{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:TJTI4KBGBBYAMZSEDB2MNRX3BS","short_pith_number":"pith:TJTI4KBG","schema_version":"1.0","canonical_sha256":"9a668e282608700666441874c6c6fb0c9f9fa44c2f807ee23746bd29ade077ad","source":{"kind":"arxiv","id":"0912.2437","version":1},"attestation_state":"computed","paper":{"title":"Synchrosqueezed Wavelet Transforms: a Tool for Empirical Mode Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Hau-Tieng Wu, Ingrid Daubechies, Jianfeng Lu","submitted_at":"2009-12-12T17:38:44Z","abstract_excerpt":"The EMD algorithm, first proposed in [11], made more robust as well as more versatile in [12], is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time-frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies. The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies -- see, e.g. [13]. On the other hand, the EMD algorithm con"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"0912.2437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2009-12-12T17:38:44Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"50612e81224594dee6670e5b04bcc4cc62c1093439dc5fbe8b45153a545daa5b","abstract_canon_sha256":"5463c381fc4310a37ea477ffb1d8b8096b89b8b173352e324b053a14abdcd194"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T23:06:31.483085Z","signature_b64":"ptPlRtGRAXC8KX9FiFWYkNk7iYIB7XcYGtp96G2zxdpoUamOP316ebHpIVA4KQtaLHHRHuF+O6ZC30BfeAZAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a668e282608700666441874c6c6fb0c9f9fa44c2f807ee23746bd29ade077ad","last_reissued_at":"2026-06-03T23:06:31.482615Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T23:06:31.482615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Synchrosqueezed Wavelet Transforms: a Tool for Empirical Mode Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Hau-Tieng Wu, Ingrid Daubechies, Jianfeng Lu","submitted_at":"2009-12-12T17:38:44Z","abstract_excerpt":"The EMD algorithm, first proposed in [11], made more robust as well as more versatile in [12], is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time-frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies. The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies -- see, e.g. [13]. On the other hand, the EMD algorithm con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0912.2437","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/0912.2437/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"0912.2437","created_at":"2026-06-03T23:06:31.482691+00:00"},{"alias_kind":"arxiv_version","alias_value":"0912.2437v1","created_at":"2026-06-03T23:06:31.482691+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0912.2437","created_at":"2026-06-03T23:06:31.482691+00:00"},{"alias_kind":"pith_short_12","alias_value":"TJTI4KBGBBYA","created_at":"2026-06-03T23:06:31.482691+00:00"},{"alias_kind":"pith_short_16","alias_value":"TJTI4KBGBBYAMZSE","created_at":"2026-06-03T23:06:31.482691+00:00"},{"alias_kind":"pith_short_8","alias_value":"TJTI4KBG","created_at":"2026-06-03T23:06:31.482691+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS","json":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS.json","graph_json":"https://pith.science/api/pith-number/TJTI4KBGBBYAMZSEDB2MNRX3BS/graph.json","events_json":"https://pith.science/api/pith-number/TJTI4KBGBBYAMZSEDB2MNRX3BS/events.json","paper":"https://pith.science/paper/TJTI4KBG"},"agent_actions":{"view_html":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS","download_json":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS.json","view_paper":"https://pith.science/paper/TJTI4KBG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0912.2437&json=true","fetch_graph":"https://pith.science/api/pith-number/TJTI4KBGBBYAMZSEDB2MNRX3BS/graph.json","fetch_events":"https://pith.science/api/pith-number/TJTI4KBGBBYAMZSEDB2MNRX3BS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS/action/storage_attestation","attest_author":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS/action/author_attestation","sign_citation":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS/action/citation_signature","submit_replication":"https://pith.science/pith/TJTI4KBGBBYAMZSEDB2MNRX3BS/action/replication_record"}},"created_at":"2026-06-03T23:06:31.482691+00:00","updated_at":"2026-06-03T23:06:31.482691+00:00"}