النورسي ومعالجته النقدية الايجابية البناءة للقضايا

From the very first day, the scholars of the Ummah, Particularly from the time of Im฀m Sh฀f฀ movements of Islamic thought originated, which affected not only the Arabic world but the whole Islamic world. There had been movements of severe revenge and bloodshed and a lot of people were killed. Im฀m Nawras฀ is one of those unique people who served the Islamic thought from such dangerous storms. Day and night he made selfless efforts. He criticized the falsehood and injustice. The period of Im฀m Nawras฀ was plagued with severe gales of argumentations. This became the cause of Invitational, reformative and renewing movement of Im฀m Nawras฀. It faced the western and European attacks which appeared after Industrial and ideological revolutions of Europe. Before starting the movement, he did deep study of current affairs, Islamic thought and history. He studied the reasons due to which chaos of Islamic thought began. It was necessary to study all the situations and to fight with the contemporary Atheistic thought and wipe out its effects. So this article discusses intellectual contributions of Im฀m Nawras฀. He is great in handling the critical situation, and his conservative positive criticism is excellent. He is one of those luckiest persons who survived and got a chance to serve humanity. He was unique in handling intellectual issues away from dialectical demagoguery. Im฀m Nawras฀ really worked great for Islam. His principles regarding intellectual positive criticism, his philosophical thoughts, his criticism on mystic issues are presented here in this article. It is important to study and analyze Nawras฀ ’s amazing ability and his critical positive approach and treatment of constructive issues away from the ego.

Automatic Modulation Classification Using Feature Based Approach

Automatic Modulation Classification (AMC) is a scheme to classify the modulated signal by observing its received signal features. The received signal is usually corrupted by influence of various sources, such as, white guassian noise and fading, which degrades the signal quality. Automatic modulation classification plays an important role in cognitive radio communication. Due to amassed usage of digital signals in different technologies, such as, cognitive radios, scientists have focused on recognizing these signal types. AMC is expected to be incorporated in the upcoming cognitive communication. Generally, digital signal type classification can be categorized into two major categories: decision theoretic (DT) methods and pattern classification (PC) methods. In this research we focused on PC methods which are based upon features extraction. The feature extraction based modulation classification is accomplished in two modules. The first module is the feature extraction and second is classification process which gives decision based upon the features extracted. The features extracted from the received signal are higher order moments, higher order cummulants, spectral features, cyclo-stationary features and novel Gabor features. The classification of digital modulation formats such as pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM) and phase shift keying (PSK) and frequency shift keying (FSK) are considered throughout the research. The performance of proposed classifier are analyzed on additive white guassian noise channel (AWGN), Rayleigh flat fading channel, Rician flat fading channel and log normal fading channel. The proposed classifier algorithm for classification of different unknown modulated signals is based on normalized higher even order cummulants features and spectral features. The proposed classifiers are based on likelihood function, vi multilayer perceptron and linear discriminant analysis. The simulation results show that the proposed algorithms have high classification accuracy even at low signal to noise ratio (SNR). The proposed classifier algorithms perform efficiently as compared to the existing classifiers. A novel joint feature extraction and classification technique is proposed to classify the digital modulated signals by adaptively tuning the parameters of Gabor filter network. The Gabor atom parameters are tuned using delta rule and weights of the Gabor filter using least mean square (LMS) algorithm. The proposed algorithm classifies efficiently the PSK, FSK and QAM signals with 100% classification. The Modified gabor filter network is proposed for classification of M-PAM signals. The proposed HMM and Gabor filter network formulates an optimal classifier structure. The proposed classifier use Baum-Welch algorithm and Genetic algorithm (GA) to update the Gabor filter network and hidden markov model (HMM) parameters. The fitness function for the genetic algorithm is probability of observation sequence given the model. The objective is to maximize the probability of observation sequence. To improve the classification accuracy, three parameters of Gabor filters (GFs) network and one HMM parameter are adjusted simultaneously such that the probability of observation sequence is maximized. The proposed classifiers are compared with well-known techniques in the literature and simulation results show the supremacy of the proposed schemes over the contemporary techniques.