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Applications of chaotic maps in communications and biomedical signal processing.

机译：混沌图在通信和生物医学信号处理中的应用。

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摘要

The dissertation proposes a novel technique of using chaotic functions to solve pivotal issues in wireless communications and biomedical signal processing applications. In this dissertation three critical issues are discussed. The dissertation proposes an effective interleaver structure which uses the chaotic circle map to generate scrambled uncorrelated randomized data. An Interleaver is a functional entity which improves the performance of error correcting codes under short bursts error due to channel effects. The information is interleaved after the orthogonal frequency division multiplexing (OFDM) modulation block. The contribution of the proposed technique is better randomization with minimal information requirement between transmitter and receiver for de-interleaving. The performance improvement of the proposed technique is compared in terms of the bit error rate and field programmable gate array (FPGA) implementation resource utilization.;The dissertation proposes an approach to alleviate the impact of distortion on the transmitted signal caused by peak to average power ration (PAPR) in OFDM systems. This is achieved with the aid of chaotic functional map, called circle map. The proposed approach is compared with the well known techniques, such as selective mapping (SLM), amplitude clipping, tone reservation (TR), partial transmit sequence (PTS), and active constellation extension (ACE). The previous methods require large side information vector and high complexity. The proposed approach reduces the PAPR by exploiting the inherent properties of chaotic signals. It is shown that the proposed method results in an overall error rate that is superior to those of the existing techniques at a lower overhead cost of the side information.;In recent years, numerous studies have been carried out on effective means of pattern detection and characterization for biomedical signal sets. With the aid of chaos theory, the dissertation presents two significant contributions in characterizing electrocardiogram (ECG) signal sets. The first contribution is the introduction of a functional map representation of atrial fibrillation ECG signal sets, which in turn can be brought to bear for the estimation of the probabilities of the future state vectors. The effectiveness of the proposed model is tested using mean-squared error metric. The Second contribution is to demonstrate the effectiveness of the recurrence period density entropy (RPDE) index as an effective tool in tracking the onset of changes in the condition of the patient from the state of normal ECG rhythm to the state of atrial fibrillation and sudden cardiac arrest.
机译：本文提出了一种利用混沌函数解决无线通信和生物医学信号处理应用中的关键问题的新​​技术。本文讨论了三个关键问题。本文提出了一种有效的交织器结构，该结构使用混沌圆图生成加扰的不相关随机数据。交织器是一种功能实体，可在由于信道效应而导致的短突发错误下提高纠错码的性能。在正交频分复用（OFDM）调制块之后对信息进行交织。所提出的技术的贡献是更好的随机化，并且在发射机和接收机之间用于解交织的信息需求最少。从误码率和现场可编程门阵列（FPGA）实现资源的利用率两方面对所提技术的性能进行了比较。论文提出了一种减轻峰均功率引起的失真对传输信号影响的方法。 OFDM系统中的比率（PAPR）。这是借助称为圆图的混沌功能图实现的。将所提出的方法与众所周知的技术进行比较，例如选择性映射（SLM），幅度削波，音调保留（TR），部分发射序列（PTS）和有效星座扩展（ACE）。先前的方法需要较大的辅助信息向量和高复杂度。所提出的方法通过利用混沌信号的固有特性来降低PAPR。结果表明，所提出的方法产生的总错误率优于现有技术，并且边信息的开销成本更低。近年来，关于模式检测和图像检测的有效手段已经进行了大量研究。生物医学信号集的表征。借助混沌理论，本文在表征心电图（ECG）信号集方面提出了两个重要的贡献。第一个贡献是引入了心房颤动ECG信号集的功能图表示，而后者又可以用于估计未来状态向量的概率。使用均方误差度量标准测试了所提出模型的有效性。第二项贡献是证明复发期密度熵（RPDE）指数作为追踪从正常心电图节律状态到心房颤动和心脏骤停状态患者病情变化发作的有效工具的有效性逮捕。

著录项

  • 作者

    Nair, Anish.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Biomedical engineering.;Engineering.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 97 p.
  • 总页数 97
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

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