Browsing by Author "Khan, Rahim"
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- 3D convolutional neural networks based automatic modulation classification in the presence of channel noisePublication . Khan, Rahim; Yang, Qiang; Ullah, Inam; Rehman, Ateeq Ur; Tufail, Ahsan Bin; NOOR, ALAM; Rehman, Abdul; Cengiz, KorhanAutomatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre-optic, next-generation 5G or 6G systems, cognitive radio as well as multimedia internet-ofthings networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D-CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.
- An efficient adaptive modulation technique over realistic wireless communication channels based on distance and SINRPublication . Khan, Rahim; Yang, Qiang; NOOR, ALAM; Altaf Khattak, Sohaib Bin; Guo, Liang; Tufail, Ahsan BinA growing trend has been observed in recent research in wireless communication systems. However, several limitations still exist, such as packet loss, limited bandwidth and inefficient use of available bandwidth that needs further investigation and research. In light of the above limitations, this paper uses adaptive modulation under various parameters, such as signal to interference plus noise ratio (SINR), and communication channel 19s distances. The primary goal is to minimize bit error rate (BER), improve throughput and utilize the available bandwidth efficiently. Additionally, the impact of Additive White Gaussian Noise (AWGN), Rayleigh and Rician fading channels on the performance of various modulation schemes are also studied. The simulation results demonstrate that our proposed technique optimally improves the BER and spectral efficiency in the long-range communication as compared to the fixed modulation schemes under the co-channel interference of surrounding base stations. The results indicate that the performance of fixed modulation schemes is suitable only either at high SINR and low distance or at low SINR and high distance values. Moreover, on the other hand, its performance was suboptimal in the entire wireless communication channel due to high distortion and attenuation. Lastly, we also noted that BER performance in the AWGN channel is better than Rayleigh and Rician channels with Rayleigh channel exhibiting poor performance than the Rician channel.
- Automated sheep facial expression classification using deep transfer learningPublication . Noor, Alam; Zhao, Yaqin; Koubaa, Anis; Wu, Longwen; Khan, Rahim; Abdalla, Fakheraldin Y.O.Digital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial expression is an efficient and reliable mark of animal life. However, the manual assessment is lack of accuracy, time-consuming, and monotonous. Hence, the recent advancement of deep learning in computer vision helps to classify facial expression as fast and accurate. In this paper, we proposed a sheep face dataset and framework that uses transfer learning with fine-tuning for automating the classification of normal (no pain) and abnormal (pain) sheep face images. Current state-of-the-art convolutional neural networks (CNN) based architectures are used to train the sheep face dataset. The data augmentation, L2 regularization, and fine-tuning has been used to prepare the models. The experimental results related to the sheep facial expression dataset achieved 100% training, 99.69% validation, and 100% testing accuracy using the VGG16 model. While employing other pre-trained models, we gained 93.10% to 98.4% accuracy. Thus, it shows that our proposed model is optimal for high-precision classification of normal and abnormal sheep faces and can check on a comprehensive dataset. It can also be used to assist other animal life with high accuracy, save time and expenses.