Pulse-Sequence Detection Circuit for Parallel Image Edge Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image edge detection methods require extensive calculation steps and result in low calculation efficiency and high power consumption due to the need for large-scale convolution operations in image processing.
Innovation Solution
A detection circuit that utilizes pulse sequences with different amplitudes and frequencies to generate conductance differences, allowing for indirect detection of signal differences between pixels, thereby reducing calculation steps and power consumption by applying the circuit to each pixel pair in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional convolution operations are used for image edge detection, then detection precision can be achieved, but calculation amount increases significantly leading to low calculation efficiency and high power consumption
Solution Approach 1:
The patent replaces conventional software-based convolution calculations with a hardware circuit implementation. The detection circuit uses parallel processing of pixel pairs through dedicated hardware components (pulse sequence generators, conductance devices, difference detection circuits) to perform edge detection operations simultaneously, substituting sequential software computation with parallel hardware execution, thereby dramatically improving calculation efficiency while maintaining detection precision
Solution Approach 2:
The patent divides the image processing task into discrete pixel pairs and processes each pair independently through separate detection circuits. By segmenting the image into multiple pixel pairs and using parallel detection circuits for each segment, the system achieves simultaneous processing of multiple regions, significantly increasing overall calculation efficiency without sacrificing detection accuracy
2Measurement precision
If conventional convolution operations are used for image edge detection, then detection precision can be achieved, but power consumption of the device increases due to large calculation amount
Solution Approach 1:
The patent replaces energy-intensive software convolution calculations with a dedicated hardware detection circuit that processes pixel pairs directly. The hardware implementation uses parallel operations and specialized components (conductance devices, pulse sequence generators) that perform edge detection with minimal computational overhead, significantly reducing power consumption while maintaining detection precision
Solution Approach 2:
The patent performs preliminary conversion of pixel grayscale values into pulse sequences before comparison. By pre-processing the input data into a suitable format (pulse sequences with frequencies proportional to grayscale values), the circuit avoids repeated calculations during the detection phase, reducing overall power consumption while ensuring accurate edge detection
3Loss of information
If convolution kernel moves within image range to complete feature extraction, then comprehensive feature extraction is achieved, but calculation steps increase leading to low efficiency
Solution Approach 1:
The patent segments the feature extraction process by dividing the image into discrete pixel pairs and processing each pair independently through dedicated detection circuits. This segmentation allows simultaneous extraction of features from multiple locations without requiring sequential convolution kernel movements, dramatically reducing calculation time while maintaining comprehensive feature extraction through parallel processing
Solution Approach 2:
The patent transitions from the conventional sequential spatial dimension of convolution kernel movement to a parallel processing dimension where multiple pixel pairs are processed simultaneously. By adding the dimension of parallelism, the system extracts features from multiple image regions at the same time, eliminating the time loss associated with sequential kernel movements while maintaining complete feature extraction
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
This approach significantly reduces the number of calculation operations required for image edge detection, improving efficiency and reducing power consumption by enabling parallel processing of pixel pairs, thus enhancing the overall performance of image processing devices.
Implementation Method 1
a first conductance device, configured to receive the first pulse sequence to generate a first conductance; a second conductance device, configured to receive the second pulse sequence to generate a second conductance
Data Source
AI summary
A detection circuit includes a first pulse sequence generator configured to generate a first pulse sequence based on a first signal and a second pulse sequence generator configured to generate a second pulse sequence based on a second signal. Amplitudes and frequencies of the first signal and the second signal are different. The detection circuit further includes a first conductance device configured to receive the first pulse sequence to generate a first conductance, a second conductance device configured to receive the second pulse sequence to generate a second conductance, and a difference detection circuit configured to, when both the first conductance device and the second conductance device receive a third signal, output a voltage representing a difference between the first conductance and the second conductance. The detection circuit can be applied to an image edge detection scenario.