Paper Citation Record · LEDGER

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.05435.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.05435 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:36:36.978986Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact1
verified fuzzy17
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f740b88-60fc-4986-b3aa-f7ef332d450e · outbound

This paper cites Smart packaging: Opportunities and challenges,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Smart packaging: Opportunities and challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.246230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ae990b2-9932-4be3-878a-5d81d8a8e707 · outbound

This paper cites Design and implementation of an accurate real time gps tracking system,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Design and implementation of an accurate real time gps tracking system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.238299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b2e62e5-113d-4175-9afb-b294738b4d63 · outbound

This paper cites Indoor multiple sound source tracking using refined tdoa measurements,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Indoor multiple sound source tracking using refined tdoa measurements,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.229154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ac25f719-03d2-42c7-b6cb-7cfddbf4866d · outbound

This paper cites Iot-td: Iot dataset for multiple model ble-based indoor localization/tracking,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Iot-td: Iot dataset for multiple model ble-based indoor localization/tracking,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.220632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e431a83f-f00c-4f29-96b6-a122f69582b9 · outbound

This paper cites Wearable sport activity classification based on deep convolutional neural network,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Wearable sport activity classification based on deep convolutional neural network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.211815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 035a442f-e581-4db7-b4ae-b76a4f305f14 · outbound

This paper cites Machinery value estimation method based on iiot system utilizing 1d-cnn model for low sampling rate vibration signals from mems,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Machinery value estimation method based on iiot system utilizing 1d-cnn model for low sampling rate vibration signals from mems,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.203197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9c59eee-2b97-4b23-9cc3-ce4b3da7f118 · outbound

This paper cites An empirical study on ai-powered edge computing architectures for real-time iot applications,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning An empirical study on ai-powered edge computing architectures for real-time iot applications,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.193935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf3a0d39-6a76-426b-b6a0-77627a07b4bc · outbound

This paper cites Tinyml smart sensor for energy saving in internet of things precision agriculture platform,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Tinyml smart sensor for energy saving in internet of things precision agriculture platform,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.184972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c9c2e05-e020-4219-b556-fdf98a7abfd7 · outbound

This paper cites tinycare: A tinyml-based low-cost continu- ous blood pressure estimation on the extreme edge,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning tinycare: A tinyml-based low-cost continu- ous blood pressure estimation on the extreme edge,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.174697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8dd727b-4e68-4901-b58d-854b26c76caa · outbound

This paper cites A comprehensive survey on tinyml,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning A comprehensive survey on tinyml,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.165002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cfa1c1fd-1fe2-4515-8a9f-a3c3d88c5fe1 · outbound

This paper cites SMOTE: Synthetic minority over-sampling technique,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning SMOTE: Synthetic minority over-sampling technique,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.155345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e1e71f07-31b4-4cee-b289-577916660984 · outbound

This paper cites ADASYN: Adaptive synthetic sampling approach for imbalanced learning,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning ADASYN: Adaptive synthetic sampling approach for imbalanced learning,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-07T10:36:37.082676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfad3f62-dd1b-415c-b892-099adc195ae9 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:36:36.953775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 131b74b3-40a1-408f-b057-81c23224f5fa · outbound

This paper cites Accessed: 2024-09-13.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Accessed: 2024-09-13

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.145759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99d5b7d5-03b9-404c-8b14-f15677c458a3 · outbound

This paper cites Accessed: 2024-09-13.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Accessed: 2024-09-13

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.136127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34a4890a-a32c-4e31-a2f4-b884c1e85f06 · outbound

This paper cites Optimal brain damage,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Optimal brain damage,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.127180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0dd14dab-1b88-4d65-930f-9052754636a8 · outbound

This paper cites Inter- operability of compression techniques for efficient deployment of CNNs on microcontrollers,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Inter- operability of compression techniques for efficient deployment of CNNs on microcontrollers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.118004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2dd30d97-60bd-48f2-a97f-81bf6f48f912 · outbound

This paper cites Learning both weights and con- nections for efficient neural network,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Learning both weights and con- nections for efficient neural network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.107820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64761a32-605d-4e96-84ef-4489c88d2bed · outbound

This paper cites Quantized Convolutional Neural Networks for Mobile Devices.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Quantized Convolutional Neural Networks for Mobile Devices

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:36:36.972746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d92d8834-c67b-4bf8-994b-9a7c68191875 · outbound

This paper cites A method for the construction of minimum-redundancy codes,.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning A method for the construction of minimum-redundancy codes,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:36:36.976020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e713826-c2f5-47d2-a228-6f990082ef94 · outbound

This paper cites Accessed: 2024-11-27.

Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning Accessed: 2024-11-27

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:36:37.093301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.