Data Rate Reduction
Data Rate Reduction - Related Videos
03:11
Oxidation-Reduction Reactions
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2020
Oxidation–Reduction Reactions Earth’s atmosphere contains about 20% molecular oxygen, O2, a chemically reactive gas that plays an essential role in the metabolism of aerobic organisms and in many environmental processes that shape the world. The term oxidation was originally used to describe chemical reactions involving O2, but its meaning has evolved to refer to a broad and important reaction class known as oxidation–reduction (redox) reactions. Some redox reactions involve the transfer of...
08:41
Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
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Cited by 17 •
2018
Electrochemical impedance spectroscopy (EIS) of species that undergo reversible oxidation or reduction in solution was used for determination of rate constants of oxidation or reduction.
08:32
Measurement of Outgassing Rates of Steels
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Cited by 5 •
2016
A protocol for the measurement of outgassing rates of hydrogen from ordinary steel vacuum chambers using the rate-of-pressure rise method is presented.
03:09
Measuring Reaction Rates
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2020
Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical field in...
11:53
Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling
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Cited by 38 •
2014
Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type.
FAQs
Compression identifies repeated or statistically predictable information so it can be represented with fewer bits. A system can also lower sampling density or numerical precision when the application does not require the original level of detail. These choices reduce transmission or storage demand, while the acceptable reduction depends on which information the downstream task must preserve.
A lossy method removes information judged less important to the application, so the reconstructed signal may not match the original exactly. This can achieve greater reductions in bit requirements, but the distortion is irreversible. Engineers therefore apply such methods only when the remaining detail is sufficient for the intended task, such as communicating or storing multimedia.
The main trade-offs involve fidelity, available transmission capacity, storage limits, processing resources, and application requirements. More aggressive reduction can lower bandwidth or memory use but may increase computational overhead, latency, or distortion. Engineers must evaluate these factors together rather than optimizing bit count alone, because a compact representation is useful only if the system still meets its performance needs.
Engineers first identify the detail required by the task, then compare whether compression, reduced sampling, reduced precision, or a combination is appropriate. They can assess the resulting bit demand against available bandwidth and storage while considering processing capability and latency. This selection process helps match the representation to the requirements of sensors, communications links, multimedia systems, or embedded devices.
Sampling or precision should be reduced only when the application can tolerate the associated loss of detail. The decision requires examining the information needed by the task and the limits of the receiving or storage system. If the reduction removes necessary content, the system may no longer preserve adequate fidelity, even though it achieves lower data and bandwidth requirements.
The approach supports sensor networks, digital communications, multimedia, and embedded devices. In sensor networks and communications, lowering bit requirements can help fit information within transmission capacity. For multimedia and embedded systems, it can reduce storage or communication demands under resource constraints. Its practical value depends on balancing those savings against latency, computation, and acceptable signal distortion.