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Добірка наукової літератури з теми "Automatic threshold"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "Automatic threshold"

1

Waller, James K. "Intelligent automatic threshold circuit." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 616. http://dx.doi.org/10.1121/1.410401.

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2

Zhao, Shuang Ping, Xiang Wei Li, Jing Hong Xing, and Gong Zheng. "An Wavelet Image Automatic Threshold Selection Denoising Method." Advanced Materials Research 482-484 (February 2012): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.780.

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A wavelet image automatic threshold selection denoising method based on wavelet transform and genetic algorithm. Firstly, wavelet transition is introduced to an original signal and selecting a wavelet and a level of wavelet decomposition. Secondly, the automatic thresholds of every level of wavelet resolved are obtained by using genetic algorithms. At the same time, the high coefficients of every level were quantized. Thirdly, inverse transition of the coefficients was processed and achieves the final resulting signals. Compared to traditional threshold methods, the proposed method has advantages that it can implement quickly optimal threshold and good capability and stabilization. The final experiments results show that using the proposed algorithm can obtain satisfactory denoising effect.

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3

Midena, E., P. P. Radin, E. Convento, and F. Cavarzeran. "Macular Automatic Fundus Perimetry Threshold versus Standard Perimetry Threshold." European Journal of Ophthalmology 17, no. 1 (January 2007): 63–68. http://dx.doi.org/10.1177/112067210701700109.

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4

Adali, Tulay. "Automatic threshold selection using histogram quantization." Journal of Biomedical Optics 2, no. 2 (April 1, 1997): 211. http://dx.doi.org/10.1117/12.268965.

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5

Lopes, N. V., P. A. Mogadouro do Couto, H. Bustince, and P. Melo-Pinto. "Automatic Histogram Threshold Using Fuzzy Measures." IEEE Transactions on Image Processing 19, no. 1 (January 2010): 199–204. http://dx.doi.org/10.1109/tip.2009.2032349.

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6

Wan, Yan, Li Yao, and Bugao Xu. "Automatic Segmentation of Fiber Cross Sections by Dual Thresholding." Journal of Engineered Fibers and Fabrics 7, no. 1 (March 2012): 155892501200700. http://dx.doi.org/10.1177/155892501200700113.

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In a microscopic image, fiber cross sections are often surrounded by borders distinctively darker than their bodies and the background. Fiber borders can be utilized to separate cross-sections properly so that accurate fiber shape and size information can be obtained. Hence, locating correct fiber borders is one of the most critical steps in cross-sectional analysis for fiber characterization and identification. This paper introduces a dual-thresholding algorithm that performs automatic fiber border segmentation from noisy cross-sectional images. The dual thresholds include a low threshold calculated based on the histogram of the difference from the average grayscale, and a high threshold computed by a bisection algorithm. With the low threshold, part of fiber border pixels, regarded as seeds, can be reliably located. The seeds can be further expanded by using the high threshold to form complete borders surrounding individual cross-sections. The experimental results show that the dual-thresholding algorithm can obtain cleaner and more fiber borders than other connectional thresholding algorithms, and improves the detection accuracy from 52.78% and 88.88%.

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7

KANATANI, KENICHI, and YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION." International Journal of Image and Graphics 04, no. 01 (January 2004): 21–33. http://dx.doi.org/10.1142/s0219467804001270.

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We study the problem of thresholding the residual of template matching as a preprocess for selecting the correct matches between feature points in two images. In order to determine the threshold dynamically, we introduce a statistical model of the residual and compute an optimal threshold according to that model. The model parameters are estimated from the histogram of the residuals of candidate matches. Using real images, we show that our method can substantially upgrade the quality of the initial matches by simply adjusting the threshold.

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8

Qiu, Y., A. R. Whittaker, M. Lucas, and K. Anderson. "Automatic wheeze detection based on auditory modelling." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 219, no. 3 (March 1, 2005): 219–27. http://dx.doi.org/10.1243/095441105x28551.

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Automatic wheeze detection has several potential benefits compared with reliance on human auscultation: it is experience independent, an automated historical record can easily be kept, and it allows quantification of wheeze severity. Previous attempts to detect wheezes automatically have had partial success but have not been reliable enough to become widely accepted as a useful tool. In this paper an improved algorithm for automatic wheeze detection based on auditory modelling is developed, called the frequency- and duration-dependent threshold algorithm. The mean frequency and duration of each wheeze component are obtained automatically. The detected wheezes are marked on a spectrogram. In the new algorithm, the concept of a frequency- and duration-dependent threshold for wheeze detection is introduced. Another departure from previous work is that the threshold is based not on global power but on power corresponding to a particular frequency range. The algorithm has been tested on 36 subjects, 11 of whom exhibited characteristics of wheeze. The results show a marked improvement in the accuracy of wheeze detection when compared with previous algorithms.

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9

Bardy, Fabrice, Bram Van Dun, Harvey Dillon, Mark Seeto, Humphry Qin, Teck Loi, and Robert Cowan. "The Cortical Automatic Threshold Estimation in Adults." Hearing Journal 69, no. 6 (June 2016): 32. http://dx.doi.org/10.1097/01.hj.0000484550.21043.23.

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10

Jung, G. S., and R. H. Park. "Automatic edge extraction using locally adaptive threshold." Electronics Letters 24, no. 11 (May 26, 1988): 711–12. http://dx.doi.org/10.1049/el:19880480.

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Дисертації з теми "Automatic threshold"

1

Braseth, Jørgen. "Automatic Configuration for Collective Construction : Automatic parameter setting for response threshold agents in collective construction." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8748.

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2

Xie, Kaicheng. "Automatic Utility Meter Reading." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1270587412.

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3

Jeuthe, Julius. "Automatic Tissue Segmentation of Volumetric CT Data of the Pelvic Region." Thesis, Linköpings universitet, Medicinsk informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133153.

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Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationtreatment planning, as it adds prior information about the material composition of imaged tissues. For instance, the separation of tissues into bone, adipose tissue and remaining soft tissues allows to use tabulated material compositions of those tissues. This approximation is not perfect because of variability of tissue composition among patients, but is still better than no approximation at all. Another use for automated tissue segmentationis in model based iterative reconstruction algorithms. An example of such an algorithm is DIRA, which is developed at the Medical Radiation Physics and the Center for Medical Imaging Science and Visualization(CMIV) at Linköpings University. DIRA uses dual-energy computed tomography (DECT) data to decompose patient tissues into two or three base components. So far DIRA has used the MK2014 algorithm which segments human pelvis into bones, adipose tissue, gluteus maximus muscles and the prostate. One problem was that MK2014 was limited to 2D and it was not very robust. Aim: The aim of this thesis work was to extend the MK2014 to 3D as well as to improve it. The task was structured to the following activities: selection of suitable segmentation algorithms, evaluation of their results and combining of those to an automated segmentation algorithm. Of special interest was image registration usingthe Morphon. Methods: Several different algorithms were tested. For instance: Otsu's method followed by threshold segmentation; histogram matching followed by threshold segmentation, region growing and hole-filling; affine phase-based registration and the Morphon. The best-performing algorithms were combined into the newly developed JJ2016. Results: For the segmentation of adipose tissue and the bones in the eight investigated data sets, the JJ2016 algorithm gave better results than the MK2014. The better results of the JJ2016 were achieved by: (i) a new segmentation algorithm for adipose tissue which was not affected by the amount of air surrounding the patient and segmented smaller regions of adipose tissue and (ii) a new filling algorithm for connecting segments of compact bone. The JJ2016 algorithm also estimates a likely position for the prostate and the rectum by combining linear and non-linear phase-based registration for atlas based segmentation. The estimated position (center point) was in most cases close to the true position of the organs. Several deficiencies of the MK2014 algorithm were removed but the improved version (MK2014v2) did not perform as well as the JJ2016. Conclusions: JJ2016 performed well for all data sets. The JJ2016 algorithm is usable for the intended application, but is (without further improvements) too slow for interactive usage. Additionally, a validation of the algorithm for clinical use should be performed on a larger number of data sets, covering the variability of patients in shape and size.

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4

SILVA, Joberth de Nazaré. "Detecção automática de massas em mamografias digitais usando Quality Threshold clustering e MVS." Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/1834.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T18:29:06Z No. of bitstreams: 1 JoberthSilva.pdf: 6383640 bytes, checksum: f18918eb45c49cb426b560e4daddf994 (MD5)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Breast cancer is worldwide the most common form of cancer affecting woman, sometimes in their lives, at the proportion of either one to nine or one to thirteen women who reach the age of ninety in the west world (LAURENCE, 2006). Breast cancer is caused by frequent reproduction of cells in various parts of the human body. At certain times, and for reasons yet unknown, some cells begin to reproduce at a higher speed, causing the onset of cellular masses called neoplasias, or tumors, which are new tissue formation, but from pathological origin. This work has proposed a method of automatic detection of masses in digital mammograms, using the Quality Threshold (QT), and the Supporting Vector Machine (MVS). The images processing steps were as follows: firstly, the pre-processing phase took place which consisted of removing the background image, smoothing it with a low pass filter, to increase the degree of contrast, and then, in sequence, accomplishing an enhancement of the Wavelet Transform (WT) by changing their coefficients with a linear function. After the pre-processing phase, came the segmentation with the use of the QT which divided the image in to clusters with pre-defined diameters. Then, the post-processing occurred with the selection of the best candidates to mass formed by the MVS analysis of the shape descriptors. For the extraction phase of texture features the Haralick descriptors and the function correlogram were used. As for the classification stage, the MVS was used again for training, validation of the MVS model and final test. The achieved results were: sensitivity of 92. 31%, specificity of 82.2%, accuracy of 83,53%, a false positive rate per image of 1.12 and an area under a FROC curve of 0.8033.
O câncer de mama é, mundialmente, a forma mais comum de câncer em mulheres afetando, em algum momento suas vidas, aproximadamente uma em cada nove a uma em cada treze mulheres que atingem os noventa anos no mundo ocidental (LAURANCE, 2006). O câncer de mama é ocasionado pela reprodução frequente de células de diversas partes do corpo humano. Em certos momentos e por motivos ainda desconhecidos algumas células começam a se reproduzir com uma velocidade maior, ocasionando o surgimento de massas celulares denominadas de neoplasias ou tumores que são tecidos de formação nova, mas de origem patológica. Neste trabalho foi proposto um método de detecção automática de massas em mamografias digitais usando o Quality Threshold (QT), e a Máquina de Vetores de Suporte (MVS). As etapas de processamento das imagens foram as seguintes: primeiramente veio a fase de pré-processamento que consiste em retirar o fundo da imagem, suavizá-la com um filtro passa-baixa, aumentar a escala de contraste, e na sequencia realizar um realce com a Transformada de Wavelet (WT) através da alteração dos seus coeficientes com uma função linear. Após a fase de pré-processamento vem a seguimentação utilizando o QT que segmenta a imagem em clusters com diâmetros pré-definidos. Em seguida, vem o pós-processamento com a seleção dos melhores candidatos à massa feita através da análise dos descritores de forma pela MVS. Para fase de extração de características de textura foram utiliza os descritores de Haralick e a função correlograma. Já na fase de classificação a MVS novamente foi utilizada para o treinamento, validação do modelo MVS e teste final. Os resultados alcançados foram: sensibilidade de 92,31%, especificidade de 82,2%, Acurácia de 83,53%, uma taxa de falsos positivos por imagem de 1,12 e uma área sob a curva FROC de 0,8033.

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5

Zhang, Zai Yong. "Simultaneous fault diagnosis of automotive engine ignition systems using pairwise coupled relevance vector machine, extracted pattern features and decision threshold optimization." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493967.

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6

Anderson, Foery Kristen R. "Triggering the Lombard effect: Examining automatic thresholds." Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1460856.

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7

Schairer, Kim, Elizabeth Kolberg, Douglas H. Keefe, Denis Fitzpatrick, Daniel Putterman, and Patrick Feeney. "Automated Wideband Acoustic Reflex Threshold Test." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/1803.

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Acoustic reflex thresholds (ARTs) are used clinically as a cross check for behavioral results and as a measure of 7th and 8th cranial nerve function. In clinical test batteries, ARTs are measured as a change in middle ear admittance of a pure tone probe in the presence of a pure tone or broadband noise (BBN) reflex activator. ARTs measured using a wideband probe may yield lower thresholds because the criterion change for 'present' reflexes can be observed across a range of frequencies rather than at a single frequency. ARTs were elicited in a group of 25 adults with normal hearing using a 226-Hz probe and a wideband (250 to 8000 Hz) probe, and activators of 500, 1000, and 2000-Hz and broadband noise (BBN). Wideband ARTs were estimated using an automated adaptive method. Lower mean ARTs were obtained for the wideband method compared to the clinical method by as much as 5-10 dB for tonal activators and 15 dB for BBN. Clinical benefits of lower ARTs include reduced activator levels during threshold estimation, and present rather than absent responses in some ears with absent ART using the clinical method. Results are encouraging for the automated adaptive ART procedure.

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8

Djiallis, Caroline Helen. "Variability of the automated perimetric threshold response." Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/54548/.

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The thesis investigated aspects of the perimetric threshold estimate with the aim of facilitating the outcome of the visual field examination. The difference in performance of the three current short-duration commercially available algorithms, SITA Standard, SITA Fast and TOP was investigated, relative to their respective 'gold standards' and to each other, in two separate studies of normal individuals and of patients with open angle glaucoma (OAG). The results for the normal individuals suggested that the TOP algorithm will overestimate the severity of the field loss relative to the Octopus Threshold and SITA Fast algorithms. However, for the patients with OAG, SITA Fast represented a good compromise between performance and examination duration. The inherent differences within- and between-algorithm for TOP suggests that an alternative should be utilised in clinical practice. The characteristics of the Frequency-of-seeing (FOS) curves for W-W perimetry and for SWAP were investigated for varying eccentricities in normal individuals and in patients with OAG. In the normal individuals, the slope of the FOS curve flattened and the magnitude of the 50th percentile decreased with increase in eccentricity for W-W perimetry and for SWAP. The magnitude of the slope was flatter at any given eccentricity for SWAP than for W-W perimetry. In patients with OAG, the magnitude of the slope was moderately correlated with the severity of field loss for W-W perimetry and for SWAP. The flatter slope of the FOS curve will always yield greater variability for SWAP than for W-W perimetry. The number of incorrect responses to the False-negative catch trials was investigated in patients with OAG as a function of the fatigue effect. No significant difference was found in the prevalence of incorrect responses with increase in fatigue. The prevalence of incorrect responses was modestly correlated with increasing severity of field loss. W-W perimetry, SWAP, SITA, TOP, FOS, False-negative catch trials.

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9

Van, Tonder Jessica Jacqueline. "Automated smartphone threshold audiometry : validity and time-efficiency." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/60435.

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Automated smartphone-based threshold audiometry has the potential to provide affordable audiometric services in underserved contexts where adequate resources and infrastructure are lacking. This study investigated the validity of the threshold version (hearTest) of the hearScreen™ smartphone-based application using inexpensive smartphones (Android OS) and calibrated supra-aural headphones. A repeated-measures, within-subject, study design was employed, comparing automated smartphone audiometry air conduction thresholds (0.5 to 8 kHz) to conventional audiometry thresholds. A total of 95 participants, with varying degrees of hearing sensitivity, were included in the study. 30 participants were adults, with known bilateral hearing losses of varying degrees (mean age of 59 years, 21.8 SD; 56.7% female). 65 participants were adolescents (mean age of 16.5 years, 1.2 SD; 70.8% female), of which 61 had normal hearing and 4 had mild hearing losses. Within the adult sample, 70.6% of thresholds obtained through smartphone and conventional audiometry corresponded within 5 dB. There was no significant difference between smartphone (6.75 min average, 1.5 SD) and conventional audiometry test duration (6.65 min average, 2.5 SD). Within the adolescent sample, 84.7% of audiometry thresholds obtained at 0.5, 2 and 4 kHz corresponded within 5 dB. At 1 kHz 79.3% of the thresholds differed by 10 dB or less. There was a significant difference (p<.01) between smartphone (7.09 min, 1.2 SD) and conventional audiometry test duration (3.23 min, 0.6 SD). The hearTest application using calibrated supra-aural headphones provided valid air conduction hearing thresholds. Therefore, it is evident that using inexpensive smartphones with calibrated headphones provides a cost-effective way to provide access to threshold air conduction audiometry.
Dissertation (M Communication Pathology)--University of Pretoria, 2016.
Speech-Language Pathology and Audiology
M Communication Pathology
Unrestricted

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Pierce, Luke. "NANOPIPELINED THRESHOLD SYNTHESIS USING GATE REPLICATION." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/694.

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Threshold logic gates allow for complex multi-input functions to be implemented using a single gate reducing the power and area of the circuit. Clocked based threshold gates have the additional advantage of its capability of being nanopipelined to increase network throughput. To produce a threshold network the proposed algorithm accepts a traditional algebraic boolean network as an input and resynthesizes it into a nanopipelined threshold logic network. The algorithm is the first to our knowledge that synthesizes in a manner to not only minimize the number of clusters produced from synthesizing the algebraic boolean network but also to minimize associated buffer insertion overhead in producing a clocked threshold gate network.

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Книги з теми "Automatic threshold"

1

Fortinsky, Michael. Eye-movements and automated threshold perimetry. Ottawa: National Library of Canada, 1990.

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2

Bragg, Eric W. At the Threshold of Liquid Geology: And Other Automatic Tales. Writers Advantage, 2002.

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3

Rimašauskas, Marius, Rūta Rimašauskienė, and Tomas Kuncius. Additive Manufacturing. Guidelines for Laboratory Works. KTU leidykla „Technologija“, 2022. http://dx.doi.org/10.5755/e01.9786090217979.

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We are currently on the threshold of the fourth industrial revolution. As the consumers’ expectations are rising, each and every consumer will be able not only to participate in the product development and production processes, but also to propose their ideas, wishes and comments. Due to the changing global production trends, it is clear that the ‘usual’ production will not be able to meet all of its challenges. The development of new shapes, automation, novel non-standard materials, the freedom of design and production, the ability to reorient and react flexibly to the changing conditions are the factors that will give some production companies the competitive advantage, while others will create unmanageable difficulties. The methodological guidelines for laboratory works are intended for Master degree students in the study field of Production Engineering under study programmes Mechatronics, Industrial Engineering and Management, Production Engineering in the framework of the module Computer Integrated Manufacturing. This book can also be useful for bachelor students and anyone interested in the possibilities offered by new technologies and their practical applications.

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4

Mann, Elizabeth C. L. An investigation into test frequency effects on the corrosion fatigue crack growth threshold of 7075-T6 aluminium-alloy using a personal computer based automated system. 1985.

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5

Financial management: DOD needs to lower the disbursement prevalidation threshold : report to congressional requesters. Washington, D.C: The Office, 1996.

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6

Gajewski, Zbigniew. Prognozowanie wystąpień faz fenologicznych pierwiosnki omączonej Primula farinosa L. (Primulaceae) – krytycznie zagrożonego gatunku - w odniesieniu do fenologii innych składników lokalnej flory i panujących warunków termicznych. Publishing House of the University of Agriculture in Krakow, 2018. http://dx.doi.org/10.15576/978-83-66602-32-8.

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In Poland, the bird’s-eye primrose (P. farinosa) is a rare and critically endangered species. Currently, it occurs only in one location in the area of the Jaworki village in Radziejowa Range (of Beskid Sądecki mountains). This is the last of the nine previously existing locations, and the only one occurring in the mountains. To maintain the species, as well as the moutain fen on which it grows, a multiannual conservation program has been implemented, including, among other measures, conducting environmental monitoring and performing active protection procedures. In 2012–2014, studies were carried out, aiming to identify the phenology of P. farinosa in its natural location amid other elements of local flora, and to elaborate reasonably accurate forecast methods of its phenological phases, in relation to the performed procedures. One of the methods was based on comparing subsequent phenological phases of P. farinosa, mainly the dates of flowering and opening of the fruits, with the dates of flowering of other species that commonly occur in the vicinity. On the basis of temperature data from the IMGW meteorological station in Krościenko nad Dunajcem, available via the Internet, an attempt to forecast the phenophases of P. farinosa has also been made, based on the developed network of nearby meteorological stations. A degree-days method was used. Prior to that, a value of base temperature (Tb) characteristic for P. farinosa was assessed, as necessary for further calculations, and the values of SAT (Sum of Active Temperatures, using the formula for GDD values) have been determined for the dates of occurrence of subsequent phenological phases of P. farinosa. These parameters were calculated using temperature data recorded at the location. During the observations, it was established that the dates of flowering for P. farinosa were dependent on the air temperatures during spring. The flowering period of the population in Jaworki took place in the months of April through to June, and it did not exceed 7.5 weeks. P. farinosa is one of the earliest blooming species in that location. Other species, also flourishing during the blooming thereof, included Eriophorum angustifolium, Caltha laeta, Chrysosplenium alternifolium, Oxalis acetosella, and Primula elatior. The full bloom of P. farinosa lasted for about 2 weeks, and it took place between the end of April and the end of the second decade of May. During its full bloom, in that same location, Cardamine pratensis, Geum rivale, and Valeriana simplicifolia also flourished. Trees and bushes from the rose (Rosaceae) family proved to be especially useful in the forecasting of P. farinosa flowering period. During the flowering of P. farinosa, in lower locations, species from the Prunus and Cerasus genera subsequently flourished, followed by the Malus, Sorbus, and Crataegus genera. The opening of P. farinosa fruits was observed in the last days of June or at the beginning of July. The date of commencement and duration of this phase is probably subject also to the degree of air humidity. Although in this period numerous species flourish such as Ononis arvensis, Valeriana sambucifolia, Cichorium intybus, Melilotus alba, Achillea millefolium, Daucus carota, Geranium pratense, and Agrimonia eupatoria, the forecasts based on those are not accurate. When forecasting this phase, as well as the majority of other phases, the SAT value calculation based on the degree-days method works well. The Tb base temperature (threshold temperature) for P. farinosa from Jaworki, determined for its calculation, is 0.75°C. However, for the calculation purposes, the rounded value of Tb = 1.0°C can also be used. When adopting the value of Tb = 0.75°C for calculations, the full bloom of P. farinosa falls within the period in which the SAT values remain between 310°C and 469°C, the optimum (culmination) of the full bloom occurs at about 408°C, and the beginning of capsules opening and release of P. farinosa seeds occur when the SAT values reach 1049°C. For Tb = 1.0°C the full bloom falls within the period in which the SAT values are between 295°C and 449°C, the optimum of full bloom occurs at 390°C, and the capsules begin opening at 1018°C. When using this method, the differences between the forecasted and the observed dates of P. farinose blooming were between –4 and +3 days, and the beginning of opening of its fruits, between –1 and 0 days. In case of no temperature data available from the given location, the forecasting can be also performed based on the data from the IMGW meteorological station in Krościenko. The accuracy of calculations is increased through the implementation of appropriate temperature data adjustments, resulting from the difference of location and height. The temperatures in Jaworki are 2°C lower compared to the station in Krościenko. The dates determined in this way are less accurate than those determined directly at the location, and they differ from the actual dates: in case of full bloom, the difference is in the range of –6 to +2 days, and in case of the beginning of capsules’ opening, it is –2 to –1 days. Probably, it is also possible to implement forecasting based on the data from new automated meteorological stations situated closer to the location, but this can be done only after the adjustment appropriate for every station has been determined and implemented. As demonstrated, the elaborated forecasting methods of P. farinosa phenological phases at the location in Jaworki (Beskid Sądecki) are sufficiently accurate and they may be used in the future as a tool supporting the implementation of measures related to active protection of the species.

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Частини книг з теми "Automatic threshold"

1

Wilkinson, Michael H. F. "Gaussian-Weighted Moving-Window Robust Automatic Threshold Selection." In Computer Analysis of Images and Patterns, 369–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_46.

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2

Xie, Pengyi, Jiangbin Zheng, Qianru Wei, and Yuke Wang. "Automatic Threshold Selection Method for SAR Edge Detection." In Advances in Brain Inspired Cognitive Systems, 530–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_51.

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3

Whitehead, Anthony, Prosenjit Bose, and Robert Laganiere. "Feature Based Cut Detection with Automatic Threshold Selection." In Lecture Notes in Computer Science, 410–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27814-6_49.

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4

Park, Seung-Jin, Kyung-Sik Seo, and Jong-An Park. "Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold." In Lecture Notes in Computer Science, 934–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_116.

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5

Hu, Jianping, and Jindan Chen. "Near-Threshold XOR and XNOR Circuits." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 1675–81. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_217.

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6

Joaquín, Pérez, Pazos Rodolfo, Velez Laura, and Guillermo Rodríguez. "Automatic Generation of Control Parameters for the Threshold Accepting Algorithm." In MICAI 2002: Advances in Artificial Intelligence, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46016-0_13.

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7

M.D., Arpitha, Megha P. Arakeri, and G. Ram Mohan Reddy. "An Approach for Color Edge Detection with Automatic Threshold Detection." In Lecture Notes in Computer Science, 117–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29280-4_13.

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8

Lorbeer, Boris, Ana Kosareva, Bersant Deva, Dženan Softić, Peter Ruppel, and Axel Küpper. "A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm." In Advances in Big Data, 169–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47898-2_18.

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9

Seo, Kyung-Sik. "Improved Fully Automatic Liver Segmentation Using Histogram Tail Threshold Algorithms." In Lecture Notes in Computer Science, 822–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428862_115.

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10

Zhang, Gui-Mei, Jun Chu, and Jun Miao. "Recognizing a Planar Curve Based on NRLCTI and Area Threshold." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 567–75. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_72.

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Тези доповідей конференцій з теми "Automatic threshold"

1

de Azevedo, D. F. G., S. Helegda, F. Glock, and T. Russomano. "Automatic DarkAdaptation Threshold Detection Algorithm." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616844.

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Mahmood, Z., G. Thoonen, and P. Scheunders. "Automatic threshold selection for morphological attribute profiles." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352502.

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3

Chang, Ku-Yaw, Hao-Han Zhang, Shao-Jer Chen, Lih-Shyang Chen, and Jia-Hong Chen. "Automatic Colon Segmentation Using Isolated-Connected Threshold." In 2011 First International Conference on Robot, Vision and Signal Processing (RVSP). IEEE, 2011. http://dx.doi.org/10.1109/rvsp.2011.65.

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4

Subramanian, R., and Rajiv Mehrotra. "Automatic Threshold Selection Based On Information Gain." In SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, edited by Wayne Wiitanen. SPIE, 1988. http://dx.doi.org/10.1117/12.947682.

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5

Zhang, Tianxu, Xinsai Wang, and Yuehuan Wang. "Automatic threshold estimation for gradient image segmentation." In Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bir Bhanu, and Ning Shu. SPIE, 2001. http://dx.doi.org/10.1117/12.441435.

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6

Bejinariu, Silviu-Ioan, Hariton Costin, Florin Rotaru, Ramona Luca, and Cristina Diana Nita. "Automatic multi-threshold image segmentation using metaheuristic algorithms." In 2015 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2015. http://dx.doi.org/10.1109/isscs.2015.7204016.

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7

Kiwanuka, Fred N., and Michael H. F. Wilkinson. "Automatic Attribute Threshold Selection for Blood Vessel Enhancement." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.566.

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8

Lu, Zhang. "Improved automatic white balance based on Otsu threshold." In 2012 National Conference on Computing and Communication Systems (NCCCS). IEEE, 2012. http://dx.doi.org/10.1109/ncccs.2012.6412996.

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9

Aggoun, Amar, Mohammad K. Ibrahim, and Mohammad F. Daemi. "New automatic threshold selection algorithm for edge detection." In Optical Tools for Manufacturing and Advanced Automation, edited by David P. Casasent. SPIE, 1993. http://dx.doi.org/10.1117/12.150156.

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10

Olivo, Jean-Christophe. "Image segmentation by wavelet-based automatic threshold selection." In Visual Communications '93, edited by Barry G. Haskell and Hsueh-Ming Hang. SPIE, 1993. http://dx.doi.org/10.1117/12.157872.

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Звіти організацій з теми "Automatic threshold"

1

Baader, Franz, Oliver Fernández Gil, and Pavlos Marantidis. Approximation in Description Logics: How Weighted Tree Automata Can Help to Define the Required Concept Comparison Measures in FL₀. Technische Universität Dresden, 2016. http://dx.doi.org/10.25368/2022.230.

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Recently introduced approaches for relaxed query answering, approximately defining concepts, and approximately solving unification problems in Description Logics have in common that they are based on the use of concept comparison measures together with a threshold construction. In this paper, we will briefly review these approaches, and then show how weighted automata working on infinite trees can be used to construct computable concept comparison measures for FL₀ that are equivalence invariant w.r.t. general TBoxes. This is a first step towards employing such measures in the mentioned approximation approaches.

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2

Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.

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3

Roth, Christian. Evaluation of the In-vehicle Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, May 2023. http://dx.doi.org/10.4271/epr2023009.

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<div class="section abstract"><div class="htmlview paragraph">Modern in-vehicle experiences are brimming with functionalities and convenience driven by automation, digitalization, and electrification. While automotive manufacturers are competing to provide the best systems to their customers, there is no common ground to evaluate these in-vehicle experiences as they become increasingly complex. Existing automotive guidelines do not offer thresholds for cognitive distraction, or—more appropriately—“disengagement.” What can researchers can do to change this?</div><div class="htmlview paragraph"><b>Evaluation of the In-vehicle Experience</b> discusses acceptable levels of disengagement by evaluating the driving context and exploring how system reliability can translate to distraction and frustration. It also covers the need to test systems for their complexity and ease of use, and to prevent users from resorting to alternative systems while driving (e.g., smartphones). It highlights the value in naturalistic data generation using vehicles already sold to customers and the issues around privacy and trust concerning such methods. Lastly, it talks about the opportunities and challenges behind developing automated testing methods for in-vehicle experiences that simulate human behavior and how to shorten evaluation timelines to enabling a much larger scale of systems testing.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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4

Casper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Isle Royale National Park. National Park Service, December 2022. http://dx.doi.org/10.36967/2295506.

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Amphibians are a Vital Sign indicator for monitoring long-term ecosystem health in seven national park units that comprise the Great Lakes Network. We present here the results for 2019 amphibian monitoring at Isle Royale National Park (ISRO). Appendices contain tabular summaries for six years of cumulative results. The National Park Service Great Lakes Inventory and Monitoring Network established 10 permanent acoustic amphibian monitoring sites at ISRO in 2015. Acoustic samples are collected by placing automated recorders with omnidirectional stereo microphones at each of the 10 sampling sites. Temperature loggers co-located with the recorders also collect air temperature during the sampling period. The monitoring program detected all seven species of frog and toad known to occur at ISRO in 2019, with Eastern American Toad, Green Frog and Spring Peeper occurring at almost every site sampled, and Wood Frog at six sites. Gray Treefrog, Mink Frog, and Boreal Chorus Frog were found at only one or two sites each. Northern Leopard Frog has yet to be confirmed at ISRO in this GLKN monitoring program. We expanded analyses and reporting in 2018 to address calling phenology and to provide a second metric for tracking changes in abundance (as opposed to occupancy) across years. Occupancy analyses track whether or not a site was occupied by a species. Abundance is tracked by assessing how the maximum call intensity changes on sites across years, and by how many automated detections are reported from sites across years. Using two independent survey methods, manual and automated, with large sample sizes continues to return reliable results, providing a confident record of site occupancy for most species. There were no significant data collection issues in 2019. Three units stopped collecting data early but these data gaps did not compromise sampling rigor or analysis. Since temperature logs show that the threshold of ≥40°F was often exceeded by 1 April in 2019, making 15 March a start date for data collection may be considered if park personnel feel snow and ice cover would be reduced enough by that date as well. We do recommend making sure that temperature logger solar shields in future are not hanging in such a manner as to be banging against anything in a breeze, as this contaminates the soundscape

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Casper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Sleeping Bear Dunes National Lakeshore. National Park Service, December 2022. http://dx.doi.org/10.36967/2295512.

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Amphibians are a Vital Sign indicator for monitoring long-term ecosystem health in seven national park units that comprise the Great Lakes Network. We present here the results for 2019 amphibian monitoring at Sleeping Bear Dunes National Lakeshore (SLBE). Appendices contain tabular summaries for six years of cumulative results. The National Park Service Great Lakes Inventory and Monitoring Network established 10 permanent acoustic amphibian monitoring sites at SLBE in 2013. Acoustic samples are collected by placing automated recorders with omnidirectional stereo microphones at each of the 10 sampling sites. Temperature loggers co-located with the recorders also collect air temperature during the sampling period. We expanded analyses and reporting in 2018 to address calling phenology and to provide a second metric for tracking changes in abundance across years. Occupancy analyses track whether or not a site was occupied by a species. Abundance is tracked by assessing how the maximum call intensity changes on sites across years, and by how many automated detections are reported from sites across years. Using two independent survey methods, manual and automated, with large sample sizes continues to return reliable results, providing a confident record of site occupancy for most species. The monitoring program detected five of the six species of frog and toad known to occur at SLBE in 2019, with Eastern American Toad, Gray Treefrog, Green Frog and Spring Peeper occurring at almost every site sampled. Wood Frog was found at one new site, and Northern Leopard Frog was not confirmed in 2019 but was detected at five sites in 2018. There were no significant data collection issues in 2019 except for late deployment of SLBE11, which limited data analyses for this site. Remaining sites successfully collected data as programmed. Cumulative data collection result summaries since inception are provided in appendices. Since temperature logs show that the threshold of ≥40°F was often exceeded by 1 April in 2019, making 15 March a start date for data collection may be considered if park personnel feel snow and ice cover would be reduced enough by that date as well.

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Casper, Gary, Stefanie Nadeau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Pictured Rocks National Lakeshore. National Park Service, December 2022. http://dx.doi.org/10.36967/2295509.

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Amphibians are a Vital Sign indicator for monitoring long-term ecosystem health in seven national park units that comprise the Great Lakes Network. We present here the results for 2019 amphibian monitoring at Pictured Rocks National Lakeshore (PIRO). Appendices contain tabular summaries for six years of cumulative results. The National Park Service Great Lakes Inventory and Monitoring Network established 10 permanent acoustic amphibian monitoring sites at PIRO in 2013. Acoustic samples are collected by placing automated recorders with omnidirectional stereo microphones at each of the 10 sampling sites. Temperature loggers co-located with the recorders also collect air temperature during the sampling period. We expanded analyses and reporting in 2018 to address calling phenology and to provide a second metric for tracking changes in abundance across years. Occupancy analyses track whether or not a site was occupied by a species. Abundance is tracked by assessing how the maximum call intensity changes on sites across years, and by how many automated detections are reported from sites across years. Using two independent survey methods, manual and automated, with large sample sizes continues to return reliable results, providing a confident record of site occupancy for most species. The monitoring program detected five of the six species of frog and toad known to occur at PIRO in 2019, with Eastern American Toad, Gray Treefrog, Green Frog, and Spring Peeper occurring at almost every site sampled. Wood Frog was found at five sites. Mink Frog is known to occur at Sand Point but has never been confirmed at sites monitored by this GLKN program. Additional species of potential occurrence remain hypothetical (i.e., Northern Leopard Frog). The only significant data collection issue in 2019 was at PIRO02, where the equipment recorded only intermittently resulting in only partial data analysis possible. Remaining sites successfully collected data as programmed. Cumulative program result summaries since inception are provided in appendices. Temperature logs in 2019 showed that the threshold of ≥40°F was uniformly exceeded by 1 May, hence we recommend making 10 April the target start date for data collection in future. This could be accomplished by fall deployment of recorders on delayed starts. We also recommend making sure that recorders are mounted 6–10 feet high to better survey the soundscape with less interference from foliage, and that temperature loggers be placed within solar shields.

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Casper, Gary, Stfani Madau, and Thomas Parr. Acoustic amphibian monitoring, 2019 data summary: Mississippi National River and Recreation Area. National Park Service, December 2022. http://dx.doi.org/10.36967/2295507.

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Amphibians are a Vital Sign indicator for monitoring long-term ecosystem health in seven national park units that comprise the Great Lakes Network. We present here the results for 2019 amphibian monitoring at Mississippi National River and Recreation Area (MISS). Appendices contain tabular summaries for six years of cumulative results. The National Park Service Great Lakes Inventory and Monitoring Network established 10 permanent acoustic amphibian monitoring sites at MISS in 2015. Acoustic samples are collected by placing automated recorders with omnidirectional stereo microphones at each of the 10 sampling sites. Temperature loggers co-located with the recorders also collect air temperature during the sampling period. Eight of the nine species of frog and toad known to occur at MISS were found in 2019. The most well distributed species were Eastern American Toad, Gray Treefrog, Green Frog, and Northern Leopard Frog. Rarer are Blanchard’s Cricket Frog, Cope’s Gray Treefrog, Wood Frog, and Boreal Chorus Frog. American Bullfrog has not yet been detected on GLKN monitored sites but has been recently confirmed nearby (Pigs Eye Lake). Two of the ten sites—MISS02, MISS04—were not sampled in 2019 due to flooding, and occupancy of early calling species at MISS06 was determined inconclusive due to a late sampling start. MISS07 was also deployed late and results may contain some false absences due to late sampling. We expanded analyses and reporting in 2018 to address calling phenology and to provide a second metric for tracking changes in abundance (as opposed to occupancy) across years. Occupancy analyses track whether or not a site was occupied by a species. Abundance is tracked by assessing how the maximum call intensity changes on sites across years, and by how many automated detections are reported from sites across years. Using two independent survey methods, manual and automated, with large sample sizes continues to return reliable results, providing a confident record of site occupancy for most species. There were some data collection issues in 2019, with two ARS units not deployed and two others with late start dates. This did reduce our ability to assess some sites and species. Summaries of 2019 data are shown in Appendices A, B and C, and cumulative data collection result summaries are provided in Appendix E. Since temperature logs show that the threshold of ≥40°F was already exceeded by 1 April in 2019, we recommend a 15 March start date for future data collection.

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