Blood-Sample Optical Speckle Imaging for Fast Random Number Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing random number generators (RNGs) face challenges in achieving high-speed, cost-effective, and secure random number generation due to high production costs and limited bandwidth, with existing technologies failing to effectively utilize biological systems for enhanced security and unpredictability.
Innovation Solution
A system utilizing a blood sample to generate random numbers through a two-pass tuple-output von Neumann (2P-TO-VN) method, involving a sample loading module, light irradiation, image generation, and random number generation module, which includes binary image processing and von Neumann extraction to enhance randomness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical random number generators are used to achieve high security and unpredictability, then randomness quality is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the random number generation function from complex physical systems and implements it using a simple blood sample-based optical system. By taking out only the essential randomness source (blood flow patterns) and processing it through a camera and algorithm, the system achieves high randomness quality without the complexity of traditional physical RNG devices
Solution Approach 2:
The patent uses a disposable blood sample as the randomness source instead of expensive, complex physical devices. The blood sample serves as a single-use randomness source that can be easily obtained and processed, significantly reducing device complexity and cost while maintaining high randomness quality
2Productivity
If optical speckle pattern methods are used to generate random numbers, then generation speed is improved, but cost and acquisition time increase
Solution Approach 1:
The patent employs the von Neumann extractor algorithm that operates autonomously on the captured speckle pattern data to generate random numbers. The system self-processes the optical data through binary conversion and debiasing algorithms, eliminating the need for additional acquisition time or external processing resources
Solution Approach 2:
The patent performs preliminary processing of the speckle pattern data by converting it to binary values and applying the von Neumann extractor in real-time during the imaging process. This preliminary action ensures that random numbers are generated immediately from the captured image without requiring separate acquisition or processing phases
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high-speed random number generation with improved randomness and cost-efficiency by leveraging biological complexity, minimizing costs and acquisition time while ensuring strong security and unpredictability of generated keys.
Implementation Method 1
irradiating the blood sample moved to a pre-designated region of interest (ROI) with light
Implementation Method 2
acquire an original speckle image by capturing an image of the blood sample located in the region of interest
Data Source
AI summary
According to an aspect of the present invention, there is provided a system for generating random numbers based on blood samples including a sample loading module configured to supply a preset blood sample, a light irradiation module configured to irradiate the blood sample provided by the sample loading module with light, the blood sample moved to a pre-designated region of interest (ROI) being irradiated with light, an image generation module configured to acquire an original speckle image by capturing an image of the blood sample located in the region of interest in a preset method using the light irradiation module, and a random number generation module configured to generate random numbers by processing the original speckle image generated from the image generation module in a preset method.