Earticle

Sphere decoding is a very powerful algorithm in searching the optimal solution of multiple input and multiple output systems. However, it cannot perform in parallel directly. Sphere decoding can be depicted as searching in a tree. In this paper, we propose a parallel mode of the sphere decoding algorithm. We proposed that the searching tree can be partitioned into several sub-trees. The searching is divided into two stages. In the first stage, the partial Euclidean distances of sub-tree root nodes are calculated. In the second stage, several sub-trees perform their searching simultaneously. The Euclidean distance of the early finished sub-tree helps to reduce the calculation in the later finished sub-trees search. Simulation results sho

Wireless power transfer (WPT) efficiencies for multiple-input multiple-output (MIMO) systems are formulated with a goal of achieving their maximums using Z matrices. The maximum efficiencies for any arbitrarily given configurations are obtained using optimum loads, which can be determined numerically through adequate optimization procedures in general. For some simpler special cases (single-input single-output, single-input multiple-output, and multiple-input single-output) of the MIMO systems, the efficiencies and optimum loads to maximize them can be obtained using closed-form expressions. These closed-form solutions give us more physical insight into the given WPT problem. These efficiencies are evaluated theoretically based on the presented formulation and also verified with comparisons with circuit- and EM-simulation results. They are shown to lead to a good agreement. This work may be useful for construction of the wireless Internet of Things, especially employed with energy autonomy.

In this paper, the efficiency of single-input multiple-output (SIMO) wireless power transfer systems is examined. Closed-form solutions for the receiver loads that maximize either the total efficiency or the efficiency for a specific receiver are derived. They are validated with the solutions obtained using genetic algorithm (GA) optimization. The optimum load values required to maximize the total efficiency are found to be identical for all the receivers. Alternatively, the loads of receivers can be adjusted to deliver power selectively to a receiver of interest. The total efficiency is not significantly affected by this selective power distribution. A SIMO system is fabricated and tested; the measured efficiency matches closely with the efficiency obtained from the theory.

The optimal output tracking control (OOTC) problem for a class of discrete-time systems with state and input delays is addressed. An augmented system is constructed such that the OOTC problem can be transformed into a two-point boundary value (TPBV) problem with both advance and delay terms from the necessary optimality conditions. The successive approximating method recently developed is extended to obtain an approximate solution of the TPBV problem, which is then used to obtain a feedforward and feedback tracking controller. An observer is designed for the uncertain reference input such that the feedforward controller is physically realizable. Simulations show the results are effective even with long time-delays.

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.