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Pulse computation is a hybrid of digital and analog computation that uses aperiodic electrical spikes, as opposed to the periodic voltages in a digital computer or the continuously varying voltages in an analog computer. Pulse streams are unclocked, so they can arrive at arbitrary times and can be generated by analog processes, although each ...
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
Examples of pulse shapes: (a) rectangular pulse, (b) cosine squared (raised cosine) pulse, (c) Dirac pulse, (d) sinc pulse, (e) Gaussian pulse. A pulse in signal processing is a rapid, transient change in the amplitude of a signal from a baseline value to a higher or lower value, followed by a rapid return to the baseline value. [1]
The unit interval is the minimum time interval between condition changes of a data transmission signal, also known as the pulse time or symbol duration time.A unit interval (UI) is the time taken in a data stream by each subsequent pulse (or symbol).
Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. [90] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science , and computer science .
During each symbol, the phase either remains the same, encoding a 0, or jumps by 180°, encoding a 1. Again, only one bit of data (i.e., a 0 or 1) is transmitted by each symbol. This is an example of data being encoded in the transitions between symbols (the change in phase), rather than the symbols themselves (the actual phase).
Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1] [2] Making the data usable usually involves substantial compute and storage, as well as data processing.
Chip manufacturers publish a "maximum clock rate" specification, and they test chips before selling them to make sure they meet that specification, even when executing the most complicated instructions with the data patterns that take the longest to settle (testing at the temperature and voltage that gives the lowest performance).