Search results for: depth sensing camera

Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Juniarti Yuni

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Simawang area is one of the critical areas (sub-optimal) that experienced drought from climate changes. Potential dry land belonging to sub-optimal in Simawang, West Sumatera, Indonesia not been fully utilized for agricultural cultivation. Simawang village, West Sumatera, Indonesia is formerly known as the rice barn, due to the climate change area is experiencing a drought, so the rice fields that were once productive now a grazing paddock because of lack of water. This study aims to calculate the soil carbon stock in Simawang village, West Sumatera Indonesia. The study was conducted in Simawang village, Tanah Datar regency, West Sumatera from October 2014 until December 2017. The study was conducted on sub-optimal land to be planted with Cymbopogon nardus L. (Sereh wangi in Indonesian language). Composite soil sampling conducted at a depth of 0-20 cm, 20–40 cm. Based on the depth of soil carbon stocks gained higher ground 6473 T/Ha at a depth of 0-20 cm at a depth of 20-40 cm. Efforts to increase soil carbon is expected to be cultivated through Cymbopogon nardus L. planting has been done.

Keywords: climate changes, sereh wangi (Cymbopogon nardus L.), soil carbon stock, sub optimal land

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Authors: E. D. Paul, H. Otanwa, O. F. Paul, A. J. Salifu, J. E. Toryila, C. E. Gimba

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The concentration levels of six heavy metals (Cd, Cr, Fe, Ni, Pb, and Zn) and two mineral elements (Ca and Mg) were determined in soil samples collected from the vicinity of two auto mechanic workshops in Sabon-Gari, Kaduna state, Nigeria, using Atomic Absorption Spectrometry (AAS), in order to compare the gradation of their concentrations with distance and depth of soil from the workshop sites. At site 1, concentrations of lead, chromium, iron, and zinc were generally found to be above the World Health Organization limits, while those of Nickel and Cadmium fell within the limits. Iron had the highest concentration with a range of 176.274 ppm to 489.127 ppm at depths of 5 cm to 15 cm and a distance range of 5 m to 15 m, while the concentration of cadmium was least with a range of 0.001 ppm to 0.008 ppm at similar depth and distance ranges. In addition, there was more of calcium (11.521 ppm to 121.709 ppm), in all the samples, than magnesium (11.293 ppm to 21.635 ppm). Similar results were obtained for site II. The concentrations of all the metals analyzed showed a downward gradient with an increase in depth and distance from both workshop sites except for iron and zinc at site 2. The immediate and remote implications of these findings on the biota are discussed.

Keywords: AAS, heavy metals, mechanic workshops, soil, variation

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Authors: Md Abdullah Al Faruque

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A very large percentage of bridge failures are attributed to scouring around bridge piers and this directly influences public safety. Experiments are carried out in a 12-m long rectangular open channel flume made of transparent tempered glass. A 300 mm thick bed made up of sand particles is leveled horizontally to create the test bed and a 50 mm hollow plastic cylinder is used as a model bridge pier. Tests are carried out with varying flow depths and velocities. Data points of various scour parameters such as scour depth, width, and length are collected based on different flow conditions and visual observations of changes in the stream bed downstream the bridge pier are also made as the scour progresses. Result shows that all three major flow characteristics (flow depth, Froude number and densimetric Froude number) have one way or other affect the scour profile.

Keywords: bridge pier scour, densimetric Froude number, flow depth, Froude number, sand

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Authors: Benyelles Zakaria, Yousfi Djaafar, Karoui Moussa Sofiane

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Agriculture is fundamental and remains an important objective in the Algerian economy, based on traditional techniques and structures, it generally has a purpose of consumption. Collection of agricultural statistics in Algeria is done using traditional methods, which consists of investigating the use of land through survey and field survey. These statistics suffer from problems such as poor data quality, the long delay between collection of their last final availability and high cost compared to their limited use. The objective of this work is to develop a processing chain for a reliable inventory of agricultural land by trying to develop and implement a new method of extracting information. Indeed, this methodology allowed us to combine data from remote sensing and field data to collect statistics on areas of different land. The contribution of remote sensing in the improvement of agricultural statistics, in terms of area, has been studied in the wilaya of Sidi Bel Abbes. It is in this context that we applied a method for extracting information from satellite images. This method is called the non-negative matrix factorization, which does not consider the pixel as a single entity, but will look for components the pixel itself. The results obtained by the application of the MNF were compared with field data and the results obtained by the method of maximum likelihood. We have seen a rapprochement between the most important results of the FMN and those of field data. We believe that this method of extracting information from satellite data leads to interesting results of different types of land uses.

Keywords: blind source separation, hyper-spectral image, non-negative matrix factorization, remote sensing

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Authors: Segun Steven Bodunde

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Mudlogging is the continuous analysis of rock cuttings and drilling fluids to ascertain the presence or absence of oil and gas from the formation penetrated by the drilling bit. This research highlighted a case study of Well BSS-99ST from ‘Bisas Field’, Niger Delta, with depth extending from 1950m to 3640m (Measured Depth). It was focused on identifying the lithologies encountered at specified depth intervals and to accurately delineate the targeted potential reservoir on the field and prepare the lithology and Master log. Equipment such as the Microscope, Fluoroscope, spin drier, oven, and chemicals, which includes: hydrochloric acid, chloroethene, and phenolphthalein, were used to check the cuttings for their calcareous nature, for oil show and for the presence of Cement respectively. Gas analysis was done using the gas chromatograph and the Flame Ionization Detector, which was connected to the Total Hydrocarbon Analyzer (THA). Drilling Parameters and Gas concentration logs were used alongside the lithology log to predict and accurately delineate the targeted reservoir on the field. The result showed continuous intercalation of sand and shale, with the presence of small quantities of siltstone at a depth of 2300m. The lithology log was generated using Log Plot software. The targeted reservoir was identified between 3478m to 3510m after inspection of the gas analysis, lithology log, electric logs, and the drilling parameters. Total gas of about 345 units and five Alkane Gas components were identified in the specific depth range. A comparative check with the Gamma ray log from the well further confirmed the lithologic sequence and the accurate delineation of the targeted potential reservoir using mudlogging.

Keywords: mudlogging, chromatograph, drilling fluids, calcareous

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Authors: Asma Lamin, Anna H. Kaksonen, Ivan Cole, Paul White, Xiao-Bo Chen

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Microbiologically influenced corrosion (MIC) is a process in which microorganisms initiate, facilitate, or accelerate the electrochemical corrosion reactions of metallic components. Several reports documented that MIC accounts for about 20 to 40 % of the total cost of corrosion. Biofilm formation due to the presence of microorganisms on the surface of metal components is known to play a vital role in MIC, which can lead to severe consequences in various environmental and industrial settings. Quorum sensing (QS) system plays a major role in regulating biofilm formation and control the expression of some microbial enzymes. QS is a communication mechanism between microorganisms that involves the regulation of gene expression as a response to the microbial cell density within an environment. This process is employed by both Gram-positive and Gram-negative bacteria to regulate different physiological functions. QS involves production, detection, and responses to signalling chemicals, known as auto-inducers. QS controls specific processes important for the microbial community, such as biofilm formation, virulence factor expression, production of secondary metabolites and stress adaptation mechanisms. The use of QS inhibitors (QSIs) has been proposed as a possible solution to biofilm related challenges in many different applications. Although QSIs have demonstrated some strength in tackling biofouling, QSI-based strategies to control microbially influenced corrosion have not been thoroughly investigated. As such, our research aims to target the QS mechanisms as a strategy for mitigating MIC on metal surfaces in engineered systems.

Keywords: quorum sensing, quorum quenching, biofilm, biocorrosion

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Authors: Baiyeri R. M, Oloriegbe Y. A., Saad A. O., Yusuf, K. O.

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Most farmers that produce food crops in Nigeria live in rural areas where potable water is not available. The farmers in some areas have problem of water borne diseases which could affect their health and could lead to death. This study was conducted to determine the impact of incorporating Granular Activated Carbon(GAC) and Magnetic Field(MF) in Slow Sand Filter(SSF) on the purification of water for rural dwellers. The SSF was developed using PVC pipe with diameter 152.4 mm and 1100 mm long, with layers of fine sand with size 0.25 mm and 350 mm depth, followed by GAC 10 mm size and 100 mm depth, fine sand 0.25mm with 500 mm depth and gravel grain size 10-14 mm and 100 mm depth. The SSF was kept moist for 21 days for biofilm layer (schmutzdecke) to fully develop, which is essential for trapping bacteria. Two SSFs fabricated consist of SSF+GAC as Filter 1, SSF+GAC+MF as Filter 2 and Control (Raw water without passing through filter. Water samples were collected from the filter and analyzed. The flow rate of Filter was 25 litres/h Total bacteria counts(TBC) for Filter 1 and Filter 2 and control were 2.4, 4.6 and 8.1 cfu/mg, respectively. Total coliform count for Filter 1 and Filter 2 and control were 1.7, 3.0 and 6.4 cfu/100mL, respectively. The filters reduced water hardness, turbidity, lead, copper, electrical conductivity and TBC by 53.13-73.44% but increased pH from 5.8 to 7.1-7.3. SSF is recommended for water purification in the rural areas.

Keywords: magnetised water, sow sand filter, portable water, activated carbon

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Authors: Meenal Surawar, Rajashree Kotharkar

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Urban Heat Island (UHI) is found more pronounced as a prominent urban environmental concern in developing cities. To study the UHI effect in the Indian context, the Nagpur urban area has been explored in this paper using Landsat 7 ETM+ satellite images through Remote Sensing and GIS techniques. This paper intends to study the effect of LU/LC pattern on daytime Land Surface Temperature (LST) variation, contributing UHI formation within the Nagpur Urban area. Supervised LU/LC area classification was carried to study urban Change detection using ENVI 5. Change detection has been studied by carrying Normalized Difference Vegetation Index (NDVI) to understand the proportion of vegetative cover with respect to built-up ratio. Detection of spectral radiance from the thermal band of satellite images was processed to calibrate LST. Specific representative areas on the basis of urban built-up and vegetation classification were selected for observation of point LST. The entire Nagpur urban area shows that, as building density increases with decrease in vegetation cover, LST increases, thereby causing the UHI effect. UHI intensity has gradually increased by 0.7°C from 2000 to 2006; however, a drastic increase has been observed with difference of 1.8°C during the period 2006 to 2013. Within the Nagpur urban area, the UHI effect was formed due to increase in building density and decrease in vegetative cover.

Keywords: land use/land cover, land surface temperature, remote sensing, urban heat island

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Authors: O. J. G. Somsen

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One of the tasks of optical surveillance is to detect anomalies in large amounts of image data. However, if the size of the anomaly is very small, limited information is available to distinguish it from the surrounding environment. Spectral detection provides a useful source of additional information and may help to detect anomalies with a size of a few pixels or less. Unfortunately, spectral cameras are expensive because of the difficulty of separating two spatial in addition to one spectral dimension. We investigate the possibility of modifying a simpler spectral line detector for outdoor detection. This may be especially useful if the area of interest forms a line, such as the horizon. We use a monochrome CCD that also enables detection into the near infrared. A simple camera is attached to the setup to determine which part of the environment is spectrally imaged. Our preliminary results indicate that sensitive detection of very small targets is indeed possible. Spectra could be taken from the various targets by averaging columns in the line image. By imaging a set of lines of various width we found narrow lines that could not be seen in the color image but remained visible in the spectral line image. A simultaneous analysis of the entire spectra can produce better results than visual inspection of the line spectral image. We are presently developing calibration targets for spatial and spectral focusing and alignment with the spatial camera. This will present improved results and more use in outdoor application

Keywords: anomaly detection, spectroscopic line imaging, image analysis, outdoor detection

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Authors: Goban Kumar Panneer Selvam

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In past years, there is lots of death caused due to harmful gases. So its very important to monitor harmful gases for human safety, and semiconductor material play important role in producing effective gas sensors.A novel solvothermal synthesis method based on sol-gel processing was prepared to deposit tin oxide thin films on glass substrate at high temperature for gas sensing application. The structure and morphology of tin oxide were analyzed by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The SEM analysis of how spheres shape in tin oxide nanoparticles. The structure characterization of tin oxide studied by X-ray diffraction shows 8.95 nm (calculated by sheers equation). The UV visible spectroscopy indicated a maximum absorption band shown at 390 nm. Further dope tin oxide with selected metals to attain maximum sensitivity using dip coating technique with different immersion and sensing characterization are measured.

Keywords: tin oxide, gas sensor, chlorine free, sensitivity, crystalline size

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Authors: Mokhlisur Rahman

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The growing consensus among scientists and world leaders indicates that immediate action should be considered regarding the climate change phenomenon. However, climate change is no more a global issue but a personal one. Thus, individual participation is necessary to address such a significant issue. Studies show that individuals who perceive climate change as a personal issue are more likely to act toward it. This abstract presents augmented reality (A.R.) technology in the social media platform Facebook video advertising. The idea involves creating a video advertisement that enables users to interact with the video by navigating its features and experiencing the result uniquely and engagingly. This advertisement uses A.R. to bring changes, such as people making changes in real-life scenarios by simple clicks on the video and hearing an instant rewarding fact about their choices. The video shows three options: room, lawn, and driveway. Users select one option and engage in interaction based on while holding the camera in their personal spaces: Suppose users select the first option, room, and hold their camera toward spots such as by the windows, balcony, corners, and even walls. In that case, the A.R. offers users different plants appropriate for those unoccupied spaces in the room. Users can change the options of the plants and see which space at their house deserves a plant that makes it more natural. When a user adds a natural element to the video, the video content explains a piece of beneficiary information about how the user contributes to the world more to be livable and why it is necessary. With the help of A.R., if users select the second option, lawn, and hold their camera toward their lawn, the options are various small trees for their lawn to make it more environmentally friendly and decorative. The video plays a beneficiary explanation here too. Suppose users select the third option, driveway, and hold their camera toward their driveway. In that case, the A.R. video option offers unique recycle bin designs using A.I. measurement of spaces. The video plays audio information on anthropogenic contribution to greenhouse gas emission. IoT embeds tracking code in the video ad on Facebook, which stores the exact number of views in the cloud for data analysis. An online survey at the end collects short qualitative answers. This study helps understand the number of users involved and willing to change their behavior; It makes personalized advertising in social media. Considering the current state of climate change, the urgency for action is increasing. This ad increases the chance to make direct connections with individuals and gives a sense of personal responsibility for climate change to act

Keywords: motivations, climate, iot, personalized-advertising, action

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Authors: Qasem A. Alyazji

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One of the main techniques for Positron emission tomography (PET), Single photon emission computed tomography (SPECT) is the development of radiation detectors. The NaI(Tl) scintillator crystal coupled to an array of photomultiplier tubes known as the Anger camera, is the most dominant detectors system in PET and SPECT devices. Technological advances in many materials, in addition to the emerging importance of specialized applications such as preclinical imaging and cardiac imaging, have encouraged innovation so that alternatives to the anger camera are now part in alternative imaging systems. In this paper we will discuss the main performance characteristics of detectors devices and scanning developments in both scintillation detectors, semiconductor (solid state) detectors, and Photon Transducers such as photomultiplier tubes (PMTs), position sensitive photomultiplier tubes (PSPMTs), Avalanche photodiodes (APDs) and Silicon photomultiplier (SiPMT). This paper discussed the detectors that showed promising results. This study is a review of recent developments in the detectors used in single photon emission computed tomography (SPECT) imaging system.

Keywords: SPECT, scintillation, PMTs, SiPMT, PSPMTs, APDs, semiconductor (solid state)

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Authors: Catalina Albornoz, Giacomo Barbieri

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Crop monitoring has shown to reduce vulnerability to spreading plagues and pathologies in crops. Remote sensing with Unmanned Aerial Vehicles (UAVs) has made crop monitoring more precise, cost-efficient and accessible. Nowadays, remote monitoring involves calculating maps of vegetation indices by using different software that takes either Truecolor (RGB) or multispectral images as an input. These maps are then used to segment the crop into management zones. Finally, knowing the spectral signature of a crop (the reflected radiation as a function of wavelength) can be used as an input for decision-making and crop characterization. The calculation of vegetation indices using software such as Pix4D has high precision for monoculture plantations. However, this paper shows that using this software on mixed crops may lead to errors resulting in an incorrect segmentation of the field. Within this work, authors propose to filter all the elements different from the main crop before the calculation of vegetation indices and the spectral signature. A filter based on the Sobel method for border detection is used for filtering a coffee crop. Results show that segmentation into management zones changes with respect to the traditional situation in which a filter is not applied. In particular, it is shown how the values of the spectral signature change in up to 17% per spectral band. Future work will quantify the benefits of filtering through the comparison between in situ measurements and the calculated vegetation indices obtained through remote sensing.

Keywords: coffee, filtering, mixed crop, precision agriculture, remote sensing, spectral signature

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Authors: Nasibeh Azizi Khereshki

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Olive leaf (OL) extract as a green reductant agent was utilized for the biogenic synthesis of silver nanoparticles (Ag NPs) for the first time in this study, and then its performance was evaluated for colorimetric detection of Fe3+ in different media. Some analytical methods were used to characterize the nanosensor. The effective sensing parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) application. Then, the prepared material's applicability in antibacterial and optical chemical sensing for naked-eye detection of Fe3+ ions in aqueous solutions were evaluated. Furthermore, OL-Ag NPs-loaded paper strips were successfully applied to the colorimetric visualization of Fe3+. The colorimetric probe based on OL-AgNPs illustrated excellent selectivity and sensitivity towards Fe3+ ions, with LOD and LOQ of 0.81 μM and 2.7 μM, respectively. In addition, the developed method was applied to detect Fe3+ ions in real water samples and validated with a 95% confidence level against a reference spectroscopic method.

Keywords: Ag NPs, colorimetric detection, Fe(III) ions, green synthesis, olive leaves

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Authors: Sadaf Nawaz, Adnan Ahmed Khan, Asad Mahmood, Chaudhary Farrukh Javed

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Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions.

Keywords: cognitive radio, energy detector, periodogram, spectrum sensing

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Authors: Linhang Zhu, Hongyu Zhu, Jiahe Liu

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This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control.

Keywords: Manhattan world hypothesis, real-time positioning and mapping, feature matching, loopback detection

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Authors: Yongqin Zhang, John Lett

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Geospatial technologies have been increasingly used in agriculture for various applications and purposes in recent years. Unmanned aerial vehicles (drones) fit the needs of farmers in farming operations, from field spraying to grow cycles and crop health. In this research, we conducted a practical research project that used drone technology to design and map optimal locations and layouts of irrigation systems for agriculture farms. We flew a DJI Mavic 2 Pro drone to acquire aerial remote sensing images over two agriculture fields in Forest, Mississippi, in 2022. Flight plans were first designed to capture multiple high-resolution images via a 20-megapixel RGB camera mounted on the drone over the agriculture fields. The Drone Deploy web application was then utilized to develop flight plans and subsequent image processing and measurements. The images were orthorectified and processed to estimate the area of the area and measure the locations of the water line and sprinkle heads. Field measurements were conducted to measure the ground targets and validate the aerial measurements. Geospatial analysis and photogrammetric measurements were performed for the study area to determine optimal layout and quantitative estimates for irrigation systems. We created maps and tabular estimates to demonstrate the locations, spacing, amount, and layout of sprinkler heads and water lines to cover the agricultural fields. This research project provides scientific guidance to Mississippi farmers for a precision agricultural irrigation practice.

Keywords: drone images, agriculture, irrigation, geospatial analysis, photogrammetric measurements

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Authors: B. Medjadji, L. Aminallah, B. Serier, M. Benlebna

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In this study, the finite element method in 3-D is used to calculate the integral J in the semi-elliptical crack in a pipe subjected to internal pressure. The stress-strain curve of the pipe has been determined experimentally. The J-integral was calculated in two fronts crack (Ф = 0 and Ф = π/2). The effect of the configuration of the crack on the J integral is analysed. The results show that an external longitudinal crack in a pipe is the most dangerous. It also shows that the increase in the applied pressure causes a remarkable increase of the integral J. The effect of the depth of the crack becomes important when the ratio between the depth of the crack and the thickness of the pipe (a / t) tends to 1.

Keywords: J integral, pipeline, crack, MEF

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Authors: Si Yin Tee

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Bimetallic nanostructures have received tremendous interests as a new class of nanomaterials which may have better technological usefulness with distinct properties from those of individual atoms and molecules or bulk matter. They excelled over the monometallic counterparts because of their improved electronic, optical and catalytic performances. The properties and the applicability of these bimetallic nanostructures not only depend on their size and shape, but also on the composition and their fine structure. These bimetallic nanostructures are potential candidates for bio-applications such as biosensing, bioimaging, biodiagnostics, drug delivery, targeted therapeutics, and tissue engineering. Herein, gold-incorporated copper (Cu/Au) nanostructures were synthesized through the controlled disproportionation of Cu⁺-oleylamine complex at 220 ºC to form copper nanowires and the subsequent reaction with Au³⁺ at different temperatures of 140, 220 and 300 ºC. This is to achieve their synergistic effect through the combined use of the merits of low-cost transition and high-stability noble metals. Of these Cu/Au nanostructures, Cu/Au nanotubes display the best performance towards electrochemical non-enzymatic glucose sensing, originating from the high conductivity of gold and the high aspect ratio copper nanotubes with high surface area so as to optimise the electroactive sites and facilitate mass transport. In addition to high sensitivity and fast response, the Cu/Au nanotubes possess high selectivity against interferences from other potential interfering species and excellent reproducibility with long-term stability. By introducing gold into copper nanostructures at a low level of 3, 1 and 0.1 mol% relative to initial copper precursor, a significant electrocatalytic enhancement of the resulting bimetallic Cu/Au nanostructures starts to occur at 1 mol%. Overall, the present fabrication of stable Cu/Au nanostructures offers a promising low-cost platform for sensitive, selective, reproducible and reusable electrochemical sensing of glucose.

Keywords: bimetallic, electrochemical sensing, glucose oxidation, gold-incorporated copper nanostructures

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Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

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Authors: Alemaw Kefale, Aramde Fetene, Hayal Desta

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Monitoring the amount of green space in urban areas is important for ensuring sustainable development and proper management. The study analyzed changes in urban green space coverage over the past 20 years in two rapidly urbanizing cities in Ethiopia, Debre Berhan and Debre Markos, using GIS and remote sensing. The researchers used Landsat 5 and 8 data with a spatial resolution of 30 m to determine different land use and land cover classes, including urban green spaces, barren and croplands, built-up areas, and water bodies. The classification accuracy ranged between 90% and 91.4%, with a Kappa Statistic of 0.85 to 0.88. The results showed that both cities experienced significant decreases in vegetation cover in their urban cores between 2000 and 2020, with radical changes observed from green spaces and croplands to built-up areas. In Debre Berhan, barren and croplands decreased by 32.96%, while built-up and green spaces increased by 357.9% and 37.4%, respectively, in 2020. In Debre Markos, built-up areas increased by 224.2%, while green spaces and barren and croplands decreased by 41% and 5.71%, respectively. The spatial structure of cities and planning policies were noticed as the major factors for big green cover change. Thus it has an implication for other rapidly urbanized cities in Africa and Asia. Overall, rapid urbanization threatens green spaces and agricultural areas, highlighting the need for ecological-based spatial planning in rapidly urbanizing cities.

Keywords: green space coverage, GIS and remote sensing, Landsat, LULC, Ethiopia

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Authors: Mohamed Mediouni

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Evaluating the performance of a simulator in the CAVE has to be confirmed by encouraging people to live the experience of virtual reality. In this paper, a detailed procedure of recording video is presented. Limitations of the experimental device are firstly exposed. Then, solutions for improving this idea are finally described.

Keywords: CAVE, stereoscopics, camera, recording

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Authors: Prashant Parhad, Vinayak Dakre, Ajay Likhite, Jatin Bhatt

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This work mainly focuses on machinability studies of Austempered Ductile Iron (ADI). The Ductile Iron (DI) was austempered at 250 oC for different durations and the process window for austempering was established by studying the microstructure. The microstructural characterization of the material was done using optical microscopy, SEM and XRD. The samples austempered as per the process window were then subjected to turning using a TiAlN-coated tungsten carbide insert to study the effect of cutting parameters, namely the cutting speed and the depth of cut. The effect was investigated in terms of cutting forces required as well as the surface roughness obtained. The turning was conducted on a CNC turning machine and primary (Fx), radial (Fy) and feed (Fz) cutting forces were quantified with a three-component dynamometer. It was observed that the magnitude of radial force was more than that of primary cutting force for all cutting speed and for various depths of cut studied. It has also been seen that increasing the cutting speed improves the surface quality. The observed machinability behaviour was investigated in light of the microstructure of the material obtained under the given austempering conditions and a structure-property- co-relation was established between the two. For all cutting speed and depth of cut, the best machining response in terms of cutting forces and surface quality was obtained towards the centre of process window.

Keywords: process window, cutting speed, depth of cut, surface roughness

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Authors: Ajibola Isola, Shattri Mansor, Ojogbane Sani, Olugbemi Tope

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The cadastral map is the rationale behind city management planning and development. For years, cadastral maps have been produced by ground and photogrammetry platforms. Recent evolution in photogrammetry and remote sensing sensors ignites the use of Unmanned Aerial Vehicle systems (UAVs) for cadastral mapping. Despite the time-saving and multi-dimensional cost-effectiveness of the UAV platform, issues related to cadastral map accuracy are a hindrance to the wide applicability of UAVs' cadastral mapping. This study aims to present an approach leading to the generation and assessing the credibility of UAV cadastral mapping. Different sets of Red, Green, and Blue (RGB) photos were obtained from the Tarot 680-hexacopter UAV platform flown over the Universiti Putra Malaysia campus sports complex at an altitude range of 70 m, 100 m, and 250. Before flying the UAV, twenty-eight ground control points were evenly established in the study area with a real-time kinematic differential global positioning system. The second phase of the study utilizes an image-matching algorithm for photos alignment wherein camera calibration parameters and ten of the established ground control points were used for estimating the inner, relative, and absolute orientations of the photos. The resulting orthoimages are exported to ArcGIS software for digitization. Visual, tabular, and graphical assessments of the resulting cadastral maps showed a different level of accuracy. The results of the study show a gradual approach for generating UAV cadastral mapping and that the cadastral map acquired at 70 m altitude produced better results.

Keywords: aerial mapping, orthomosaic, cadastral map, flying altitude, image processing

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Authors: Linyu Wang, Jiahui Ma, Jianhong Xiang, Hanyu Jiang

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the traditional greedy compressed sensing algorithm needs to know the signal sparsity when recovering the signal, but the signal sparsity in the practical application can not be obtained as a priori information, and the recovery accuracy is low, which does not meet the needs of practical application. To solve this problem, this paper puts forward Piecewise adaptive backtracking orthogonal least squares algorithm. The algorithm is divided into two stages. In the first stage, the sparsity pre-estimation strategy is adopted, which can quickly approach the real sparsity and reduce time consumption. In the second stage iteration, the correction strategy and adaptive step size are used to accurately estimate the sparsity, and the backtracking idea is introduced to improve the accuracy of signal recovery. Through experimental simulation, the algorithm can accurately recover the estimated signal with fewer iterations when the sparsity is unknown.

Keywords: compressed sensing, greedy algorithm, least square method, adaptive reconstruction

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Authors: Parimalah Velo, Ahmad Zakaria

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Introduction: Monte Carlo simulations of preclinical imaging systems allow opportunity to enable new research that could range from designing hardware up to discovery of new imaging application. The simulation system which could accurately model an imaging modality provides a platform for imaging developments that might be inconvenient in physical experiment systems due to the expense, unnecessary radiation exposures and technological difficulties. The aim of present study is to validate the Monte Carlo simulation of thyroid phantom imaging using Geant4-GATE for Siemen’s e-cam single head gamma camera. Upon the validation of the gamma camera simulation model by comparing physical characteristic such as energy resolution, spatial resolution, sensitivity, and dead time, the GATE simulation of thyroid phantom imaging is carried out. Methods: A thyroid phantom is defined geometrically which comprises of 2 lobes with 80mm in diameter, 1 hot spot, and 3 cold spots. This geometry accurately resembling the actual dimensions of thyroid phantom. A planar image of 500k counts with 128x128 matrix size was acquired using simulation model and in actual experimental setup. Upon image acquisition, quantitative image analysis was performed by investigating the total number of counts in image, the contrast of the image, radioactivity distributions on image and the dimension of hot spot. Algorithm for each quantification is described in detail. The difference in estimated and actual values for both simulation and experimental setup is analyzed for radioactivity distribution and dimension of hot spot. Results: The results show that the difference between contrast level of simulation image and experimental image is within 2%. The difference in the total count between simulation and actual study is 0.4%. The results of activity estimation show that the relative difference between estimated and actual activity for experimental and simulation is 4.62% and 3.03% respectively. The deviation in estimated diameter of hot spot for both simulation and experimental study are similar which is 0.5 pixel. In conclusion, the comparisons show good agreement between the simulation and experimental data.

Keywords: gamma camera, Geant4 application of tomographic emission (GATE), Monte Carlo, thyroid imaging

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Sheheryar Khan, Weidong Li, Fanqian Meng

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The Urban Heat Island (UHI) effect is one of the foremost problems out of other ecological and socioeconomic issues in urbanization. Due to this phenomenon that human-made urban areas have replaced the rural landscape with the surface that increases thermal conductivity and urban warmth; as a result, the temperature in the city is higher than in the surrounding rural areas. To affect the evidence of this phenomenon in the Zhengzhou city area, an observation of the temperature variations in the urban area is done through a scientific method that has been followed. Landsat 8 satellite images were taken from 2013 to 2015 to calculate the effect of Urban Heat Island (UHI) along with the NPP-VRRIS night-time remote sensing data to analyze the result for a better understanding of the center of the built-up area. To further support the evidence, the correlation between land surface temperatures and the normalized difference vegetation index (NDVI) was calculated using the Red band 4 and Near-infrared band 5 of the Landsat 8 data. Mono-window algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data using Band 10 and 11 accordingly to convert the top-of-atmosphere radiance (TOA) and to convert the satellite brightness temperature. Along with Landsat 8 data, NPP-VIIRS night-light data is preprocessed to get the research area data. The analysis between Landsat 8 data and NPP night-light data was taken to compare the output center of the Built-up area of Zhengzhou city.

Keywords: built-up area, land surface temperature, mono-window algorithm, NDVI, remote sensing, threshold method, Zhengzhou

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Authors: Nurin Amalina Widityani, Adinda Syifa Azhari, Twin Aji Kusumagiani, Eleonora Agustine

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Fertilizer industry activities contribute to environmental changes. Changes to the environment became one of a few problems in this era of globalization. Parameters that can be seen as criteria to identify changes in the environment can be seen from the aspects of physics, chemistry, and biology. One aspect that can be assessed quickly and efficiently to describe environmental change is the aspect of physics, one of which is the value of magnetic susceptibility (χ). The rock magnetism method can be used as a proxy indicator of environmental changes, seen from the value of magnetic susceptibility. The rock magnetism method is based on magnetic susceptibility studies to measure and classify the degree of pollutant elements that cause changes in the environment. This research was conducted in the area around the fertilizer plant, with five coring points on each track, each coring point a depth of 15 cm. Magnetic susceptibility measurements were performed by in-situ and ex-situ. In-situ measurements were carried out directly by using the SM30 tool by putting the tools on the soil surface at each measurement point and by that obtaining the value of the magnetic susceptibility. Meanwhile, ex-situ measurements are performed in the laboratory by using the Bartington MS2B tool’s susceptibility, which is done on a coring sample which is taken every 5 cm. In-situ measurement shows results that the value of magnetic susceptibility at the surface varies, with the lowest score on the second and fifth points with the -0.81 value and the highest value at the third point, with the score of 0,345. Ex-situ measurements can find out the variations of magnetic susceptibility values at each depth point of coring. At a depth of 0-5 cm, the value of the highest XLF = 494.8 (x10-8m³/kg) is at the third point, while the value of the lowest XLF = 187.1 (x10-8m³/kg) at first. At a depth of 6-10 cm, the highest value of the XLF was at the second point, which was 832.7 (x10-8m³/kg) while the lowest XLF is at the first point, at 211 (x10-8m³/kg). At a depth of 11-15 cm, the XLF’s highest value = 857.7 (x10-8m³/kg) is at the second point, whereas the value of the lowest XLF = 83.3 (x10-8m³/kg) is at the fifth point. Based on the in situ and exsit measurements, it can be seen that the highest magnetic susceptibility values from the surface samples are at the third point.

Keywords: magnetic susceptibility, fertilizer plant, Bartington MS2B, SM30

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