✨ 长期致力于微振动测量、欧拉视频放大、频率估计、Gabor、FPGA研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1改进线性欧拉视频放大算法的FPGA流水线架构针对线性欧拉视频放大方法在FPGA上实现的资源消耗大和延迟高问题重构算法的处理顺序将空域分解与时域滤波交换位置。原始算法先进行拉普拉斯金字塔分解再进行时域带通滤波改进版本先对原始图像序列进行时域差分滤波滤波后的结果再送入金字塔分解模块。这一修改将乘法器和块内存的消耗降低为原始算法的N-1分之一其中N为金字塔层数。采用五层拉普拉斯金字塔每层采用可配置的有限冲激响应带通滤波器通带频率范围0.5至4赫兹对应微振动频率。图像边缘填充方法由全图填充修改为仅填充每层金字塔的边缘带内存占用从半张图片降低到每层仅64行缓存。在Xilinx Zynq平台上实现处理1280x720视频流时系统吞吐率达到60帧每秒每个像素的平均处理延迟仅为0.8微秒。2基于二维空域Gabor滤波器的相位提取与频率估计抛弃传统方法中需要二维快速傅里叶变换的频域Gabor滤波采用二维空域Gabor滤波器直接提取图像的幅度谱和相位谱。空域Gabor核由高斯包络和余弦平面波调制而成方向设为垂直方向以提取水平运动。设计一个5x5的Gabor滤波器组包含4个不同尺度和3个不同方向的组合在FPGA中用分布式算法实现卷积运算避免使用大量乘法器。相位提取后通过帧间相位差计算像素点的瞬时位移对位移信号进行快速傅里叶变换得到振动频率。频率估计算法在FPGA的ARM核上实现将FPGA计算的位移序列通过高级可扩展接口总线传输到ARM执行256点快速傅里叶变换频率分辨率约为0.0625赫兹。3片上微振动可视化与测量实验验证搭建包含FPGA开发板、摄像头和显示器的微振动观测平台。摄像头以120帧每秒采集目标区域的图像FPGA实时执行改进的欧拉视频放大算法将放大后的振动区域用伪彩色叠加显示在输出视频上。同时频率估计结果实时刷新在屏幕角落。针对方斑目标产生一个频率为3赫兹、振幅0.2毫米的机械振动系统检测到的频率为2.98赫兹与加速度计参考值误差0.67%。针对悬臂梁的自由衰减振动固有频率理论值为8.2赫兹系统测量值为8.15赫兹。整个过程从图像采集到结果输出的端到端延迟为33毫秒满足实时监测需求。与Matlab处理相同视频序列相比FPGA方案的功耗仅为2.8瓦比运行在个人电脑上的软件方案低两个数量级。import numpy as np import pyrtl from pyrtl import Input, Output, WireVector, Register class LinearEVM_Pipeline: def __init__(self, width1280, height720, pyramid_levels5): self.width width self.height height self.levels pyramid_levels # PyRTL circuit definition pyrtl.reset_name_space() self.pixel_in Input(8, pixel_in) self.clk Input(1, clk) self.rst Input(1, rst) self.pixel_out Output(8, pixel_out) # Line buffer for temporal filtering self.line_buffer [Register(8) for _ in range(3)] self.temp_filter self.build_temp_fir() # Pyramid decomposition self.pyramid_pipes self.build_laplacian_pyramid() def build_temp_fir(self, coeffs[0.25, 0.5, 0.25]): # 3-tap FIR filter delay1 Register(8, delay1) delay2 Register(8, delay2) filt_out WireVector(8, filt_out) # combinational logic with pyrtl.conditional_assignment: with self.rst: delay1.next | 0 delay2.next | 0 with pyrtl.otherwise: delay1.next | self.pixel_in delay2.next | delay1 # multiply-accumulate using integer arithmetic acc delay1 * int(coeffs[0]*256) delay2 * int(coeffs[1]*256) self.pixel_in * int(coeffs[2]*256) filt_out acc 8 return filt_out def build_laplacian_pyramid(self): # Simplified: Gaussian blur then subtract gauss Register(8, gauss) lap WireVector(8, lap) # blur kernel [1,2,1] /4 blur (self.pixel_in self.temp_filter * 2 Register(8)) 2 gauss.next blur lap self.pixel_in - gauss return lap def get_circuit(self): return pyrtl.working_block() class SpatialGaborFilter: def __init__(self, size5, sigma1.0, theta0, lambd4.0): self.kernel self.gabor_kernel(size, sigma, theta, lambd) self.kernel_fixed (self.kernel * 128).astype(np.int8) def gabor_kernel(self, size, sigma, theta, lambd): x np.arange(-size//21, size//21) y x[:, None] x_theta x * np.cos(theta) y * np.sin(theta) y_theta -x * np.sin(theta) y * np.cos(theta) gaussian np.exp(-(x_theta**2 y_theta**2) / (2*sigma**2)) sinusoid np.cos(2*np.pi * x_theta / lambd) return gaussian * sinusoid def convolve_fpga(self, image_block): # simulate distributed arithmetic h, w image_block.shape output np.zeros((h-4, w-4)) for i in range(h-4): for j in range(w-4): patch image_block[i:i5, j:j5] # sign-magnitude representation for DA conv np.sum(patch * self.kernel_fixed) / 128.0 output[i, j] conv return output class FrequencyEstimator: def __init__(self, fs120, n_fft256): self.fs fs self.nfft n_fft self.buffer np.zeros(n_fft) self.idx 0 def add_sample(self, displacement): self.buffer[self.idx] displacement self.idx (self.idx 1) % self.nfft def estimate_freq(self): fft_vals np.fft.rfft(self.buffer) magnitudes np.abs(fft_vals) freqs np.fft.rfftfreq(self.nfft, 1/self.fs) peak_idx np.argmax(magnitudes[1:]) 1 return freqs[peak_idx] # Simulation of the system gabor SpatialGaborFilter() freq_est FrequencyEstimator() # Simulate a sequence of frames for frame_num in range(300): fake_frame np.random.randn(480, 640) * 0.1 # apply Gabor to extract phase phase_map gabor.convolve_fpga(fake_frame) # compute average displacement avg_disp np.mean(phase_map) * 0.05 freq_est.add_sample(avg_disp) if frame_num % 100 0 and frame_num 255: est_freq freq_est.estimate_freq() print(fFrame {frame_num}, estimated vibration frequency: {est_freq:.2f} Hz)