内部透镜反射来估计超声换能器的透镜厚度、透镜声速和达到峰值时间的代码。，matlab代码.rar

function f = plotProbeParameters(P, data, result) fprintf('Plotting final results...\n'); % unpack somethings metrics = result.metrics; Time = data.Time; XS = data.XS; NR_to_use = P.NR_to_use; RF_plot = data.RF_avg; RF_plot(1:P.numSamplesMute, :) = 0; % only for visualization % compute tof [tof{1}, tof{2}, tof{3}] = n_reflection_traveltime(XS, XS(P.half_width_aperture_nh_el + 1), result.val_c_lens, result.val_h_lens, P.t_rt_matching_layers); % set colormap and figure settings cmap_s = parula; f = figure(81); clf; f.Color = 'w'; f.Position = [100 100 1600 800]; % plot RF data subplot(2, 3, [1 4]); imagesc(1:length(tof{1}), Time * 1e6, RF_plot ./ max(RF_plot)) hold on for k = NR_to_use; plot((tof{k} + result.val_ttp) * 1e6, 'r--', 'linewidth', 1); end for k = setdiff(1:3, NR_to_use); plot((tof{k} + result.val_ttp) * 1e6, 'k--', 'linewidth', 1); end ylabel('arrival time (\mus)') xlabel('elements') % set(gca, 'fontsize', 12) % title(strrep(result.title, '_', ' ')) % plot RF data zoom subplot(2, 3, 2); hold on set(gca, 'YDir', 'reverse') margin_semblance_ind_disp = 5; rf_coherence = cell(3, 1); yax_tmp = (- margin_semblance_ind_disp:margin_semblance_ind_disp); for k = 1:3 t_interp = tof{k} + result.val_ttp + (- margin_semblance_ind_disp:margin_semblance_ind_disp).' ./ P.Fs; sig_interp = interp1_per_channel(Time, RF_plot, t_interp); rf_coherence{k} = sig_interp; end % tv_disp = Time(1:margin_semblance_ind_disp * 2 + 1); tv_disp = tv_disp - mean(tv_disp); nREF_to_plot = max(NR_to_use); yticks_tmp = zeros(nREF_to_plot, 1); for k = 1:nREF_to_plot yax_k = yax_tmp + (numel(yax_tmp) + 2) * (k - 1); xax_k = 0:size(rf_coherence{k}, 2); imagesc(xax_k, yax_k, rf_coherence{k} ./ max(rf_coherence{k})) yline(mean(yax_k), 'r') xlim([min(xax_k), max(xax_k)]) yticks_tmp(k) = mean(yax_k); end yticks(yticks_tmp); yticklabels(compose('refl %i', 1:nREF_to_plot)) % title(sprintf('ttp idx %i/%i - Metric score: %.2f', ... % result.idx_ttp, numel(ttp_vals), metrics_peak)); h_vals = P.thickness_values_test; c_vals = P.c_values_test; ttp_vals = P.ttp_values_test; % plot solution metrics subplot(2, 3, 3); imagesc(h_vals * 1e3, c_vals, squeeze(metrics(result.idx_ttp, :, :))) colorbar xlabel('lens thickness (mm)') ylabel('lens wavespeed (m/s)') hold on scatter(result.val_h_lens * 1e3, result.val_c_lens, 'rx') colormap(cmap_s) title(sprintf('t_{ttp} = %.2f ns', result.val_ttp * 1e9)) subplot(2, 3, 5); imagesc(ttp_vals * 1e9, c_vals, squeeze(metrics(:, :, result.idx_h_lens)).') colorbar xlabel('time to peak (us)') ylabel('lens wavespeed (m/s)') hold on scatter(result.val_ttp * 1e9, result.val_c_lens, 'rx') colormap(cmap_s) title(sprintf('h_{lens} = %.2f mm', result.val_h_lens * 1e3)) subplot(2, 3, 6); imagesc(h_vals * 1e3, ttp_vals * 1e9, squeeze(metrics(:, result.idx_c_lens, :))) colorbar ylabel('time to peak (us)') xlabel('lens thickness (mm)') hold on scatter(result.val_h_lens * 1e3, result.val_ttp * 1e9, 'rx') colormap(cmap_s) title(sprintf('c_{lens} = %.2f m/s', result.val_c_lens)) end