Inter-humanoid robot interaction with emphasis on detection: a comparison study – ADDENDUM

Shangari, Taher Abbas; Shams, Vida; Azari, Bita; Shamshirdar, Faraz; Baltes, Jacky; Sadeghnejad, Soroush

doi:10.1017/s0269888917000078

“…The result is shown in Figure 6, which shows that the error in the visible light band was lower than that in the near infrared band. There are various active methods to reflect a beam from air-suspended solids, such as laser, sonar, and radar; however, in these methods, the acquisition price is high, and it is difficult to interpret the returning output signal independently [43]. In contrast, the passive method discussed in this study is easy to interpret, has no interference problems with the environment, and is cheaper to build.…”

Section: Estimated Results Of Pm25 and Pm10mentioning

confidence: 97%

Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging

Chen

¹

,

Tseng

²

,

Mukundan

³

et al. 2021

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This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection.

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“…passive. Active sensors provide their own energy for illumination (Shangari et al, 2017), whereas passive sensors rely on the availability of energy that is reflected back from an object's surface.…”

Section: Capturing Image and Videomentioning

confidence: 99%

Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors

Khan

2024

Preprint

0

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<p>This thesis examines the feasibility of installing imaging sensors in sewers, combined with innovative machine learning techniques, to detect and identify non-flushable consumer products in sewers. A Raspberry Pi microprocessor with an off-the-shelf camera module was used, and Edge Impulse was applied to process captured imagery. The results indicated that optimal placement of the system (camera and lights) can vary depending on whether the products of interest float near the surface of the water or more towards the deep end of the sewer maintenance holes. The application of such a system for urban wastewater collection systems will be to proactively detect the areal hotspots of rising influxes of non-flushable consumer products (e.g., wet wipes and tissues). Building on knowledge gained on the performance and functionality of such a monitoring tool, utility managers can perform targeted public outreach, as opposed to general calls to the public to stop flushing consumer products.</p>

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“…Passive sensors, on the other hand, are more commonly used in thermal, infrared, and seismic imaging since they work best when natural light is available. Sensors working in the visible spectrum are best used in daylight (Shangari et al, 2017). On the contrary, thermal infrared sensors can work throughout the day or night as long as the amount of energy available is sufficient to be recorded by the sensor's receiver.…”

Section: Capturing Image and Videomentioning

confidence: 99%

Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors

Khan

2024

Preprint

0

View full text Add to dashboard Cite

<p>This thesis examines the feasibility of installing imaging sensors in sewers, combined with innovative machine learning techniques, to detect and identify non-flushable consumer products in sewers. A Raspberry Pi microprocessor with an off-the-shelf camera module was used, and Edge Impulse was applied to process captured imagery. The results indicated that optimal placement of the system (camera and lights) can vary depending on whether the products of interest float near the surface of the water or more towards the deep end of the sewer maintenance holes. The application of such a system for urban wastewater collection systems will be to proactively detect the areal hotspots of rising influxes of non-flushable consumer products (e.g., wet wipes and tissues). Building on knowledge gained on the performance and functionality of such a monitoring tool, utility managers can perform targeted public outreach, as opposed to general calls to the public to stop flushing consumer products.</p>

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Inter-humanoid robot interaction with emphasis on detection: a comparison study – ADDENDUM

Cited by 3 publications

References 1 publication

Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging

Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging

Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors

Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors

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