Auto-Generation of Pipelined Hardware Designs for Polar Encoder

This paper presents a general framework for auto-generation of pipelined polar encoder architectures. The proposed framework could be well represented by a general formula. Given arbitrary code length $N$ and the level of parallelism $M$, the formula could specify the corresponding hardware architecture. We have written a compiler which could read the formula and then automatically generate its register-transfer level (RTL) description suitable for FPGA or ASIC implementation. With this hardware generation system, one could explore the design space and make a trade-off between cost and performance. Our experimental results have demonstrated the efficiency of this auto-generator for polar encoder architectures

Reduction of Thermal Deflection and Random Response of Composite Structures With Embedded Shape Memory Alloy at Elevated Temperature

A feasibility study on the reduction of thermal deflection and random response of the composite structures using shape memory alloys (SMA) at elevated temperatures is presented in this dissertation. The characteristics of SMA are introduced and the structural problems, static and dynamic, of SMA fiber reinforced composites are investigated. The stress-strain relations are developed for a composite lamina with embedded SMA fibers. The finite element system equations including shape memory effect are derived. A consistent two-step solution procedure is developed for solving the static and dynamic problems of composite structures with embedded SMA fibers subjected to combined acoustic and thermal loads. With the consideration of nonlinear material properties of SMA and geometrically nonlinear deflection, an incremental technique and the Newton-Raphson iteration method have been employed to determine the static response of the SMA embedded composite structures. Thermal buckling behavior of composite plates with and without embedded SMA has been studied first. The results show that the change of the austenite start temperature of SMA results in the increase of the critical buckling temperature of composite structures with embedded SMA. The study of thermal deflection of SMA hybrid composite structures has revealed that the thermal deflection can be reduced by changing the volume fraction, prestrain, austenite start temperature of SMA, as well as stacking sequence and boundary condition of structures. The random response analysis of SMA hybrid composite structures indicates that the random response of composite structure with embedded SMA can be significantly reduced by combining proper percentages of SMA volume fraction and prestrain and also altering the austenite start temperature. Thus the benefits of using SMA will maintain the originally designed optimal aerodynamic efficiency at high temperatures during cruise and result in lower noise and longer service life

Reduction of hematite (Fe2O3) to metallic iron (Fe) by CO in a micro fluidized bed reaction analyzer: A multistep kinetics study

A better understanding of the reduction kinetics of iron oxides in a fluidized bed promotes the development of relevant industrial processes, e.g. chemical looping combustion and non-blast furnace iron making. The reduction of iron oxides into iron is complex because the process is heterogeneous and several elementary reactions take place simultaneously. It is hard to figure out the reduction kinetics under fluidization in a fixed bed reactor such as in a thermogravimetry analyzer (TGA) which suffers from the limitations of heating rate, external diffusion, thermal pretreatment before reaction occurs. In this study, the reduction kinetics of hematite to metallic iron at different temperatures and carbon monoxide concentrations are experimentally investigated in a micro fluidized bed reaction analyzer (MFBRA), developed by the Institute of Process Engineering (IPE), Chinese Academy of Sciences (CAS) to develop the kinetics of fast gas-solid reactions. Results indicate that the reduction process has to be described by multistep kinetics and separated into several elementary reactions (i.e. hematite-magnetite, magnetite-wüsitite and wüsitite-iron), which proceed in parallel with different controlling mechanisms as well as with different time dependences. A multistep kinetics model based on Johnson-Mehl-Avrami (JMA) model is developed for the isothermal reduction process of hematite to metallic iron by taking into account the influences of reduction temperature and reducing gas concentration, using statistical analysis tools in the Statistical Product and Service Solutions (SPSS). The kinetics parameters, i.e. activation energy and pre-exponential factor, are determined for each elementary reaction. The contribution of each individual reaction to the whole reduction process is further discussed. The results also suggest that the reduction of hematite to wüsitite takes place fast and dominates the initial part of the entire reduction while the reduction of wüsitite to iron plays a less important role in the initial stage but controls the whole reduction in the late stage. The conclusions obtained in this study are comparable with that in the literature and indicate that the multistep kinetics model is able to capture the properties of both elementary reactions and the integrated process, providing an analysis strategy for revealing detail characteristics of the comple

An Actor-Critic-Based UAV-BSs Deployment Method for Dynamic Environments

In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) for optimizing the throughput of mobile users is investigated for UAV networks. This problem is formulated as a time-varying mixed-integer non-convex programming (MINP) problem, which is challenging to find an optimal solution in a short time with conventional optimization techniques. Hence, we propose an actor-critic-based (AC-based) deep reinforcement learning (DRL) method to find near-optimal UAV positions at every moment. In the proposed method, the process searching for the solution iteratively at a particular moment is modeled as a Markov decision process (MDP). To handle infinite state and action spaces and improve the robustness of the decision process, two powerful neural networks (NNs) are configured to evaluate the UAV position adjustments and make decisions, respectively. Compared with the heuristic algorithm, sequential least-squares programming and fixed UAVs methods, simulation results have shown that the proposed method outperforms these three benchmarks in terms of the throughput at every moment in UAV networks