QoE Driven Multimedia Service Schemes in Wireless Networks Resource Allocation: Evolution from Optimization, Game Theory, to Economics

In order to deal with the Quality of Experience (QoE) improvement issue in the wireless networks services. In this dissertation we first investigated the Device to Device (D2D) relaying approach in the conventional Base Station (BS) to User Equipment (UE) two entities multimedia service system. In this part, the Multiple Input Multiple Output (MIMO) technology will be implemented in the D2D communication. Furthermore, factors such as the multimedia content distribution (i.e., Quad-tree fractal image compression method), the power allocation strategy, and modulation size are jointly considered to improve the QoE performance and energy efficiency. In addition, the emerging Non-Orthogonal Multiple Access (NOMA) transmission method is becoming very popular and being considered as one of the most potential technologies for the next generation of wireless networks. For the purpose of improving the QoE of UE in the wireless multimedia service, the power allocation method and the corresponding limitations are studied in detail in the wireless system where the traditional Orthogonal Multiple Access (OMA) technology and the promising NOMA technology are compared. At last, facing the real business model in the wireless network services, where the Content Provider (CP), Wireless Carrier (WC), and UE are included, we extend on work from the conventional BS-UE two entities research model to the CP-WC-UE three entities model. More specifically, a generalized best response Smart Media Pricing (SMP) method is studied in this dissertation. In our work, the CP and WC are treated as the service provider alliance. The SMP approach and the game theory are utilized to determine the data length of UE and the data price rate determined by the CP-WC union. It is worth pointing out that the concavity of utility function is no longer necessary for seeking the game equilibrium under the proposed best response game solution. Numerical simulation results also validate the system performance improvement of our proposed transmission schemes

Improvement of Decision on Coding Unit Split Mode and Intra-Picture Prediction by Machine Learning

High efficiency Video Coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The reference software (i.e., HM) have included the implementations of the guidelines in appliance with the new standard. The software includes both encoder and decoder functionality. Machine learning (ML) works with data and processes it to discover patterns that can be later used to analyze new trends. ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. In this research project, in compliance with H.265 standard, we are focused on improvement of the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. We used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in our convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in HM software, and it was proved to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications

Process–Structure–Properties in Polymer Additive Manufacturing II

Additive manufacturing (AM) methods have grown and evolved rapidly in recent years. AM for polymers is particularly exciting and has great potential in transformative and translational research in many fields, such as biomedicine, aerospace, and even electronics. The current methods for polymer AM include material extrusion, material jetting, vat polymerization, and powder bed fusion. In this Special Issue, state-of-the-art reviews and current research results, which focus on the process–structure–properties relationships in polymer additive manufacturing, are reported. These include, but are not limited to, assessing the effect of process parameters, post-processing, and characterization techniques

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing