{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IB2A4PJJQXDICOCTZFBQQ5MMWG","short_pith_number":"pith:IB2A4PJJ","schema_version":"1.0","canonical_sha256":"40740e3d2985c6813853c94308758cb19f56286af83030812dca23eabd606bd8","source":{"kind":"arxiv","id":"2408.04210","version":4},"attestation_state":"computed","paper":{"title":"Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Manjun Cui, Yangfan He, Zhichao Zhang","submitted_at":"2024-08-08T04:31:00Z","abstract_excerpt":"Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising, with a particular focus on non-stationary signal processing in practical applications}. We integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal auto"},"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":"2408.04210","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-08-08T04:31:00Z","cross_cats_sorted":[],"title_canon_sha256":"380bcf114d1606779884a5649cc7ad9bb8a15cde9b2a107fc17c75a6bdb1fe7b","abstract_canon_sha256":"a88e753c5c7a0e6f253db4e710a682c4145883a120b8f0a5efd0b5a8cffd9b75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:03.618057Z","signature_b64":"iWSViUtHTnLs/ryMT5/2tahuQ9/w/UAkhez48Y6dP7EcgZhgLfzgIagXh6AgzxEaLZugFmETLGLVPd6ua7r2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40740e3d2985c6813853c94308758cb19f56286af83030812dca23eabd606bd8","last_reissued_at":"2026-06-03T01:05:03.617644Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:03.617644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Manjun Cui, Yangfan He, Zhichao Zhang","submitted_at":"2024-08-08T04:31:00Z","abstract_excerpt":"Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising, with a particular focus on non-stationary signal processing in practical applications}. We integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal auto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04210","kind":"arxiv","version":4},"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/2408.04210/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":"2408.04210","created_at":"2026-06-03T01:05:03.617702+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04210v4","created_at":"2026-06-03T01:05:03.617702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04210","created_at":"2026-06-03T01:05:03.617702+00:00"},{"alias_kind":"pith_short_12","alias_value":"IB2A4PJJQXDI","created_at":"2026-06-03T01:05:03.617702+00:00"},{"alias_kind":"pith_short_16","alias_value":"IB2A4PJJQXDICOCT","created_at":"2026-06-03T01:05:03.617702+00:00"},{"alias_kind":"pith_short_8","alias_value":"IB2A4PJJ","created_at":"2026-06-03T01:05:03.617702+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/IB2A4PJJQXDICOCTZFBQQ5MMWG","json":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG.json","graph_json":"https://pith.science/api/pith-number/IB2A4PJJQXDICOCTZFBQQ5MMWG/graph.json","events_json":"https://pith.science/api/pith-number/IB2A4PJJQXDICOCTZFBQQ5MMWG/events.json","paper":"https://pith.science/paper/IB2A4PJJ"},"agent_actions":{"view_html":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG","download_json":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG.json","view_paper":"https://pith.science/paper/IB2A4PJJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04210&json=true","fetch_graph":"https://pith.science/api/pith-number/IB2A4PJJQXDICOCTZFBQQ5MMWG/graph.json","fetch_events":"https://pith.science/api/pith-number/IB2A4PJJQXDICOCTZFBQQ5MMWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG/action/storage_attestation","attest_author":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG/action/author_attestation","sign_citation":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG/action/citation_signature","submit_replication":"https://pith.science/pith/IB2A4PJJQXDICOCTZFBQQ5MMWG/action/replication_record"}},"created_at":"2026-06-03T01:05:03.617702+00:00","updated_at":"2026-06-03T01:05:03.617702+00:00"}