Digital signal (signal processing)

Summary

In the context of digital signal processing (DSP), a digital signal is a discrete time, quantized amplitude signal. In other words, it is a sampled signal consisting of samples that take on values from a discrete set (a countable set that can be mapped one-to-one to a subset of integers). If that discrete set is finite, the discrete values can be represented with digital words of a finite width. Most commonly, these discrete values are represented as fixed-point words (either proportional to the waveform values or companded) or floating-point words. The process of analog-to-digital conversion produces a digital signal. The conversion process can be thought of as occurring in two steps:

sampling, which produces a continuous-valued discrete-time signal, and

quantization, which replaces each sample value with an approximation selected from a given discrete set (for example, by truncating or rounding).

It can be shown that an analog signal can be reconstructed after conversion t

Official source

https://en.wikipedia.org/wiki/Digital_signal_(signal_processing)

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Introduction to the Issue on Advanced Signal Processing in Microscopy and Cell Imaging

Michaël Unser

Microscopy imaging, including fluorescence microscopy and electron microscopy, has a prominent role in life science and medical research. During the past two decades, biological imaging has undergone a revolution by way of the development of new microscopy techniques that allow the visualization of tissues, cells, proteins and macromolecular structures at all levels of resolution, physiological states, chemical composition and dynamic analysis. Thanks to recent advances in optics, digital sensing technologies and labeling probes (i.e., XFP—Colored Fluorescence Protein), we can now visualize subcellular components and organelles at the scale of a few nanometers to several hundreds of nanometers. As a result, fluorescent microscopy has become the workhorse of modern biology. Further technological advances include structured and coherent light sources, faster and more sensitive detectors, smaller and more specific molecular probes and automation processes for image acquisition. Additionally, there is a push towards multimodal imaging in order to gather complementary information such as the concentration of fluorophores at various wavelengths and the refractive index of a given sample (phase imaging).

IEEE2016

Highly energy-efficient wireless sensor nodes are a prerequisite for a sustainable operation of the Internet of things. Therefore, classical approaches for system design based on digital signal processing are not a viable solution, but system design has to follow entirely new paradigms. In this regard, we present a sensory system with analog spike-based signal processing for sensing and communication, encoding the sensory information in the pulse repetition frequency (PRF), getting rid of energy hungry A/D and D/A conversion. Our spiking sensory system can generate spikes from any conventional analog output sensor using a compact, highly tunable voltage-controlled oscillator based on vanadium dioxide, and an analog differentiator circuit performing the transmit pulse shaping. The sole conversion from analog to digital takes place at the base station followed by the estimation of the PRF, for which we compare a conventional receiver design consisting of an analog-to-digital converter (ADC) with the use of an integrate-and-fire time encoding machine (IFTEM). Results show the successful communication of sensory information from the edge node over an additive white Gaussian noise channel to the base station, with the IF-TEM outperforming the conventional ADC for a signal-to-noise ratio above 0 dB.

IEEE2022

Ieee-Inst Electrical Electronics Engineers Inc2016