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Related Concept Videos

Downsampling01:20

Downsampling

767
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...
767
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.8K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
3.8K
Upsampling01:22

Upsampling

691
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...
691
Reducing Line Loss01:18

Reducing Line Loss

438
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...
438
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.6K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.6K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

1.1K
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Lensless Fluorescent Microscopy on a Chip
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Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication.

Xianming Liu, Deming Zhai, Jiantao Zhou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 22, 2016
    PubMed
    Summary
    This summary is machine-generated.

    A novel compressive sampling image coding scheme offers efficient, error-resilient visual communication for low-resource devices. This method preserves image details and supports multiple descriptions for robust data transmission.

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    Area of Science:

    • Digital image processing
    • Signal compression
    • Information theory

    Background:

    • Conventional image coding often sacrifices high-frequency details for compression efficiency.
    • Resource-deficient devices require efficient and error-resilient visual communication methods.
    • Existing compressive sensing techniques may not fully preserve image details or offer inherent error resilience.

    Purpose of the Study:

    • To develop a compressive sampling-based image coding scheme with high coding efficiency and low computational complexity.
    • To enhance error resilience in image transmission for visual communication systems.
    • To enable effective image recovery with fine detail preservation, especially at low bit-rates.

    Main Methods:

    • Replaced the conventional low-pass filter with a local random binary convolution kernel during image down-sampling.
    • Generated local random measurements that preserve high-frequency features and form a conventional image structure.
    • Employed a unified sparsity-based soft-decoding technique within a compressive sensing framework for image reconstruction.

    Main Results:

    • The proposed scheme achieves competitive coding efficiency with lower encoder complexity.
    • High-frequency image features and sharp edges are preserved, outperforming existing methods at low bit-rates.
    • The scheme inherently supports multiple description coding, enhancing error resilience.

    Conclusions:

    • The developed compressive sampling-based image coding scheme provides an efficient and error-resilient solution for visual communication.
    • It effectively balances coding efficiency, computational complexity, and the preservation of image details.
    • This technique is particularly advantageous for applications involving resource-constrained devices and unreliable transmission channels.