{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:KHO2PLPKKLXA7NZHUGORHLMV4A","short_pith_number":"pith:KHO2PLPK","schema_version":"1.0","canonical_sha256":"51dda7adea52ee0fb727a19d13ad95e02a9a4da6ad15ffc18df2b5ad79ca6eff","source":{"kind":"arxiv","id":"1303.1719","version":1},"attestation_state":"computed","paper":{"title":"Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Gaetano Scarano, Giorgia Ruggiero, Roberto Cusani, Stefania Colonnese, Stefano Rinauro","submitted_at":"2013-01-25T09:04:30Z","abstract_excerpt":"Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically eval"},"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":"1303.1719","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2013-01-25T09:04:30Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"5f1fb5ac40285fc0560dc400b642a0fc7a4354c7e91ebf28c42fd343c09f3eb7","abstract_canon_sha256":"8d2ec39511a7d6409256261c53e498e69288fd5be07c4bc0d2c44e18432040cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:08.629734Z","signature_b64":"yuzTlr2zrXMzXpfUBhKaI6YxRmwLHIW/72hyKt+E45awj+ToluL1p+Olm39GRdhAJmXMJBHJJTDrUhrtiPdDDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51dda7adea52ee0fb727a19d13ad95e02a9a4da6ad15ffc18df2b5ad79ca6eff","last_reissued_at":"2026-05-18T02:51:08.629296Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:08.629296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Gaetano Scarano, Giorgia Ruggiero, Roberto Cusani, Stefania Colonnese, Stefano Rinauro","submitted_at":"2013-01-25T09:04:30Z","abstract_excerpt":"Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically eval"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.1719","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":""},"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":"1303.1719","created_at":"2026-05-18T02:51:08.629366+00:00"},{"alias_kind":"arxiv_version","alias_value":"1303.1719v1","created_at":"2026-05-18T02:51:08.629366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.1719","created_at":"2026-05-18T02:51:08.629366+00:00"},{"alias_kind":"pith_short_12","alias_value":"KHO2PLPKKLXA","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_16","alias_value":"KHO2PLPKKLXA7NZH","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_8","alias_value":"KHO2PLPK","created_at":"2026-05-18T12:27:49.015174+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/KHO2PLPKKLXA7NZHUGORHLMV4A","json":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A.json","graph_json":"https://pith.science/api/pith-number/KHO2PLPKKLXA7NZHUGORHLMV4A/graph.json","events_json":"https://pith.science/api/pith-number/KHO2PLPKKLXA7NZHUGORHLMV4A/events.json","paper":"https://pith.science/paper/KHO2PLPK"},"agent_actions":{"view_html":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A","download_json":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A.json","view_paper":"https://pith.science/paper/KHO2PLPK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1303.1719&json=true","fetch_graph":"https://pith.science/api/pith-number/KHO2PLPKKLXA7NZHUGORHLMV4A/graph.json","fetch_events":"https://pith.science/api/pith-number/KHO2PLPKKLXA7NZHUGORHLMV4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A/action/storage_attestation","attest_author":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A/action/author_attestation","sign_citation":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A/action/citation_signature","submit_replication":"https://pith.science/pith/KHO2PLPKKLXA7NZHUGORHLMV4A/action/replication_record"}},"created_at":"2026-05-18T02:51:08.629366+00:00","updated_at":"2026-05-18T02:51:08.629366+00:00"}