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The current generation of light wave systems benefit from increased transmission distance by using optical amplification and increased capacity by using dense wavelength division multiplexing (DWDM) technology. The reach of present systems is limited by the noise contributed by the used amplifiers, combined with nonlinear effects from transmission. This paper aims to address these issues.

The nature and extent of degradations in the optical DWDM systems due to these limiting factors have been discussed in this paper.

It has been learnt that stimulated Raman scattering (SRS), four wave mixing (FWM) and amplified spontaneous emission (ASE) are the important factors in optical DWDM systems. These factors limit the system capacity of the transmission systems drastically.

It can be concluded from the discussion that while designing an efficient DWDM system, an optimization of the channel separation and the amplifier separation is required to minimize the nonlinear effects (FWM and SRS) along with the ASE noise introduced by inline optical amplifications.

Fiber nonlinearities are anticipated to impose transmission limitations due to the enhanced total interaction length in long‐haul dense wavelength division multiplexing (DWDM) optical transmission systems. The purpose of this paper is to analytically study the combined effect of stimulated Raman scattering (SRS) and four‐wave mixing (FWM) in the presence of amplified spontaneous emission (ASE) noise generated by erbium‐doped fiber amplifiers (EDFAs).

The paper presents analytical analysis of DWDM optical transmission systems in the presence of two significant fiber nonlinearities (SRS and FWM).

Simple expressions are derived to study the dependence of signal‐to‐noise ratio (SNR) on the amplifier spacing between two consecutive amplifiers.

The authors have analytically studied the combined effect of SRS and FWM in the presence of ASE noise generated by EDFAs. The novelty of the work is that it has considered all the three factors simultaneously and the expressions are derived for calculation of SNR.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications.

Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA.

The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated.

This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.