{"id":106,"date":"2026-01-29T09:40:33","date_gmt":"2026-01-29T14:40:33","guid":{"rendered":"https:\/\/carleton.ca\/adrianchan\/?page_id=106"},"modified":"2026-01-29T09:42:35","modified_gmt":"2026-01-29T14:42:35","slug":"research-resources","status":"publish","type":"page","link":"https:\/\/carleton.ca\/adrianchan\/research-resources\/","title":{"rendered":"Research Resources"},"content":{"rendered":"\n<section class=\"w-screen px-6 cu-section cu-section--white ml-offset-center md:px-8 lg:px-14\">\n    <div class=\"space-y-6 cu-max-w-child-5xl  md:space-y-10 cu-prose-first-last\">\n\n            <div class=\"cu-textmedia flex flex-col lg:flex-row mx-auto gap-6 md:gap-10 my-6 md:my-12 first:mt-0 max-w-5xl\">\n        <div class=\"justify-start cu-textmedia-content cu-prose-first-last\" style=\"flex: 0 0 100%;\">\n            <header class=\"font-light prose-xl cu-pageheader md:prose-2xl cu-component-updated cu-prose-first-last\">\n                                    <h1 class=\"cu-prose-first-last font-semibold !mt-2 mb-4 md:mb-6 relative after:absolute after:h-px after:bottom-0 after:bg-cu-red after:left-px text-3xl md:text-4xl lg:text-5xl lg:leading-[3.5rem] pb-5 after:w-10 text-cu-black-700 not-prose\">\n                        Research Resources\n                    <\/h1>\n                \n                                \n                            <\/header>\n\n                    <\/div>\n\n            <\/div>\n\n    <\/div>\n<\/section>\n\n\n\n<h2 id=\"data\" class=\"wp-block-heading\">Data<\/h2>\n\n\n\n<p>If you would like access to particular data, please contact me via&nbsp;<a href=\"https:\/\/carleton.ca\/adrianchan\/contact\/\">email<\/a>.<\/p>\n\n\n\n<h2 id=\"biomedical-signal-quality-analysis\" class=\"wp-block-heading\">Biomedical Signal Quality Analysis<\/h2>\n\n\n\n<p>The Matlab files will enable people researching biomedical signal quality analysis to have a common methodology to compare against.<\/p>\n\n\n\n<p><strong>Keywords:<\/strong>&nbsp;biological signal, biosignal, electrocardiogram (EMG), Matlab, signal quality index (SQI), signal processing, signal quality analysis<\/p>\n\n\n\n<h3 id=\"usage\" class=\"wp-block-heading\">Usage<\/h3>\n\n\n\n<p>If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/doi.org\/10.1109\/MeMeA.2013.6549694\" target=\"_blank\" rel=\"noreferrer noopener\">Quesnel P, Chan ADC, Yang H, &#8220;Real-Time Biosignal Quality Analysis of Ambulatory ECG for Detection of Myocardial Ischemia&#8221;, IEEE International Symposium on Medical Measurements and Applications, Ottawa, Canada, pp. 1-5, 2013.<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/doi.org\/10.1109\/TBME.2016.2602283\" target=\"_blank\" rel=\"noreferrer noopener\">Abdelazez M, Quesnel PX, Chan ADC, Yang H, &#8220;Signal Quality Analysis of Ambulatory Electrocardiograms to Gate False Myocardial Ischemia Alarms&#8221;, IEEE Transactions on Biomedical Engineering, vol. 64. no. 6. pp 1318-1325, 2017. doi:10.1109\/TBME.2016.2602283<\/a><\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/SQIexample.zip\">SQIexample.zip<\/a>&nbsp;(20-07-16) Example code of SQI that contaminates ECG with motion artifact at different levels of SNR and shows how the SQI varies with SNR.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/ecgsqi.m\">ecgsqi.m<\/a>&nbsp;signal quality index for ECG<\/p>\n\n\n\n<h2 id=\"motion-artifact-signal-generation-toolkit\" class=\"wp-block-heading\">Motion Artifact Signal Generation Toolkit<\/h2>\n\n\n\n<p>These files include simulated motion artifact, pretrained models to simulate motion artifact, and the ability to train new models. The models include an autoregressive (AR) model, a Markov chain model, and a recurrent neural network (RNN). The simulated motion artifact can be added to bioelectric signal recordings (e.g., ECG, EMG) for biomedical signal quality analysis research.<\/p>\n\n\n\n<p><strong>Keywords:<\/strong>&nbsp;biological signal, biosignal, electrocardiogram (EMG), motion artifact, python, signal processing, signal quality analysis<\/p>\n\n\n\n<h3 id=\"usage\" class=\"wp-block-heading\">Usage<\/h3>\n\n\n\n<p>If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/doi.org\/10.1109\/MeMeA54994.2022.9856579\" target=\"_blank\" rel=\"noreferrer noopener\">Kulpa J, Farago E, Chan ADC, &#8220;A Toolkit for Motion Artifact Signal Generation&#8221;,&nbsp;IEEE International Symposium on Medical Measurements and Applications, Taormina, Italy, 2022.<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/doi.org\/10.1016\/j.bspc.2021.102611\" target=\"_blank\" rel=\"noreferrer noopener\">Farago E, Chan ADC, &#8220;Motion artifact synthesis for research in biomedical signal quality analysis&#8221;, Journal of Biomedical Signal Processing and Control, vol. 68, 2021.<\/a><\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/MotionArtifactModels.zip\">MotionArtifactModels.zip<\/a>&nbsp;(22-08-08) Simulated motion artifact, pretrained models to simulate motion artifact, and software to train and simulate motion artifact.<\/p>\n\n\n\n<h2 id=\"motion-artifact-signal-database\" class=\"wp-block-heading\">Motion Artifact Signal Database<\/h2>\n\n\n\n<p>These files include motion artifact signals obtained by taking 84 ambulatory ECG recordings from the&nbsp;<a href=\"https:\/\/physionet.org\/content\/ltafdb\/1.0.0\/\" target=\"_blank\" rel=\"noreferrer noopener\">Physionet Long Term AF database<\/a>&nbsp;and removing the ECG. Each recording is about 24-hours in length.<\/p>\n\n\n\n<p><strong>Keywords:<\/strong>&nbsp;biological signal, biosignal, motion artifact, signal quality analysis<\/p>\n\n\n\n<h3 id=\"usage\" class=\"wp-block-heading\">Usage<\/h3>\n\n\n\n<p>If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Kulpa J, Chan ADC, &#8220;Electrocardiogram Removal to Establish a Motion Artifact Database&#8221;,&nbsp;<em>submitted to<\/em>&nbsp;IEEE Canadian Conference on Electrical and Computer Engineering, Regina SK, Canada, 2023.<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/MotionArtifactSignals.zip\">MotionArtifactSignals.zip<\/a>&nbsp;(23-04-22) A sample of 10 motion artifact signals, each around 24-hours in length.<\/p>\n\n\n\n<h2 id=\"myoelectric-control-meclab\" class=\"wp-block-heading\">Myoelectric Control (MECLab)<\/h2>\n\n\n\n<p>The Matlab files will enable people researching MES\/EMG classification methods to have a common methodology to compare against. The methodology used is a relatively simple and direct approach using ULDA feature reduction and an LDA classifier; however, it has shown to be quite effective.<\/p>\n\n\n\n<p><strong>Keywords:<\/strong>&nbsp;biological signal, electromyography (EMG), feature reduction, Matlab, myoelectric control, myoelectric signals (MES), pattern classification, prosthetic control, prosthesis, signal processing<\/p>\n\n\n\n<h3 id=\"usage\" class=\"wp-block-heading\">Usage<\/h3>\n\n\n\n<p>If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/myoelectric%20control%20development%20toolbox.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Chan ADC, Green GC, &#8220;Myoelectric control development toolbox&#8221;, 30th Conference of the Canadian Medical &amp; Biological Engineering Society, Toronto, Canada, M0100, 2007.<\/a><\/li>\n<\/ul>\n\n\n\n<h3 id=\"myoelectric-control-example\" class=\"wp-block-heading\">Myoelectric Control Example<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/MECexample.zip\">MECexample.zip<\/a>&nbsp;(10-02-08) Example code and data to classify eight channels of myoelectric data to predict seven upper arm motions (i.e. using electromyography (EMG) signals for control of upper limb prostheses).<\/p>\n\n\n\n<h3 id=\"feature-extraction\" class=\"wp-block-heading\">Feature Extraction<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getrmsfeat.m\">getrmsfeat.m<\/a>&nbsp;root mean square feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getmavfeat.m\">getmavfeat.m<\/a>&nbsp;mean absolute value feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getiavfeat.m\">getiavfeat.m<\/a>&nbsp;integrated absolute value feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getarfeat.m\">getarfeat.m<\/a>&nbsp;autoregressive feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getzcfeat.m\">getzcfeat.m<\/a>&nbsp;zero crossing feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getsscfeat.m\">getsscfeat.m<\/a>&nbsp;slope sign change feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getwlfeat.m\">getwlfeat.m<\/a>&nbsp;waveform length feature<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/scatterplot.m\">scatterplot.m<\/a>&nbsp;creates a scatter plot of the feature vector (likely want to use pca or ulda feature reduction first)<\/p>\n\n\n\n<h3 id=\"feature-reduction\" class=\"wp-block-heading\">Feature Reduction<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/pca_feature_reduction.m\">pca_feature_reduction.m<\/a>&nbsp;principal component analysis feature reduction<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/ulda_feature_reduction.m\">ulda_feature_reduction.m<\/a>&nbsp;uncorrelated linear discriminant analysis feature reduction<\/p>\n\n\n\n<h3 id=\"classification\" class=\"wp-block-heading\">Classification<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/score_classify.m\">score_classify.m<\/a>&nbsp;converts columns of scores into classification outputs (numbers)<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/confmat.m\">confmat.m<\/a>&nbsp;generates a confusion matrix<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/plotconfmat.m\">plotconfmat.m<\/a>&nbsp;plots a confusion matrix<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/plotconfmattext.m\">plotconfmattext.m<\/a>&nbsp;plots a confusion matrix in text format<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/find_rank.m\">find_rank.m<\/a>&nbsp;find the rank of a particular class given columns of scores<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/rank_classify.m\">rank_classify.m<\/a>&nbsp;converts columns of scores into ranks<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/majority_vote.m\">majority_vote.m<\/a>&nbsp;performs majority vote post processing on classification decisions<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/classification_timeplot.m\">classification_timeplot.m<\/a>&nbsp;plots classification results as a function of time<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/ldaclassify.m\">lda_classify.m<\/a>&nbsp;classification performed by linear discriminant analysis<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/knnclassify.m\">knn_classify.m<\/a>&nbsp;classification performed by k-nearest neighbors<\/p>\n\n\n\n<h3 id=\"myoelectric-signal-processing\" class=\"wp-block-heading\">Myoelectric signal processing<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/meanfrequency.m\">meanfrequency.m<\/a>&nbsp;computes the mean frequency<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/medianfrequency.m\">medianfrequency.m<\/a>&nbsp;(09-10-07) computes the median frequency<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/SMratio.m\">SMratio.m<\/a>&nbsp;computes the signal-to-motion artifact ratio<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/DPratio.m\">DPratio.m<\/a>&nbsp;computes the maximum-to-minimum drop in power density<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/SNratio.m\">SNratio.m<\/a>&nbsp;computes the signal-to-noise ratio<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/OHMratio.m\">OHMratio.m<\/a>&nbsp;computes the spectral deformation<\/p>\n\n\n\n<h3 id=\"miscellaneous\" class=\"wp-block-heading\">Miscellaneous<\/h3>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/remove_transitions.m\">remove_transitions.m<\/a>&nbsp;(10-02-08) will remove transitional data (e.g. from a time series of feature vectors)<\/p>\n\n\n\n<h2 id=\"ecg-person-identification\" class=\"wp-block-heading\">ECG Person Identification<\/h2>\n\n\n\n<p><strong>Keywords:<\/strong>&nbsp;biological signal, electrcardiography (ECG), electrcardiogram, wavelet, Matlab, biometric, person identification<\/p>\n\n\n\n<h3 id=\"usage\" class=\"wp-block-heading\">Usage<\/h3>\n\n\n\n<p>If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"http:\/\/dx.doi.org\/10.1109\/TIM.2007.909996\" target=\"_blank\" rel=\"noreferrer noopener\">Chan ADC, Hamdy MM, Badre A, Badee V, &#8220;Wavelet distance measure for person identification using electrocardiograms&#8221;, IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 2, pp. 248-253, 2008.<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/doi.org\/10.1109\/CCECE.2006.277291\" target=\"_blank\" rel=\"noreferrer noopener\">Chan ADC, Hamdy MM, Badre A, Badee V, &#8220;Person identification using electrocardiograms&#8221;, IEEE Canadian Conference on Electrical and Computer Engineering, Ottawa, Canada, DRDC Workshop S. No.1, 2006.<\/a><\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/ECGBiometricData.zip\">ECG Biometric Data Example<\/a>&nbsp;ECG from 10 subjects from three sessions on separate days.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/wavelet_dist.m\">wavelet_dist.m<\/a>&nbsp;wavelet distance measure<\/p>\n\n\n\n<h2 id=\"adaptive-signal-processing\" class=\"wp-block-heading\">Adaptive Signal Processing<\/h2>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/anc_lms.m\">anc_lms.m<\/a>&nbsp;adaptive filter using the LMS algorithm<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/anc_rls.m\">anc_rls.m<\/a>&nbsp;adaptive filter using the RLS algorithm<\/p>\n\n\n\n<h2 id=\"miscellaneous\" class=\"wp-block-heading\">Miscellaneous<\/h2>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/find_delay.m\">find_delay.m<\/a>&nbsp;finds the delay (in samples) between two signals<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/approxequal.m\">approxequal.m<\/a>&nbsp;logical function to compare numbers to see if they are within a certain tolerance of each other<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/remove_mean.m\">remove_mean.m<\/a>&nbsp;removes the mean from signals that are arranged in columns<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/gausspdf.m\">gausspdf.m<\/a>&nbsp;computes the Gaussian probability distribution function<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/loggausspdf.m\">loggausspdf.m<\/a>&nbsp;computes the log Gaussian probability distribution function<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/prd.m\">prd.m<\/a>&nbsp;computes the percent residual difference<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/fft_freq.m\">fft_freq.m<\/a>&nbsp;computes fft with corresponding frequencies (fftshift is optional)<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/M15GUI.zip\">M15GUI.zip<\/a>&nbsp;software to configure the Grass-Telefactor Model 15 Neurodata Amplifier System<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/findqrs_mobd.m\">findqrs_mobd.m<\/a>&nbsp;this function is an implementation of the MOBD algorithm for QRS detection<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sce.carleton.ca\/faculty\/chan\/matlab\/getaxondata.m\">getaxondata.m<\/a>&nbsp;this function loads data from Axon file (generated from AxoScope)<\/p>\n\n\n\n<h2 id=\"disclaimer\" class=\"wp-block-heading\">Disclaimer<\/h2>\n\n\n\n<p>The files provided are distributed &#8220;AS IS&#8221; and &#8220;WITH ALL FAULTS&#8221;. We do not offer a warranty for the content or use of these files, nor do we guarantee their quality, accuracy, fitness for a particular purpose, or safety, either expressed or implied. All questions, complaints, issues, and claims related to files should be directed to the contributing author.<\/p>\n\n\n\n<p>You assume all risk associated with downloading these files from this site.<\/p>\n\n\n\n<p>You are solely responsible for protecting yourself against viruses and backing up data, files, and hardware used in conjunction with the files.<\/p>\n\n\n\n<p>Matlab is a registered trademark of <a href=\"https:\/\/www.mathworks.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">The Mathworks, Inc.<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data If you would like access to particular data, please contact me via&nbsp;email. Biomedical Signal Quality Analysis The Matlab files will enable people researching biomedical signal quality analysis to have a common methodology to compare against. Keywords:&nbsp;biological signal, biosignal, electrocardiogram (EMG), Matlab, signal quality index (SQI), signal processing, signal quality analysis Usage If you are [&hellip;]<\/p>\n","protected":false},"author":397,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_cu_dining_location_slug":"","footnotes":"","_links_to":"","_links_to_target":""},"cu_page_type":[],"class_list":["post-106","page","type-page","status-publish","hentry"],"acf":{"cu_post_thumbnail":""},"_links":{"self":[{"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/pages\/106","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/users\/397"}],"replies":[{"embeddable":true,"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/comments?post=106"}],"version-history":[{"count":3,"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/pages\/106\/revisions"}],"predecessor-version":[{"id":110,"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/pages\/106\/revisions\/110"}],"wp:attachment":[{"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/media?parent=106"}],"wp:term":[{"taxonomy":"cu_page_type","embeddable":true,"href":"https:\/\/carleton.ca\/adrianchan\/wp-json\/wp\/v2\/cu_page_type?post=106"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}