Ötz-T: 3D-printed open-source turbidity sensor with Arduino shield for suspended sediment monitoring

Droujko, Jessica; Kunz, Felix; Molnár, Péter

doi:10.1016/j.ohx.2023.e00395

Cited by 11 publications

(5 citation statements)

References 39 publications

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“…After we get a good machine learning model, the next step is to integrate it with the mechanical system of opening or closing the door. The mechanical system utilizes an Arduino microcontroller programmed using the C++ programming language, while the language used for hand gesture classification uses the Python programming language [17], [27], [28]. This paper adds a connecting library between the Python and Arduino C++ programming languages.…”

Section: Mechanical Systemmentioning

confidence: 99%

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Prihanto,

Effendy,

Nopriadi

2024

IJ-AI

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Viruses can be transmitted due to various aspects; one spreads through airborne droplets or the touch of multiple objects. This can occur in any area, including the entrance to the house or access to a room or deposit box. The spread of viruses that cause diseases like Covid-19 has caused many human casualties, and there is still the possibility of similar conditions appearing in the future. Several things need to be done to reduce the chances of spreading disease due to viruses, including developing contactless security support methods. This paper proposes a security system using hand gesture recognition using squeeze and excitation residual networks (SE-ResNet). This research offers a hand gesture recognition system for an automatic door system using SE-ResNet and the residual network (ResNet).

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Section: Mechanical Systemmentioning

confidence: 99%

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Prihanto,

Effendy,

Nopriadi

2024

IJ-AI

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“…A wide range of sensors and associated data loggers have been proposed for monitoring water quantity and quality (Chan et al, 2021), from non-contact water level sensors using ultrasonic (Pereira et al, 2022) or Light Detection and Ranging (Lidar) technology (Paul et al, 2020) to water quality sondes (Kinar and Brinkmann, 2022). However, most developments have been linked to physical or electrochemical sensors, with optical sensors for monitoring water quality largely neglected, except for turbidity (Kelley et al, 2014;Droujko et al, 2023).…”

Section: Low-cost Sensors-testing and Field Validationmentioning

confidence: 99%

“…Conversely, absorbance can detect a larger pool of organic and inorganic compounds across a wider range of concentrations, albeit with limited specificity (Dai et al, 2022;Carter et al, 2023). Nephelometric sensors detect undissolved/suspended solid/sediment particles in water by measuring scattering at different angles to the incident light (0°), including back-scattering (30° ± 15° angle), sidescattering (90° angle) and forward-scattering (120° ± 15° angle) (Kitchener et al, 2017;Droujko et al, 2023). These are based on inverse attenuation, i.e., a higher concentration of total suspended solids (TSS) will cause higher scattering and vary considerably with particle size, shape, and wavelength of incident light.…”

Section: Introductionmentioning

confidence: 99%

In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities

Kumar,

Khamis,

Stevens

et al. 2024

Front. Water

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Water quality issues remain a major cause of global water insecurity, and real-time low-cost monitoring solutions are central to the remediation and management of water pollution. Optical sensors, based on fluorescence, absorbance, scattering and reflectance-based principles, provide effective water quality monitoring (WQM) solutions. However, substantial challenges remain to their wider adoption across scales and environments amid cost and calibration-related concerns. This review discusses the current and future challenges in optical water quality monitoring based on multi-peak fluorescence, full-spectrum absorbance, light-scattering and remotely sensed surface reflectance. We highlight that fluorescence-based sensors can detect relatively low concentrations of aromatic compounds (e.g., proteins and humic acids) and quantify and trace organic pollution (e.g., sewage or industrial effluents). Conversely, absorbance-based sensors (Ultraviolet-Visible-Infra-red, UV-VIS-IR) are suitable for monitoring a wider range of physiochemical variables (e.g., nitrate, dissolved organic carbon and turbidity). Despite being accurate under optimal conditions, measuring fluorescence and absorbance can be demanding in dynamic environments due to ambient temperature and turbidity effects. Scattering-based turbidity sensors provide a detailed understanding of sediment transport and, in conjunction, improve the accuracy of fluorescence and absorbance measurements. Recent advances in micro-sensing components such as mini-spectrometers and light emitting diodes (LEDs), and deep computing provide exciting prospects of in-situ full-spectrum analysis of fluorescence (excitation-emission matrices) and absorbance for improved understanding of interferants to reduce the signal-to-noise ratio, improve detection accuracies of existing pollutants, and enable detection of newer contaminants. We examine the applications combining in-situ spectroscopy and remotely sensed reflectance for scaling Optical WQM in large rivers, lakes and marine bodies to scale from point observations to large water bodies and monitor algal blooms, sediment load, water temperature and oil spills. Lastly, we provide an overview of future applications of optical techniques in detecting emerging contaminants in treated and natural waters. We advocate for greater synergy between industry, academia and public policy for effective pollution control and water management.

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“…Commercial loggers cost around $400 USD and provide centimeter accuracy. Open-source designs for water depth sensors are also prevalent in the literature and have seen more widespread adoption than turbidity sensors 11,[25][26][27][28][29] . Unlike the variety of custom designs used to measure turbidity by the devices in Table 1, these open-source depth sensors all use the MS5803 pressure sensor by TE Connectivity.…”

Section: Depth Sensorsmentioning

confidence: 99%

Increased scale and accessibility of sediment transport research in rivers through practical, open-source turbidity and depth sensors

Langhorst,

Pavelsky,

Eidam

et al. 2023

Nat Water

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Open-source designs for turbidity and depth sensors are becoming increasingly capable and available, but the knowledge required to construct them limits their use compared to expensive, commercial sensors. Here, we present an open-source optical backscatter and water pressure sensor that can be ordered almost fully assembled, requires no coding to deploy, and costs approximately $50 USD. We share three examples of these sensors' ability to facilitate new research. First, we observed complex changes in spatial and temporal patterns of suspended sediment transport in the Arctic Sagavanirktok River using a network of sensors. Second, we measured turbidity during the freeze-up period in the Tanana River, a period of high risk to sensors. Last, we built and deployed sensors with middle-school students to monitor turbidity under full ice cover on the Tanana River. The success of open-source sensors in these examples shows a marked increase in scale and accessibility of river science.

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Ötz-T: 3D-printed open-source turbidity sensor with Arduino shield for suspended sediment monitoring

Cited by 11 publications

References 39 publications

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Hand gesture-based automatic door security system using squeeze and excitation residual networks

In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities

Increased scale and accessibility of sediment transport research in rivers through practical, open-source turbidity and depth sensors

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