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Lossless Lines

Lossless Lines

In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...

Boundary Conditions: Lossless Lines

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...

Reducing Line Loss

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...

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Downsampling

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...

Lossy Lines and Overvoltages

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...

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Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

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Lossless compression of AVIRIS images.

R E Roger¹, M C Cavenor

¹Dept. of Electr. Eng., New South Wales Univ., Canberra, ACT.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

Adaptive Differential Pulse Code Modulation (DPCM) methods achieve lossless compression for hyperspectral images by leveraging spectral correlations. These techniques offer efficient data reduction for remote sensing applications.

Area of Science:

Remote Sensing
Image Processing
Data Compression

Background:

Hyperspectral imaging generates large datasets due to numerous spectral bands.
Lossless compression is crucial for preserving data integrity in scientific applications.
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data presents unique compression challenges.

Purpose of the Study:

To develop and evaluate adaptive Differential Pulse Code Modulation (DPCM) methods for lossless hyperspectral image compression.
To investigate the effectiveness of linear prediction in exploiting spectral correlations.
To optimize encoding strategies for improved compression ratios.

Main Methods:

Adaptive DPCM with linear prediction for predictive decorrelation.

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Generation and encoding of residual data.

Utilization of sensor noise characteristics for codebook design in variable-length coding (VLC).

Main Results:

Developed predictors that approach sensor noise limits, effectively utilizing spectral correlations.
Achieved improved compression using eight custom-designed VLC codebooks.
Compared VLC with Rice coding, noting a slight compression loss but significant simplicity and speed benefits for Rice coding.

Conclusions:

Adaptive DPCM methods provide effective lossless compression for hyperspectral imagery.
Linear prediction and tailored VLC codebooks are key to maximizing compression efficiency.
The trade-off between compression performance, simplicity, and speed (VLC vs. Rice coding) is a critical consideration.