The dynamics of digital circuits with memory for ternary signals

The paper examines a digital automaton with memory operating in the ternary alphabet (0, 1, ). The problem of analytical determination of ternary processes on automaton outputs and inside the automaton from given ternary processes on the automaton inputs is considered. An algorithm is proposed reducing this problem to standard determination of the processes in an automaton with the alphabet (0, 1).Translated from Kibernetika, No. 4, pp. 19–25, July–August, 1989.     

Direct synthesis of pseudo-random ternary perturbation signals with harmonic multiples of two and three suppressed

This paper proposes an analytical method to directly synthesise pseudo-random ternary perturbation signals for the identification of frequency response functions, through the multiplication of two components signals which satisfy a prescribed set of properties. The ternary signals generated have harmonic multiples of two and three suppressed; this specification is useful for reducing the effects of nonlinear distortion on the linear estimate. The signals have uniform magnitude in all, except two, of the nonzero harmonics. The method is significant in overcoming the existing problem of sparsity in the available periods when analytical methods are used, as well as the relatively long computational time required in approaches based on exhaustive search or computer optimisation. The proposed technique presents a breakthrough as it eliminates the sequence-to-signal conversion stage required in the existing conventional methods. A direct consequence is the increased signal power within amplitude constraints, which now equals the theoretical limit for the specification considered. If it is necessary to further increase the number of available periods, the mathematical derivation can be extended to a class of suboptimal direct synthesis signals; however, these possess reduced signal power compared with direct synthesis signals.     

适用于十字型正交幅度调制信号的多模盲均衡算法

在加权多模盲均衡算法的基础上,采用多模算法在处理十字型正交幅度调制(QAM)信号时的改进方案,充分利用十字型QAM信号的符号统计特性,采用分段线性模值,使误差模型与十字型QAM信号星座更加匹配.此外,还给出了不同信噪比下均方误差估计值与权值的对应关系.仿真结果表明,该算法更适合处理十字型QAM信号,具有较好的稳态性能.     

Unconstrained and noninvasive measurement of bioelectric signals from small fish

Recently, the technique of fish bioassay has attracted attention as a method for constant monitoring of aquatic contamination. The respiratory rhythms of fish are considered to be an efficient indicator for the monitoring of water quality, since they are sensitive to chemicals and can be measured indirectly from the bioelectric signals generated by their breathing. However, no method has yet been established to measure signals in small free-swimming fish. In this article, we propose a system to measure bioelectric signals in small fish and monitor the frequency component in real time. To cover the large measurement range required in a free-swimming environment, the signals are measured using multiple electrodes. Further, the system focuses on the frequency component of the signal to assess the condition of the fish using frequency analysis and a band-pass filter. Experiments were conducted with the purpose of enabling remote sensing and environment estimation. First, it was verified that the measured signals were synchronized with the breathing of the fish. Then, a remote sensing experiment was performed using medaka (Oryzias latipes) that were allowed to swim freely in a measurement aquarium. The results confirmed that bioelectric signals which were synchronized with breathing could be measured in unconstrained and noninvasive conditions.     

Feasibility of Recurrent Neural Network for the Binary classification of Non stationary signals

Spectral domain techniques are best suited for the examination of signals which are non-stationary in nature. Features extracted from spectral decomposition can be represented with statistical parameters. These features are then fed to classifiers for classifying the non-stationary signals. In this work, Recurrent Neural Network is used as the classifier for labeling signals as normal or abnormal and also embedded. Bursts and saw tooth non-stationary signals are considered and the performance is studied. Also the significant parameters are identified based on sensitivity, specificity and accuracy. From the analysis, it is found that Discreet Framelet Transform (DFT) co-efficients are best suited for binary classification of burst signals while Discrete Wavelet Transform (DWT) is the best suited technique for saw tooth non-stationary signals. This methods can be used for the non-stationary signals like ECG, speech signals etc.     

Building with quantum correlations

‘Correlations without correlata’ is an influential way of thinking of quantum entanglement as a form primitive correlation which nonetheless maintains locality of quantum theory. A number of arguments have sought to suggest that such a view leads either to internal inconsistency or to conflict with the empirical predictions of quantum mechanics. Here we explicate and provide a partial defence of the notion, arguing that these objections import unwarranted conceptions of correlation properties as hidden variables. A more plausible account sees the properties in terms of Everettian relative states. The ontological robustness of entanglement is also defended from recent objections.     

Binary search with delayed and missing answers

How many questions are necessary and sufficient to guess an unknown number x in the set S={1,2,…,n}, by using only comparison questions, that is questions of the type “Is x?a?”, a∈S, when answers to questions are received with a delay of d time units and up to c of the answers can be lost, i.e., can not be received at all? We exactly solve this problem for all integers d?0 and c=1.     

Optical time-series signals classification based on data augmentation for small sample

Science China Information Sciences -     

Binary sequences with optimal autocorrelation

Sequences have important applications in ranging systems, spread spectrum communication systems, multi-terminal system identification, code-division multiple access communication systems, global positioning systems, software testing, circuit testing, computer simulation, and stream ciphers. Sequences and error-correcting codes are also closely related. In this paper, we give a well rounded treatment of binary sequences with optimal autocorrelation. We survey known ones and construct new ones.     

Surface modeling with ternary interpolating subdivision

In this paper, a new interpolatory subdivision scheme, called ternary interpolating subdivision, for quadrilateral meshes with arbitrary topology is presented. It can be used to deal with not only extraordinary faces but also extraordinary vertices in polyhedral meshes of arbitrary topologies. It is shown that the ternary interpolating subdivision can generate a C¹-continuous interpolatory surface. Some applications with open boundaries and curves to be interpolated are also discussed.     

三参数binary细分法

通过在曲线细分过程中引入三个参数,给出一种新的细分曲线构造的算法,并利用生成多项式等方法对细分法的一致收敛性、Ck连续性进行了分析.在给定初始控制数据的条件下,可以通过对形状参数的适当选择来实现对细分极限曲线形状的调控.该方法可以生成C4连续的细分曲线,增加了曲线造型的灵活性.数值试验表明这种算法是有效的.     

Binary trees with choosable edge lengths

For a set of two-element sets of non-negative real numbers we consider rooted strict binary trees with the property that the two edges leading from every non-leaf to its two children are assigned lengths l₁ and l₂ with {l₁,l₂}∈L.For choices of L for which l₁+l₂ is constant for every {l₁,l₂}∈L which models that a certain total length can be distributed with some degree of freedom specified by L to incident edges, we study the asymptotic growth of the maximum number of leaves of bounded depths in such trees and the existence of such trees with leaves at individually specified maximum depths.     

基于实例与目标相关性的多目标稀疏回归算法

针对传统多目标回归算法无法处理输入与多输出间的非线性关系,且忽视了数据点在输入与输出之间的结构信息,导致算法泛化性能受限、缺乏稳健性等问题,提出一种基于实例与目标相关性的多目标稀疏回归(multi-target sparse regression with instances and targets correlations,MTR-ITC)算法.首先,通过嵌入潜变量空间来对复杂的输入与输出以及输出间的关联结构解耦,并利用核技巧和稀疏回归学习输入输出间的非线性关系和输出间的相关结构;然后,引入流形正则化项探索不同实例在输入与输出变量间的相关性,确保模型输出与真实结果在局部和全局结构的一致性,以提升模型泛化性能;最后,提出一种交替优化算法来对目标函数进行求解,使其能快速收敛至全局最优.在基准测试数据集上的实验表明,所提算法在不同MTR数据集上均具有较好的测试性能.     

Distortion-invariant pattern recognition with local correlations

Local adaptive correlations for distortion-invariant and robust pattern recognition are presented. An iterative training algorithm for design of adaptive filters is utilized. The recognition performance of the proposed filters in noisy environment is compared with that of linear composite filters in terms of noise robustness and discrimination capability. Computer simulation results are provided and discussed.     

MERTA: micro-expression recognition with ternary attentions

Multimedia Tools and Applications - Micro-expression is a spontaneous and uncontrollable way to convey emotions. It contains abundant psychological information, whose recognition has significant...     

Adaptive detection of small sinusoidal signals in non-Gaussiannoise using an RBF neural network

This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density. The estimation of the noise density is a major part of the computational burden of LO detection rules. In this paper, adaptive estimation of the noise density is implemented using a radial basis function neural network. Unlike existing algorithms, the present technique places few assumptions on the properties of the noise, and performs well under a wide variety of circumstances. Experimental results are shown which illustrate the system performance as a variety of noise densities are encountered.     

Binary search trees with binary comparison cost

We introduce a new variant of the cost measure usually associated with binary search trees. This cost measure BCOST, results from the observation that during a search, a decision to branch left need require only one binary comparison, whereas branching right or not branching at all requires two binary comparisons. This is in contrast with the standard cost measure TCOST, which assumes an equal number of comparisons is required for each of the three possible actions. With BCOST in mind we re-examine its effect with respect to minimal and maximal BCOST trees, minimal and maximal BCOST-height trees, and introduce a class of BCOST-height balanced trees, which have a logarithmically maintainable stratified subclass. Finally, a number of other issues are briefly touched upon.This work was partially supported by NATO Grant GR 155.81, Natural Sciences and Engineering Research Council of Canada Grant No. A-5692, and National Science Foundation Grant No. MCS-8116522.