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Neuharth, Dalton B., Wade A. Ryberg, Connor S. Adams, Toby J. Hibbitts, Danielle K. Walkup, Shelby L. Frizzell, Timothy E. Johnson, Brian L. Pierce, Josh B. Pierce, and D. Craig Rudolph. "Searching for rare and secretive snakes: are camera-trap and box-trap methods interchangeable?" Wildlife Research 47, no. 6 (2020): 476. http://dx.doi.org/10.1071/wr19230.
Abstract ContextAdvancements in camera-trap technology have provided wildlife researchers with a new technique to better understand their study species. This improved method may be especially useful for many conservation-reliant snake species that can be difficult to detect because of rarity and life histories with secretive behaviours. AimsHere, we report the results of a 6-month camera-trapping study using time lapse-triggered camera traps to detect snakes, in particular the federally listed Louisiana pinesnake (Pituophis ruthveni) in eastern Texas upland forests in the USA. MethodsSo as to evaluate the efficacy of this method of snake detection, we compared camera-trap data with traditional box-trapping data collected over the same time period across a similar habitat type, and with the same goal of detecting P. ruthveni. Key resultsNo differences in focal snake species richness were detected across the trap methods, although the snake-detection rate was nearly three times higher with camera traps than with the box traps. Detection rates of individual snake species varied with the trapping method for all but two species, but temporal trends in detection rates were similar across the trap methods for all but two species. Neither trap method detected P. ruthveni in the present study, but the species has been detected with both trap methods at other sites. ConclusionsThe higher snake-detection rate of the camera-trap method suggests that pairing this method with traditional box traps could increase the detection of P. ruthveni where it occurs. For future monitoring and research on P. ruthveni, and other similarly rare and secretive species of conservation concern, we believe these methods could be used interchangeably by saturating potentially occupied habitats with camera traps initially and then replacing cameras with box traps when the target species is detected. ImplicationsThere are financial and logistical limits to monitoring and researching rare and secretive species with box traps, and those limits are far less restrictive with camera traps. The ability to use camera-trap technologies interchangeably with box-trap methods to collect similar data more efficiently and effectively will have a significant impact on snake conservation.
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McIntyre, T., T. L. Majelantle, D. J. Slip, and R. G. Harcourt. "Quantifying imperfect camera-trap detection probabilities: implications for density modelling." Wildlife Research 47, no. 2 (2020): 177. http://dx.doi.org/10.1071/wr19040.
Abstract ContextData obtained from camera traps are increasingly used to inform various population-level models. Although acknowledged, imperfect detection probabilities within camera-trap detection zones are rarely taken into account when modelling animal densities. AimsWe aimed to identify parameters influencing camera-trap detection probabilities, and quantify their relative impacts, as well as explore the downstream implications of imperfect detection probabilities on population-density modelling. MethodsWe modelled the relationships between the detection probabilities of a standard camera-trap model (n=35) on a remotely operated animal-shaped soft toy and a series of parameters likely to influence it. These included the distance of animals from camera traps, animal speed, camera-trap deployment height, ambient temperature (as a proxy for background surface temperatures) and animal surface temperature. We then used this detection-probability model to quantify the likely influence of imperfect detection rates on subsequent population-level models, being, in this case, estimates from random encounter density models on a known density simulation. Key resultsDetection probabilities mostly varied predictably in relation to measured parameters, and decreased with an increasing distance from the camera traps and speeds of movement, as well as heights of camera-trap deployments. Increased differences between ambient temperature and animal surface temperature were associated with increased detection probabilities. Importantly, our results showed substantial inter-camera (of the same model) variability in detection probabilities. Resulting model outputs suggested consistent and systematic underestimation of true population densities when not taking imperfect detection probabilities into account. ConclusionsImperfect, and individually variable, detection probabilities inside the detection zones of camera traps can compromise resulting population-density estimates. ImplicationsWe propose a simple calibration approach for individual camera traps before field deployment and encourage researchers to actively estimate individual camera-trap detection performance for inclusion in subsequent modelling approaches.
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Welsh, Taylor J., Daniel Bentall, Connor Kwon, and Flore Mas. "Automated Surveillance of Lepidopteran Pests with Smart Optoelectronic Sensor Traps." Sustainability 14, no. 15 (August 4, 2022): 9577. http://dx.doi.org/10.3390/su14159577.
Several lepidopterans are pests in horticulture and pose biosecurity risks to trading countries worldwide. Efficient species-specific semiochemical lures are available for some of these pests, facilitating the implementation of surveillance programmes via trapping networks. These networks have a long history of success in detecting incursions of invasive species; however, their reliance on manual trap inspections makes these surveillance programmes expensive to run. Novel smart traps integrating sensor technology are being developed to detect insects automatically but are so far limited to expensive camera-based sensors or optoelectronic sensors for fast-moving insects. Here, we present the development of an optoelectronic sensor adapted to a delta-type trap to record the low wing-beat frequencies of Lepidoptera, and remotely send real-time digital detection via wireless communication. These new smart traps, combined with machine-learning algorithms, can further facilitate diagnostics via species identification through biometrics. Our laboratory and field trials have shown that moths flying in/out of the trap can be detected automatically before visual trap catch, thus improving early detection. The deployment of smart sensor traps for biosecurity will significantly reduce the cost of labour by directing trap visits to the locations of insect detection, thereby supporting a sustainable and low-carbon surveillance system.
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Stokeld, Danielle, Anke S. K. Frank, Brydie Hill, Jenni Low Choy, Terry Mahney, Alys Stevens, Stuart Young, Djelk Rangers, Warddeken Rangers, and Graeme R. Gillespie. "Multiple cameras required to reliably detect feral cats in northern Australian tropical savanna: an evaluation of sampling design when using camera traps." Wildlife Research 42, no. 8 (2015): 642. http://dx.doi.org/10.1071/wr15083.
Context Feral cats are a major cause of mammal declines and extinctions in Australia. However, cats are elusive and obtaining reliable ecological data is challenging. Although camera traps are increasingly being used to study feral cats, their successful use in northern Australia has been limited. Aims We evaluated the efficacy of camera-trap sampling designs for detecting cats in the tropical savanna of northern Australia. We aimed to develop a camera-trapping method that would yield detection probabilities adequate for precise occupancy estimates. Methods First, we assessed the influence of two micro-habitat placements and three lure types on camera-trap detection rates of feral cats. Second, using multiple camera traps at each site, we examined the relationship between sampling effort and detection probability by using a multi-method occupancy model. Key results We found no significant difference in detection rates of feral cats using a variety of lures and micro-habitat placement. The mean probability of detecting a cat on one camera during one week of sampling was very low (p = 0.15) and had high uncertainty. However, the probability of detecting a cat on at least one of five cameras deployed concurrently on a site was 48% higher (p = 0.22) and had a greater precision. Conclusions The sampling effort required to achieve detection rates adequate to infer occupancy of feral cats by camera trap is considerably higher in northern Australia than has been observed elsewhere in Australia. Adequate detection of feral cats in the tropical savanna of northern Australia will necessitate inclusion of more camera traps and a longer survey duration. Implications Sampling designs using camera traps need to be rigorously trialled and assessed to optimise detection of the target species for different Australian biomes. A standard approach is suggested for detecting feral cats in northern Australian savannas.
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Wilton, Clay M., Jeff Beringer, Emily E. Puckett, Lori S. Eggert, and Jerrold L. Belant. "Spatiotemporal factors affecting detection of black bears during noninvasive capture–recapture surveys." Journal of Mammalogy 97, no. 1 (November 16, 2015): 266–73. http://dx.doi.org/10.1093/jmammal/gyv176.
Abstract Accounting for low and heterogeneous detection probabilities in large mammal capture–recapture sampling designs is a persistent challenge. Our objective was to improve understanding of ecological and biological factors driving detection using multiple data sources from an American black bear ( Ursus americanus ) DNA hair trap study in south-central Missouri. We used Global Positioning System telemetry and remote camera data to examine how a bear’s distance to traps, probability of space use, sex-specific behavior, and temporal sampling frame affect detection probability and number of hair samples collected at hair traps. Regression analysis suggested that bear distance to nearest hair trap was the best predictor of detection probability and indicated that detection probability at encounter was 0.15 and declined to < 0.05 at nearest distances > 330 m from hair traps. From remote camera data, number of hair samples increased with number of visits, but the proportion of hair samples from known visits declined 39% from early June to early August. Bears appeared attracted to lured hair traps from close distances and we recommend a hair trap density of 1 trap/2.6 km 2 with spatial coverage that encompasses potentially large male home ranges. We recommend sampling during the late spring and early summer molting period to increase hair deposition rates.
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Hahn, Federico, Salvador Valle, Roberto Rendón, Oneyda Oyorzabal, and Alondra Astudillo. "Mango Fruit Fly Trap Detection Using Different Wireless Communications." Agronomy 13, no. 7 (June 28, 2023): 1736. http://dx.doi.org/10.3390/agronomy13071736.
Fruit flies cause production losses in mango orchards affecting fruit quality. A National Campaign against Fruit Flies (NCFF) evaluates farm status using the fruit flies per trap per day index (FTD). Traps with attractant are installed manually within orchards in Mexico, but counting the flies trapped every week requires excessive numbers of trained personal. Electronic traps (e-traps) use sensors to monitor fruit fly population, saving labor and obtaining the real-time orchard infestation. The objective of this work was to acquire an image within a e-trap at 17:00 when an insect was detected and binarize the information in real-time to count the number of flies. Each e-trap was implemented with a polyethylene PET bottle screwed to a tap containing an ESP32-CAM camera. E-traps from several hectares of mango trees were sampled and transmitted through WSN wireless sensor networks. This original system presents a star topology network within each hectare with the long range LoRa transceiver at the central tower. It receives the fly count from five e-traps and finally transmits data to the house tower end point. Another contribution of this research was the use of a DJI mini2 for acquiring the e-trap data, and the 8-ha flight took 15 min and 35 s. This period can be reduced if the drone flies higher.
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Moll, Remington J., Waldemar Ortiz-Calo, Jonathon D. Cepek, Patrick D. Lorch, Patricia M. Dennis, Terry Robison, and Robert A. Montgomery. "The effect of camera-trap viewshed obstruction on wildlife detection: implications for inference." Wildlife Research 47, no. 2 (2020): 158. http://dx.doi.org/10.1071/wr19004.
Abstract ContextCamera traps are one of the most popular tools used to study wildlife worldwide. Numerous recent studies have evaluated the efficiency and effectiveness of camera traps as a research tool. Nonetheless, important aspects of camera-trap methodology remain in need of critical investigation. One such issue relates to camera-trap viewshed visibility, which is often compromised in the field by physical obstructions (e.g. trees) or topography (e.g. steep slopes). The loss of visibility due to these obstructions could affect wildlife detection rates, with associated implications for study inference and management application. AimsWe aimed to determine the effect of camera-trap viewshed obstruction on wildlife detection rates for a suite of eight North American species that vary in terms of ecology, commonness and body size. MethodsWe deployed camera traps at 204 sites throughout an extensive semi-urban park system in Cleveland, Ohio, USA, from June to September 2016. At each site, we quantified camera-trap viewshed obstruction by using a cover-board design. We then modelled the effects of obstruction on wildlife detection rates for the eight focal species. Key resultsWe found that detection rates significantly decreased with an increasing viewshed obstruction for five of the eight species, including both larger and smaller mammal species (white-tailed deer, Odocoileus virginianus, and squirrels, Sciurus sp., respectively). The number of detections per week per camera decreased two- to three-fold as visibility at a camera site decreased from completely free of obstruction to mostly obstructed. ConclusionsThese results imply that wildlife detection rates are influenced by site-level viewshed obstruction for a variety of species, and sometimes considerably so. ImplicationsResearchers using camera traps should address the potential for this effect to ensure robust inference from wildlife image data. Accounting for viewshed obstruction is critical when interpreting detection rates as indices of abundance or habitat use because variation in detection rate could be an artefact of site-level viewshed obstruction rather than due to underlying ecological processes.
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Levi-Zada, A., A. Sadowsky, S. Dobrinin, T. Ticuchinski, M. David, D. Fefer, E. Dunkelblum, and J. A. Byers. "Monitoring and mass-trapping methodologies using pheromones: the lesser date moth Batrachedra amydraula." Bulletin of Entomological Research 108, no. 1 (May 11, 2017): 58–68. http://dx.doi.org/10.1017/s0007485317000487.
AbstractThe lesser date moth (LDM) Batrachedra amydraula is a significant pest of date palm fruits. Previously, detection and monitoring of the pest was inaccurate due to high costs of sampling with lifting machines. We report a practical system for detection and monitoring of LDM based on pheromone traps and relevant models. Dose–response experiments with LDM pheromone traps indicated a 1 mg lure is optimal for monitoring. Delta traps with adhesive covering their entire inner surface gave the highest captures while trap colour was unimportant. Sampling pheromone traps throughout the night indicated male flight began at 1:00–2:00 and reached a peak 2 h before sunrise. Monitoring traps exposed all year long in Israel revealed three generations with different abundance. Trapping transects in a date plantation indicated interference from a monitoring trap became minimal at distances >27 m away. Inter-trap distances closer than this may lower efficiency of monitoring and mass trapping in control programs. Our estimate of the circular effective attraction radius (EARc) of a 1 mg delta trap for LDM (3.43 m) shows this bait is among the most attractive compared with baits for other insects. We developed encounter-rate equations with the pheromone trap EARc to model the interplay between population levels, trap density and captures that are useful for detection of invasive LDM and its control by mass trapping. The integrated methodologies are applicable to many pest species.
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Huang, Renjie, Tingshan Yao, Cheng Zhan, Geng Zhang, and Yongqiang Zheng. "A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies." Agriculture 11, no. 5 (May 19, 2021): 460. http://dx.doi.org/10.3390/agriculture11050460.
Citrus flies are important quarantine pests in citrus plantations. Electronic traps (e-traps) based on computer vision are the most popular types of equipment for monitoring them. However, most current e-traps are inefficient and unreliable due to requiring manual operations and lack of reliable detection and identification algorithms of citrus fly images. To address these problems, this paper presents a monitoring scheme based on automatic e-traps and novel recognition algorithms. In this scheme, the prototype of an automatic motor-driven e-trap is firstly designed based on a yellow sticky trap. A motor autocontrol algorithm based on Local Binary Pattern (LBP) image analysis is proposed to automatically replace attractants in the e-trap for long-acting work. Furthermore, for efficient and reliable statistics of captured citrus flies, based on the differences between two successive sampling images of the e-trap, a simple and effective detection algorithm is presented to continuously detect the newly captured citrus flies from the collected images of the e-trap. Moreover, a Multi-Attention and Multi-Part convolutional neural Network (MAMPNet) is proposed to exploit discriminative local features of citrus fly images to recognize the citrus flies in the images. Finally, extensive simulation experiments validate the feasibility and efficiency of the designed e-trap prototype and its autocontrol algorithm, as well as the reliability and effectiveness of the proposed detection and recognition algorithms for citrus flies.
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Ramsey, Dave, Murray Efford, Steve Ball, and Graham Nugent. "The evaluation of indices of animal abundance using spatial simulation of animal trapping." Wildlife Research 32, no. 3 (2005): 229. http://dx.doi.org/10.1071/wr03119.
We apply a new algorithm for spatially simulating animal trapping that utilises a detection function and allows for competition between animals and traps. Estimates of the parameters of the detection function from field studies allowed us to simulate realistically the expected range of detection probabilities of brushtail possums caught in traps. Using this model we evaluated a common index of population density of brushtail possums based on the percentage of leg-hold traps catching possums. Using field estimates of the parameters of the detection function, we simulated the relationship between the trap-catch index and population density. The relationship was linear up to densities of 10 possums ha–1. We also investigated the accuracy (bias and precision) of the trap-catch index for possums to estimate relative changes in population density (relative abundance) under conditions of varying detection probability, and compared these results with those obtained using a removal estimate of the population in the vicinity of trap lines. The ratio of trap-catch indices was a more precise estimator of relative abundance than the ratio of removal estimates but was positively biased (i.e. overestimated relative abundance). In contrast, the ratio of removal estimates was relatively unbiased but imprecise. Despite the positive bias, the trap-catch index had a higher power to determine the correct ranking between population densities than the removal estimate. Although varying detection probability can bias estimates of relative abundance using indices, we show that the potential for bias to lead to an incorrect result is small for indices of brushtail possum density based on trapping.
Dissertations / Theses on the topic "Trap detection":
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Örn, Fredrik. "Computer Vision for Camera Trap Footage : Comparing classification with object detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447482.
Monitoring wildlife is of great interest to ecologists and is arguably even more important in the Arctic, the region in focus for the research network INTERACT, where the effects of climate change are greater than on the rest of the planet. This master thesis studies how artificial intelligence (AI) and computer vision can be used together with camera traps to achieve an effective way to monitor populations. The study uses an image data set, containing both humans and animals. The images were taken by camera traps from ECN Cairngorms, a station in the INTERACT network. The goal of the project is to classify these images into one of three categories: "Empty", "Animal" and "Human". Three different methods are compared, a DenseNet201 classifier, a YOLOv3 object detector, and the pre-trained MegaDetector, developed by Microsoft. No sufficient results were achieved with the classifier, but YOLOv3 performed well on human detection, with an average precision (AP) of 0.8 on both training and validation data. The animal detections for YOLOv3 did not reach an as high AP and this was likely because of the smaller amount of training examples. The best results were achieved by MegaDetector in combination with an added method to determine if the detected animals were dogs, reaching an average precision of 0.85 for animals and 0.99 for humans. This is the method that is recommended for future use, but there is potential to improve all the models and reach even more impressive results.Teknisk-naturvetenskapliga
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Murray, Jacolin Ann. "High Flow Air Sampling for Field Detection Using Gas Chromatography-Mass Spectrometry." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2414.
The ability to rapidly detect and identify hazardous analytes in the field has become increasingly important. One of the most important analytical detection methods in the field is gas chromatography-mass spectrometry (GC-MS). In this work, a hand-portable GC-MS system is described that contains a miniature toroidal ion trap mass analyzer and a low thermal mass GC. The system is self-contained within the dimensions of 47 x 36 x 18 cm and weighs less than 13 kg. Because the instrument has a small footprint, it was used as the detector for an automated near-real-time permeation testing system. In permeation testing, materials that are used to make individual protective equipment such as gloves, masks, boots, and suits are exposed to hazardous analytes to determine how long the equipment can be worn safely. The system described herein could test five samples simultaneously. A multi-position valve rotated among the various sample streams and delivered time aliquots into the MS for quantitation. Current field air sampling techniques suffer from long desorption times, high pressure drops, artifact formation and water retention. These disadvantages can be avoided by concentrating the analytes in short open tubular traps containing thick films. There are several advantages to using polymer coated capillaries as traps, including fast desorption, inertness and low flow restriction. An air sampling trap was constructed utilizing open tubular traps for the concentration of semi-volatile organic compounds. The system consisted of multiple capillary traps bundled together, providing high sample flow rates. The analytes were desorbed from the multi-capillary bundle and refocused in a secondary trap. The simultaneous focusing and separation effect of a trap subjected to a negative temperature gradient was also explored. In this configuration, analytes were focused because the front of the peak was at a lower temperature than the rear of the peak and, hence, moved slower. In addition to the focusing effect, analytes with different volatilities focused at different temperatures within the gradient, allowing for separation.
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Sudholz, Ashlee. "Machine learning for the automated detection of deer in drone and camera trap imagery." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212981/1/Ashlee_Sudholz_Thesis.pdf.
To effectively manage the growing population of invasive deer in Australia, adequate monitoring techniques are essential. Traditional methods of detecting and monitoring deer such as scat surveys, spotlighting, or piloted aerial surveys can be expensive and time consuming. To overcome these issues, camera traps and remotely piloted aircraft systems (RPAS or drones) are increasingly being used to detect and monitor deer populations. This thesis presents a new method for assessing the imagery provided by RPAS and camera traps using Machine Learning, reducing the time and cost of assessing deer populations, providing the opportunity for more efficient management.
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Goeders, James E. "Resolved sideband spectroscopy for the detection of weak optical transitions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49082.
This thesis reports on the setup of a new ion trap apparatus designed for experiments with single ⁴⁰Ca⁺ ions to perform molecular spectroscopy. The calcium ion is laser cooled, allowing for sympathetic cooling of the nonfluorescing molecular ion. The aim of these experiments is to explore loading and identifying molecular ions in RF-Paul traps, as well as developing new spectroscopic tools to measure transitions of molecular ions via the fluorescence of co-trapped ⁴⁰Ca⁺ ions. Ground state cooling of a mixed ion pair is implemented as a first step towards increasing the sensitivity of our technique to the level necessary to measure transitions with low scattering rates (like those present in molecular ions). Doppler cooling on the S(1/2)->P(1/2) transition of the calcium ion results in the formation of a Coulomb crystal, the behavior of which may be used to infer properties of the molecular ion. Following cooling, sideband spectroscopy on the narrow S(1/2)->D(5/2) quadrupole transition of calcium may be used to identify the mass of single molecular ions. This method is verified via a non-destructive measurement on ⁴⁰CaH⁺ and ⁴⁰Ca¹⁶O⁺. The normal modes of the Coulomb crystal can also be used to extract information from the target ion to the control ion. By driving the blue side of a transition, laser induced heating can be put into the two ion system, which leads to changes in fluorescence of the ⁴⁰Ca⁺ ion, first demonstrated with two Ca⁺ isotopes. Increasing the sensitivity of this technique requires ground state cooling of both the ⁴⁰Ca⁺ ion and the ion of interest, enabling the transfer of the ion's motional state into the ground state with high probability. This thesis demonstrates ground state cooling of the atomic ion and sympathetic cooling of a second ion (⁴⁴Ca⁺). Once in the ground state, heating of the Coulomb crystal by scattering photons off of the spectroscopy ion can be measured by monitoring the resolved motional sidebands of the S(1/2)->D(5/2) transition of ⁴⁰Ca⁺, allowing for spectral lines to be inferred. Future experiments will investigate this technique with molecular ions.
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Kracke, Holger [Verfasser]. "Detection of individual spin transitions of a single proton confined in a cryogenic Penning trap / Holger Kracke." Mainz : Universitätsbibliothek Mainz, 2013. http://d-nb.info/1032368268/34.
Arnesson, Pontus, and Johan Forslund. "Edge Machine Learning for Wildlife Conservation : Detection of Poachers Using Camera Traps." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177483.
This thesis presents how deep learning can be utilized for detecting humans ina wildlife setting using image classification. Two different solutions have beenimplemented where both of them use a camera-equipped microprocessor to cap-ture the images. In one of the solutions, the deep learning model is run on themicroprocessor itself, which requires the size of the model to be as small as pos-sible. The other solution sends images from the microprocessor to a more pow-erful computer where a larger object detection model is run. Both solutions areevaluated using standard image classification metrics and compared against eachother. To adapt the models to the wildlife environment,transfer learningis usedwith training data from a similar setting that has been manually collected andannotated. The thesis describes a complete system’s implementation and results,including data transfer, parallel computing, and hardware setup. One of the contributions of this thesis is an algorithm that improves the classifi-cation performance on images where a human is far away from the camera. Thealgorithm detects motion in the images and extracts only the area where thereis movement. This is specifically important on the microprocessor, where theclassification model is too simple to handle those cases. By only applying theclassification model to this area, the task is more simple, resulting in better per-formance. In conclusion, when integrating this algorithm, a model running onthe microprocessor gives sufficient results to run as a camera trap for humans.However, test results show that this implementation is still quite underperform-ing compared to a model that is run on a more powerful computer.
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Cummings, Elizabeth Ann. "Optical Detection of Ultracold Neutral Calcium Plasmas." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd692.pdf.
Heck, Michael [Verfasser], and Klaus [Akademischer Betreuer] Blaum. "Investigation of various excitation and detection schemes of stored ions in a Penning trap / Michael Heck ; Betreuer: Klaus Blaum." Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://d-nb.info/1177148544/34.
Chalkha, Achouak. "Glow discharge electron impact ionisation and improvements of linear ion trap operating mode for in-the-field detection of illegal substances." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4704/document.
Anđelković, Zoran [Verfasser]. "Setup of a Penning trap for precision laser spectroscopy at HITRAP : trapping, cooling and electronic detection of externally produced ions / Zoran Andelkovic." Mainz : Universitätsbibliothek Mainz, 2012. http://d-nb.info/1023187825/34.
H, LaGasa Eric, Washington (State). Dept. of Agriculture. Laboratory Services Division., and Washington State Library. Electronic State Publications., eds. 2001 Western Washington exotic pest detection survey: A pheromone-trap survey for proeulia spp. (lepidoptera: tortricidae). [Olympia, Wash.]: Laboratory Services Division, Pest Program, Washington State Dept. of Agriculture, 2001.
H, LaGasa Eric, Washington (State). Plant Protection Division., and Washington State Library. Electronic State Publications., eds. 2002 pheromone-trap detection survey for leek moth, acrolepiopsis assectella (Zeller, 1893) (lepidoptera: acrolepiidae), an exotic pest of allium spp. [Olympia, Wash.]: Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.
H, LaGasa Eric, Washington (State). Plant Protection Division., and Washington State Library. Electronic State Publications., eds. 2002 pheromone-trap detection survey for plum fruit moth, grapholita funebrana (Treitschke, 1835) (lepidoptera: tortricidae), an exotic pest of prunus spp. [Olympia, Wash.]: Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.
LaGasa, Eric H. 2002 light-trap detection survey for European chafer, rhizotrogus majalis (raz.) (coleoptera: scarabeidae), a turf and grain pest recently found in B.C., Canada. [Olympia, Wash.]: Plant Protection Divison, Pest Program, Washington State Dept. of Agriculture, 2003.
United States. Animal and Plant Health Inspection Service. Plant Protection and Quarantine Programs., ed. Exotic pest detection manual. [Beltsville, Md.?]: APHIS Plant Protection and Quarantine, 1986.
He, Shibo, Jiming Chen, Junkun Li, and Youxian Sun. "Energy-Efficient Trap Coverage in Sensor Networks." In Energy-Efficient Area Coverage for Intruder Detection in Sensor Networks, 35–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04648-8_3.
Wang, Chun-Chia, H. W. Lin, Timothy K. Shih, and Wonjun Lee. "Automatic Trap Detection of Ubiquitous Learning on SCORM Sequencing." In Ubiquitous Intelligence and Computing, 1164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11833529_117.
Figueroa, Karina, Antonio Camarena-Ibarrola, Jonathan García, and Héctor Tejeda Villela. "Fast Automatic Detection of Wildlife in Images from Trap Cameras." In Advanced Information Systems Engineering, 940–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_114.
Meats, A. "Fruit Fly Detection Programs: The Potentials and Limitations of Trap Arrays." In Trapping and the Detection, Control, and Regulation of Tephritid Fruit Flies, 253–75. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9193-9_8.
Brockerhoff, Eckehard G., Juan C. Corley, Hervé Jactel, Daniel R. Miller, Robert J. Rabaglia, and Jon Sweeney. "Monitoring and Surveillance of Forest Insects." In Forest Entomology and Pathology, 669–705. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-11553-0_19.
AbstractMonitoring of insect populations is widely used in forest entomology in the context of biodiversity studies, as an aspect of pest management, and for the detection and surveillance of non-native invasive species. In particular, monitoring is undertaken to obtain information on the presence or abundance of particular species, to study their phenology (e.g. the time of oviposition or flight periods), to predict pest population size, spread and damage, or to determine if pest management activities are required. A wide variety of methods are being used for these purposes including physical surveys, the use of insect traps, molecular methods, as well as aerial surveys and remote sensing. This chapter focusses on some of the more important methods to provide an overview of the objectives and applications of monitoring and surveillance of forest insects. The principles of each method and common uses are explained and illustrated with case studies on prominent forest insects including the pine processionary moth (Thaumetopoea pityocampa), the Sirex wood wasp (Sirex noctilio), spongy moth (Lymantria dispar), bark beetles such as Ips typographus, and the brown spruce longhorn beetle (Tetropium fuscum). The chapter also explores statistical considerations and issues such as imperfect relationships between trap catch and the local population size of target species. Niche methods that are not widely used but have strengths in some situations (e.g. detector dogs for detection of Anoplophora glabripennis and other invasive species) or are still in development (e.g. e-noses and acoustic detection) are also discussed.
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Anand, P. Mohan, P. V. Sai Charan, Hrushikesh Chunduri, and Sandeep K. Shukla. "RTR-Shield: Early Detection of Ransomware Using Registry and Trap Files." In Information Security Practice and Experience, 209–29. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7032-2_13.
Blaum, Klaus, and Günter Werth. "Precision Physics in Penning Traps Using the Continuous Stern-Gerlach Effect." In Molecular Beams in Physics and Chemistry, 247–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63963-1_13.
Abstract“A single atomic particle forever floating at rest in free space” (H. Dehmelt) would be the ideal object for precision measurements of atomic properties and for tests of fundamental theories. Such an ideal, of course, can ultimately never be achieved. A very close approximation to this ideal is made possible by ion traps, where electromagnetic forces are used to confine charged particles under well-controlled conditions for practically unlimited time. Concurrently, sensitive detection methods have been developed to allow observation of single stored ions. Various cooling methods can be employed to bring the trapped ion nearly to rest. Among different realisations of ion traps we consider in this chapter the so-called Penning traps which use static electric and magnetic fields for ion confinement. After a brief discussion of Penning-trap properties, we consider various experiments including the application of the “continuous Stern-Gerlach effect”, which have led recently to precise determinations of the masses and magnetic moments of particles and antiparticles. These serve as input for testing fundamental theories and symmetries.
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Tonouchi, M., M. Hangyo, J. Ramos, R. Ijsselsteijn, V. Schultze, H. G. Meyer, and H. E. Hoenig. "Design of Superconductive Optical Flux Trap Memory for Femtosecond Laser Pulse Detection." In Advances in Superconductivity XI, 1297–300. Tokyo: Springer Japan, 1999. http://dx.doi.org/10.1007/978-4-431-66874-9_304.
Brandl, Martin, M. Mayer, C. Fabian, and D. Falkenhagen. "Optical detection of ferromagnetic and fluorescently labeled microparticles, simulation of a magnetic trap." In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 591–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68017-8_148.
Whitley, Kevin D., Matthew J. Comstock, and Yann R. Chemla. "High-Resolution “Fleezers”: Dual-Trap Optical Tweezers Combined with Single-Molecule Fluorescence Detection." In Optical Tweezers, 183–256. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6421-5_8.
Ramkaow, Tanutpong, Surangkana Philaphang, Peerapakorn Phornphikun, and Jirawath Parnklang. "Mosquitoes Flapping Detection Electrocuting Trap." In 2022 7th International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2022. http://dx.doi.org/10.1109/iccre55123.2022.9770258.
Xu, Zhongke, Liang Sun, Xinwei Wang, Han Dong, Pingshun Lei, and Yan Zhou. "Binocular camera trap for wildlife detection." In Optoelectronic Imaging and Multimedia Technology VI, edited by Qionghai Dai, Tsutomu Shimura, and Zhenrong Zheng. SPIE, 2019. http://dx.doi.org/10.1117/12.2537428.
N, Veena, Mahalakshmi S, and OmBhargava. "Human Trap Detection using Convolution Neural Networks." In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2023. http://dx.doi.org/10.1109/iccmc56507.2023.10083927.
Gu, Tong, Min Han, Songlin He, and Xiaotong Chen. "Trap Contract Detection in Blockchain with Improved Transformer." In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437216.
Loos, Alexander, Christian Weigel, and Mona Koehler. "Towards Automatic Detection of Animals in Camera-Trap Images." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553439.
Chunlong, Fan, Yu Zhouhua, and Xu Lei. "An evaluating method of spider detection techniques by trap." In 2010 2nd International Conference on Future Computer and Communication. IEEE, 2010. http://dx.doi.org/10.1109/icfcc.2010.5497315.
Lermer, N., M. D. Barnes, C.-Y. Kung, W. B. Whitten, and J. M. Ramsey. "High-Speed Single Molecule Detection in Microdroplet Streams." In Laser Applications to Chemical and Environmental Analysis. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/lacea.1996.lwb.7.
The detection of individual fluorescent molecules in liquids has been of great interest in recent years. Various fluorescence-based techniques shown to provide single molecule sensitivities include confocal microscopy [1], flow cell techniques [2], and levitated microdroplets [3]. The application of the microdroplet technique to single molecule detection offers many advantages. First, fluoresence decay rates and total fluoresence yield have been shown to be enhanced in glycerol microdroplets [4]. Additionally, the droplet confines the single fluorophore to a small volume thereby removing difficulties arising from diffusion of the fluorophore. Furthermore, the discrete detection unit of the droplet is ideally suited to the application of digital molecular detection for the analysis of ultradilute solutions [5]. Previous liquid microdroplet work has exhibited single molecule detection with signal-to-noise ratios in the range of 10-40 [3]. In our previous work, an electrodynamic trap was employed to trap glycerol microdroplets for a period much longer than the average photochemical lifetime, thus obtaining the maximum possible signal from the analyte. However, the application of digital molecular analysis to real systems requires tens of thousands of droplet measurements [5]; the time required to trap (and to size) the droplet in a levitated system prohibits its application in a high-speed molecular counting technique. In addition, many biological applications of single molecule fluorescence detection require aqueous samples. The present work discusses the development of an instrument designed to permit single molecule detection in water microdroplets at count rates in the range of 10 - 1000 Hz.
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Tekeli, Ulas, and Yalin Bastanlar. "Detection of images with animals in raw camera-trap data." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404214.
Schneider, Stefan, Graham W. Taylor, and Stefan Kremer. "Deep Learning Object Detection Methods for Ecological Camera Trap Data." In 2018 15th Conference on Computer and Robot Vision (CRV). IEEE, 2018. http://dx.doi.org/10.1109/crv.2018.00052.
Xu, Xiaoxiao, Zhenyu Li, Nalinikanth Kotagiri, Pinaki Sarder, Samuel Achilefu, and Arye Nehorai. "Microfluidic microsphere-trap arrays for simultaneous detection of multiple targets." In SPIE MOEMS-MEMS, edited by Holger Becker and Bonnie L. Gray. SPIE, 2013. http://dx.doi.org/10.1117/12.2006628.
Lammert, S. A., R. Merriweather, E. W. Sarver, and M. B. Wasseman. Detection of chemical agents, precursors and by-products using ion trap technology. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/34294.
Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/907881.
Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. US: University Of Alabama, March 2004. http://dx.doi.org/10.2172/898354.
Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. US: University Of Alabama, December 2004. http://dx.doi.org/10.2172/898355.
Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), March 2006. http://dx.doi.org/10.2172/877656.
Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/834185.
Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/840256.
Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/829959.
Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/840803.
Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/859241.
