The resolution of the converter indicates the number of discrete values it can produce. It is usually expressed in bits. For example, an ADC that encodes an analog input to one of 256 discrete values has a resolution of eight bits, since 28 = 256. Resolution can also be defined electrically, and expressed in volts. The voltage resolution of an ADC is equal to its overall voltage measurement range divided by the number of discrete values. Some examples may help: Example 1 Full scale ...

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Analog-to-digital converter, Analog-to-digital converter - Resolution, Analog-to-digital converter - Response type, Analog-to-digital converter - Linear ADCs, Analog-to-digital converter - Non-linear ADCs, Analog-to-digital converter - Accuracy, Analog-to-digital converter - Sampling rate, Analog-to-digital converter - Aliasing, Analog-to-digital converter - Dither, Analog-to-digital converter - Oversampling, Analog-to-digital converter - ADC structures, Analog-to-digital converter - Application to music recording, Analog-to-digital converter - Other applications

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Analog-to-digital converter - Linear ADCs. Most ADCs are of a type known as linear, although analog-to-digital conversion is an inherently non-linear process (since the mapping of a continuous space to a discrete space is a non-invertible and therefore non-linear operation). In the sense of the term "linear" as used here, it means that the range of the input values that map to each output value has a linear relationship with the output value, i.e., that the output value k is used for the range of input values fro ...

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Analog-to-digital converter, Analog-to-digital converter - Resolution, Analog-to-digital converter - Response type, Analog-to-digital converter - Linear ADCs, Analog-to-digital converter - Non-linear ADCs, Analog-to-digital converter - Accuracy, Analog-to-digital converter - Sampling rate, Analog-to-digital converter - Aliasing, Analog-to-digital converter - Dither, Analog-to-digital converter - Oversampling, Analog-to-digital converter - ADC structures, Analog-to-digital converter - Application to music recording, Analog-to-digital converter - Other applications

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There is also a simple logical characterization of NP: it contains precisely those languages expressible in second order logic restricted to exclude universal quantification over relations, functions, and subsets. NP can be seen as a very simple type of interactive proof system, where the prover comes up with the proof certificate and the verifier is a deterministic polynomial-time machine that checks it. It is complete because the right proof string will make it accept if there is one, and it is sound because the verif ...

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NP complexity, NP complexity - Introduction and applications, NP complexity - Why some NP problems are hard to solve, NP complexity - Other characterizations, NP complexity - Example

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Accuracy depends on the error in the conversion. If the ADC is not broken, this error has two components: quantization error and (assuming the ADC is intended to be linear) non-linearity. These errors are measured in a unit called the LSB, which is an abbreviation for least significant bit. In the above example of an eight-bit ADC, an error of one LSB is 1/256 of the full signal range, or about 0.4%. Quantization error is due to the finite resolution of the ADC, and is an unavoidable imperfection in all types of ADC. The magnitude of the quantization error at t ...

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Analog-to-digital converter, Analog-to-digital converter - Resolution, Analog-to-digital converter - Response type, Analog-to-digital converter - Linear ADCs, Analog-to-digital converter - Non-linear ADCs, Analog-to-digital converter - Accuracy, Analog-to-digital converter - Sampling rate, Analog-to-digital converter - Aliasing, Analog-to-digital converter - Dither, Analog-to-digital converter - Oversampling, Analog-to-digital converter - ADC structures, Analog-to-digital converter - Application to music recording, Analog-to-digital converter - Other applications

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One variation of divide and conquer is called decrease and conquer, where a solution of problem depends on only one subproblem. There are two advantages of treating this variant separately. Some problems does not need to solve all subproblems, and have a simpler conquer strategy. They can be generally solved with tail recursion. Analysis of these problems is simpler than divide and conquer.See also:

Divide and conquer algorithm, Divide and conquer algorithm - Implementation, Divide and conquer algorithm - Variations, Divide and conquer algorithm - Advantages, Divide and conquer algorithm - Solving difficult problems, Divide and conquer algorithm - Algorithm efficiency, Divide and conquer algorithm - Parallelism, Divide and conquer algorithm - Memory access, Divide and conquer algorithm - Disadvantages

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