A non-uniform quantization hybrid analog-to-digital converter and its control method

Abstract

The present invention relates to the field of analog-to-digital conversion technology, and particularly to a non-uniform quantization hybrid analog-to-digital converter and its control method. The hybrid analog-to-digital converter includes: a data processing and control module, an analog signal sampling module, a non-uniform quantization module, and a data encoding module; the method includes first performing denoising processing on the input continuous analog signal, then sampling the signal through the sampling module to convert the analog quantity that changes continuously over time into an analog quantity that is discrete in time; determining the quantization interval according to different intervals of the sampled signal to discretize the sampled continuous analog signal; representing the quantized result in binary coding and outputting it by the hybrid analog-to-digital converter. The present invention improves the signal-to-noise ratio of the signal through denoising processing and precise sampling technology; non-uniform quantization makes the quantization process more in line with the statistical characteristics of the signal, and can improve the quantization accuracy of small signals and reduce the quantization error while keeping the total number of quantization levels unchanged.

Description

Technical Field

[0001] The present invention relates to the technical field of analog-to-digital conversion, and particularly to a non-uniform quantization hybrid analog-to-digital converter and a control method thereof. Background Art

[0002] An analog-to-digital converter, i.e., an analog-digital converter, an A / D converter, abbreviated as ADC, generally refers to an electronic component that converts an analog signal into a digital signal. The function of the analog-to-digital converter is to convert an analog signal that is continuous in time and amplitude into a digital signal that is discrete in time and amplitude. There are various types of analog-to-digital converters. Although many structures are fast, their power consumption and cost are relatively high, and they are not suitable for real-time application scenarios. In high-speed analog-to-digital converters, the fully parallel structure is fast, but the resolution is low. The pipelined analog-to-digital converter has improved resolution, but its conversion speed is limited. The dual-slope ADC has high accuracy but low conversion speed and is suitable for occasions requiring high accuracy. Although it has strong anti-interference ability and stability, cross-interference still needs to be avoided in actual applications. Therefore, a new type of analog-to-digital converter is needed to improve the current situation.

[0003] In the prior art one, Patent Application No. CN202210164304.6 discloses an analog-to-digital converter, including a logic control module, an input voltage module, a reference voltage generation module, a reference voltage selection module, and a comparison module. Under the control of the logic control module, the Q signal and the corresponding reference voltage are selected as the inputs of the comparison module to confirm the polarity of the Q signal. Thus, at the beginning of the next timing, in a successive approximation manner, the I signal and a suitable reference voltage are selected as the inputs of the comparison module to facilitate confirming the reference voltage closest to the I signal, and then the analog signal is quantized into the corresponding digital signal. Although the reference voltage is generated by the reference voltage generation module and the reference voltage is used to quantize the analog signal into a digital signal, which avoids consuming static power and is beneficial to reducing static power consumption, and the structure of the reference voltage generation module is simple, which is beneficial to reducing dynamic power consumption. However, the conversion speed is relatively slow, and the cost is relatively high at a resolution higher than 14 bits.

[0004] Prior Art Two, Application No. CN202211598509.1 discloses a high-speed Flash-SAR hybrid analog-to-digital converter, which includes a sampling switch, a Flash ADC, a comparator, a capacitive digital-to-analog converter, a successive approximation register, and a digital error correction circuit; it adopts a fully differential structure. The input signal quickly completes the conversion of 3-bit digital codes through the Flash ADC, and at the same time controls the conversion of the switches corresponding to the three high-order capacitors of the capacitive digital-to-analog converter, and completes the conversion of the subsequent bits through the comparator. Although the capacitor array is divided into two segments, and each segment of the capacitor is designed with a capacitor architecture based on binary error compensation, which reduces the errors brought to the system by the offset of the comparator, the incomplete establishment of the top plate pressure, and the thermal noise of the conversion switch itself. And due to the existence of the encoding circuit in the Flash ADC, the conversion from thermometer code to binary code is realized, saving the chip area and reducing the power consumption. However, the cost is relatively high, and the conversion rate is limited.

[0005] Prior Art Three, Application No. CN202210873729.4 discloses a non-uniform phase-shifted optical quantization analog-to-digital converter, which includes an electro-optic modulator, a non-uniform phase shifter, and a non-uniform threshold decision module; the analog electrical signal to be quantized and the optical pulse signal are input into the electro-optic modulator for modulation, and then the obtained modulated optical signal is input into the non-uniform phase shifter for non-uniform phase-shifting operation, and finally the phase-shifted signal is compared and judged by the non-threshold decision module to output non-uniform quantization coding. Although it reduces the quantization error, improves the quantization performance, uses optoelectronic technology to reduce the clock jitter, and has better anti-pulse amplitude jitter performance compared with the uniform phase-shifted optical quantization analog-to-digital converter, and can better meet the requirements of high-speed and high-precision analog-to-digital conversion application scenarios. However, the cost is relatively high, and the complexity of calculation and implementation is relatively high.

[0006] Currently, Prior Art One, Prior Art Two, and Prior Art Three have the problems of relatively high production costs, relatively high complexity of calculation and implementation, deficiencies in various types of analog-to-digital converters, and easy distortion of small signals during uniform quantization; therefore, the present invention provides a non-uniform quantization hybrid analog-to-digital converter to improve the signal quality, avoid distortion, make full use of the dynamic range of the signal, and improve the utilization rate of the dynamic range of the signal. Summary of the Invention

[0007] The main purpose of the present invention is to provide a non-uniform quantization hybrid analog-to-digital converter to solve the problems of relatively high production costs, relatively high complexity of calculation and implementation, deficiencies in various types of analog-to-digital converters, and easy distortion of small signals during uniform quantization in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A non-uniform quantization hybrid analog-to-digital converter, which includes:

[0010] A data processing and control module for denoising the input continuous analog signal to improve the signal-to-noise ratio of the signal; and providing an accurate clock signal to synchronize the signal acquisition and output processing processes;

[0011] An analog signal sampling module for converting the continuously varying analog quantity over time into a discrete analog quantity in time, and the sampling process follows the Nyquist law;

[0012] A non-uniform quantization module for determining the quantization interval according to different intervals of the signal and discretizing the sampled continuous analog signal;

[0013] A data encoding module for representing the quantized result in binary coding and outputting it by the hybrid analog-to-digital converter.

[0014] As a further improvement of the present invention, the data processing and control module includes:

[0015] A filter sub-module for denoising the analog signal input to the hybrid analog-to-digital converter;

[0016] A clock signal generator sub-module for generating a clock signal to synchronize signal acquisition and control the conversion rate.

[0017] As a further improvement of the present invention, the analog sampling module includes:

[0018] A function input sub-module, before sampling, the input continuous analog signal is a continuous function containing multiple frequency components; according to the Nyquist law, a sampling frequency is selected to ensure that this frequency is at least twice the highest frequency component in the signal;

[0019] A sampling execution sub-module for setting the sampling time and extracting the sampling value; according to the selected sampling frequency, setting the sampling time, and extracting the voltage value of the analog signal at each set sampling time to obtain discrete signal samples;

[0020] A signal composition sub-module for obtaining a set of discrete sample values, which together constitute a discrete signal representing the voltage value of the original continuous signal at the specified sampling time.

[0021] As a further improvement of the present invention, the non-uniform quantization module includes:

[0022] A signal compression sub-module for performing a non-linear transformation on the input signal, using a compression function to compress a wide range of signal values into a smaller range;

[0023] A compression mapping sub-module, which is used to determine the size of each quantization interval according to the compressed signal distribution, and map the compressed signal values to a series of equally spaced discrete points;

[0024] An amplitude distribution sub-module, which is used to reverse the operation of the compressor at the receiving end, and restore the quantized signal to the amplitude distribution of the original signal through an expander.

[0025] As a further improvement of the present invention, a signal compression sub-module includes:

[0026] A signal range identification unit, which is used to analyze the input continuous analog signal and determine the amplitude range of the input continuous analog signal; the compression function adopts the A-law compression algorithm;

[0027] A compression function application unit, which is used to apply the compression function to each sampling value of the input continuous analog signal;

[0028] An output range control unit, which is used to normalize the processed compressed value to make it within a specific range; after the processing is completed, the resulting continuous analog signal represents the signal after non-linear compression processing, and the compressed continuous analog signal will then be transmitted to the compression mapping sub-module for quantization and processing.

[0029] As a further improvement of the present invention, a compression mapping sub-module includes:

[0030] A signal characteristic identification unit, which is used to obtain the continuous analog signal data after being processed by the compression function, including all sample values after non-linear compression; analyze the distribution characteristics of the compressed signal using the probability density function to find the signal peak, average value and variance characteristics;

[0031] A quantization interval determination unit, which is used to determine the size and number of each quantization interval according to the statistical characteristics of the signal distribution; set smaller quantization intervals near the signal values that appear frequently; set larger quantization intervals in the regions where the signal values appear less frequently or are sparse;

[0032] A discrete point mapping unit, which is used to quantize each compressed continuous analog signal value using the determined quantization interval, and map the compressed signal value to a discrete point according to the quantization interval; convert the mapping result into a quantized discrete signal output, and the discrete signal is represented by the median or central value of each quantization interval;

[0033]

[0034] where, y j represents the median value of the jth quantization interval, y min and y maxare the minimum and maximum quantization values of the continuous analog signal, respectively, and the discrete points Q[i].

[0035] As a further improvement of the present invention, the signal characteristic recognition unit includes:

[0036] A signal data acquisition sub-unit, which is used to extract all sample values from the continuous analog signal processed by the compression function, divide the extracted continuous analog signal data into several small intervals, and count the number of data points in each interval; the height of each bar of the histogram corresponds to the number of data points in that interval;

[0037] A probability density function calculation sub-unit, which is used to convert the height of the histogram into a probability density function, and divide the number of sample points in each interval by the product of the total number of samples and the interval width;

[0038] A statistical characteristic calculation sub-unit, which is used to calculate the peak value, average value and variance.

[0039] As a further improvement of the present invention, the quantization interval determination unit includes:

[0040] A signal characteristic recognition sub-unit, which is used to obtain the continuous analog signal data processed by the compression function, and extract all non-linearly compressed sample values; use the probability density function to analyze the distribution characteristics of the continuous analog signal, including finding out the statistical characteristics of the peak value, average value and variance;

[0041] A frequency analysis and data distribution sub-unit, which is used to observe the height of each interval of the histogram when analyzing the signal data and its frequency distribution, and find out the intervals where the signal appears at high frequency and low frequency;

[0042] A quantization interval setting sub-unit, which is used to set the quantization interval according to the characteristics of the signal distribution, especially the different distribution situations in the high-frequency and low-frequency regions; according to the determined quantization interval, finally generate the quantization mode.

[0043] As a further improvement of the present invention, the data encoding module includes:

[0044] A quantization level confirmation sub-module, where each quantization interval corresponds to a specific digital representation, that is, the quantization level; by establishing a mapping relationship from the quantization value to the digital value, determine the number corresponding to each quantization value;

[0045] A quantization level allocation sub-module, which is used to assign a number to each interval, and the quantized sampled value corresponds to a number;

[0046] A binary encoding sub-module, which is used to convert the digital number of each quantization value into a binary encoding, use the number of bits to perform binary encoding on each quantization level; combine and output all the generated binary representations to form the final digital signal.

[0047] To achieve the above object, the present invention also provides the following technical solutions:

[0048] A control method for a non-uniform quantization hybrid analog-to-digital converter, the non-uniform quantization hybrid analog-to-digital converter control method comprising:

[0049] The data processing and control module performs denoising processing on the input continuous analog signal;

[0050] The analog signal sampling module samples according to the Nyquist law, and converts the analog quantity that changes continuously with time into an analog quantity that is discrete in time;

[0051] The non-uniform quantization module determines the quantization interval according to different intervals of the signal, and discretizes the sampled continuous analog signal; the data encoding module represents the quantized result in binary encoding and outputs it by the hybrid analog-to-digital converter.

[0052] The data processing and control module of the present invention performs denoising on the input continuous analog signal to improve the signal-to-noise ratio (SNR) of the signal, reduce environmental noise and interference, and make the signal clearer and more stable; provides an accurate clock signal to synchronize the signal acquisition and output processing processes, ensuring the accuracy and real-time performance of data processing. The achieved significance: The denoising process can effectively improve the signal quality, reduce errors, and provide a more reliable basis for subsequent sampling and quantization. This is crucial for high-precision applications (such as medical, industrial, and communication, etc.); the accurate clock signal synchronization can eliminate data misalignment caused by inaccurate sampling timing, ensure the stability and consistency of data processing, and thus improve the performance of the entire system. The analog signal sampling module converts the analog signal that changes over time into a discrete analog signal in time. The sampling process follows the Nyquist law to ensure the effectiveness of signal sampling and avoid aliasing. The achieved significance: Through compliant sampling, the main features and information of the input signal can be faithfully retained, providing accurate data for subsequent quantization and encoding steps. This is a necessary step to achieve high-quality digital signals; the discretized signal reduces the amount of data, making subsequent data processing and encoding more efficient, while reducing storage and bandwidth requirements. The non-uniform quantization module 3 determines the quantization interval according to different intervals of the signal, can dynamically adjust the quantization precision, and discretizes it non-uniformly according to the signal characteristics to improve the resolution of important signal intervals. The achieved significance: Through non-uniform quantization, while ensuring the key signal information, the redundant information in the low-importance area can be reduced, thus effectively using the storage space and reducing the amount of data; through higher resolution quantization for important areas, a more accurate signal representation can be achieved, improving the quality and effectiveness of the final digital signal, and contributing to the accuracy of subsequent processing. The data encoding module converts the quantized signal result into a binary coding form and outputs it by the hybrid analog-to-digital converter for subsequent processing and applications. The achieved significance: The standard binary coding form enables the signal to be conveniently processed, stored, and transmitted digitally, with strong compatibility and easy integration into various digital systems; the encoded signal can be further applied to various subsequent operations such as digital signal processing, data analysis, and decision-making, supporting a wider range of application scenarios and system integrations. Description of the Drawings

[0053] Figure 1 It is a schematic diagram of the functional modules of an embodiment of the non-uniform quantization hybrid analog-to-digital converter of the present invention;

[0054] Figure 2 It is a schematic diagram of the 13-segment broken line of the A-law compression algorithm in an embodiment of the control method of the non-uniform quantization hybrid analog-to-digital converter of the present invention;

[0055] Figure 3 It is a schematic diagram of the step flow of an embodiment of the control method of the non-uniform quantization hybrid analog-to-digital converter of the present invention;

[0056] Figure 4 Schematic diagram of the step flow for denoising the continuous analog signal input for an embodiment of the non-uniform quantization hybrid analog-to-digital converter control method of the present invention;

[0057] Figure 5 Schematic diagram of the step flow for converting an analog quantity that varies continuously over time into a discrete analog quantity in time for an embodiment of the non-uniform quantization hybrid analog-to-digital converter control method of the present invention;

[0058] Figure 6 Schematic diagram of the step flow for discretizing the sampled continuous analog signal for an embodiment of the non-uniform quantization hybrid analog-to-digital converter control method of the present invention;

[0059] Figure 7 Schematic diagram of the structure of an embodiment of the electronic device of the present invention;

[0060] Figure 8 Schematic diagram of the structure of an embodiment of the storage medium of the present invention. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] The terms "first", "second", and "third" in the present invention are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products, or devices.

[0063] References to "embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0064] As Figure 1 shown, this embodiment provides an embodiment of a non-uniform quantization hybrid analog-to-digital converter. In this embodiment, the non-uniform quantization hybrid analog-to-digital converter includes: a data processing and control module 1, an analog signal sampling module 2, a non-uniform quantization module 3, and a data encoding module 4;

[0065] Among them, the data processing and control module 1 performs denoising processing on the input continuous analog signal to improve the signal-to-noise ratio of the signal; and provides an accurate clock signal to synchronize the signal acquisition and output processing processes; the analog signal sampling module 2 converts the continuously varying analog quantity over time into a discrete analog quantity in time, and the sampling process follows the Nyquist law; the non-uniform quantization module 3 determines the quantization interval according to different intervals of the signal and discretizes the sampled continuous analog signal; the data encoding module 4 represents the quantized result in binary coding and outputs it by the hybrid analog-to-digital converter.

[0066] Preferably, the data processing and control module 1 in this embodiment performs denoising processing on the input continuous analog signal to improve the signal-to-noise ratio (SNR) of the signal, reduce environmental noise and interference, and make the signal clearer and more stable; provide an accurate clock signal to synchronize the signal acquisition and output processing processes, and ensure the accuracy and real-time performance of data processing. The significance achieved: The denoising processing can effectively improve the quality of the signal, reduce errors, and provide a more reliable basis for subsequent sampling and quantization. This is crucial for high-precision applications (such as medical, industrial, and communication, etc.); the accurate clock signal synchronization can eliminate data misalignment caused by inaccurate sampling timing, ensure the stability and consistency of data processing, and thus improve the performance of the entire system. The analog signal sampling module 2 converts the analog signal that changes over time into an analog signal that is discrete in time. The sampling process follows the Nyquist law to ensure the effectiveness of signal sampling and avoid aliasing. The significance achieved: Through compliant sampling, the main features and information of the input signal can be faithfully retained, providing accurate data for subsequent quantization and encoding steps. This is a necessary step to achieve high-quality digital signals; the discretized signal reduces the amount of data, making subsequent data processing and encoding more efficient, while reducing storage and bandwidth requirements. The non-uniform quantization module 3 determines the quantization interval according to different intervals of the signal, can dynamically adjust the quantization accuracy, and discretizes it non-uniformly according to the signal characteristics to improve the resolution of important signal intervals. The significance achieved: Through non-uniform quantization, while ensuring the key signal information, redundant information in low-importance regions can be reduced, thus effectively using storage space and reducing the amount of data; through higher-resolution quantization for important regions, a more accurate signal representation can be achieved, improving the quality and effectiveness of the final digital signal, and contributing to the accuracy of subsequent processing. The data encoding module 4 converts the quantized signal result into a binary coding form and outputs it through the hybrid analog-to-digital converter for subsequent processing and applications. The significance achieved: The standard binary coding form enables the signal to be conveniently processed, stored, and transmitted digitally, with strong compatibility and easy integration into various digital systems; the encoded signal can be further applied to various subsequent operations such as digital signal processing, data analysis, and decision-making, supporting a wider range of application scenarios and system integrations.

[0067] In summary, through the collaborative work of these four modules, the non-uniform quantization hybrid analog-to-digital converter of this embodiment realizes an efficient analog signal digitization process. The data processing and control module 1 provides precise sampling and denoising processing of the signal, improving the system's response ability to the signal; the analog signal sampling module 2 ensures the accuracy and stability of signal conversion; the non-uniform quantization module 3 optimizes the efficiency and accuracy of signal quantization; and the data encoding module 4 provides an encoding form that facilitates digital signal processing and transmission. Overall, this system performs excellently in maintaining signal quality and effectiveness and is applicable to a wide range of high-precision application fields. The non-uniform quantization hybrid analog-to-digital converter first denoises the input continuous analog signal, samples it following the Nyquist law after denoising, then performs non-uniform quantization, and encodes the signal after quantization, and outputs the encoded digital signal.

[0068] The non-uniform quantization hybrid analog-to-digital converter of this embodiment performs denoising processing on the input signal, improving the signal-to-noise ratio, enhancing the signal quality, and avoiding distortion; non-uniform quantization can flexibly perform quantization according to the dynamic range of the signal, using a smaller quantization interval for low-amplitude signals and a larger quantization interval for high-amplitude signals, making full use of the signal's dynamic range and improving the utilization rate of the signal's dynamic range; for low-amplitude signals, non-uniform quantization can reduce quantization errors, thereby improving the signal-to-noise ratio and enhancing the signal quality; compared with uniform quantization, non-uniform quantization can better adapt to the statistical characteristics of the signal and reduce quantization distortion.

[0069] Furthermore, the data processing and control module 1 of this embodiment specifically includes:

[0070] A filter sub-module, which is used to denoise the analog signal input to the hybrid analog-to-digital converter, improving the reliability and accuracy of the signal and the signal-to-noise ratio;

[0071] A clock signal generator sub-module, which is used to generate an accurate clock signal to synchronize signal acquisition, control the conversion rate, and a high-quality clock signal can reduce noise and jitter, improving stability and anti-interference ability.

[0072] In summary, the filter sub-module in this embodiment denoises the input continuous analog signal, filtering out unnecessary frequency components, thereby improving the signal-to-noise ratio (SNR) of the signal. Significance achieved: By removing noise, the filter can ensure a clearer and more accurate signal input, providing a high-quality basis for subsequent signal sampling and quantization; the reliability and accuracy of the signal are crucial in fields such as industry, healthcare, and communication; improving the signal quality can effectively reduce the risk of misjudgment in subsequent processing, especially when measuring important parameters, which has a direct impact on the safety and effectiveness of the device. The clock signal generator sub-module generates a high-precision clock signal for synchronizing signal acquisition and controlling the conversion rate. Significance achieved: The precise clock signal can ensure the timing consistency of the signal acquisition process, avoid data misalignment, making the sampled data more reliable. It is particularly important in high-frequency signal processing, which can effectively improve the reduction degree of digital signals; a high-quality clock signal can reduce the uncertainty introduced by clock jitter and noise, thereby improving the overall system stability and anti-interference ability; it is particularly important for applications that need to operate in unstable or noisy environments, such as communication devices or industrial monitoring systems.

[0073] Through the efficient combination of the filter sub-module and the clock signal generator sub-module, the data processing and control module 1 in this embodiment realizes the effective processing of analog signals and high-quality clock synchronization. The filter sub-module improves the reliability and accuracy of the signal, reduces the influence of noise, thereby laying a solid foundation for subsequent signal processing. The clock signal generator sub-module enhances the system stability and working efficiency by providing a precise clock signal. Overall, these two sub-modules jointly improve the performance and application breadth of the hybrid analog-to-digital converter, especially playing an important role in scenarios with extremely high requirements for data quality and real-time performance. The data processing and control module can denoise the input continuous analog signal, improve the signal-to-noise ratio, enhance the reliability and accuracy of the signal, improve the usability of the signal, thereby increasing the sampling efficiency; through a high-frequency clock signal, it synchronizes the signal acquisition and output processing processes, and reduces noise and jitter, improving stability and anti-interference ability, thereby enhancing the performance of the ADC.

[0074] Furthermore, the analog sampling module 2 specifically includes:

[0075] The function input sub-module is used to input a continuous analog signal before sampling. The continuous analog signal is a continuous function containing multiple frequency components; according to the Nyquist theorem, a sampling frequency is selected to ensure that this frequency is at least twice the highest frequency component in the signal;

[0076] The sampling execution sub-module is used to set the sampling time and extract the sampling value; according to the selected sampling frequency, set the sampling time, and extract the voltage value of the analog signal at each set sampling time to obtain discrete signal samples;

[0077] The signal composition sub-module is used to obtain a set of discrete sample values, which together constitute a discrete signal, representing the voltage value of the original continuous signal at the specified sampling time.

[0078] Preferably, the function input sub-module of this embodiment selects an appropriate sampling frequency according to the Nyquist theorem to ensure that this frequency is at least twice the highest frequency component in the signal, and calculates the obtained sampling frequency; The significance achieved: By identifying the frequency components of the input signal, it can provide necessary information for the subsequent sampling process and ensure the effectiveness of the sampling strategy; Selecting a sampling frequency that conforms to the Nyquist criterion can effectively avoid aliasing phenomena and ensure that key signal information will not be lost during the digitization process. The sampling execution sub-module extracts the voltage value of the analog signal at each set sampling time, converting the continuous signal into discrete signal samples. The significance achieved: Ensure accurate sampling of the signal at the given sampling time, so that each sample can accurately reflect the state of the continuous signal; By setting appropriate sampling frequencies and times, the signal conversion process can be optimized to ensure the efficiency and reliability of data acquisition. The signal composition sub-module integrates the extracted sampling values to form a set of discrete sample values, and these sample values together constitute a discrete signal. The significance achieved: By forming a discrete signal, the original continuous signal is converted into a series of digital sample values, providing data support for the subsequent non-uniform quantization and encoding modules; The generated discrete signal can be conveniently used in subsequent digital signal processing, analysis, and applications, forming the basis of the digital signal processing chain, making it applicable to various application scenarios such as data storage, transmission, and processing.

[0079] In summary, the analog sampling module 2 of this embodiment realizes the effective conversion from a continuous analog signal to a discrete signal through the collaborative work of these three sub-modules. The function input sub-module is responsible for signal input and sampling frequency selection to ensure compliance with the Nyquist theorem; the sampling execution sub-module is responsible for setting the sampling time and extracting samples to efficiently capture the signal; the signal composition sub-module integrates the sample values into a discrete signal, laying the foundation for subsequent processing. This process is one of the core functions of the analog-to-digital converter, providing a reliable digital representation for signal processing, transmission, and analysis.

[0080] Furthermore, the non-uniform quantization module 3 specifically includes:

[0081] The signal compression sub-module is used to perform a non-linear transformation on the input signal, using a compression function to compress a wide range of signal values into a smaller range, so that small signal amplitudes receive more attention;

[0082] The compression mapping sub-module is used to determine the size of each quantization interval according to the compressed signal distribution, and map the compressed signal values to a series of equally spaced discrete points;

[0083] The amplitude distribution sub-module is used to reverse the operation of the compressor at the receiving end, and restore the quantized signal to the amplitude distribution of the original signal through an expander.

[0084] Preferably, the signal compression sub-module of this embodiment uses a compression function to perform a non-linear transformation on the input signal, compressing the values in a large range of the signal into a smaller range. This transformation helps to enhance the perceptibility of small-amplitude signals. Significance achieved: Through the compression function, small-amplitude signal components receive more attention during the quantization process, and even weak signals can be better processed and analyzed in subsequent processing. This is particularly important when dealing with weak signals with noise; since the signal value range is compressed, the amount of data transmitted and stored decreases, thus effectively utilizing system resources, especially when the bandwidth or storage capacity is limited. The compression mapping sub-module determines the size of each quantization interval according to the compressed signal distribution, and maps the compressed signal values to a series of equally spaced discrete points for quantization. Significance achieved: By reasonably dividing the quantization intervals, this sub-module ensures higher quantization accuracy in important signal regions, making the quantization result more representative. This non-uniform quantization helps to retain more information during signal quantization; it makes the representation of low-amplitude signals and important signal parts more refined, reduces information loss, improves the overall signal quality, and lays a good foundation for subsequent signal processing. In the receiving end, the amplitude distribution sub-module is responsible for performing the expansion operation, and restoring the quantized signal to the amplitude distribution of the original signal through an expander. Significance achieved: This sub-module ensures that the signal can be restored to its original state as much as possible during the decoding stage, improving the decoding efficiency and accuracy of the entire analog-to-digital converter; restoring the amplitude distribution of the signal makes subsequent processing more accurate, and can improve the signal quality in a wider range of applications, especially in signal parsing after data transmission and storage, ensuring the information integrity of the signal.

[0085] In summary, through the collaborative work of the signal compression sub-module, the compression mapping sub-module, and the amplitude distribution sub-module, the non-uniform quantization module 3 of this embodiment enhances the effectiveness of the signal quantization process. The signal compression sub-module enables weak signals to receive more attention during processing. The compression mapping sub-module ensures quantization accuracy and data validity, while the amplitude distribution sub-module improves the signal recovery ability. This series of operations not only improves the reliability and effectiveness of the signal during quantization but also optimizes the resource utilization for data storage and transmission, thus better serving the needs of complex signal processing. The quantization interval of non-uniform quantization varies with the magnitude of the signal sampling value. When the signal sampling value is small, the quantization interval is correspondingly small to provide more quantization levels in the small-signal range, thereby reducing the quantization error and improving the quantization signal-to-noise ratio. When the signal sampling value is large, the quantization interval is correspondingly increased to reduce the number of quantization levels in the large-signal range.

[0086] In summary, by using non-uniform quantization in this embodiment, it is possible to improve the quantization accuracy and signal-to-noise ratio of small signals while maintaining the total number of quantization levels unchanged. At the same time, sufficient quantization levels are maintained in the large-signal range to avoid signal distortion. The non-uniform quantization method helps to more accurately recover the original signal.

[0087] Further, the signal compression sub-module specifically includes:

[0088] A signal range identification unit, which is used to analyze the input continuous analog signal and determine the amplitude range of the input continuous analog signal. The compression function uses the A-law compression algorithm.

[0089] The expression of the A-law compression algorithm is:

[0090]

[0091] In the formula, x is the normalized input voltage of the compressor, y is the normalized output voltage of the compressor, A is the companding parameter, representing the degree of compression. In the international standard, A = 87.6 is taken. From the above formula, it can be seen that it has a linear characteristic for small signals and an approximate logarithmic characteristic for large signals. The A-law can be approximated by 13 line segments (equivalent to A = 87.6), which is convenient for implementation with digital circuits (specific reference appendix Figure 2 ); The non-uniform quantization signal-to-noise ratio of the A-law compression characteristic:

[0092]

[0093] Where is the signal-to-noise ratio improvement:

[0094]

[0095] Where x e is the effective value of the signal;

[0096] A compression function application unit for applying a compression function to each sampled value of an input continuous analog signal;

[0097] An output range control unit for normalizing the processed compressed value to be within a specific range; after processing, the resulting continuous analog signal represents a signal that has undergone non-linear compression processing. The compressed continuous analog signal will then be passed to the compression mapping sub-module for quantization and processing.

[0098] Preferably, the signal range identification unit of this embodiment analyzes the input continuous analog signal, identifies the amplitude range of the signal, and determines the minimum and maximum values of the signal, providing necessary information for subsequent signal compression processing. Significance achieved: Accurately understanding the signal range can help design a reasonable compression strategy to ensure the effective use of the compression parameter (A); understanding the dynamic range of the signal enables better normalization and compression, thereby enhancing the effect of subsequent processing; by analyzing the signal distribution, the goal is to identify signal regions that require key attention, optimize the compression process, and thus better capture and process small signal amplitudes. The compression function application unit applies the A-law compression algorithm to non-linearly compress the previously identified input signal (x), and performs transformation using a formula. This compression characteristic exhibits a linear characteristic for small signals and a logarithmic characteristic for large signals. Significance achieved: Since small signals retain the linear characteristic, they can better reflect the actual small-amplitude input, ensuring that weak signals are not ignored in subsequent processing; the conversion of large signals to logarithmic characteristics means that at high-amplitude signals, compression can reduce the dynamic range of the signal, thereby avoiding distortion caused by excessive amplitude during the digitization process and improving the overall signal quality. The output range control unit normalizes the compressed signal so that its output value is limited within a certain range (such as [0, 1] or [-1, 1]); by adjusting the output value, the stability and consistency of subsequent signal processing can be controlled to a certain extent. Significance achieved: Ensuring that the compressed signal is within a unified range facilitates subsequent quantization, processing, and analysis; the normalized signal improves the processing accuracy of digital circuits for signals, contributing to achieving a higher quantization level; by reasonably controlling the output range, signal overflow can be avoided, thereby ensuring that key information is not lost during signal processing and improving the reliability and integrity of the signal during subsequent mapping and quantization processes.

[0099] In summary, through the coordinated work of the signal range identification unit, compression function application unit, and output range control unit, the signal compression sub-module in this embodiment completes the non-linear compression process of the input signal. The signal range identification unit provides the necessary basic data for compression, the compression function application unit realizes the core non-linear transformation of the signal, and the output range control unit ensures that the signal adapts to subsequent processing requirements. The comprehensive effect of this module not only improves the effectiveness and accuracy of signal compression but also provides a reliable information basis for subsequent quantization and processing, thus playing an important role in the overall signal processing system.

[0100] This embodiment adopts the A-law compression algorithm to achieve compression by non-uniform quantization of the input signal. It uses a non-linear mapping method to convert continuous analog signals into discrete digital signals, resulting in smaller quantization intervals for small signals and larger quantization intervals for large signals. Through the characteristics of non-uniform quantization and approximate logarithm, the quantization interval increases with the increase of the signal amplitude in the A-law compression algorithm, which helps to maintain high precision for small signals and can better process signals of different amplitudes, especially small-amplitude signals. Using this algorithm effectively reduces the number of bits required for data transmission while ensuring signal quality.

[0101] Furthermore, the compression mapping sub-module specifically includes:

[0102] The signal characteristic identification unit is used to obtain the continuous analog signal data after being processed by the compression function, including all sample values after non-linear compression; it analyzes the distribution characteristics of the compressed signal using the probability density function to find features such as the signal peak value, average value, and variance.

[0103] The quantization interval determination unit is used to determine the size and number of each quantization interval according to the statistical characteristics of the signal distribution; set smaller quantization intervals near the signal values that appear frequently at high frequencies; set larger quantization intervals in areas where the signal values appear infrequently or are sparse in the compressed signal.

[0104] The discrete point mapping unit is used to quantize each compressed continuous analog signal value using the determined quantization intervals, map the compressed signal values to discrete points according to the quantization intervals, and convert the mapping result into a quantized discrete signal for output. The discrete signal is represented by the median or central value of each quantization interval.

[0105]

[0106] Among them, y j represents the median value of the j-th quantization interval, y min and y max are the minimum and maximum quantization values of the continuous analog signal respectively, and Q[i] is the discrete point.

[0107] Preferably, the signal characteristic recognition unit of this embodiment obtains the signal data after being processed by the compression function, analyzes the distribution characteristics of the compressed signal by using the probability density function, and recognizes the statistical characteristics such as the peak value, average value, and variance of the signal. Significance achieved: Understanding the signal distribution can assist in the design of subsequent quantization processes; by identifying the peak value and wide distribution of the signal, quantization resources can be appropriately allocated within different amplitude intervals. Important signals (high-frequency parts) can be encoded with finer quantization, while unimportant or sparse signals can be processed with larger quantization steps, thereby optimizing the quantization effect; through statistical analysis, non-uniform quantization can be better achieved, enabling more detailed representation of frequently occurring important signal values, and thus maintaining high signal quality. The quantization interval determination unit determines the size and number of each quantization interval according to the signal distribution characteristics; smaller quantization intervals are set in regions with higher signal frequencies, while larger quantization intervals are set in regions with lower frequencies or sparse signals. Significance achieved: Through this mechanism, efficient non-uniform quantization of the signal can be performed, focusing on protecting and expressing the information of important signal parts, thereby reducing information loss and improving the quality of the encoded data; based on the existing analysis of compressed signal characteristics, reasonably allocating the size of the quantization interval can avoid the problem of unclear important signal characteristics caused by overly rough quantization, and optimize data storage and transmission resources. The discrete point mapping unit uses the previously determined quantization intervals to perform quantization mapping on each compressed continuous analog signal value, and finally converts these values into discrete quantization signals. Significance achieved: Through this mapping, the compressed signal data is converted into discrete signals that can be used for processing and storage. Discrete signals are usually more suitable for digital circuits and computer systems, facilitating subsequent processing and analysis; during the mapping process, the use of the intermediate value of each quantization interval is emphasized to ensure that the quantization signal can reflect the characteristics of the original signal as much as possible, helping to reduce information loss and improve the accuracy of signal recovery.

[0108] In summary, the compression mapping sub-module of this embodiment realizes the effective non-uniform quantization of the signal through the collaborative work of the signal characteristic recognition unit, the quantization interval determination unit, and the discrete point mapping unit. The signal characteristic recognition unit provides the basis for signal analysis, the quantization interval determination unit flexibly designs the quantization interval according to the analysis results, and the discrete point mapping unit effectively maps the compressed signal into discrete signals. Overall, this module optimizes the signal processing process, improves the effectiveness and quality of information, and lays a solid foundation for subsequent signal recovery and processing.

[0109] Furthermore, the signal characteristic recognition unit specifically includes:

[0110] A signal data acquisition subunit, configured to extract all sample values from a continuous analog signal processed by a compression function, divide the extracted continuous analog signal data into several small intervals, and count the number of data points in each interval; the height of each bar of the histogram corresponds to the number of data points in that interval;

[0111] A probability density function calculation subunit, configured to convert the height of the histogram into a probability density function by dividing the number of sample points in each interval by the product of the total number of samples and the interval width;

[0112]

[0113] Where: is the number of data points in the th interval, is the total number of samples, is the width of each interval;

[0114] A statistical characteristic calculation subunit, configured to calculate the peak value, average value, and variance;

[0115] Peak value: Identify the position of the maximum value point of the probability density function, which represents the value where the continuous analog signal appears most concentratedly, that is, the "peak value" of the signal;

[0116] Average value (expected value): The average value reflects the central position of the signal and represents the "average" performance of the signal values;

[0117] Variance: Reflects the degree of dispersion of the distribution of the continuous analog signal. The larger the value, the more dispersed the distribution of data points around the average value.

[0118] Preferably, the signal data acquisition subunit of this embodiment extracts all sample values from the continuous analog signal processed by the compression function, divides them into several small intervals, and counts the number of data points in each interval, so as to obtain the preliminary distribution characteristics of the signal values. Significance achieved: Displaying the distribution of the signal through a histogram helps to quickly identify in which intervals the signal is more concentrated and provides basic data for the subsequent calculation of the probability density function; it can reveal which values in the signal are common and which signals have a higher occurrence frequency in specific intervals, thus providing a direction for non-uniform quantization. The probability density function calculation subunit converts the number of data points in the histogram into a probability density function (PDF), which reflects the relative probability of each interval and ensures that the total area under the curve is 1. Significance achieved: Converting the quantification into probability density improves the standardization and consistency of subsequent analysis. With the PDF, the distribution characteristics of the signal can be described more precisely; the PDF is the basis for further statistical characteristic calculations (such as peak value, average value, variance, etc.). Through this function, the characteristics of the signal can be quantified and visual data support can be provided. The statistical characteristic calculation subunit calculates the peak value, average value (expected value), and variance of the signal to determine the key statistical characteristics of the signal. Significance achieved: By calculating the peak value, the position where the signal appears concentrated can be understood, which helps to design the quantization interval. The average value provides the central tendency of the signal, while the variance reveals the degree of dispersion of the signal around the average value, respectively reflecting the concentration and extensiveness of the signal; these statistical characteristics provide a basis for the design of non-uniform quantization. Understanding the statistical characteristics of the signal can help to reasonably allocate the size of the quantization interval, better protect important signal information, and improve the efficiency and quality of quantization.

[0119] In summary, through the collaborative work of the signal data acquisition subunit, the probability density function calculation subunit, and the statistical characteristic calculation subunit, the signal characteristic recognition unit of this embodiment realizes a comprehensive feature analysis of the compressed signal. Each subunit plays a key role, from the extraction and arrangement of the original data, the construction of a standardized probability density function to the in-depth calculation of statistical characteristics, forming a systematic analysis framework. This framework enables more effective capture of important information in signal processing and lays a good foundation for subsequent quantization and signal processing.

[0120] Furthermore, the quantization interval determination unit specifically includes:

[0121] A signal characteristic recognition subunit, configured to acquire continuous analog signal data processed by a compression function, and extract all non-linearly compressed sample values; use the probability density function to analyze the distribution characteristics of the continuous analog signal, including finding out statistical characteristics such as peak value, average value, and variance;

[0122] Frequency analysis and data distribution subunit, which is used to observe the height of each interval in the histogram and find the intervals with higher (high frequency) and lower (low frequency) signal occurrence frequencies when analyzing signal data and its frequency distribution;

[0123] Quantization interval setting subunit, which is used to set the quantization interval according to the characteristics of signal distribution, especially the different distribution situations in high-frequency and low-frequency regions; and generate the final quantization pattern according to the determined quantization interval;

[0124] High-frequency signal region: In the intervals where the signal values appear with higher frequencies, smaller quantization intervals are set; by setting smaller intervals, more discretization details can be provided for these important signal values; Example: If the signal frequently appears near a certain specific value, the quantization interval near this value can be set to a width of, for example, 0.1 to capture more information.

[0125] Low-frequency signal region: In the regions where the signal values are sparse or appear with lower frequencies, larger quantization intervals are set. This is because the signal changes little in these regions, and larger quantization intervals can effectively reduce the quantization complexity without significantly affecting the expression of overall information; Example: For signal values that rarely appear, the quantization interval can be set to a width of 1.0, which can reduce the storage overhead while maintaining approximate information integrity.

[0126] Preferably, the signal characteristic identification subunit of this embodiment extracts all sample values from the continuous analog signal processed by the compression function; analyzes the signal data using the probability density function (PDF) to identify the main statistical characteristics (such as peaks, averages, variances, etc.). Significance achieved: Provides the basic characteristic information of the signal, which will guide the subsequent quantization interval setting, signal processing, and analysis; Identifies the main characteristics of the signal (such as central tendency, dispersion), provides a basis for non-uniform quantization, and ensures that important features will not be lost during the quantization process. The frequency analysis and data distribution subunit analyzes the frequency distribution of the signal, especially observes the number of samples in each interval through the histogram to determine the high-frequency and low-frequency regions of the signal. Significance achieved: Provides a necessary basis to determine the width and number of quantization intervals according to the signal occurrence frequency. The high-frequency region requires finer quantization, while the low-frequency region can be simplified; Can reasonably allocate system resources, make the storage and transmission of signal data more efficient, and reduce unnecessary overhead. The quantization interval setting subunit sets the quantization interval according to the signal distribution situation, where the high-frequency region is set to small intervals to capture details, and the low-frequency region is set to large intervals to reduce data complexity. Significance achieved: Ensures the retention of more important signal information and improves the overall quality of the data by adopting appropriate quantization strategies for different regions; Effectively reduces the over-quantization of low-frequency signals, thereby saving storage space and computing resources without affecting the availability of overall information.

[0127] In summary, through the collaborative action of the signal characteristic identification subunit, the frequency analysis and data distribution subunit, and the quantization interval setting subunit, the quantization interval determination unit of this embodiment realizes the complete process from the extraction and analysis of the original signal to the practical quantization setting. Each subunit plays its respective role in the whole process. By extracting and analyzing signal characteristics, identifying frequency distributions, and optimizing quantization strategies, it ensures the integrity and availability of important information while achieving signal compression and processing. Such a design brings more efficient signal processing results and also provides more favorable conditions for the transmission and storage of signals in practical applications.

[0128] Further, the data encoding module 4 specifically includes:

[0129] The quantization level confirmation sub-module is used to ensure that each quantization interval corresponds to a specific digital representation, that is, the quantization level; by establishing a mapping relationship from the quantization value to the digital value, the number corresponding to each quantization value is determined;

[0130] The quantization level allocation sub-module is used to assign a number to each interval, and the quantized sampled value corresponds to a number;

[0131] The binary encoding sub-module is used to convert the digital number of each quantization value into a binary encoding, use the number of bits to perform binary encoding on each quantization level; combine and output all the generated binary representations to form the final digital signal.

[0132] Preferably, the quantization level confirmation sub-module of this embodiment establishes a corresponding digital representation for each quantization interval, usually a certain number or level; by analyzing the quantization result, the digital representation corresponding to each quantization value is determined. Significance achieved: Ensure that each quantized sampled value has a unique digital representation, so that the signal maintains its structure and characteristics when converted to digital; provide a clear mapping relationship to ensure that the digital representation can be accurately obtained from the quantization value during the encoding process, thereby achieving efficient encoding. Only by accurately confirming the quantization level can subsequent data processing and transmission proceed smoothly. The quantization level allocation sub-module assigns a unique number to each quantization interval, and this number can be used for the digital representation of the quantized signal value, so that each sampled value corresponds one-to-one with its corresponding quantization level. Significance achieved: By converting the quantization value into a digital number, the subsequent processing process is simplified, providing a clearer and more structured input for the encoding module; by uniformly numbering each interval, a standard is established, enabling the system to follow consistent rules when processing different signals, thus ensuring compatibility and scalability. The binary encoding sub-module converts each digital number into a binary code, uses a specific number of bits to represent each quantization level, and combines all the generated binary representations into the final digital signal. Significance achieved: Binary encoding is the most effective and common format in computer processing. Converting the quantized signal into binary can reduce storage space and transmission energy consumption, and improve signal processing efficiency; the generated binary signal can be further processed, stored, or transmitted, and is applicable to subsequent digital signal processing, signal compression, and communication system scenarios.

[0133] In summary, each sub-module of the data encoding module 4 in this embodiment works together to jointly achieve the conversion process from the quantized signal to the binary digital signal. Each sub-module not only technically undertakes specific functions, but also constructs a rigorous and systematic process through a series of steps such as mapping, numbering, and encoding. Generally speaking, the design of this module not only ensures the accuracy and integrity of the signal, but also provides strong support for the subsequent storage, transmission, and processing of the signal.

[0134] As Figure 3 shown, this embodiment also provides an embodiment of the non-uniform quantization hybrid analog-to-digital converter control method. In this embodiment, the non-uniform quantization hybrid analog-to-digital converter control method is applied to the non-uniform quantization hybrid analog-to-digital converter in the above embodiment. The specific steps of this non-uniform quantization hybrid analog-to-digital converter control method include:

[0135] Step S1: The data processing and control module performs denoising processing on the input continuous analog signal to improve the signal-to-noise ratio of the signal; and provides an accurate clock signal to synchronize the signal acquisition and output processing processes;

[0136] Step S2: The analog signal sampling module samples in accordance with the Nyquist law, converting the continuously varying analog quantity over time into a discrete analog quantity in time.

[0137] Step S3: The non-uniform quantization module determines the quantization interval according to different intervals of the signal, discretizing the sampled continuous analog signal; the data encoding module represents the quantized result in binary encoding and outputs it through the hybrid analog-to-digital converter.

[0138] Preferably, for the data processing and control module in step S1 of this embodiment, noise in the input analog signal is eliminated through filtering or other signal processing techniques, thereby improving the signal-to-noise ratio (SNR) of the signal; an accurate clock signal is generated to ensure the synchronization of signal acquisition and subsequent processing. Significance achieved: The denoising process improves the quality of the signal, making subsequent sampling, quantization, and encoding more accurate; the high-quality clock signal ensures the coordinated and synchronous operation of all modules, thus avoiding signal loss or misalignment. This synchronization is a prerequisite for achieving high-precision and high-reliability signal conversion. In step S2, the analog signal sampling module follows the Nyquist law, converting the continuously varying analog signal into a discrete signal in time, ensuring that the sampling frequency is at least twice the highest frequency of the signal. Significance achieved: Ensuring that important signal information is not lost during the sampling process, being able to accurately restore the original signal, which is the basic principle of digital signal conversion and is crucial for avoiding aliasing; discretizing the signal provides a data basis for the subsequent quantization and encoding stages, ensuring reliable input for subsequent processing steps. In step S3, the non-uniform quantization module and the data encoding module flexibly set the quantization interval according to the different interval characteristics of the signal, discretizing the sampled continuous analog signal, especially adopting different quantization strategies in the high-frequency and low-frequency signal regions; converting the quantized result into binary encoding to form the final digital signal output. Significance achieved: Non-uniform quantization can more flexibly adapt to the characteristics of the signal, providing higher resolution especially in regions where the signal changes violently, thereby better retaining useful information and reducing information loss; effectively converting the data into binary format not only makes subsequent storage and transmission more efficient but also provides a unified data format for signal processing; this process ensures that the finally output digital signal meets high standards in terms of accuracy, effectiveness, and storage efficiency, enabling the hybrid analog-to-digital converter to be widely applied in practical systems, such as audio processing, communication, sensors, and other fields.

[0139] In summary, through these three steps, the non-uniform quantization hybrid analog-to-digital converter in this embodiment achieves comprehensive processing of analog signals into digital signals. Each step has unique technical effects and important significance, jointly ensuring the quality, accuracy, and effectiveness of the signal during the conversion process, laying a solid foundation for subsequent digital processing, storage, and transmission. The overall process reflects the efficiency and effectiveness of the signal processing system and demonstrates the application potential of modern signal conversion technology. The control method of the non-uniform quantization hybrid analog-to-digital converter first denoises the analog signal to improve the signal-to-noise ratio of the signal and generates an accurate clock signal to ensure the synchronization of the entire conversion process; after sampling, it enters the non-uniform quantization module, dynamically adjusts the quantization interval according to different intervals of the signal, and finally the data encoding module converts the quantized structure into binary code to complete the conversion of the analog signal to the digital signal.

[0140] In the control method of the non-uniform quantization hybrid analog-to-digital converter in this embodiment, through denoising processing and precise sampling technology, the signal-to-noise ratio of the signal is improved; non-uniform quantization makes the quantization process more in line with the statistical characteristics of the signal, and can improve the quantization accuracy of small signals and reduce quantization errors while keeping the total number of quantization levels unchanged.

[0141] Furthermore, as Figure 4 shown, the process of denoising the input continuous analog signal in step S1 specifically includes the following steps:

[0142] Step S11: Denoise the analog signal input to the hybrid analog-to-digital converter to improve the reliability and accuracy of the signal and increase the signal-to-noise ratio;

[0143] Step S12: Generate an accurate clock signal to synchronize signal acquisition, control the conversion rate, and a high-quality clock signal can reduce noise and jitter, improving stability and anti-interference ability.

[0144] In summary, for the denoising process in step S11 of this embodiment, various digital filters (such as low-pass filters, median filters, adaptive filters, etc.) are used to remove unnecessary noise from the input analog signal; through the denoising process, after reducing the interference components, the signal-to-noise ratio is improved, making the important signal components more prominent and clear. The achieved significance is that the denoising process directly improves the signal quality, making the signal more accurate in subsequent conversion and processing, reducing errors and distortions, and providing a reliable data basis for subsequent digital signal processing; improving the signal-to-noise ratio means that effective information can be extracted more efficiently and the ineffective interference can be reduced. This is particularly important in fields such as high-precision measurement, audio processing, and communication, which helps to improve the performance and stability of the entire system. Step S12 generates an accurate clock signal to generate a high-precision and low-jitter clock signal, which is used to synchronize signal acquisition and control the data conversion rate to ensure the coordinated operation of each module; a stable clock signal can reduce the signal noise and the influence of jitter, thereby improving the anti-interference ability of the system. The achieved significance is that the accurate clock signal ensures that signal acquisition is carried out at fixed time intervals, thereby improving the consistency and reliability of sampling and reducing the risk of information loss or distortion caused by inaccurate sampling; a high-quality clock signal provides a stable time reference for the signal processing process, thereby enhancing the anti-interference ability of the system. Especially when facing environmental interference or electrical noise between devices, it can effectively reduce the influence of these factors on the signal quality.

[0145] In summary, in the process of denoising the input continuous analog signal in this embodiment, steps S11 and S12 effectively improve the performance of the entire system through two aspects: denoising and clock generation respectively. The denoising process ensures the clarity and accuracy of the signal, laying a solid foundation for the subsequent digitization process, while the stable clock signal maintains the synchronization and consistency of signal acquisition, ultimately promoting the efficiency and reliability of signal processing and conversion. Overall, these two steps are the key links to ensure the normal and efficient operation of the hybrid analog-to-digital converter.

[0146] Furthermore, as Figure 5 shown, the process of converting the analog quantity that changes continuously over time into a discrete analog quantity in step S2 specifically includes the following steps:

[0147] Step S21: Before sampling, the input continuous analog signal is a continuous function containing multiple frequency components; according to the Nyquist law, a sampling frequency is selected to ensure that this frequency is at least twice the highest frequency component in the signal;

[0148] Step S22: Set the sampling moment and extract the sampling value; according to the selected sampling frequency, set the sampling moment, and extract the voltage value of the analog signal at each set sampling moment to obtain discrete signal samples;

[0149] Step S23: Obtain a set of discrete sample values, which together constitute a discrete signal, representing the voltage values of the original continuous signal at specified sampling instants.

[0150] Preferably, in step S21 of this embodiment, the sampling frequency is selected. Based on the Nyquist law, the sampling frequency is selected to ensure that the sampling frequency is at least twice the highest frequency component in the signal; determining an appropriate sampling frequency can avoid aliasing and ensure that the characteristics of the original signal can be accurately reflected during the discretization process. The significance achieved: Selecting an appropriate sampling frequency ensures that the original signal can be accurately reconstructed and restored, avoiding information loss introduced by improper sampling and resulting in signal distortion; conforming to the Nyquist law is one of the most basic principles in digital signal processing, and ensuring the accuracy of the signal during the digitization process is the basis for high-quality signal processing. In step S22, the sampling instants are set and the sample values are extracted. According to the selected sampling frequency, the actual sampling instants are determined to obtain signal values at regular intervals; at each set sampling instant, the corresponding voltage value is extracted from the continuous analog signal and recorded. The significance achieved: By sampling at precise instants, multiple sample values can be obtained throughout the signal time series, and these samples form a discrete signal, laying a foundation for subsequent processing; sampling according to the set instants helps to capture the dynamic changes of the signal during the continuous change process, ensuring that the discrete samples can reflect the important characteristics of the signal. In step S23, the discrete sample values are obtained by integrating the extracted voltage values to form a set of discrete samples, which together constitute a discrete signal. The significance achieved: These discrete sample values can effectively represent the characteristics of the original continuous signal at specific time points through means such as encoding, making it suitable for subsequent digital processing; the formed discrete signal provides basic data for subsequent quantization, encoding, and signal processing, enabling subsequent analysis, storage, and transmission to be carried out in the digital domain, facilitating the implementation of various applications of digital signal processing.

[0151] In summary, the above steps in this embodiment ensure the process of effectively and efficiently converting a continuous analog signal into a discrete signal. Each step jointly promotes the smooth progress of the conversion of the analog signal to the digital signal, providing a reliable foundation for subsequent digital signal processing, analysis, and applications. The implementation of these steps directly affects the quality of the final digital signal, so it plays a crucial role in various signal processing and data acquisition systems.

[0152] Furthermore, as Figure 6 shown, the process of discretizing the sampled continuous analog signal in step S3 specifically includes the following steps:

[0153] Step S31: Perform a non-linear transformation on the input signal. Using a compression function, compress the signal values in a large range into a smaller range, so that more attention is paid to the small signal amplitudes;

[0154] Step S32: Determine the size of each quantization interval according to the compressed signal distribution, and map the compressed signal values to a series of equally spaced discrete points;

[0155] Step S33: Reverse the operation of the compressor at the receiving end, and restore the quantized signal to the original signal amplitude distribution through an expander.

[0156] Preferably, for the non-linear transformation and compression function in step S31 of this embodiment, a non-linear compression function is used to compress a large range of signal values into a smaller range, optimizing the dynamic range of the signal and paying more attention to the changes in the small-signal amplitude; small-signal amplitudes may be ignored in subsequent processing, and compression can reduce the influence of large signals, thereby enhancing the relative importance and recognizability of small signals. Significance achieved: The non-linear transformation amplifies small signals, which helps to improve the quantization accuracy, reduce quantization errors, and improve the accuracy of subsequent digital processing; during the signal processing process, especially in some specific applications (such as audio processing, medical signals), focusing on small signals can reduce signal distortion and maintain the original characteristics of the signal. Step S32 determines the quantization interval and discrete point mapping. Determine the size of each quantization interval according to the compressed signal distribution, optimizing the quantization process so that frequently occurring small signals are represented by appropriate multiples; map the compressed signal values to a series of equally spaced discrete points to form a quantized discrete signal. Significance achieved: Reasonably mapping the compressed signal to discrete points helps to effectively retain important information in the discretized signal and improve the quality of the quantized signal; dynamically adjusting the quantization interval according to the actual distribution of the signal makes the discrete signal more adaptable to actual application scenarios and improves the flexibility and applicability of the system. Step S33 reverse expander operation. At the receiving end, use the expander to perform the reverse operation, and restore the quantized signal to an amplitude distribution close to the original signal through correction and expansion; although the quantization process will inevitably introduce certain distortion, through the expander, the amplitude close to the original signal can be restored as much as possible, improving the usability of the signal. Significance achieved: In practical applications, the signal restored by the expander can better reflect the characteristics of the original signal, thereby improving the effect of subsequent processing; in some applications (such as audio signal, video signal processing), a good restoration function helps to maintain the integrity of the signal, improve the user experience, and enhance the overall performance of the device.

[0157] In summary, in the process of discretizing the sampled analog signal in this embodiment, an efficient and accurate signal processing framework is constructed through steps S31 to S33. Nonlinear compression, optimization of quantization intervals, and signal recovery enable more systematic retention of important information, reduction of distortion, and improvement of effectiveness in signal processing. The reasonable implementation of these steps is of crucial significance for obtaining high-quality digital signals and subsequent processing, and is applicable to multiple fields such as audio processing, communication systems, and image processing.

[0158] As Figure 7 shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 5 includes a processor 51 and a memory 52 coupled to the processor 51.

[0159] The memory 52 stores program instructions for implementing the non-uniform quantization hybrid analog-to-digital converter in any of the above embodiments.

[0160] The processor 51 is configured to execute the program instructions stored in the memory 52 to perform the non-uniform quantization hybrid analog-to-digital converter control method.

[0161] Among them, the processor 51 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 51 may be an integrated circuit chip with signal processing capabilities. The processor 51 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0162] Furthermore, Figure 8 is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium 6 of the embodiment of the present application stores program instructions 61 capable of implementing all the above methods. Among them, the program instructions 61 may be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0163] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0164] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only the implementation manner of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0165] The specific implementation manners of the invention have been described in detail above, but they are only examples. The present invention is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution to the invention is also within the scope of the present invention. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present invention should all be covered by the scope of the present invention.

Claims

1. A non-uniform quantization hybrid analog-to-digital converter, characterized in that, The non-uniform quantization hybrid analog-to-digital converter includes: A data processing and control module for denoising the input continuous analog signal to improve the signal-to-noise ratio of the signal; and providing an accurate clock signal to synchronize the signal acquisition and output processing processes; An analog signal sampling module for converting the analog quantity that changes continuously over time into an analog quantity that is discrete in time, and the sampling process follows the Nyquist law; A non-uniform quantization module for determining the quantization interval according to different intervals of the signal and discretizing the sampled continuous analog signal; A data encoding module for representing the quantized result in binary code and outputting it by the hybrid analog-to-digital converter; The non-uniform quantization module includes: A signal compression sub-module for performing a non-linear transformation on the input signal, using a compression function to compress a large range of signal values into a smaller range; A compression mapping sub-module for determining the size of each quantization interval according to the compressed signal distribution and mapping the compressed signal values to a series of equally spaced discrete points; An amplitude distribution sub-module for inversely performing the operation of the compressor at the receiving end and restoring the quantized signal to the original signal amplitude distribution through an expander; The compression mapping sub-module includes: A signal characteristic identification unit for obtaining the continuous analog signal data processed by the compression function, including all sample values after non-linear compression; analyzing the distribution characteristics of the compressed signal using the probability density function to find the signal peak, average value, and variance characteristics; A quantization interval determination unit for determining the size and number of each quantization interval according to the statistical characteristics of the signal distribution; setting a smaller quantization interval near the signal values that appear frequently at high frequencies; setting a larger quantization interval in the regions where the signal values appear less frequently or are sparse after compression; A discrete point mapping unit for quantizing each compressed continuous analog signal value using the determined quantization interval, mapping the compressed signal values to discrete points according to the quantization interval; converting the mapping result into a quantized discrete signal output, and the discrete signal is represented by the median or central value of each quantization interval; where y j represents the midpoint of the j-th quantization interval, y min and y max are the minimum and maximum quantization values of the continuous analog signal, respectively, and Q[i] is the discrete point.

2. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, characterized in that, The data processing and control module includes: A filter sub-module for denoising the analog signal input to the hybrid analog-to-digital converter; A clock signal generator sub-module for generating a clock signal to synchronize signal acquisition and control the conversion rate.

3. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, wherein The analog sampling module includes: A function input sub-module for, before sampling, the input continuous analog signal is a continuous function containing multiple frequency components; according to the Nyquist law, selecting a sampling frequency to ensure that this frequency is at least twice the highest frequency component in the signal; A sampling execution sub-module for setting the sampling time and extracting the sampling value; setting the sampling time according to the selected sampling frequency, and extracting the voltage value of the analog signal at each set sampling time to obtain discrete signal samples; A signal composition sub-module for obtaining a set of discrete sample values that together constitute a discrete signal, representing the voltage value of the original continuous signal at the specified sampling time.

4. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, wherein The signal compression sub-module includes: A signal range identification unit for analyzing an input continuous analog signal to determine the amplitude range of the input continuous analog signal; the compression function adopts the A-law compression algorithm; A compression function application unit for applying the compression function to each sampled value of the input continuous analog signal; An output range control unit for normalizing the processed compressed value to be within a specific range; after processing, the resulting continuous analog signal represents the signal after non-linear compression processing, and the compressed continuous analog signal will then be passed to the compression mapping sub-module for quantization and processing.

5. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, wherein A signal characteristic identification unit, including: A signal data acquisition sub-unit for extracting all sample values from the continuous analog signal processed by the compression function, dividing the extracted continuous analog signal data into several small intervals, and counting the number of data points in each interval; the height of each bar of the histogram corresponds to the number of data points in that interval; A probability density function calculation sub-unit for converting the height of the histogram into a probability density function by dividing the number of sample points in each interval by the product of the total number of samples and the interval width; A statistical characteristic calculation sub-unit for calculating the peak value, average value, and variance.

6. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, wherein A quantization interval determination unit, including: A signal characteristic identification sub-unit for obtaining the continuous analog signal data processed by the compression function and extracting all non-linearly compressed sample values; analyzing the distribution characteristics of the continuous analog signal using the probability density function, including finding the peak value, average value, and variance statistical characteristics; A frequency analysis and data distribution sub-unit for observing the height of each interval of the histogram when analyzing the signal data and its frequency distribution to find the intervals where the signal appears with high frequency and low frequency; A quantization interval setting sub-unit for setting the quantization interval according to the characteristics of the signal distribution and the different distribution situations in the high-frequency and low-frequency regions; finally generating the quantization pattern according to the determined quantization interval.

7. The non-uniform quantization hybrid analog-to-digital converter according to claim 1, wherein A data encoding module, including: A quantization level confirmation sub-module, where each quantization interval corresponds to a digital representation, i.e., the quantization level; by establishing a mapping relationship from the quantization value to the digital value, determining the number corresponding to each quantization value; A quantization level allocation sub-module for assigning a number to each interval, and the quantized sampled value corresponds to a number; A binary encoding sub-module for converting the digital number of each quantization value into a binary encoding, using the number of bits to perform binary encoding on each quantization level; combining and outputting all the generated binary representations to form the final digital signal.

8. A control method for a non-uniform quantization hybrid analog-to-digital converter, characterized in that, The non-uniform quantization hybrid analog-to-digital converter control method includes: The data processing and control module performs denoising processing on the input continuous analog signal; The analog signal sampling module samples according to the Nyquist law, converting the analog quantity that changes continuously with time into an analog quantity that is discrete in time; The non-uniform quantization module determines the quantization interval according to different intervals of the signal, discretizing the sampled continuous analog signal; the data encoding module represents the quantized result in binary encoding and outputs it by the hybrid analog-to-digital converter; The non-uniform quantization module includes: The signal compression sub-module is used to perform a non-linear transformation on the input signal. By using a compression function, it compresses a wide range of signal values into a smaller range, so that more attention is paid to the amplitude of small signals; The compression mapping sub-module is used to determine the size of each quantization interval according to the distribution of the compressed signal, and map the compressed signal values to a series of equally spaced discrete points; The amplitude distribution sub-module is used to reverse the operation of the compressor at the receiving end, and restore the quantized signal to the amplitude distribution of the original signal through an expander; The compression mapping sub-module specifically includes: The signal characteristic recognition unit is used to obtain the continuous analog signal data processed by the compression function, including all sample values after non-linear compression; analyze the distribution characteristics of the compressed signal using the probability density function, and find out the signal peak, average value and variance characteristics; The quantization interval determination unit is used to determine the size and number of each quantization interval according to the statistical characteristics of the signal distribution; set smaller quantization intervals near the signal values that appear frequently at high frequencies; set larger quantization intervals in the regions where the signal values appear less frequently or are sparse after compression; The discrete point mapping unit is used to quantize each compressed continuous analog signal value using the determined quantization interval, and map the compressed signal values to discrete points according to the quantization interval; convert the mapping result into a quantized discrete signal output, and the discrete signal is represented by the median or central value of each quantization interval; where y j represents the midpoint of the j-th quantization interval, y min and y max are the minimum and maximum quantization values of the continuous analog signal, respectively, and Q[i] is the discrete point.

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