There are two types of quantization .They are,
1. Truncation.
2.Round off.
Vector quantization lowers the bit rate of the signal being quantized thus making it more bandwidth efficient than scalar quantization. But this however contributes to it's implementation complexity (computation and storage).
We describe basic ideas of the stochastic quantization which was originally proposed by Parisi and Wu. We start from a brief survey of stochastic-dynamical approaches to quantum mechanics, as a historical background, in which one can observe important characteristics of the Parisi-Wu stochastic quantization method that are different from others. Next we give an outline of the stochastic quantization, in which a neutral scalar field is quantized as a simple example. We show that this method enables us to quantize gauge fields without resorting to the conventional gauge-fixing procedure and the Faddeev-Popov trick. Furthermore, we introduce a generalized (kerneled) Langevin equation to extend the mathematical formulation of the stochastic quantization: It is illustrative application is given by a quantization of dynamical systems with bottomless actions. Finally, we develop a general formulation of stochastic quantization within the framework of a (4 + 1)-dimensional field theory.
Scalar
Time is scalar
No, a millilitre is a measure, so it is neither scalar nor vector. It is a measure of volume and that is a scalar.
Vector quantization lowers the bit rate of the signal being quantized thus making it more bandwidth efficient than scalar quantization. But this however contributes to it's implementation complexity (computation and storage).
theory were all proved by taking longer and longer sequences of inputs. This indicates that a quantization strategy that works with sequences or blocks of output would provide some improvement in performance over scalar quantization. In other words, we wish to generate a representative
APPLES
We describe basic ideas of the stochastic quantization which was originally proposed by Parisi and Wu. We start from a brief survey of stochastic-dynamical approaches to quantum mechanics, as a historical background, in which one can observe important characteristics of the Parisi-Wu stochastic quantization method that are different from others. Next we give an outline of the stochastic quantization, in which a neutral scalar field is quantized as a simple example. We show that this method enables us to quantize gauge fields without resorting to the conventional gauge-fixing procedure and the Faddeev-Popov trick. Furthermore, we introduce a generalized (kerneled) Langevin equation to extend the mathematical formulation of the stochastic quantization: It is illustrative application is given by a quantization of dynamical systems with bottomless actions. Finally, we develop a general formulation of stochastic quantization within the framework of a (4 + 1)-dimensional field theory.
one syllable LOL
Sampling Discritizes in time Quantization discritizes in amplitude
The ideal Quantization error is 2^N/Analog Voltage
Quantization noise is a model of quantization error introduced by quantization in the analog-to-digital conversion(ADC) in telecommunication systems and signal processing.
A scalar variable can hold only one piece of data at a time. So in C, C++ and Java scalar data types include int, char, float and double, along with others. Scalar variables of the same type can be arranged into ascending or descending order based on the value. Prasangax
A scalar variable can hold only one piece of data at a time. So in C, C++ and Java scalar data types include int, char, float and double, along with others. Scalar variables of the same type can be arranged into ascending or descending order based on the value. Prasangax
quantisation noise decrease and quantization density remain same.
scalar lol