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Related Concept Videos

Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

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Related Experiment Videos

Learning a multi-dimensional companding function for lossy source coding.

Shin-ichi Maeda1, Shin Ishii

  • 1Kyoto University, Gokasyo, Uji, Kyoto, Japan. ichi@sys.i.kyoto-u.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society
|June 27, 2009
PubMed
Summary
This summary is machine-generated.

Companding vector quantization (CVQ) offers a practical solution for lossy source coding, outperforming traditional methods like Karhunen-Loève transformation (KLT) and non-structured vector quantization (VQ) in various scenarios.

Related Experiment Videos

Area of Science:

  • Information Theory
  • Signal Processing
  • Data Compression

Background:

  • Lossy source coding is crucial, but practical design methodologies remain challenging.
  • Vector Quantization (VQ) is effective but computationally intensive.
  • Companding Vector Quantization (CVQ) reduces VQ complexity by using scalar quantizations.

Purpose of the Study:

  • To propose and validate a CVQ optimization method for practical lossy source coding.
  • To introduce a new distortion formula for bit allocation, generalizing Bennett's formula.
  • To compare the performance of optimized CVQ against KLT-based and non-structured VQ.

Main Methods:

  • Developed a CVQ optimization technique using data samples.
  • Incorporated a novel distortion formula for bit allocation.
  • Applied the method to transform coding and compared with KLT and non-structured VQ.

Main Results:

  • Trained CVQ outperformed KLT-based coding and non-structured VQ in high bit-rate coding of uniform sources.
  • Trained CVQ surpassed KLT-based coding in low bit-rate coding of Gaussian sources.
  • Demonstrated advantages over non-structured VQ and highlighted limitations of high bit-rate theoretical analyses.

Conclusions:

  • The proposed CVQ optimization method provides a practical and efficient approach to lossy source coding.
  • Optimized CVQ demonstrates superior performance compared to established methods, particularly in high bit-rate applications.
  • The study addresses limitations in theoretical analysis and practical design of VQ.