Mohamed Ben Haj Rhouma

Coexistence and extinction in a competitive exclusion Leslie/Gower model with harvesting and stocking

The principle of competitive exclusion states that when the competition between species is sufficiently strong, only the dominant species survives. In this paper, we examine the strategies of using stocking and harvesting to prevent the extinction of the weak species in a competitive exclusion environment. We find that in a system governed by the Leslie/Gower model careful stocking will ensure the coexistence between all species. We also find that constant harvesting of the dominant species may guarantee the survival of the weaker species when the parameters of the model are in a certain range. Mixed strategies and conditional harvesting strategies are also discussed.

The Discrete Beverton-Holt Model with Periodic Harvesting in a Periodically Fluctuating Environment

We investigate the effect of constant and periodic harvesting on the Beverton-Holt model in a periodically fluctuating environment. We show that in a periodically fluctuating environment, periodic harvesting gives a better maximum sustainable yield compared to constant harvesting. However, if one can also fix the environment, then constant harvesting in a constant environment can be a better option, especially for sufficiently large initial populations. Also, we investigate the combinatorial structure of the periodic sequence of carrying capacities and its effect on the maximum sustainable yield. Finally, we leave some questions worth further investigations.

The Beverton-Holt model with periodic and conditional harvesting.

In this theoretical study, we investigate the effect of different harvesting strategies on the discrete Beverton–Holt model in a deterministic environment. In particular, we make a comparison between the constant, periodic and conditional harvesting strategies. We find that for large initial populations, constant harvest is more beneficial to both the population and the maximum sustainable yield. However, periodic harvest has a short-term advantage when the initial population is low, and conditional harvest has the advantage of lowering the risk of depletion or extinction. Also, we investigate the periodic character under each strategy and show that periodic harvesting drives population cycles to be multiples (period-wise) of the harvesting period.

GIS-based estimation of roof-PV capacity & energy production for the Seeb region in Oman

This paper assesses the potential solar power for the PV roof integration system using the Geographic Information System (GIS). The residential area of Seeb (Muscat-Oman) was investigated as a case study. The constraint of the roof optimization for the optimal design of solar photovoltaic fields consisting of multiple rows was analyzed and the optimal area of deployment of PV collectors was calculated taking into account orientation, shading and masking of adjacent rows. A MATLAB program was developed in order to estimate the total roof-PV power capacity and total annual energy generation. The program takes into account the different factors that affect the efficiency of the PV. The results show that, if we consider 10 hours as a daily average of sunshine, the total roof PV panels in the Seeb area will be equivalent to a 300MW power generation plant.

On a (2,2)-rational recursive sequence

We investigate the asymptotic behavior of the recursive difference equation yn+1 = (α+βyn)/(1+yn-1) when the parameters α < 0 and β ∈ ℝ. In particular, we establish the boundedness and the global stability of solutions for different ranges of the parameters α and β. We also give a summary of results and open questions on the more general recursive sequences yn+1 = (a + byn)/(A + Byn-1), when the parameters a, b, A, B ∈ ℝ and abAB ≠ 0.

Feature generation using the Laplacian operator with neumann boundary condition

The eigenvalues of the Neumann Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation, translation, and size invariant and are shown to be tolerant of boundary deformation. The effectiveness of these features is demonstrated by using them to classify 5 types of computer generated and hand drawn shapes. The classification was done using 4 to 20 features fed to a simple feedforward neural network. Correct classification rates ranging from 94.4% to 100% were obtained on computer generated shapes and 67.5% to 95.5% on hand drawn shapes.

Moment invariants for multi-component shapes with applications to leaf classification

Abstract In this paper we introduce seven new invariants for multi-component shapes, and apply them to the leaf classification problem. One of the new invariants is an area based analogue of the already known boundary based anisotropy measure, defined for the multi-component shapes (Rosin and Žunic, 2011). The other six invariants are completely new. They are derived following the concept of the geometric interpretation (Xu and Li, 2008) of the first two Hu moment invariants (Hu, 1961). All the invariants introduced are computable from geometric moments corresponding to the shape components. This enables an easy and straightforward computation of translation, rotation, and scaling invariants. Also, being area based, the new invariants are robust to noise and mild deformations. Several desirable properties of the new invariants are discussed and evaluated experimentally on a number of synthetic examples. The usefulness of the new multi-component shape invariants, in the shape based object analysis tasks, is demonstrated on a well-known leaf data set.

ANN-based extraction approach of PV cell equivalent circuit parameters

In most practical cases, a PV array is constructed from a combination of standard PV units (solar cells or panels) connected in series and/or in parallel to obtain the desired power, voltage and current ratings. However, the non-linear behavior of solar cells or panels makes accurate determination of the model parameters a difficult task. A precise determination of the internal physical parameters of cells and panels is not always possible because of the non-uniqueness of the solution. As a result, some parameters of the PV panel model are estimated with non-negligible errors. In this paper, an advanced ANN-based determination approach of PV array parameters from individual cell/panel characteristics is developed for single-diode model. A proper selection of the training points for the ANN is also introduced to improve the estimation of the model. Simulation results obtained for simple case studies using Matlab/Simulink software are presented and discussed. The proposed ANN-based technique made possible more accurate determination of the parameters of the single-diode model with mean errors below 1% for all parameters except for the diode saturation and diffusion current which reached 11%.

Shape Recognition Based on Eigenvalues of the Laplacian

Abstract Recently there has been a surge in the use of the eigenvalues of linear operators in problems of pattern recognition. In this chapter, we discuss the theoretical, numerical, and experimental aspects of using four wellknown linear operators and their eigenvalues for shape recognition. In particular, the eigenvalues of the Laplacian operator under Dirichlet and Neumann boundary conditions, as well as those of the clamped plate and buckling of a clamped plate, are examined. Since the ratios of eigenvalues for each of these operators are translation, rotation, and scale invariant, four feature vectors are extracted for the purpose of shape recognition. These feature vectors are then fed into a basic neural network for training and measuring the performance of each of the feature vectors, which in turn were all shown to be reliable features for shape recognition. We also offer a review of the literature on finite difference schemes for these operators and summarize key facts about their eigenvalues that are of relevance in image recognition.