Random number generator and noise generator

Abstract

The invention discloses a random number generator and a noise generator. The random number generator comprises a preprocessing circuit and a chaotic random number generator. The preprocessing circuit comprises a disturbance source and a control circuit, and the control circuit is coupled to the disturbance source. The chaotic random number generator is coupled to the preprocessing circuit and comprises a chaotic system and a post-processing circuit, and the post-processing circuit is coupled to the chaotic system.

Description

Technical Field

[0001] The present invention relates to a random number generator and a noise generator, and in particular to a random number generator and a noise generator which can be realized by matching a chaos system. Background Art

[0002] True Random Number Generator (TRNG) is a hardware random number generator used to generate non-periodic and unpredictable random numbers. There are two common types of true random number generators: metastable true random number generators (metastability-based TRNG) and jitter-based true random number generators (jitter-based TRNG). Metastable true random number generators have a latch circuit in a metastable state, which enters a stable state due to slight noise changes to generate random bit values. Jitter-based true random number generators use a ring oscillator to generate random signals based on the jitter in the circuit. Both metastable true random number generators and jitter-based true random number generators require a lot of time to adjust circuit parameters and are difficult to implement in field programmable gate arrays (Field Programmable Gate Array, FPGA). In addition, these true random number generators are all analog circuits, which require a lot of time and cost to adjust when converting between different processes.

[0003] Therefore, it is necessary to propose another random number generator that has high randomness and high unpredictability and is easy to implement in hardware circuits. Summary of the invention

[0004] Therefore, the main purpose of the present invention is to propose a new random number generator and a related noise generator that can be implemented in conjunction with a chaos system to solve the above problems.

[0005] An embodiment of the present invention discloses a random number generator, which includes a preprocessing circuit and a chaos random number generator (CRNG). The preprocessing circuit includes a disturbance source and a control circuit, wherein the control circuit is coupled to the disturbance source. The chaos random number generator is coupled to the preprocessing circuit and includes a chaotic system and a post-processing circuit, wherein the post-processing circuit is coupled to the chaotic system.

[0006] Another embodiment of the present invention discloses a noise generator, which includes a random number generator and a noise generating circuit. The random number generator includes a preprocessing circuit and a chaotic random number generator. The preprocessing circuit includes a disturbance source and a control circuit, wherein the control circuit is coupled to the disturbance source. The chaotic random number generator is coupled to the preprocessing circuit and includes a chaotic system and a post-processing circuit, wherein the post-processing circuit is coupled to the chaotic system. The noise generating circuit is coupled to the random number generator. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 Schematic diagram of a random number generator according to an embodiment of the present invention.

[0008] Figure 2 An exemplary implementation of converting a chaotic signal into a random signal according to an embodiment of the present invention is shown.

[0009] Figure 3 Schematic diagram of the circuit structure of the modulo division circuit.

[0010] Figure 4 Schematic diagram of a noise generator according to an embodiment of the present invention.

[0011] Figure 5 A random number generator is shown outputting a random signal to a lidar system.

[0012] Figure 6 A noise generator is shown outputting a noise signal to a lidar system.

[0013] Figure 7 A detailed implementation of an application for realizing a lidar system is shown.

[0014] The reference numerals are described as follows:

[0015] 10. Random Number Generator

[0016] 100 Pre-processing circuit

[0017] 110 Chaotic Random Number Generator

[0018] 102 Disturbance Source

[0019] 104 Control Circuit

[0020] P disturbance signal

[0021] 112 Chaotic System

[0022] 114 Post-processing circuit

[0023] X0, Y0, Z0 initial values

[0024] X out, Y out , Z out Chaotic Signal

[0025] u1~u3 random signal

[0026] M1~M3 Modulus division circuit

[0027] X n [32:0], X n-1 [32:0], U1[31:0] signal bits

[0028] R1, R2, R3 calculation result

[0029] XOR1~XOR3 logic gate

[0030] MUX1 Multiplexer

[0031] FF1 Flip-flop

[0032] 40 Noise Generator

[0033] 400 Noise Generation Circuit

[0034] P1~P3 Signal Processor

[0035] w1~w6 Noise signal

[0036] 500, 600, 700 LiDAR systems

[0037] 502, 602 Parallel to Serial Converters

[0038] 702 Digital to Analog Converter

[0039] 704 Direct Modulation Laser

[0040] 706 Photodiode

[0041] 708 Analog-to-digital converter

[0042] 710 Circuit System

[0043] 720 Noise Generator

[0044] 712 Filter

[0045] 714 Related Units

[0046] 716 Interpolation Unit

[0047] 722 Preprocessing Circuit

[0048] 724 Chaotic Random Number Generator

[0049] 726 Noise Generating Circuit

[0050] TS Target signal

[0051] RS reference signal DETAILED DESCRIPTION

[0052] Chaos Random Number Generator (CRNG) uses a chaos system to generate random output. A chaotic system is a nonlinear system that generates output values ​​that can form a messy trajectory. A chaotic system is very sensitive to initial conditions and has non-periodic characteristics. If the initial value changes slightly, the chaotic system will produce a completely different trajectory, making the output of the chaotic system essentially random. However, if the initial value and parameters of the chaotic random number generator are known, its output value can still be predicted. In addition, the behavior of a chaotic system can be mathematically represented by a difference equation, which is difficult to generate in a hardware circuit, so some approximate methods are needed to implement it. The fourth-order Runge-Kutta method (RK4) is an example used to approximate the output value of a chaotic system. This method is quite complex, so it requires a high hardware cost and limits the throughput of the system.

[0053] The present invention can utilize a chaotic system to implement a random number generator to realize a chaotic random number generator with optimized unpredictability. In some embodiments, the random number generator can be integrated with a noise generation circuit to realize a noise generator, wherein the noise generation circuit can convert a series of random numbers with uniform distribution into a noise signal with normal distribution.

[0054] Figure 1 1 is a schematic diagram of a random number generator 10 according to an embodiment of the present invention. The random number generator 10 includes a preprocessing circuit 100 and a chaotic random number generator 110, wherein the chaotic random number generator 110 is coupled to the preprocessing circuit 100. More specifically, the chaotic random number generator 110 is coupled to the output end of the preprocessing circuit 100 to receive one or more disturbance signals from the preprocessing circuit 100.

[0055] The preprocessing circuit 100 includes a disturbance source 102 and a control circuit 104. The disturbance source 102 can sample a physical random noise to generate a disturbance signal P, and output the disturbance signal P to the chaotic random number generator 110, wherein the disturbance signal P can be used to disturb the chaotic random number generator 110. The random noise can be generated by any appropriate noise source with sufficient randomness. In an exemplary embodiment, the disturbance signal P can be generated from an environmental noise. The disturbance source 102 can include an analog-to-digital converter (ADC) for sampling the environmental noise and converting the environmental noise into a noise signal in digital form, so that the generated disturbance signal P becomes unpredictable. The disturbance signal P with unpredictability can be used as an initial value or used to modify the initial value to change the output curve of the chaotic random number generator 110. Based on the disturbance signal P, the random number generated by the chaotic random number generator 110 will become more unpredictable.

[0056] The control circuit 104 is coupled to the output end of the disturbance source 102 and may include a multiplexer or a switch circuit for controlling the output of the disturbance signal P according to the operation mode or setting of the random number generator 10. For example, based on the control of the control circuit 104, the pre-processing circuit 100 may periodically or non-periodically output the disturbance signal P to the chaotic random number generator 110. Figure 1 As shown, the control circuit 104 includes a plurality of switch circuits, which can enable the input of the disturbance signal P in a first operation mode and stop the input of the disturbance signal P (ie, receive the signal 0) in a second operation mode.

[0057] The chaotic random number generator 110 includes a chaotic system 112 and a post-processing circuit 114. Figure 1 As shown, the chaotic system 112 can receive initial values ​​X0, Y0 and Z0 on different input channels, and simultaneously receive a disturbance signal P from the preprocessing circuit 100, which means that the received disturbance signal P can be used as a factor in the iterative process of the chaotic system 112, and / or can be used to modify the initial values ​​X0, Y0 and Z0 received by the chaotic system 112, that is, to increase or decrease the initial values ​​X0, Y0 and Z0. According to the initial values ​​X0, Y0 and Z0 (and in combination with the disturbance signal P), the chaotic system 112 can generate and output chaotic signals X0, Y0 and Z0 respectively. out , Y out and Z outIn another embodiment, the preprocessing circuit 100 may output three disturbance signals as three initial values ​​for iterating and generating the chaotic signal output. Alternatively, the disturbance signals may be first combined with the initial values ​​X0, Y0 and Z0 respectively, and then received by the chaotic system 112 to iterate and generate the chaotic signal output. In this case, the initial values ​​X0, Y0 and Z0 will become unpredictable.

[0058] The chaotic system 112 may use any suitable algorithm to generate the chaotic signal X out , Y out and Z out , and the Lorenz system is an example of the chaotic system 112. In this example, the chaotic system 112 may be an Euler-based chaos system, which uses the Euler method to solve the difference equations of the Lorenz system, as described below:

[0059]

[0060] Wherein, x, y, z represent the output values ​​of the chaotic system 112, which can be used to represent three-dimensional coordinates to form a chaotic curve, and σ, ρ, β are the parameters of the Lorenz system. In one embodiment, these parameters can be set to (σ, ρ, β) = (10, 28, 8 / 3), and the output chaotic signal will become:

[0061]

[0062] Since the Euler method is used to solve the first-order difference equation, compared with the fourth-order operation of the fourth-order Runge-Kutta method, the hardware cost can be saved. In the Euler-based chaotic system 112, the overall throughput can also be improved.

[0063] It is worth noting that the Lorenz system is only an exemplary implementation of the chaotic system 112. In another embodiment, a modified Lorenz system may also be used, as described below:

[0064]

[0065] Here, the Euler method can be used to solve the difference equation of the modified Lorenz system, which is expressed as:

[0066]

[0067] The difference equations of the Lorenz system or modified Lorenz system can be approximated using simple hardware circuits, such as adders, bit shifters, flip-flops and / or multiplexers, so that the chaotic system 112 can be implemented in an integrated circuit (IC).

[0068] The post-processing circuit 114 is coupled to the chaotic system 112 and can process the chaotic signal X out , Y out and Z out The post-processing circuit 114 can be used to disrupt the chaotic signal X out , Y out and Z out In one embodiment, the post-processing circuit 114 may include a plurality of modulo operation circuits M1 to M3, wherein each modulo operation circuit is used to process the chaotic signal X. out , Y out and Z out The analog division circuits M1 to M3 can extract the chaotic signal X out , Y out and Z out The post-processing operation can be used to enhance the randomness of the random signals u1-u3. In other embodiments, the post-processing operation examples performed in the post-processing circuit 114 also include bit shifting, bit reversal, and any and suitable logical operations, but are not limited thereto, and these operations are all implementable in an integrated circuit.

[0069] In one embodiment, the analog division circuit M1, M2 or M3 can be used to generate the chaotic signal X out , Y out or Z out The integer part of is shifted to add the chaotic signal X out , Y out and Z out The fractional part of is used as the least significant bit (LSB), thereby generating a random number uniformly distributed between 0 and 1. For example, the chaotic signal X out Equal to (011000.001011011) b , the 6-bit integer part 011000 can be shifted to become the least significant bit of the fractional part (through the modulo division circuit M1), so the output random signal u1 will become (0.001011011011000) bIn a similar way, the analog division circuit M2 can convert the chaotic signal Y out Converted into random signal u2, the modular division circuit M3 can convert the chaotic signal Z out Converted to random signal u3.

[0070] Since each random signal u1 ˜ u3 has a series of random bits, the random number generator 10 or subsequent circuits may further combine the random signals u1 ˜ u3 (eg, by parallel-to-serial conversion) to generate a random number sequence as the output of the random number generator 10 .

[0071] In another embodiment, the modulo division circuit M1 , M2 or M3 may utilize logic operations to generate the random signal u1 , u2 or u3 , thereby improving the randomness of the output random number. Figure 2 The chaotic signal X of the embodiment of the present invention is shown out An exemplary implementation of converting into a random signal u1. Figure 2 As shown, the chaotic signal X used for calculation out Can be a 33-bit signal (with X n [32:0] indicates the most significant bit (MSB) X n

[32] is the sign bit), which can be used to generate a 32-bit random signal u1 (represented by U1[31:0]). n [31:25] Chaos Signal X out The integer part of n [24:0] is the chaotic signal X out The decimal part of .

[0072] The logical operation is as follows. First, the 7-bit integer part X n [31:25] With X n [13:7] Performs a bit-wise exclusive-OR (XOR) operation (where X n [13:7] is 7 bits taken from the fractional part) to generate a first operation result R1. The first operation result R1 is used to compare with the least significant bit X n [6:0] performs a bit XOR operation to generate a second operation result R2. The second operation result R2 is further used to generate a second chaotic signal X out The order of the 7 least significant bits of the signal is reversed and then the bit XOR operation is performed (the previous chaotic signal is X n-1 [32:0] means that the 7 least significant bits are X n-1 [6:0], the order is reversed to become Xn-1 The third operation result R3 can be used as the 7 most significant bits (i.e., U1[31:25]) of the random signal u1, and the other 25 bits (i.e., U1[24:0]) of the random signal u1 are taken from the original chaotic signal X out Based on the above multiple XOR operations, the randomness of the random signal u1 can be greatly improved.

[0073] The circuit structure of the corresponding modular division operation circuit M1 used to perform the above operation is shown in Figure 3 .Depend on Figure 3 It can be seen that the modular division circuit M1 can be easily implemented using a simple logic circuit, which includes a number of logic gates (ie, XOR gates) XOR1-XOR3, a multiplexer MUX1 and a flip-flop FF1.

[0074] In a similar manner, the chaotic signal Y can also be used by the modular division circuit M2 out To generate random signal u2, and the modular division circuit M3 uses the chaotic signal Z out To generate random signal u3.

[0075] In a first embodiment, the chaotic system 112 adopts a Lorenz system, whose parameters are (σ, ρ, β) = (10, 28, 8 / 3), and is solved by the Euler method. Each modular division circuit M1-M3 in the post-processing circuit 114 adopts the above-mentioned bit shifting method to convert the chaotic signal X out , Y out or Z out The integer part of is added to its decimal part as the least significant bit. Under such an implementation, the random numbers (including random signals u1~u3) output by the random number generator 10 can pass all items of the NIST-800-20 test issued by the National Institute of Standards and Technology (NIST), which means that the random numbers have sufficient randomness. A simulation result shows that the random numbers output by the random number generator 10 are uniformly distributed and have a high degree of autocorrelation. In addition, the random number generator 10 of the present invention can achieve a high throughput of 1.5 million samples per second (or equivalent to 3 gigabits per second (gigabits / sec)), which is better than various random number generators currently on the market.

[0076] In a second embodiment, the chaotic system 112 adopts a modified Lorenz system, whose parameters are (σ, ρ, β) = (0.5, 50, 0.25), and is solved by the Euler method. Figure 2 Multiple XOR operations shown. In addition, the chaotic system 112 receives a disturbance signal P (such as controlled by the control circuit 104) at a predetermined period. For example, the disturbance signal P can be applied once every 5 output cycles. Under such an implementation, the random numbers (including random signals u1~u3) output by the random number generator 10 can also pass all items tested by NIST-800-20. A simulation result shows that the random numbers are uniformly distributed and have a high degree of autocorrelation, and the random number generator 10 of the present invention can achieve an ultra-high throughput of 1.111 billion samples per second (or equivalent to 35.5556 billion bits per second), which is better than the first embodiment.

[0077] As described above, in some embodiments of the present invention, the random number generator may further integrate a noise generating circuit to convert a series of uniformly distributed random numbers into a noise signal with a normal distribution. Figure 4 Schematic diagram of a noise generator 40 according to an embodiment of the present invention. The noise generator 40 includes: Figure 1 The random number generator 10 shown is coupled to a noise generating circuit 400. Figure 4 In the random number generator 10, the structure is the same as Figure 1 The noise generator 400 is the same as that shown in the figure, so the related components and signals are represented by the same symbols. The noise generating circuit 400 is coupled to the output end of the random number generator 10, and includes signal processors P1-P3, which are respectively used to receive random signals u1-u3. Since the noise generator 40 generates noise output according to the random signals u1-u3 and the random signals u1-u3 are generated in a chaotic system, the noise generator 40 can be regarded as a chaos noise generator.

[0078] In detail, the random number generator 10 can output random signals u1~u3 to the noise generating circuit 400. By receiving uniformly distributed random signals u1~u3, the noise generating circuit 400 can generate and output 6 normally distributed noise signals w1~w6. In the noise generating circuit 400, the signal processor P1 is configured to convert the random signals u1 and u2 into noise signals w1 and w2, the signal processor P2 is configured to convert the random signals u2 and u3 into noise signals w3 and w4, and the signal processor P3 is configured to convert the random signals u1 and u3 into noise signals w5 and w6. An exemplary implementation of each signal processor P1~P3 is a Box-Muller processor, which uses a Box-Muller transformation to convert uniformly distributed random variables into normally distributed random variables. The formula for the Box-Muller transformation is as follows:

[0079]

[0080] Based on the above formula, each Bucks-Miller processor is responsible for converting two input random variables into two output random variables. In this example, the noise generation circuit 400 includes three Bucks-Miller processors P1-P3, which can generate six noise signals w1-w6 (with standard normal distribution) according to three random signals u1-u3 (with uniform distribution) from the chaotic random number generator 110 through appropriate configuration.

[0081] It is worth noting that the Backus-Miller processor can also be implemented in an integrated circuit using hardware circuits. For example, the cosine and sine functions can be implemented using the Coordinate Rotation Digital Computer (CORDIC) algorithm, while the natural logarithm and square root functions can be implemented using the hyperbolic CORDIC algorithm.

[0082] A simulation result for the noise generator 40 shows that the output noise signals w1-w6 have a standard normal distribution, and the noise signals w1-w6 still have high randomness and high autocorrelation. Since the noise generating circuit 400 converts the three random signals u1-u3 into six noise signals w1-w6, the system throughput can be doubled.

[0083] It is worth noting that the purpose of the present invention is to propose a new random number generator and noise generator that can be implemented with a chaotic system to achieve superior efficiency, such as high randomness and high throughput. Those skilled in the art can modify or change accordingly, but are not limited to this. For example, the above embodiment adopts (modified) Lorenz system to realize a 3-dimensional chaotic system (i.e., X, Y, Z), which includes a first-order differential equation to be solved using the Euler method. In another embodiment, the chaotic system can also be a 1-dimensional system, a 2-dimensional system, a 4-dimensional system, or a higher dimension, but is not limited to this. In addition, the method for solving the differential equation is not limited to the Euler method, and the differential equation can have any order, which is not limited to the content described in this specification. In addition, the post-processing scheme proposed in the above embodiment is only an example for illustration. In another embodiment, it is also possible to combine multiple circuit operations in any appropriate way in the post-processing circuit, such as bit shifting, bit reversal, and / or any logical operation, so as to disrupt the chaotic signal and enhance the randomness of the output random signal.

[0084] In some embodiments, the random number generator or noise generator of the present invention can be applied to an optical detector, wherein the random signal of the random number generator or the noise signal of the noise generator can be output to the optical detector to enhance its anti-interference ability and recognition. For example, in one embodiment, the random number generator or the noise generator can be applied to a LiDAR (Light Detection And Ranging, LiDAR) system. LiDAR is a long-distance sensing solution used to detect objects and determine the distance of objects. Random numbers can be used as an identifier carried by the optical signal output by the LiDAR transmitter. This identifier should be unique and recognizable by the LiDAR receiver, making it less susceptible to interference from environmental noise or interference from signals generated by other detectors or sensing systems. Therefore, random signals or noise signals with high randomness and high autocorrelation can be used to achieve the purpose of anti-interference. In one embodiment, a random number generator (such as Figure 1 The random signal (such as u1-u3) output by the random number generator 10 in the embodiment can be applied to a lidar system 500, such as Figure 5 In another embodiment, the noise generator (such as Figure 4 The noise signal (such as w1-w6) output by the noise generator 40 in FIG. 4 can be applied to a lidar system 600, such as Figure 6 shown.

[0085] like Figure 5 and Figure 6As shown, a parallel-to-serial converter 502 or 602 can be used to convert random signals u1-u3 or noise signals w1-w6 into a random number sequence, which is then output to the lidar system 500 or 600. The parallel-to-serial converter 502 or 602 can distribute the input signal in any appropriate manner, and its structure and implementation should be well known to those skilled in the art and will not be described in detail here.

[0086] Figure 7 Detailed implementation of an application for realizing a LiDAR system 700 is shown. Figure 7 As shown, the lidar system 700 includes a digital-to-analog converter (DAC) 702 and a directly modulated laser (DML) 704 at the transmitting end, and includes a photodiode (such as an avalanche photodiode (APD)) 706 and an analog-to-digital converter (ADC) 708 at the receiving end. The lidar system 700 is coupled to a circuit system 710 for receiving a random signal or a noise signal from the circuit system 710. The circuit system 710 can be implemented in a field programmable gate array (FPGA), wherein a noise generator 720 can be implemented in an integrated circuit of the field programmable gate array to be coupled to the lidar system 700. The noise generator 720 includes a pre-processing circuit 722, a chaotic random number generator 724, and a noise generating circuit 726, similar to Figure 4 The structure of the noise generator 40 in FIG. 7 can provide a noise signal to the lidar system 700. Alternatively, the noise generator 720 can also be replaced by a random number generator, such as Figure 1 The random number generator 10 in FIG. 7 can provide a random signal to the lidar system 700. The circuit system 710 can also include a filter 712 (which can be, for example, a Chebyshev filter or any other suitable type of filter), a correlation unit 714 and an interpolation unit 716.

[0087] The noise signal (or random signal) received by the lidar system 700 can be converted into an analog form by a digital-to-analog converter 702, and then the electronic signal with specific noise is converted into an optical signal by a direct modulation laser 704, and then the optical signal is output. The optical signal is reflected by a target object and received by a photodiode 706, wherein the received reflected signal is accompanied by environmental interference and / or noise. Then, the reflected signal is processed by an analog-to-digital converter 708 and a filter 712 to generate a target signal TS corresponding to the target object. More specifically, the photodiode 706 can convert the received optical signal into an electronic signal, and then convert it into a digital form by the analog-to-digital converter 708, and the filter 712 can filter out unnecessary frequency components in the electronic signal. Then, the correlation unit 714 can use a cross-correlation method to refer to a reference signal RS from a noise generator 720 to remove environmental interference and / or noise in the target signal TS. The result of the cross-correlation can be further processed by an interpolation unit 716 to obtain depth information used to indicate the distance of the object.

[0088] In one embodiment, the circuit system 710 in the lidar system 700 can calculate the time difference between the output optical signal and the received reflected signal, thereby determining the distance information. Since the optical signal carries the random noise signal generated by the noise generator 720 as a unique identifier, the receiver will not mistakenly detect another signal, and the received reflected signal is not easily interfered by other signals. With the reference signal RS, the reflected target signal TS can be correctly identified using the cross-correlation method.

[0089] As described above, the random number generator of the present invention adopts a chaotic system, which receives a random / perturbed initial signal. Since the chaotic system is very sensitive to the initial signal, the disturbance of the initial signal can ensure that the output random numbers have high autocorrelation to achieve a high degree of anti-interference ability.

[0090] A simulation result shows that a lidar system using the noise generator (or random number generator) of the present invention can achieve a low peak sidelobe level (PSL) value and a high peak sidelobe level standard deviation (PSL std ). Parameters PSL and PSL std It is often used to evaluate the efficiency of LiDAR systems. The peak sidelobe level indicates the sidelobe peak relative to the cross-correlation peak, and a lower PSL value indicates a higher anti-interference capability. std is the cross-correlation peak relative to the noise level, and a higher PSL stdThe simulation results also show that the LiDAR system using the noise generator (or random number generator) of the present invention has high accuracy and low error rate.

[0091] Based on the simulation results of the lidar system, in PSL, PSL std In terms of accuracy, error rate, etc., using noise signals seems to be more efficient than random numbers. In other words, the noise signal converted from the random signal has higher autocorrelation and higher randomness, thus achieving more optimized anti-interference ability.

[0092] It is worth noting that the LiDAR system is only one of many applications of the random number generator or noise generator of the present invention. In various embodiments, the random number generator or noise generator of the present invention can be used for coding, encryption, and / or security applications.

[0093] In summary, the present invention proposes a novel random number generator and noise generator based on a chaotic system. In the random number generator, a pre-processing circuit can be used to generate a disturbance signal to disturb the initial value and / or the iterative process of the chaotic system, thereby achieving higher randomness. The disturbance signal can be generated by sampling the ambient noise by an analog-to-digital converter. In addition, a post-processing circuit can be used to modify the chaotic signal to generate a uniformly distributed random signal. The post-processing operation may include bit shifting, bit reversal, any logical operation and / or a combination of the above operations, but is not limited thereto. In one embodiment, the chaotic system may be a Lorenz system or a modified Lorenz system, which has several differential equations and is solved using the Euler method to generate a chaotic output signal.

[0094] In addition, the random number generator of the present invention may integrate a noise generating circuit to implement the noise generator. The noise generating circuit may convert a uniformly distributed random signal into a normally distributed noise signal. In an exemplary embodiment, the noise generating circuit may use a plurality of Bucks-Mueller processors to convert the random signal into a noise signal.

[0095] Since the random number generator and noise generator of the present invention are realized by adding input disturbance to a chaotic system, they have high randomness, high autocorrelation, and high unpredictability, thereby achieving high efficiency. The Euler method is used to solve the first-order differential equation with low complexity, so it can be implemented in an integrated circuit with low circuit cost and high throughput at the same time. Low cost and high throughput can also be achieved by using modular division operations in post-processing circuits. Multiple Bucks-Miller processors used in the noise generation circuit can further improve the throughput, for example, 3 random signals can be converted into 6 noise signals, doubling the throughput. The entire system can be realized by digital circuits, so it can be implemented in integrated circuits, such as field programmable logic gate arrays. In addition, digital circuits also have the benefit of being able to easily switch between different processes.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A random number generator, characterized in that: include: A pre-processing circuit comprising: a disturbance source; and a control circuit coupled to the disturbance source; and A chaotic random number generator is coupled to the preprocessing circuit and comprises: a chaotic system; and A post-processing circuit is coupled to the chaotic system.

2. The random number generator according to claim 1, wherein: The preprocessing circuit outputs a disturbance signal to serve as an initial value of the chaotic random number generator or to modify an initial value of the chaotic random number generator.

3. The random number generator according to claim 2, characterized in that The disturbance signal is generated from an environmental noise.

4. The random number generator according to claim 2, characterized in that The chaotic system is used for generating a chaotic signal according to the initial value.

5. The random number generator according to claim 1, wherein: The chaotic system is an Euler-based chaotic system.

6. The random number generator according to claim 1, wherein: The chaotic system utilizes an Euler method to solve a difference equation of a Lorenz system or a modified Lorenz system.

7. The random number generator according to claim 1, wherein: The post-processing circuit is used for disturbing a chaotic signal of the chaotic system.

8. The random number generator according to claim 1, wherein: The post-processing circuit includes a modular division operation circuit.

9. The random number generator according to claim 8, characterized in that The modular division operation circuit generates a random signal according to the fractional part of a chaotic signal of the chaotic system.

10. The random number generator according to claim 1, wherein: The random number generator is coupled to a noise generating circuit.

11. The random number generator according to claim 10, wherein: The random number generator is used for outputting a random signal to the noise generating circuit.

12. The random number generator according to claim 1, wherein: The random number generator is used to output a random signal to an optical detector.

13. A noise generator, characterized in that: include: A random number generator comprising: A pre-processing circuit comprising: a disturbance source; and a control circuit coupled to the disturbance source; and A chaotic random number generator is coupled to the preprocessing circuit and comprises: a chaotic system; and a post-processing circuit coupled to the chaotic system; and A noise generating circuit is coupled to the random number generator.

14. The noise generator according to claim 13, characterized in that The preprocessing circuit outputs a disturbance signal to serve as an initial value of the chaotic random number generator or to modify an initial value of the chaotic random number generator.

15. The noise generator according to claim 14, characterized in that The disturbance signal is generated from an environmental noise.

16. The noise generator according to claim 14, characterized in that The chaotic system is used for generating a chaotic signal according to the initial value.

17. The noise generator according to claim 13, characterized in that The chaotic system is an Euler-based chaotic system.

18. The noise generator according to claim 13, characterized in that The chaotic system utilizes an Euler method to solve a difference equation of a Lorenz system or a modified Lorenz system.

19. The noise generator according to claim 13, characterized in that The post-processing circuit is used for disturbing a chaotic signal of the chaotic system.

20. The noise generator of claim 13, wherein: The post-processing circuit includes a modular division operation circuit.

21. The noise generator of claim 20, wherein: The modular division operation circuit generates a random signal according to the fractional part of a chaotic signal of the chaotic system.

22. The noise generator of claim 13, wherein: The random number generator is used for outputting a random signal to the noise generating circuit.

23. The noise generator of claim 13, wherein: The noise generating circuit is used for converting a random signal with uniform distribution into a noise signal with normal distribution.

24. The noise generator of claim 13, wherein: The noise generator is used for outputting a noise signal to an optical detector.

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