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Sensor data.

Pierre V. Baudin (16955278), Mircea Teodorescu (742331) · Figshare · 2023

<p>Data from sensing pipeline used to generate plots shown in the paper.</p> <p>(ZIP)</p>

Cited by 0Open access

Sensor data

yeping shi · Figshare · 2022

Active pumping electronic nose response to detect trace NO gas and pumping sensor response

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Sensor data

Mariapaola Riggio · OSF Preprints (OSF Preprints) · 2019

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sensor data

Haiyan Wu, Lei Lou, Chih-chung Chen et al. · 2013

Abstract — In order to speed up image processing in visual servoing, the distributed computational power across networks and appropriate data transmission mechanisms are of particular interest. In this paper, a high sampling rate of visual feedback is achieved by distributed computation on a cloud image processing platform. For target tracking with a networked visual servo control system, a switching control law considering the varying feedback delay caused by image processing and data transmission is applied to improve the control performance. A sending rate scheduling strategy aiming at saving the network load is proposed based on the tracking error. Experiments on a 7 degree-of-freedom (DoF) manipulator are carried out to validate the proposed approach. The proposed approach shows a similar control performance as a system without sending rate scheduling, however, beneficially with largely reduced network load.

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Sensor data.

Edward Brial (15999123), Robert Aunger (3067761), Winnie Costancia Muangi (15999126) et al. · London School of Hygiene & Tropical Medicine · 2023

<p>Data the 9 households where sensors recorded each time the device was used for at least part of the trial period.</p> <p>(XLSX)</p>

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Sensor data

shi, yeping · Figshare · 2022

Active pumping electronic nose response to detect trace NO gas and pumping sensor response

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Sensor data

Mariapaola Riggio · OSF Preprints (OSF Preprints) · 2019

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sensor data

Reemet Ammer · Figshare · 2022

Metal casting image dataset

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sensor data

Reemet Ammer · Figshare · 2022

hydraulic sensor datasets

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sensor data

Ammer, Reemet · Figshare · 2022

hydraulic sensor datasets

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Sensor Retro Data

Patel, Jamin · Figshare · 2024

This data contains objective and retrospective smartphone use data.

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Sheep sensor data

song, yang · Mendeley Data · 2025

The monitoring data of the emptying frequency of the sheep's abomasum and duodenum within 48 hours

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Linked sensor data

Harshal Kamlesh Patni, Cory Henson, Amit Sheth · 2010

A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.

Cited by 166

Mining Sensor Data

Mihai Datcu · 2016

Cited by 2

Tactile sensor data.

Nicolas Herzig (9230425), Liang He (305820), Perla Maiolino (9230428) et al. · Figshare · 2020

<p>(A) Tactile data after baseline correction per shift. (B) Normalized tactile prominence map computed at the peak time per shift.</p>

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Raw sensor data

Ilia Zhuravok · Figshare · 2021

Raw sensor data from our experiment. Sensors were started before the experiment, but the data has been cut down to include just the experiment results.

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Sensor data alignment

Deept Sharma · Mendeley Data · 2026

Data was generated by machining 314L steel. It consists 3 phase energy data and vibrations.

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Live sensor data.

Nadim Nawshad (22208986), Md. Asraf Ali (22208989), Ku Nurul Fazira (22208992) et al. · Figshare · 2025

<div> Due to limited literacy among root-level farmers, hydroponic farming in Bangladesh faces significant challenges. Therefore, there is a demand for easy-to-use technical systems to help farmers to monitor and operate smart systems. To address the issue, this study introduces a robust hydroponic system that provides automatic guidelines, monitoring, and a disease detection system. The main objective of this paper is to support farmers by making the cultivation process more convenient and less stressful. The system is structured into three phases: hardware implementation using WeMos controllers, disease detection using the Deep Learning model, and mobile application development for sensor data analysis and automatic notifications. The proposed system significantly demonstrates a high disease detection accuracy of 98.5%. Moreover, the survey report shows that around 80% of the root-level farmers find the system helpful for their cultivation process and increase the usability and monitoring of the system. These findings suggest that the proposed system can substantially improve the operational efficiency and sustainability of hydroponic farming, and it has the potential to enable more effective resource management and disease prevention strategies. </div>

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Tactile Sensor Data

Gast, Simon · OSF Preprints (OSF Preprints) · 2020

A tactile sensor based on magneto-sensitive elastomer to determine the position of an indentation

Cited by 1Open access

Sensor data requirements

Lawrence A. Klein · 2017

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