{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2008:5K5YLRQ7F3F6EPQ25EYMQ5T4WA","short_pith_number":"pith:5K5YLRQ7","schema_version":"1.0","canonical_sha256":"eabb85c61f2ecbe23e1ae930c8767cb03970ebe668262e95122aa6e34bc68a67","source":{"kind":"arxiv","id":"0809.3711","version":1},"attestation_state":"computed","paper":{"title":"Chirplet approximation of band-limited, real signals made easy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"J.M Greenberg, Laurent Gosse (CNR BARI)","submitted_at":"2008-09-22T13:59:49Z","abstract_excerpt":"In this paper we present algorithms for approximating real band-limited signals by multiple Gaussian Chirps. These algorithms do not rely on matching pursuit ideas. They are hierarchial and, at each stage, the number of terms in a given approximation depends only on the number of positive-valued maxima and negative-valued minima of a signed amplitude function characterizing part of the signal. Like the algorithms used in \\cite{gre2} and unlike previous methods, our chirplet approximations require neither a complete dictionary of chirps nor complicated multi-dimensional searches to obtain suita"},"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":"0809.3711","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2008-09-22T13:59:49Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"148ca939ebe99645a5c7280d466d9ba5fc5f39f4a45debbcffffb5e84d01937f","abstract_canon_sha256":"5c41e5bfb7724609be686f03be4f01244237081385573d57cd2dfa399dd22f9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T22:06:10.151798Z","signature_b64":"PpC73qjOa+yqDkhAY/hBLT0dT+oqcB9F9QbnkrKSyQAvOVfI+48QwkY3vrQZWwQhIzvn4NrfFBodW8sJqyJ0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eabb85c61f2ecbe23e1ae930c8767cb03970ebe668262e95122aa6e34bc68a67","last_reissued_at":"2026-06-03T22:06:10.151176Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T22:06:10.151176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chirplet approximation of band-limited, real signals made easy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"J.M Greenberg, Laurent Gosse (CNR BARI)","submitted_at":"2008-09-22T13:59:49Z","abstract_excerpt":"In this paper we present algorithms for approximating real band-limited signals by multiple Gaussian Chirps. These algorithms do not rely on matching pursuit ideas. They are hierarchial and, at each stage, the number of terms in a given approximation depends only on the number of positive-valued maxima and negative-valued minima of a signed amplitude function characterizing part of the signal. Like the algorithms used in \\cite{gre2} and unlike previous methods, our chirplet approximations require neither a complete dictionary of chirps nor complicated multi-dimensional searches to obtain suita"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0809.3711","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/0809.3711/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":"0809.3711","created_at":"2026-06-03T22:06:10.151268+00:00"},{"alias_kind":"arxiv_version","alias_value":"0809.3711v1","created_at":"2026-06-03T22:06:10.151268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0809.3711","created_at":"2026-06-03T22:06:10.151268+00:00"},{"alias_kind":"pith_short_12","alias_value":"5K5YLRQ7F3F6","created_at":"2026-06-03T22:06:10.151268+00:00"},{"alias_kind":"pith_short_16","alias_value":"5K5YLRQ7F3F6EPQ2","created_at":"2026-06-03T22:06:10.151268+00:00"},{"alias_kind":"pith_short_8","alias_value":"5K5YLRQ7","created_at":"2026-06-03T22:06:10.151268+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/5K5YLRQ7F3F6EPQ25EYMQ5T4WA","json":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA.json","graph_json":"https://pith.science/api/pith-number/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/graph.json","events_json":"https://pith.science/api/pith-number/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/events.json","paper":"https://pith.science/paper/5K5YLRQ7"},"agent_actions":{"view_html":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA","download_json":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA.json","view_paper":"https://pith.science/paper/5K5YLRQ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0809.3711&json=true","fetch_graph":"https://pith.science/api/pith-number/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/graph.json","fetch_events":"https://pith.science/api/pith-number/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/action/storage_attestation","attest_author":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/action/author_attestation","sign_citation":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/action/citation_signature","submit_replication":"https://pith.science/pith/5K5YLRQ7F3F6EPQ25EYMQ5T4WA/action/replication_record"}},"created_at":"2026-06-03T22:06:10.151268+00:00","updated_at":"2026-06-03T22:06:10.151268+00:00"}