Raymond Sheriff

Video coding effectively reduces the amount of video data while unavoidably producing compression noise. Compression noise can cause significant artifacts in compressed video, such as blocking, ringing, and blurring, which seriously affects the visual quality of videos and the value of videos for content analysis. In compressed video quality enhancement, few methods based on deep learning fully consider the relationship between video content and compression noise or the possibility of uniting the encoder or the decoder to enhance the quality of compressed video. In an approach different from existing methods, we propose a video quality enhancement framework based on the distribution characteristics of compression noise. The proposed framework consists of two parts: at the encoder, we propose a convolutional neural network (CNN)-based in-loop filtering network combined with noise distribution (IFN-ND) characteristics for the I frame instead of high efficiency video coding (HEVC) standard in-loop filters; at the decoder, we propose a CNNbased quality enhancement network combined with the noise distribution characteristics (PQEN-ND) for the P frames. The noise characteristics are extracted from the code stream to further improve the performance of the proposed networks. The experiments show that the proposed method can significantly improve the quality of HEVC compressed video, achieving an average 12.84% reduction in the BD rate and up to a 1.0476 dB increase in the peak signal-to-noise ratio (PSNR).

Fairness in bandwidth resource allocation is of high significance to the advancement in mobile and wireless technologies. It is likely that starvation of bandwidth due to some scheduling scheme would not be an appropriate option for the future development in the communication systems. However, there is need to consider an implementation that would lead to good network performance and avoid unguaranteed bandwidth delivery. This paper will be focusing on the evaluating performance of Bandwidth Allocation using Dynamic Label Switching Paths (LSPs) Tunnelling and Label Distribution Protocol (LDP) signaling in MPLS network. This will make provision for bandwidth allocation and reservation possible. An appropriate bandwidth allocation would have positive impact on throughput as well as the delay. It is an extension of the existing network model designed using network simulator. The results of an IP (Internet Protocol) Network without MPLS enabled is compared with MPLS model network. Furthermore, implementation of dynamic and static LSPs models are presented. In addition, there are models of bandwidth estimation, bandwidth allocation, delay and jitter. Therefore, performance metrics used in this respect are throughput, end-to-end delay and packet delay variation of the multimedia services (Voice and Video conferencing). These confirmed that the modified models have been improved as compared with the baseline.
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Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images—a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.

The current inefficient utilization of frequency spectrum has alerted regulatory bodies to streamline improvements. Cognitive radio (CR) has recently received considerable attention and is widely perceived as a promising improvement tool in estimating, or equivalently sensing, the frequency spectrum for wireless communication systems. The cognitive cycle in CR systems is capable of recognizing and processing better spectrum estimation (SE) and hence promotes the efficiency of spectrum utilization. Among different SE methods, the multi-taper method (MTM) shows encouraging results. Further performance improvement in the SE for CR can be achieved by applying multiple antennas and combining techniques. This paper proposes a constructive development of SE using MTM, abbreviated as MTSE, and by employing multiple-input multiple-output (MIMO), parsed into separate parallel channels using singular value decomposition (SVD), and maximum ratio combining (MRC) configurations. Deviating from these improvements, however, multicarrier systems such as orthogonal frequency division multiplexing (OFDM) show inferior sensing performances due to the noise multiplicity generated and combined from all subcarrier channels. By means of the quadrature matrix form, the probabilities for such integrated settings of SE have been derived to reach at their approximate asymptotes. Numerical simulations revealed specific better performances stemmed from coupling the fashionable MTSE and MIMO technologies.

This paper delivers an accurate approximation for adaptive threshold and optimal frame detection algorithms based on the robust multitaper method aiming at an efficient spectrum sensing in cognitive radio systems. An appropriate adaptive thresholding allows for seamless vacation of unlicensed secondary users from certain bands upon primary users’ requests, while arbitrary optimal frame detection contributes to the computational and throughput demands. Simulation exercises corroborate the given analysis over Rayleigh channel and multiple-input multiple-output configuration and emphasize the critical role of adopting applicable adaptive threshold and optimal frame detection policies.

The recent surge in the development of new technologies, most especially in the field of mobile and wireless communications, requires the adequate maintenance and overall procurement of network infrastructures. this is due to a great deal of accelerating demand from Mobile users having access to real-time information such as data, voice and video services. Therefore, the operators and service providers require seamless integration of network protocols with an improved quality of service (QoS). This paper addresses the performance of multimedia services in Multiprotocol Label Switching (MPLS) nodes and network models design using simulation approach. MPLS ensures the reliability of the communication minimizing the delays and enhancing the speed of packet transfer. It is valuable in its capability of providing Traffic Engineering (TE) for minimizing the congestion by efficient throughput. The verification of the MPLS model will be the focus of the performance evaluation. An elaborate description of MPLS and its principle of operation will be required. It will eventually address the challenges of packet loss, high latency, high operational cost, more bandwidth utilization, and poor QoS.