Signal Processing Analysis
Signal Processing Analysis - Related Videos
09:37
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
0 Views •
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.
10:33
Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
0 Views •
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.
11:17
Acquisition and Analysis of an ECG (electrocardiography) Signal
0 Views •
2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient's torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall...
09:47
Isolation, Processing and Analysis of Murine Gingival Cells
0 Views •
Cited by 56 •
2013
This study describes an efficient technique to isolate and process gingival tissues from the mouse oral cavity in order to produce a single-cell culture. The resulting cells can be further used for flow cytometry analysis and molecular studies.
06:49
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
0 Views •
Cited by 28 •
2014
Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.
FAQs
Sampling determines when a continuous measurement becomes a sequence of values, while quantization assigns those values to discrete levels. These steps make the signal suitable for computational analysis, but they also shape the data available to later operations. In practice, engineers must treat the converted representation as the basis for filtering, transforms, and statistical characterization.
Filtering changes which signal components remain prominent, helping suppress noise or isolate features. Fourier transforms emphasize frequency content, whereas time-frequency transforms relate changing frequency behavior to time. Statistical analysis adds another perspective by describing signal characteristics numerically. Selecting among these operations depends on whether the engineering task requires cleaner measurements, spectral information, or changing behavior.
Time-domain observations show how a signal changes, while frequency-domain views reveal its component frequencies. Signal quality affects how confidently those features can be interpreted, especially when noise obscures relevant behavior. Considering both representations helps engineers choose suitable processing operations and judge whether a measured pattern reflects the physical system, communication channel, or an unwanted disturbance.
A practical workflow begins with continuous measurements, followed by sampling and quantization to create analyzable data. Engineers then select filtering, Fourier or time-frequency transforms, and statistical analysis according to the feature or behavior of interest. The resulting characterization can support performance evaluation, fault detection, system identification, or control decisions, linking processing choices to a defined engineering objective.
In wireless communication, analysis can characterize signals and channels; in audio and image processing, it helps examine information-bearing data. Sensor monitoring uses processed measurements to identify meaningful changes and possible faults, while feedback control uses signal information to evaluate or regulate system behavior. These applications show how the same analytical operations serve different engineering performance goals.
Real-time technologies depend on processing signals efficiently enough to support timely interpretation or control. Signal quality, the chosen representation, and the computational demands of filtering, transforms, and statistical operations all influence that design. Engineers use these considerations to balance useful feature extraction with practical implementation needs when monitoring systems, evaluating performance, or operating feedback control.