Low-complexity joint transmit and receive antenna selection for MIMO systems

In this paper, we present a low-complexity algorithm for real-time joint transmit and receive antenna selection in MIMO systems. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. We apply Binary Particle Swarm Optimization (BPSO) to the joint transmit and receive antenna selection problem. In addition, applying the general BPSO to joint antenna selection, we also present a specific improvement to this population-based heuristic algorithm, namely, we feed cyclically shifted initial population so that the average convergence time (the number of iterations until reaching an acceptable solution) is reduced. The proposed BPSO for joint antenna selection problem has a low computational complexity, and its effectiveness is verified through simulation results.     

Random linear-quadratic mathematical models: Computing explicit solutions and applications

This paper deals with the study of linear random population models and with a random logistic model (where parameters are random variables). Assuming appropriate conditions, the stochastic processes solutions are obtained under closed form using mean square calculus. Expectation and variance expressions for the stochastic processes solutions are given and illustrative examples are included.     

Replica Exchange Light Transport

We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replica‐exchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain Monte Carlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport.     

双拼组合宽角相机低空航测实验研究

介绍无人机载双拼组合相机低空航测的实验过程,包括双拼组合相机组成结构、原理以及利用该系统进行大比例尺测图的生产流程,对此过程中出现的误差进行了重点分析,并提出消除误差的方法。理论和实践证明,无人机载双拼相机低空航测可以满足1∶1000测图精度标准,比装备普通单相机工作效率有明显提高。     

An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

Teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self-feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability.     

基于分级策略的演化算法

为了避免演化算法过早收敛,保持种群多样性,增加算法的搜索能力,本文提出基于分级策略的演化算法．即通过对种群进行分级,来度量种群的多样性,衡量算法是否陷入局部最优,协调种群多样性和精英策略之间的矛盾,再根据种群分布的多样性设计一种有效的半一致交叉算子与单重均匀变异算子。     

燃气报警器报警点漂移模型研究

根据燃气报警器报警点年漂移试验 ,探讨了报警点漂移原因及改进措施 ,建立了报警点漂移的正态分布模型 ,并经用 χ2 检验法进行了检验。依据行业标准CJ- 30 5 7,结合燃气报警器的加严试验结果 ,提出了报警器最佳标定点。     

通用扫描线多边形填充算法

传统的扫描线多边形填充算法只适用于水平扫描线的逐行填充。文章提出通用扫描线多边形填充算法,该算法可以有效地解决任意间距、任意倾角的扫描线对多边形的填充问题。通用扫描线多边形算法采用了坐标变换、浮点数舍入策略等重要方法。顶点扫描线号是该算法中的核心概念。     

浮点格式转换的分析与实现

针对不同类型浮点数表示不尽相同,不同机型浮点格式的转 换不仅涉及阶码和尾数的表达,还涉及到浮点数存储的方式、位和位域操作等实现起来较繁 琐的问题,分析了其存储方式的异同,开发了一个CBitArray位数组类,结合VAX机型浮点数 与微机浮点数的转换,说明了该方法可简洁方便地实现不同类型浮点格式的转换。