Evidence map›Paper›PMID 37514609›Full record

ArticleSensors (Basel, Switzerland)2023

A Low-Cost Radar-Based IoT Sensor for Noncontact Measurements of Water Surface Velocity and Depth.

Stephen Catsamas, Baiqian Shi, Miao Wang, Jieren Xiao, Peter Kolotelo, David McCarthy

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Stephen CatsamasBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.ORCID 0000-0001-8967-5853
Baiqian ShiBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Miao WangBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Jieren XiaoBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Peter KoloteloBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
David McCarthyBoSL Water Monitoring and Control, Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Monash University · AUQueensland University of Technology · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We designed an out-of-water radar water velocity and depth sensor, which is unique due to its low cost and low power consumption. The sensor is a first at a cost of less than USD 50, which is well suited to previously cost-prohibited high-resolution monitoring schemes. This use case is further supported by its out-of-water operation, which provides low-effort installations and longer maintenance-free intervals when compared with in-water sensors. The inclusion of both velocity and depth measurement capabilities allows the sensor to also be used as an all-in-one solution for flowrate measurement. We discuss the design of the sensor, which has been made freely available under open-hardware and open-source licenses. The design uses commonly available electronic components, and a 3D-printed casing makes the design easy to replicate and modify. Not before seen on a hydrology sensor, we include a 3D-printed radar lens in the casing, which boosts radar sensitivity by 21 dB. The velocity and depth-sensing performance were characterised in laboratory and in-field tests. The depth is accurate to within ±6% and ±7 mm and the uncertainty in the velocity measurements ranges from less than 30% to 36% in both laboratory and field conditions. Our sensor is demonstrated to be a feasible low-cost design which nears the uncertainty of current, yet more expensive, velocity sensors, especially when field performance is considered.

Indexed as

Doppler radarfield verificationIoTlow costlow powernoncontactopen hardwareradar level measurementreal-time environmental monitoringsensor designstormwater monitoringurban waterwater depthwater velocity

Identifiers

PMID37514609
PMCPMC10385264
OpenAlexW4384039118

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.