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Learning FPGAs : digital design for beginners with Mojo and Lucid HDL
\"Learn how to design digital circuits with FPGAs (field-programmable gate arrays), the devices that reconfigure themselves to become the very hardware circuits you set out to program. With this practical guide, author Justin Rajewski shows you hands-on how to create FPGA projects, whether you're a programmer, engineer, product designer, or maker. You'll quickly go from the basics to designing your own processor. Designing digital circuits used to be a long and costly endeavor that only big companies could pursue. FPGAs make the process much easier, and now they're affordable enough even for hobbyists. If you're familiar with electricity and basic electrical components, this book starts simply and progresses through increasingly complex projects\"--Publisher's description.
Book
Evaluating the Use of Drones Equipped with Thermal Sensors as an Effective Method for Estimating Wildlife
Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human-occupied aerial surveys (e.g., low detection, high operational cost, human safety risk). However, their accuracy and reliability as a valid population technique have not been adequately tested. We tested the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal drone) for surveying white-tailed deer (Odocoileus virginianus) populations using a captive deer population with a highly constrained (hereafter, known) abundance (151–163 deer, midpoint 157 [87–94 deer/km², midpoint 90 deer/km²]) at Auburn University’s deer research facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 nonoverlapping parallel transects (18.8 km) using a small fixed-wing aircraft equipped with a nonradiometric thermal infrared imager. Deer were identified by 2 separate observers by their contrast against background thermal radiation and body shape. Our average thermal drone density estimate (69.8 deer/km², 95% CI = 52.2–87.6), was 78% of the mean known value of 90.2 deer/km², exceeding most sighting probabilities observed with thermal surveys conducted using human-occupied aircraft. Thermal contrast between animals and background was improved during evening flights and our drone-based density estimate (82.7 deer/km²) was 92% of the mean known value. This indicates that time of flight, in conjunction with local vegetation types, determines thermal contrast and influences ability to distinguish deer. The method provides the ability to perform accurate and reliable population surveys in a safe and cost-effective manner compared with traditional aerial surveys and is only expected to continue to improve as sensor technology and machine learning analytics continue to advance. Furthermore, the precise replicability of autonomous flights at future dates results in methodology with superior spatial precision that increases statistical power to detect population trends across surveys.
Journal Article
Trapped : how the world rescued 33 miners from 2,000 feet below the Chilean desert
\"A middle grade nonfiction title about thirty-three miners trapped in a copper-gold mine in San Jose, Chile, and how experts from around the world--from drillers to astronauts to submarine specialists--came together to make their remarkable rescue possible\"-- Provided by publisher.
Book
ctmmweb
Estimating animal home ranges is a primary purpose of collecting tracking data. Many widely used home range estimators, including conventional kernel density estimators, assume independently-sampled data. In stark contrast, modern animal tracking datasets are almost always strongly autocorrelated. The incongruence between estimator assumptions and empirical reality often leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) directly models the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb’s capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal tracked between 2013 and 2015. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.
Journal Article
Effectiveness of Contemporary Techniques for Reducing Livestock Depredations by Large Carnivores
Mitigation of large carnivore depredation is essential to increasing stakeholder support for human–carnivore coexistence. Lethal and non-lethal techniques are implemented by managers, livestock producers, and other stakeholders to reduce livestock depredations by large carnivores. However, information regarding the relative effectiveness of techniques commonly used to reduce livestock depredations is currently lacking. We evaluated 66 published, peer-reviewed research papers that quantitatively measured livestock depredation before and after employing 4 categories of lethal and non-lethal mitigation techniques (livestock husbandry, predator deterrents and removal, and indirect management of land or wild prey) to assess their relative effectiveness as livestock protection strategies. Effectiveness of each technique was measured as the reported percent change in livestock losses. Husbandry (42–100% effective) and deterrents (0–100% effective) demonstrated the greatest potential but also the widest variability in effectiveness in reducing livestock losses. Removal of large carnivores never achieved 100% effectiveness but exhibited the lowest variation (67–83%). Although explicit measures of effectiveness were not reported for indirect management, livestock depredations commonly decreased with sparser and greater distances from vegetation cover, at greater distances from protected areas, and in areas with greater wild prey abundance. Information on time duration of effects was available only for deterrents; a tradeoff existed between the effectiveness of tools and the length of time a tool remained effective. Our assessment revealed numerous sources of bias regarding the effectiveness of techniques as reported in the peer-reviewed literature, including a lack of replication across species and geographic regions, a focus on Canid carnivores in the United States, Europe, and Africa, and a publication bias toward studies reporting positive effects. Given these limitations, we encourage managers and conservationists to work with livestock producers to more consistently and quantitatively measure and report the impacts of mitigation techniques under a wider range of environmental, economic, and sociological conditions.
Journal Article
Density Estimation of Unmarked Populations Using Camera Traps in Heterogeneous Space
Camera traps are commonly used to monitor animal populations, but statistical estimators of density from camera-trap data for species that cannot be individually identified are still in development, and few models take space use into account. We present a model to estimate the density of unmarked populations, which considers species’ space use. The model assumes that animal movements depend on space use probability. Using a hypothetical animal population, we carried out a set of computer simulations on individual movements and camera-trap monitoring to estimate population density with sampling data. Our results demonstrated that the model performed well, and highlighted the importance of adding space use information into density estimation approaches. We also tested the effect of multiple variables (i.e., day range, no. of camera traps, time span of monitoring duration) on density estimates. Increasing the number of camera traps and monitoring duration may reduce the variance of density estimate. Precision of density estimates increased when the species’ day range increased. Subject to accurate measurement of model parameters, this method provides a potential approach to estimate unmarked population density using camera-trap data.
Journal Article
Visible and Thermal Infrared Remote Sensing for the Detection of White-tailed Deer Using an Unmanned Aerial System
Wildlife management is based on various measurements representative of the health of populations and their habitats. Some agencies are focusing on animal surveys to manage species such as white-tailed deer (Odocoileus virginianus). Current survey methods are faced with the challenge of reduced operating costs as well as estimating and correcting detection biases. Our pilot study (data collected on 6 Nov 2012 at Saint-David-de-Falardeau, QC, Canada) assessed the potential of a new approach detect and count deer based on visible and thermal infrared image processing at very-high spatial resolutions using an unmanned aerial system (UAS). Supervised and unsupervised pixel-based image classification approaches as well as object-based image analysis (OBIA) were assessed for different spatial resolutions and with different combinations of spectral bands. None of the pixel-based approaches were effective for detecting deer. The OBIA approach detected deer with a rate of up to 100% under the best conditions by using a combination of visible and thermal infrared imagery at a spatial resolution of 0.8 cm/pixel. Overall, this approach had an average detection rate of 0.5, which is comparable to conventional aerial surveys. Visual obstructions by coniferous canopy and the spectral confusion associated with certain elements (e.g., bare soil, rocks) are problems that remain unresolved. Using UASs with image processing for surveys of deer and other species of large mammals is promising, but currently limited by the flight range of unmanned aerial vehicles and the associated regulations.
Journal Article
Testing a New Passive Acoustic Recording Unit to Monitor Wolves
As part of a broader trial of noninvasive methods to research wild wolves (Canis lupus) in Minnesota, USA, we explored whether wolves could be remotely monitored using a new, inexpensive, remotely deployable, noninvasive, passive acoustic recording device, the AudioMoth. We tested the efficacy of AudioMoths in detecting wolf howls and factors influencing detection by placing them at set distances from a captive wolf pack and compared those recordings with real-time, on-site howling data between 22 May and 17 June 2019. We identified 1,531 vocalizations grouped into 428 vocal events (236 solo howl series and 192 chorus howls). The on-site AudioMoth correctly recorded 100% of chorus and solo howls that were also documented in real-time. The remote array detected 49.5% of chorus and 11.9% of solo howls (≥1 unit detected the event). The closest remote AudioMoth (0.54 km, 0.33 mi) detected 37% of choruses and 8.9% of solo howls. Chorus howls (9.4%) were detected at the farthest unit (3.2 km, 2.0 mi). Favorable wind (carrying source howls to the remote units) and calm (no wind) conditions increased detectability and detection distance of chorus howls. Temperature was inversely related to detection. Given the detection distances we observed, AudioMoths are probably useful in studying specific sites during periods when wolves move less frequently (e.g., during late spring and summer at homesites or potentially during winter at kill sites of very large prey). AudioMoths would also be useful in a passive sampling array (e.g., occupancy studies), especially when used in concert with other methods such as camera-trapping. Additional research should be conducted in areas with different environmental variables (e.g., wind, temperature, habitat, topography) to determine performance under varying conditions and also when fitted with a parabolic dish.
Journal Article
Identifying Individual Jaguars and Ocelots via Pattern-Recognition Software
Camera-trapping is widespread in wildlife studies, especially for species with individually unique markings to which capture–recapture analytical techniques can be applied. The large volume of data such studies produce have encouraged researchers to increasingly look to computer-assisted pattern-recognition software to expedite individual identifications, but little work has been done to formally assess such software for camera-trap data. We used 2 sets of camera-trap images—359 images of jaguars (Panthera onca) and 332 images of ocelots (Leopardus pardalis) collected from camera traps deployed in 4 study sites in Orange Walk District, Belize, in 2015 and 2016—to compare the accuracy of 2 such programs, HotSpotter and Wild-ID, and assess the effect of image quality on matching success. Overall, HotSpotter selected a correct match as its top rank 71–82% of the time, whereas the rate for Wild-ID was 58–73%. Positive matching rates for both programs were highest for high-quality images (85–99%) and lowest for low-quality images (28–52%). False match rates were very low for HotSpotter (0–2%) but these were greater in Wild-ID (6–28%). When lower ranks were also considered, both programs performed similarly (overall 22–24% nonmatches for HotSpotter, 17–26% nonmatches for Wild-ID). We found that in both programs, images more often matched to other images of the same quality; therefore, including multiple reference images of an individual, of different qualities, improves matching success. These programs do not provide fully automatic identification of individuals and human involvement is still required to confirm matches, but we found that they are effective tools to expedite processing of camera-trap data. We also offer usage recommendations for researchers to maximize the benefits of these tools.
Journal Article
Sound anomalies of Cornell Swift recorders affect ecoacoustic studies, and a workaround solution
The Swift Terrestrial Passive Acoustic Recording Unit from The Cornell Lab of Ornithology, running firmware v. STM32 0.18.6.3, produced an initial 4-sec sound anomaly in each sound (wave file) recording, created by the power-saving features of the unit as it switches from standby to record mode. The sound anomaly had a statistically significant impact on several soundscape indices calculated from the recordings. Here, as a case study of identifying and solving this problem, I dissected the nature of the anomaly and analyzed the variable effects it has on calculated ecoacoustic soundscape indices. I used a sample of 150, 10-min sound files, recorded during my ecoacoustics study in central boreal Alaska during 2019 (June-August) and 2020 (April-September), stratified by several landscape conditions and by types of sounds representing anthrophony, biophony, and geophony conditions. The sound anomaly statistically significantly biased the calculations of 7 of 13 ecoacoustic indices analyzed from all of these landscape and soundscape conditions. There is no simple correction factor that can be applied to the calculated index values to account for the effects of the anomaly. I suggest several workarounds, notably to automate a procedure to delete a specified segment of each sound file to eliminate the anomaly prior to soundscape analysis, and in general to watch and correct for such anomalies when using Autonomous Recording Units recordings in ecoacoustic analyses.
Journal Article