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Signal Conversion Methods
Published in Clarence W. de Silva, Sensor Systems, 2016
Suppose that an analog signal within the dynamic range of a particular ADC is converted by that ADC. Since the analog input (sampled value) has an infinitesimal resolution and the digital representation has a finite resolution (one LSB), an error is introduced into the process of ADC. This is known as the “quantization error.” A digital number undergoes successive increments in constant steps of 1 LSB. If an analog value falls at an intermediate point within a step of single LSB, a quantization error is caused as a result. Rounding off the digital output can be accomplished as follows: The magnitude of the error when quantized up is compared with that when quantized down, say, using two hold elements and a differential amplifier. Then, we retain the digital value corresponding to the lower error magnitude. If the analog value is below the 1/2 LSB mark, then the corresponding digital value is represented by the value at the beginning of the step. If the analog value is above the 1/2 LSB mark, then the corresponding digital value is the value at the end of the step. It follows that with this type of rounding off, the quantization error does not exceed 1/2 LSB.
Digital equipment
Published in Michael Talbot-Smith, Audio Engineer's Reference Book, 2013
Terry Clarke, David Mellor, Francis Rumsey
In digital mixing and filtering, errors can occur which will either compromise sound quality or result in instability. When an analogue signal is quantized, the least-significant bit will contain an error with up to half the value of that bit which is signal related and, therefore, can be classed as distortion. To overcome this a noise signal is added, known as dither, which randomizes the last bit and replaces the distortion with noise. As a by-product, dithering allows signals which would otherwise have fallen below the level of the least significant bit to be captured. If during some digital signal processing operation this dither is lost, for example when a signal is attenuated, then quantization distortion will result, as though the dither had never been there. The solution is to perform digital arithmetic to a much greater degree of precision than the samples themselves, for example using 32-bit arithmetic for 16-bit samples, and ensuring that when a sample is truncated then dither, which may be digitally generated, is added correctly.
Audio Plug-ins
Published in Mike Collins, Pro Tools for Music Production, 2012
According to Ken Pohlmann in his book Principles of Digital Audio, ‘Without dither, a low level signal would be encoded by an A/D converter as a square wave. With dither, the output of the A/D is the signal with noise. Perceptually, the effects of dither are much preferred because noise is more readily tolerated by the ear than distortion.’ An added benefit of dither is that it lets the converter handle amplitudes below the lowest quantization value. As Pohlmann explains, ‘Dither changes the digital nature of the quantization error into a white noise and the ear may then resolve signals with levels well below one quantization level. So, with dither, the resolution of a digitization system is below the least significant bit. By encoding the audio signal with dither to produce modulation of the quantized signal, we may recover that information, even though it might be smaller than the smallest increment of the quantizer.’
Manufacture process quality control of interferometric fibre optic gyroscope using analyses of multi-type assembly and test data
Published in International Journal of Computer Integrated Manufacturing, 2018
Haoting Liu, Donghai Shi, Xinyu Hou, Beibei Yan, Wei Wang
The design target of SFA_AHP algorithm is to detect and locate the potential faults of IFOG optical path. Multi-type environment adaptability test experiments are used to evaluate its working performance. In this section, two environment adaptability experiments, i.e. the temperature experiment and the vibration experiment, are illustrated to test the algorithm performance; and an integrated application experiment is also considered. In Figure 11, images (a) and (b) show the control curve of temperature experiment and the vibration power spectral density curve of vibration experiment, respectively. Images (c)–(f) show the normal and abnormal output results of IFOG under different experimental conditions. Where (c) and (d) are the typical normal and abnormal outputs, respectively, of the IFOG when the experiment temperature is 30°C; (e) and (f) are the typical normal and abnormal outputs, respectively, of the IFOG when the vibration frequency is 500 Hz. Here, ‘LSB’ indicates the least significant bit. Other typical normal and abnormal outputs of the IFOG can also be accumulated using different environment adaptability test experiments.
A New Hybrid Method for Secure Data Transmission Using Watermarking based on Fuzzy Encryption in IoT
Published in IETE Journal of Research, 2023
Hossein Mohammadi, Abdulbaghi Ghaderzadeh, Amir Sheikhahmadi
LSB stands for Least Significant Bit. The byte or octet is the least significant value in the position of a multibyte number. The least significant bit gives the unit value and indicates the position of the bit in a binary integer. It determines whether the number is odd or even. The LSB is sometimes referred to as the rightmost bit, due to a positional notation convention to write the least significant digit to the right [21]. It is the same as the least significant digit of a decimal integer, which is the digit in the ones position. If the number changes even slightly, the least significant bits have the useful property of changing quickly. It is easy to understand and simple to implement [21].
Knowledge Based database of arm-muscle and activity characterization during load pull exercise using Diagnostic Electromyography (D-EMG) Signal.
Published in Cogent Engineering, 2020
Pritam Chakraborty, Biswarup Neogi, Achintya Das
Apprehending and analysis of the D-EMG signal is most commonly done digitally by computer, which requires transforming the analog signal into a digital signal using an analog to digital (A/D) converter. One of the most important factors in the A/D converter is a sampling. A slow sampling rate can result in the distortion of the signal, such as aliasing, in order to avoid aliasing and other signal distortion the sampling rate must be greater than Nyquist rate (Burden & Bartlett, 1999). The major power of the D-EMG signal is accounted for by harmonics up to 400–500 Hz range and most of the frequency components of the D-EMG signal more than 500 Hz is contributed by the electrode and equipment noise or environmental interference. Thus, for D-EMG signal analysis broadly used sampling rate is 1 kHz. Utilizing a high sampling rate involves high-frequency components of the myoelectric signals captured with the surface electrodes but concurrently add prosthesis controller processing and time complexity (Youdas et al, 2008; 2010). Thus, it is desirable in signal acquisitions of EMG signal to use a low sampling rate without compromising controller performance (Hibbs et al., 2011) Quantization of the sampled signal consists of expressing the analog value in digital forms or steps, which has limited resolution. The amplitude of each digital form is referred to as the least significant bit or LSB. The quantization presents an approximation in the reconstructed signal, since all the values of the between two subsequent values will be represented by the same digital steps, it can be modeled as an additive noise that is added to the signal in order to obtain digital representation. The effect of A/D conversion of analog D-EMG signal is then limited to signal-to-noise ratio to a value equals the value of quantization signal-to-noise ratio, for a signal with uniform amplitude distribution, equals: