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Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in Artificial Neural Networks (ANNs). Simulation results shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% using DWT, DCT and DST for some modulation schemes over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low Signal-to-Noise ratios (SNRs).

In this paper, different topologies were discussed and their effect on the design of photonic crystal applications was evaluated. They can be classified into four categories (i.e., ring resonator, selfcollimation, waveguide, and cavity-based structures). The figure of merits of each topology and its impact on design modules were explained and compared for two different applications (i.e., half-adder and decoder). Finite difference time domain (FDTD) and plane wave expansion methods are the two numerical approaches that were used to analyze the proposed designs. The truth table for each application was introduced. Comparison tables were organized to discriminate the valued characteristics for each structure. Based on the extracted tables, the appropriate topology can be chosen for the required design application according to the needed characteristic.

The early discovery of the disease is a great achievement in management of the cornea. This paper presents an efficient approach for the classification of normal and abnormal corneal patterns based on deep learning. Convolutional Neural Networks (CNNs) are utilized for this purpose. The CNN model built for this purpose comprises5 layers. The classification process is achieved through two stages. Automatic feature extraction based CNN is applied in the first stage, followed by sequence of processing layers includes: pooling layer, dropout layer and fully connected layer resulted in a diagnosis of the condition of the patient in terms of normal or abnormal. The proposed technique was tested and evaluated based MATLAB environment on a set of corneal images. These images were collected for patients based on confocal microscopy. The CNN classification results on corneal fundus images recorded an accuracy of 100 %.

This paper investigates the effect of reverberation on pitch frequency estimation. The autocorrelation function (ACF) method is used for pitch frequency estimation with and without reverberation effect. This paper modeled the reverberation effect on speech signal using a comb filter. The estimation error percentage of a comb filter at the length mild 8, moderate 10, and severe 12 in these scenarios have been investigated. The accuracy of the pitch frequency estimation is evaluated for the different scenarios for further accurate speech processing and verification.