Digital Signal Processing
Digital Signal Processing - Related Videos
10:55
Digital Microfluidics for Automated Proteomic Processing
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Cited by 30 •
2009
Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.
09:12
Calcified Artery Preparation and Processing with Preserved Morphology and RNA for Digital Spatial Profiling
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2026
This protocol includes a step-by-step workflow for calcified vascular specimens: tissue handling, decalcification, RNA validation, calcification level detection, and region-of-interest selection strategies on the Nanostring GeoMx Digital Spatial Profiler (DSP). The goal is to present a comprehensive method for preserving vascular tissue morphology and RNA for reliable spatial transcriptomic analysis.
09:37
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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Cited by 5 •
2015
Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.
04:04
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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2025
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and specificity.
10:33
Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
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Cited by 12 •
2012
The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.
FAQs
Digital signal processing starts by sampling a continuous signal, meaning it takes measurements at selected points in time and represents them as a numerical sequence. Algorithms then operate on those values rather than directly on the original waveform. This representation allows engineers to apply repeatable filtering, analysis, or modification while processors and programmable hardware execute operations for real-time signal handling.
Fourier analysis and convolution provide different algorithmic ways to work with numerical signal sequences. Fourier analysis supports examination of signal behavior through its frequency components, while convolution applies a defined operation between sequences that can modify or analyze signal behavior. Together with filtering, these tools help engineers extract information, reduce unwanted content, and shape outputs for particular systems.
Filtering changes signal content by selecting or suppressing components, which supports tasks such as noise reduction and signal enhancement. Modulation instead changes a signal for communications purposes, helping prepare information for transmission. The distinction matters in system design: filtering improves or isolates information within a signal, whereas modulation supports how that information is carried through a communications system.
A typical workflow begins with sampling a continuous input and encoding the measurements as a numerical sequence. Engineers then select algorithms such as filtering, Fourier analysis, convolution, or modulation according to the intended result. The processed sequence can support analysis, signal modification, or generation of an output, with execution assigned to a processor, microcontroller, or programmable hardware.
Digital signal processing supports a broad range of engineering applications, including noise reduction, speech enhancement, image enhancement, wireless communication, radar, biomedical instrumentation, and industrial monitoring. In each case, numerical processing helps systems analyze measurements or alter signal behavior in a controlled way. The same computational approach can therefore serve communication, sensing, medical, and industrial requirements.
The computational platform determines how a signal-processing system carries out its algorithms. Processors, microcontrollers, and programmable hardware can execute operations on numerical sequences, supporting precise and repeatable behavior. When a system requires real-time analysis or efficient data transmission, selecting an implementation that can perform the needed processing helps maintain responsive operation and adaptable engineering designs.