An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Invariant discrete flows

In this paper, we investigate the evolution of joint invariants under invariant geometric flows using the theory of equivariant moving frames and the induced invariant discrete variational complex. For certain arc length preserving planar curve flows invariant under the special Euclidean group , the special linear group , and the semidirect group , we find that the induced evolution of the discrete curvature satisfies the differential‐difference mKdV, KdV, and Burgers' equations, respectively. These three equations are completely integrable, and we show that a recursion operator can be constructed by precomposing the characteristic operator of the curvature by a certain invariant difference operator. Finally, we derive the constraint for the integrability of the discrete curvature evolution to lift to the evolution of the discrete curve itself.     

IP网络信源信道联合编码策略

面向IP网络,讨论了一种信源编码的失真估计模型,并用典型的视频编码器作了验证.提出了一种基于包的信道编码方法,并将该方法引入信道编码的失真估计中,对一种信道编码的失真估计模型作了改进.以前述内容为基础,提出了两种信源信道联合编码策略,实验结果表明,这两种策略得到的最优结果在很大程度上提高了视频重建质量.     

多载波CDMA系统中联合检测译码方法研究

为了提高多载波CDMA(MC-CDMA)系统的抗干扰能力与系统容量,提出了一种将信道参数检测与纠错码译码联合运算进行的方法,得到了一种反馈级联结构.在分析MC-CDMA系统接收性能时,通常假设信道参数能精确估计,但对于时变信道在实现上是困难的;根据本文提出的级联结构,先用导频符号进行信道参数估计,再用导频符号的译码信息作为导频符号进行信道参数的进一步优化,构成一种联合检测译码结构,能有效地降低导频符号数量,并提高了信道参数估计精度.通过仿真,与传统的方法进行了性能比较,联合检测译码方案能获得较大系统增益,是一种优化方案.     

一种联合信源信道匹配的视频传输方法

介绍了一种实现联合信源-信道编码的通用的新方法—联合信源-信道匹配。该方法思想巧妙,通过将有限的通信系统资源在信源和信道编码器之间进行巧妙合理的分配,从而获得最优的系统整体传输性能。并且,该方法并不仅局限于特定的信源-信道编码器对,可适用于任何一种信源-信道编码器对共享有限系统资源的情况,具有很好的通用性。仿真实验证明了JSCM通用视频传输系统能获得比传统的固定码率分配系统更高的整体性能增益。     

A Generalization of the Mean Size Formula of Wavelet Packets in Lp

The purpose of this paper is to investigate the mean size formula of wavelet packets in Lp for 0 〈 p ≤ ∞. We generalize a mean size formula of wavelet packets given in terms of the p-norm joint spectral radius and we also give some asymptotic formulas for the Lp-norm or quasi-norm on the subdivision trees. All results will be given in the general setting,     

线性规划联合算法的理论与应用

本在[1]的基础上．较系统的叙述了线性规划联合算法的步骤、相关理论及其应用，指出该算法具有避免人工变量、减少迭代次数、使用灵活、应用方便等特点。     

Implementation of a Nonzero-order Joint Transform Correlator Using Interferometric Technique

The implementation of a nonzero-order joint transform correlator using the double port Mach-Zehnder interferometric technique is proposed and demonstrated. This approach provides on-line processing for directly removing the zero-order components of a joint power spectrum in one step and performs the nonzero-order optical correlation. Experimental results are presented and discussed.     

A Joint Integral Test for the Locations of Extrema for Brownian Motion

Let μ⁺(t) and μ⁻(t) be the locations of the maximum and minimum, respectively, of a standard Brownian motion in the interval [0,t]. We establish a joint integral test for the lower functions of μ⁺(t) and μ⁻(t), in the sense of Paul Lévy. In particular, it yields the law of the iterated logarithm for max(μ⁺(t),μ⁻(t)) as a straightforward consequence. Our result is in agreement with well-known theorems of Chung and Erdős [(1952) Trans. Amer. Math. Soc. 72, 179–186.], and Csáki, F?ldes and Révész [(1987) Prob. Theory Relat. Fields 76, 477–497].      

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.