{"id":18325595,"url":"https://github.com/kulia/hilbert-huang-transform","last_synced_at":"2026-03-13T23:32:12.585Z","repository":{"id":84266420,"uuid":"75288346","full_name":"kulia/Hilbert-Huang-transform","owner":"kulia","description":"Implementation of Hilbert-Huang Transform software for matlab.","archived":false,"fork":false,"pushed_at":"2018-10-03T16:54:03.000Z","size":349,"stargazers_count":31,"open_issues_count":0,"forks_count":9,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-10-11T01:15:28.558Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Matlab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kulia.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-12-01T11:58:39.000Z","updated_at":"2025-06-04T07:59:28.000Z","dependencies_parsed_at":"2023-08-12T17:49:48.616Z","dependency_job_id":null,"html_url":"https://github.com/kulia/Hilbert-Huang-transform","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kulia/Hilbert-Huang-transform","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulia%2FHilbert-Huang-transform","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulia%2FHilbert-Huang-transform/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulia%2FHilbert-Huang-transform/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulia%2FHilbert-Huang-transform/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kulia","download_url":"https://codeload.github.com/kulia/Hilbert-Huang-transform/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kulia%2FHilbert-Huang-transform/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30479360,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-13T20:45:58.186Z","status":"ssl_error","status_checked_at":"2026-03-13T20:45:20.133Z","response_time":60,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-05T18:44:19.164Z","updated_at":"2026-03-13T23:32:12.570Z","avatar_url":"https://github.com/kulia.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hilbert-Huang transform\nA light version of the Hilbert-Huang Transform for Matlab. This version uses the Normalized Hilbert Transform to define and calculate the amplitude and phase. \n\n## How to use this software?\n\nThere are two essential functions to the hht code. It is the `emd(·)` and the `hilbertSpectrum(·)`. The `emd(·)` function decomposes a one-dimensional array down to the fewest monocomponents *c*\u003csub\u003e*i*\u003c/sub\u003e(*t*) and one monotonic function *r*(*t*) that is needed to describe it. \n\n## Example\n\nLets considering the equation\n\n  *v(t)* = sin(*ω\u003csub\u003e0\u003c/sub\u003e t*) + 0.5 cos(*ω\u003csub\u003e1\u003c/sub\u003e t*\u003csup\u003e2\u003c/sup\u003e)\n\nIt is shown in the figure below\n\n\u003cimg src=\"fig/examples/raw.png\" width=\"400\"\u003e\n\n### Empirical Mode Decomposition\nAs shown in the example code, we can decompose the voltage waveform *v(t)* using\n\n```c\n[intrinsicModeFunctions, res] = emd(voltageWaveform);\n```\n\nThis will decompose the voltage waveform _v(t)_ down to two intrinsic mode functions (IMFs) and a residue so that\n\n_v(t)_ = Σ_c\u003csub\u003ei\u003c/sub\u003e(t)_ + _r(t)_\n\nwhere _c\u003csub\u003ei\u003c/sub\u003e(t)_ is IMF number _i_ and _r(t)_ is the residue. The IMFs and residue of the example waveform are shown in the figure below.\n\n\u003cimg src=\"fig/examples/imf.png\" width=\"400\"\u003e\n\n### Hilbert Spectrum\nThe IMFs can be visualized using a Hilbert Spectrum. In the Hilbert Spectrum shows the instantaneous frequency _f(t)_ the frequency components power (amplitude squared) as a function of time. To use the Hilbert Spectrum function write\n```c\nmedianFilterLength = 0.02 * samplingFrequency;\nhilbertSpectrum(intrinsicModeFunctions, samplingFrequency, medianFilterLength)\n```\nwhere the `medianFilterLength` is the length of a median filter used to remove artifacts. In this example, the filter length is 2 % of the sampling rate. The figure below shows the Hilbert Spectrum of the example waveform _v(t)_.\n\n\u003cimg src=\"fig/examples/hs.png\" width=\"400\"\u003e\n\n# TODO:\n- [ ] Argument for fixed EMD\n- [ ] Ensure that residue output is correct\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkulia%2Fhilbert-huang-transform","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkulia%2Fhilbert-huang-transform","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkulia%2Fhilbert-huang-transform/lists"}