Evolutionary ensemble of diverse artificial neural networks using speciation

Recently, many researchers have designed neural network architectures with evolutionary algorithms but most of them have used only the fittest solution of the last generation. To better exploit information, an ensemble of individuals is a more promising choice because information that is derived from combining a set of classifiers might produce higher accuracy than merely using the information from the best classifier among them. One of the major factors for optimum accuracy is the diversity of the classifier set. In this paper, we present a method of generating diverse evolutionary neural networks through fitness sharing and then combining these networks by the behavior knowledge space method. Fitness sharing that shares resources if the distance between the individuals is smaller than the sharing radius is a representative speciation method, which produces diverse results than standard evolutionary algorithms that converge to only one solution. Especially, the proposed method calculates the distance between the individuals using average output, Pearson correlation and modified Kullback–Leibler entropy to enhance fitness sharing performance. In experiments with Australian credit card assessment, breast cancer, and diabetes in the UCI database, the proposed method performed better than not only the non-speciation method but also better than previously published methods.     

卷积码纠错法在校园多媒体广播系统中的应用

论述了校园多媒体广播系统中数据通信的又一差错纠错方法--卷积码，其编码器和大数逻辑译码器较简单，纠错性能更好。     

会议扩声与同声传译系统设计要点

本文对会议扩声系统、讨论和表决系统、红外线同声传译系统的设计要点作了一些探讨和总结。在论述电声设计的同时，特别阐明了电声专业对建声专业的技术要求以及扩声控制室、译音室对土建专业的设计要求。     

Analysis and optimization of weighted voting systems consisting of voting units with limited availability

The voting system studied consists of n voting units each either providing a binary decision (0 or 1) or abstaining from voting. The system output is 1 if the cumulative weight of all 1-opting units is at least a pre-specified fraction τ of the cumulative weight of all non-abstaining units. Otherwise, the system output is 0.In this paper, we study the effect of limited availability of the voting units on the entire voting system reliability. Two different types of systems are considered. In the system of type 1, the absence of unit output (unit unavailability) is interpreted by the system as abstention from voting. In the system of type 2, the unavailable state of the voting unit and its abstention from voting can be distinguished and the system parameters can be adjusted to optimize its performance for each combination of available units.There are two ways to improve reliability of weighted voting system consisting of units with the given output probability distribution: optimization of system parameters (weights of units and threshold factor value) and units availability enhancement (for example, by choosing proper maintenance policy). This paper shows a method of incorporating information about units' availability into a procedure for determining the optimal system parameters. It also presents a method for determining indices that measure importance of voting units availability for both types of systems. These indices indicate voting units for which efforts of availability enhancement are the most beneficial from the entire system reliability improvement point of view.The approach is based on using a universal generating function technique and optimization procedure presented in [5]. Examples are presented.     

Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting

Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions within a formal framework of label ranking and, under some model assumptions, derive a generalized voting strategy in which predictions are properly adapted according to the strengths of the corresponding base classifiers. We call this strategy adaptive voting and show that it is optimal in the sense of yielding a MAP prediction of the class label of a test instance. Moreover, we offer a theoretical justification for weighted voting by showing that it yields a good approximation of the optimal adaptive voting prediction. This result is further corroborated by empirical evidence from experiments with real and synthetic data sets showing that, even though adaptive voting is sometimes able to achieve consistent improvements, weighted voting is in general quite competitive, all the more in cases where the aforementioned model assumptions underlying adaptive voting are not met. In this sense, weighted voting appears to be a more robust aggregation strategy.     

Design and Optimization of Full Comparator Based on Quantum‐Dot Cellular Automata

Quantum‐dot cellular automata (QCA) is one of the few alternative computing platforms that has the potential to be a promising technology because of higher speed, smaller size, and lower power consumption in comparison with CMOS technology. This letter proposes an optimized full comparator for implementation in QCA. The proposed design is compared with previous works in terms of complexity, area, and delay. In comparison with the best previous full comparator, our design has 64% and 85% improvement in cell count and area, respectively. Also, it is implemented with only one clock cycle. The obtained results show that our full comparator is more efficient in terms of cell count, complexity, area, and delay compared to the previous designs. Therefore, this structure can be simply used in designing QCA‐based circuits.     

Classification by ensembles from random partitions of high-dimensional data

A robust classification procedure is developed based on ensembles of classifiers, with each classifier constructed from a different set of predictors determined by a random partition of the entire set of predictors. The proposed methods combine the results of multiple classifiers to achieve a substantially improved prediction compared to the optimal single classifier. This approach is designed specifically for high-dimensional data sets for which a classifier is sought. By combining classifiers built from each subspace of the predictors, the proposed methods achieve a computational advantage in tackling the growing problem of dimensionality. For each subspace of the predictors, we build a classification tree or logistic regression tree. Our study shows, using four real data sets from different areas, that our methods perform consistently well compared to widely used classification methods. For unbalanced data, our approach maintains the balance between sensitivity and specificity more adequately than many other classification methods considered in this study.     

基于虚拟基线的干涉仪测向改进方法

复杂电磁环境下对复杂电磁信号进行测向是电子信息侦测的必要手段。并针对传统数字相位干涉仪解模糊和天线布设等技术问题,根据干涉仪测向基本原理,提出了一种基于虚拟基线的相位干涉仪方法,并利用大数判决准则对干涉仪测向估计方法进行了改进,改善和提升了测向性能。通过理论分析与仿真验证了该干涉仪设计与改进方法的有效性、可行的,同时该方法有利于工程实时实现,对实际工程应用有重要的参考意义,尤其是多长基线和多种天线阵形式的干涉仪系统。     

A Weighted Feature Voting Approach for Robust and Real‐Time Voice Activity Detection

This paper concerns a robust real‐time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short‐term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not‐weighted voting scheme.