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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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Continuous -time Fourier Transform01:11

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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BIBO stability of continuous and discrete -time systems01:24

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
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使用隐藏马尔科夫模型的波纹进行压缩计算,并进行连续观测.

Luca Bello1, John Wiedenhöft2, Alexander Schliep1,3

  • 1Computer Science and Engineering, University of Gothenburg, Chalmers, Gothenburg, Sweden.

PloS one
|June 6, 2023
PubMed
概括
此摘要是机器生成的。

压缩显著加快连续数据的隐藏马尔科夫模型 (HMM) 算法. 这种新方法可以加速大数据机器学习的计算,而不会牺牲准确性.

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科学领域:

  • 计算生物学 计算生物学
  • 机器学习 机器学习
  • 统计建模 统计建模

背景情况:

  • 压缩对于加速大数据机器学习至关重要.
  • 之前的工作证明了压缩对离散隐藏马尔科夫模型 (HMM) 的好处.
  • 压缩还可以加速贝叶斯式HMM,在特定假设下提供连续数据.

研究的目的:

  • 为了将压缩计算扩展到连续值观测的频率主义HMM算法.
  • 在连续数据上为经典频率主义HMM算法提供第一个压缩方法.
  • 评估压缩HMM算法的性能与经典方法相比.

主要方法:

  • 将压缩技术应用于经典的频率主义HMM算法 (向前过,向后平滑,维特比).
  • 进行了大规模的模拟,以比较压缩和经典HMM算法.
  • 在HMM框架中使用连续值观测.

主要成果:

  • 压缩HMM算法在许多环境中显著优于经典算法.
  • 压缩方法对计算的概率和推断的状态路径没有,或者只是微不足道的影响.
  • 证明了对大数据HMM加速计算的经验证据.

结论:

  • 压缩计算为使用HMM进行大数据分析提供了一种高效的方法.
  • 开发的方法可以在连续数据上加速频率主义HMM算法,而不会影响准确性.
  • 一个开源实现可用于更广泛的采用和研究.