of fish

1729-1741The contribution (Sensitivity analysis) of four variables, namely chlorophyll-a (Chl-a), sea surface temperature (SST), photosynthetically active radiation (PAR) and diffuse attenuation coefficient (Kd_490 or Kd) in predicting the Catch per Unit Effort (CPUE) of fish was evaluated using simple General Linear Model, Generalized Linear Model (GLM), Generalized Additive Model (GAM) and different explanatory methods of Artificial Neural Networks (ANN) technique. The models were assessed for their accuracy in determining the relative importance of the four variables in predicting the CPUE. GAM was an improvement over the General Linear Model, while ANN was found better than GAM. The six explanatory methods which can give the relative contribution or importance of variables were compared using ANN modeling techniques: (i) Connection weights algorithm, (ii) Garson’s algorithm (iii) Partial derivatives (PaD) (iv) Profile method (v) Perturb method, and (vi) Classical stepwise (forward and backward) method. Our results showed that the PaD method, Profile method, Input perturbation (50 % noise), and Connection weight approaches were only consistent in identifying the two most important variables (Chlorophyll-a and Kd) in the network. The distribution of profile plot & partial derivative helped indirectly in finding the other three variables in decreasing order of importance (PAR > fishing hour > SST). It was observed that the significance (sensitivity) of independent variables under GAM and explanatory methods of ANN were similar

Forecasting quarterly landings of total fish and major pelagic fishes and modelling the impacts of climate change on Bombay duck along India’s north-western Gujarat coast

Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 – 2025) and a long term (2030 – 2050) with the last two years’ (2012 – 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term

Data mining and wireless sensor network for agriculture pest/disease predictions

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/ disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions