Simultaneous Bidirectional Link Selection in Full Duplex MIMO Systems

In this paper, we consider a point to point full duplex (FD) MIMO communication system. We assume that each node is equipped with an arbitrary number of antennas which can be used for transmission or reception. With FD radios, bidirectional information exchange between two nodes can be achieved at the same time. In this paper we design bidirectional link selection schemes by selecting a pair of transmit and receive antenna at both ends for communications in each direction to maximize the weighted sum rate or minimize the weighted sum symbol error rate (SER). The optimal selection schemes require exhaustive search, so they are highly complex. To tackle this problem, we propose a Serial-Max selection algorithm, which approaches the exhaustive search methods with much lower complexity. In the Serial-Max method, the antenna pairs with maximum "obtainable SINR" at both ends are selected in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expressions of the average weighted sum rate and the weighted sum SER are derived. The analysis is validated by simulations. Both analytical and simulation results show that as the number of antennas increases, the Serial-Max method approaches the performance of the exhaustive-search schemes in terms of sum rate and sum SER

Interface optimisation and bonding mechanism of rubber-wood-plastic composites

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe incorporation of waste tyre rubber into thermoplastics to develop a class of polymer composites with both elastomeric and thermoplastic behaviour has gained a lot of attention and is becoming one of the most straightforward and preferred options to achieve the valorisation of waste tyres. In view of the unique properties rubber possesses and the rapid expansion and versatile application of wood plastic composites (WPC) materials, the inclusion of tyre rubber as raw material into WPC to develop an entirely new generation of WPC, namely rubber-wood-plastic composites (RubWPC), was presumed to be another highly promising solution to turn waste tyres into value-added materials. This research starts with the interfacial optimisation of Rubber-PE composites and WPC by the use of maleated and silane coupling agents, aiming at addressing their poor constituent compatibility and interfacial bonding, thus enabling the optimal design of RubWPC. Chemical, physical and mechanical bonding scenarios of both untreated and treated composites were revealed by conducting ATR-FTIR, NMR, SEM and FM analyses. The contribution of the optimised interface to the bulk mechanical property of the composites were assessed by carrying out DMA and tensile property analysis. The influence of the coupling agent treatments on the in situ mechanical property of WPC was first determined by nanoindentation analysis, which led to a thorough understanding of the interfacial characteristics and the correlation between in situ and bulk mechanical properties. This research focuses on the novel formulation of RubWPC and the understanding of bonding mechanism. Chemical bonding and interface structure studies revealed that interdiffusion, molecular attractions, chemical reactions, and mechanical interlocking were mutually responsible for the enhancement of the interfacial adhesion and bonding of the coupling agent treated RubWPC. The improved interface gave rise to the increase of bulk mechanical properties, while the continuous addition of rubber particle exerted an opposite influence on the property of RubWPC. The composite with optimised interface possessed superior nanomechanical properties due to the resin penetration into cell lumens and vessels and the reaction between cell walls and coupling agents.European CIP-EIP-Eco-Innovation-201

Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

This paper proposes a low-latency secure mobile edge computing (MEC) system where multiple users offload computing tasks to a base station in the presence of an eavesdropper. We jointly optimize the users’ transmit power, computing capacity allocation, and user association to minimize the computing and transmission latencies over all users subject to security and computing resource constraints. Numerical results show that our proposed algorithm outperforms baseline strategies. Furthermore, we highlight a novel trade-off between the latency and security of MEC systems

Pressure induced superconductivity bordering a charge-density-wave state in NbTe4 with strong spinorbit coupling

Transition-metal chalcogenides host various phases of matter, such as charge-density wave (CDW), superconductors, and topological insulators or semimetals. Superconductivity and its competition with CDW in low-dimensional compounds have attracted much interest and stimulated considerable research. Here we report pressure induced superconductivity in a strong spin-orbit (SO) coupled quasi-one-dimensional (1D) transition-metal chalcogenide NbTe$_4$, which is a CDW material under ambient pressure. With increasing pressure, the CDW transition temperature is gradually suppressed, and superconducting transition, which is fingerprinted by a steep resistivity drop, emerges at pressures above 12.4 GPa. Under pressure $p$ = 69 GPa, zero resistance is detected with a transition temperature $T_c$ = 2.2 K and an upper critical field $H_{c2}$= 2 T. We also find large magnetoresistance (MR) up to 102\% at low temperatures, which is a distinct feature differentiating NbTe$_4$ from other conventional CDW materials.Comment: https://rdcu.be/LX8

Improved Federated Learning for Handling Long-tail Words

Automatic speech recognition (ASR) machine learning models are deployed on client devices that include speech interfaces. ASR models can benefit from continuous learning and adaptation to large-scale changes, e.g., as new words are added to the vocabulary. While federated learning can be utilized to enable continuous learning for ASR models in a privacy preserving manner, the trained model can perform poorly on rarely occurring, long-tail words if the distribution of data used to train the model is skewed and does not adequately represent long-tail words. This disclosure describes federated learning techniques to improve ASR model quality when interpreting long-tail words given an imbalanced data distribution. Two different approaches - probabilistic sampling and client loss weighting - are described herein. In probabilistic sampling, the federated clients that include fewer long-tail words are less likely to be selected during training. In client loss weighting, incorrect predictions on long-tail words are more heavily penalized than for other words