Artículos de revistas: "Automatic threshold"

1

Waller, James K. "Intelligent automatic threshold circuit". Journal of the Acoustical Society of America 96, n.º 1 (julio de 1994): 616. http://dx.doi.org/10.1121/1.410401.

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2

Zhao, Shuang Ping, Xiang Wei Li, Jing Hong Xing y Gong Zheng. "An Wavelet Image Automatic Threshold Selection Denoising Method". Advanced Materials Research 482-484 (febrero de 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 y F. Cavarzeran. "Macular Automatic Fundus Perimetry Threshold versus Standard Perimetry Threshold". European Journal of Ophthalmology 17, n.º 1 (enero de 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, n.º 2 (1 de abril de 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 y P. Melo-Pinto. "Automatic Histogram Threshold Using Fuzzy Measures". IEEE Transactions on Image Processing 19, n.º 1 (enero de 2010): 199–204. http://dx.doi.org/10.1109/tip.2009.2032349.

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6

Wan, Yan, Li Yao y Bugao Xu. "Automatic Segmentation of Fiber Cross Sections by Dual Thresholding". Journal of Engineered Fibers and Fabrics 7, n.º 1 (marzo de 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 y YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION". International Journal of Image and Graphics 04, n.º 01 (enero de 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 y K. Anderson. "Automatic wheeze detection based on auditory modelling". Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 219, n.º 3 (1 de marzo de 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 y Robert Cowan. "The Cortical Automatic Threshold Estimation in Adults". Hearing Journal 69, n.º 6 (junio de 2016): 32. http://dx.doi.org/10.1097/01.hj.0000484550.21043.23.

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10

Jung, G. S. y R. H. Park. "Automatic edge extraction using locally adaptive threshold". Electronics Letters 24, n.º 11 (26 de mayo de 1988): 711–12. http://dx.doi.org/10.1049/el:19880480.

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11

Ahmad, Norazlina y Zaidatun Tasir. "Threshold Value in Automatic Learning Style Detection". Procedia - Social and Behavioral Sciences 97 (noviembre de 2013): 346–52. http://dx.doi.org/10.1016/j.sbspro.2013.10.244.

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12

Henstock, P. V. y D. M. Chelberg. "Automatic gradient threshold determination for edge detection". IEEE Transactions on Image Processing 5, n.º 5 (mayo de 1996): 784–87. http://dx.doi.org/10.1109/83.499917.

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13

dos Santos, Juliana B., Carlos A. Heuser, Viviane P. Moreira y Leandro K. Wives. "Automatic threshold estimation for data matching applications". Information Sciences 181, n.º 13 (julio de 2011): 2685–99. http://dx.doi.org/10.1016/j.ins.2010.05.029.

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14

Razavi, H. M. "Self-purging redundancy with automatic threshold adjustment". IEE Proceedings G Circuits, Devices and Systems 140, n.º 4 (1993): 233. http://dx.doi.org/10.1049/ip-g-2.1993.0040.

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15

Ueki, Masao y Yoshinori Kawasaki. "Automatic grouping using smooth-threshold estimating equations". Electronic Journal of Statistics 5 (2011): 309–28. http://dx.doi.org/10.1214/11-ejs608.

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16

Olivo, J. C. "Automatic Threshold Selection Using the Wavelet Transform". CVGIP: Graphical Models and Image Processing 56, n.º 3 (mayo de 1994): 205–18. http://dx.doi.org/10.1006/cgip.1994.1019.

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17

Ejofodomi, O'tega y Godswill Ofualagba. "Design of a Real Time Feedback Automation Irrigation System". European Journal of Engineering Research and Science 4, n.º 12 (27 de diciembre de 2019): 117–20. http://dx.doi.org/10.24018/ejers.2019.4.12.1685.

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This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and commencing automatic irrigation. When the moisture content rises above a predetermined threshold for the crop, the monitoring units sends a wireless message to the control units, causing them to close the pipeline valves and cease automatic irrigation. An automatic irrigation system pipeline network optimization software has also been designed to plan, cost, and design the automatic irrigation system for a piece of land prior to installation.

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18

Ejofodomi, O'tega y Godswill Ofualagba. "Design of a Real Time Feedback Automation Irrigation System". European Journal of Engineering and Technology Research 4, n.º 12 (27 de diciembre de 2019): 117–20. http://dx.doi.org/10.24018/ejeng.2019.4.12.1685.

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This paper presents the design of a real time feedback control automated irrigation systems that consists of monitoring units, control units, irrigation pipeline valves, and a network of irrigation pipeline. The monitoring units continuously measure the soil moisture content in the irrigation blocks, and if the moisture content drops below a predetermined threshold for the particular crop under production, it sends a wireless message to the control units controlling the pipeline valves along the water flow channel, causing the control units to open the valves leading to the water source and commencing automatic irrigation. When the moisture content rises above a predetermined threshold for the crop, the monitoring units sends a wireless message to the control units, causing them to close the pipeline valves and cease automatic irrigation. An automatic irrigation system pipeline network optimization software has also been designed to plan, cost, and design the automatic irrigation system for a piece of land prior to installation.

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19

Wen, Hui Jian, Jiao Lv y Yao Kai Shi. "Automatic Identification of Vertical Fracture". Advanced Materials Research 962-965 (junio de 2014): 2961–64. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2961.

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According to the changes of grayscale or colour in imaging logging data, the principle of fracture automatically segmentation which is in control of threshold and boundary is adopted, combing threshold segmentation principle with shapes of vertical fracture , we can segment and extract fracture images accurately. This method is applied to identification of vertical fracture in logging image which takes thin interacted layer horizontal bedding as background.

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20

Cone-Wesson, Barbara, Richard C. Dowell, Dani Tomlin, Gary Rance y Wu Jia Ming. "The Auditory Steady-State Response: Comparisons with the Auditory Brainstem Response". Journal of the American Academy of Audiology 13, n.º 04 (abril de 2002): 173–87. http://dx.doi.org/10.1055/s-0040-1715962.

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Two studies are reported in which the threshold estimates from auditory steady-state response (ASSR) tests are compared to those of click- or toneburst-evoked auditory brainstem responses (ABRs). The first, a retrospective review of 51 cases, demonstrated that both the click-evoked ABR and the ASSR threshold estimates in infants and children could be used to predict the pure-tone threshold. The second, a prospective study of normal-hearing adults, provided evidence that the toneburst-evoked ABR and the modulated tone–evoked ASSR thresholds were similar when both were detected with an automatic detection algorithm and that threshold estimates varied with frequency, stimulus rate, and detection method. The lowest thresholds were obtained with visual detection of the ABA. The studies illustrate that ASSRs can be used to estimate pure-tone threshold in infants and children at risk for hearing loss and also in normal-hearing adults.

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21

Uyun, Shofwatul, Sri Hartati, Agus Harjoko, Subanar y Lina Choridah. "Comparison between Automatic and Semiautomatic Thresholding Method for Mammographic Density Classification". Advanced Materials Research 896 (febrero de 2014): 672–75. http://dx.doi.org/10.4028/www.scientific.net/amr.896.672.

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Mammographic density is a novel independent risk factor of breast cancer that reflects the amount of fibroglandular tissue. Breast Imaging Reporting and Data System (BIRADS) density is one of the mammographic density classification schemes which are most widely used by radiologists. Initially, the method used for assessing mammographic density was subjective and qualitative. Recently however, the measurement of mammographic density is more objective and quantitative. In this paper, we propose an alternative model of breast cancer risk factor assessment based on a quantitative approach of density mammogram. This model consists of pre-processing, breast area counting, fibroglandular tissue area counting that uses maximum entropy and multilevel thresholds, and finally breast density counting to determine the risk factor of breast cancer. The proposed model has been tested on a private database from Oncology Clinic Kotabaru, Yogyakarta, Indonesia consisting of 30 mammograms and has been analyzed by some radiologists using the semiautomatic threshold. The result shows that percentage of mammographic density counted by maximum entropy threshold method has the accuracy, sensitivity and specificity of about 87%, 73% and 91% respectively compared to the semiautomatic thresholding method. On the other hand, the accuracy, sensitivity and specificity resulted from using multilevel threshold is about 93%, 87% and 96% respectively. The obtained results suggest that multilevel threshold is perfectly suited for getting quantitative measurement of mammographic density as one of the strongest risk factors for breast cancer.

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22

Heise, Stephan J., Jesko L. Verhey y Manfred Mauermann. "Automatic screening and detection of threshold fine structure". International Journal of Audiology 47, n.º 8 (enero de 2008): 520–32. http://dx.doi.org/10.1080/14992020802089473.

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23

ANEGAWA, O. "Automatic Threshold Control for ASK Millimeter-Wave Transceivers". IEICE Transactions on Communications E88-B, n.º 3 (1 de marzo de 2005): 1249–52. http://dx.doi.org/10.1093/ietcom/e88-b.3.1249.

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24

Gupta, Pallav, Rui Zhang y Niraj K. Jha. "Automatic Test Generation for Combinational Threshold Logic Networks". IEEE Transactions on Very Large Scale Integration (VLSI) Systems 16, n.º 8 (agosto de 2008): 1035–45. http://dx.doi.org/10.1109/tvlsi.2008.2000671.

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25

Singh, Dhananjay Kumar y P. K. Panigrahi. "Automatic threshold technique for holographic particle field characterization". Applied Optics 51, n.º 17 (8 de junio de 2012): 3874. http://dx.doi.org/10.1364/ao.51.003874.

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26

SAUER, WILLIAM H., JOSHUA M. COOPER, REBECCA W. LAI y RALPH J. VERDINO. "Underestimation of Pacing Threshold as Determined by an Automatic Ventricular Threshold Testing Algorithm". Pacing and Clinical Electrophysiology 29, n.º 9 (septiembre de 2006): 1028–30. http://dx.doi.org/10.1111/j.1540-8159.2006.00483.x.

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27

Saleh, Marwan D., C. Eswaran y Ahmed Mueen. "An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection". Journal of Digital Imaging 24, n.º 4 (4 de junio de 2010): 564–72. http://dx.doi.org/10.1007/s10278-010-9302-9.

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28

Saha, Rupali, Roshani Thakre, Sakshi Khapekar, Shrushti Newalkar, Sarthak Nagdeote y Viprant Moon. "Automatic Plant Watering System". International Journal for Research in Applied Science and Engineering Technology 11, n.º 2 (28 de febrero de 2023): 431–36. http://dx.doi.org/10.22214/ijraset.2023.49041.

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Abstract: In India, most people depend on agricultural farming. A system is needed for resolving the many issues regarding the development of agricultural farming. Hence, an IoT based Automatic Plant Watering System. This system can be implemented on projects like green building concepts, roof farming, etc. The device will be connected to the internet so that the user can get updates about change in the moisture level. The two major parts of the water supply method is to know when and how much water to supply to the plants. This system is implemented such that it will sense the soil moisture content of the plant, and if it is less than the threshold, then it will turn the motor ON. If the soil moisture content of the plant has crossed the threshold, then it will turn the motor OFF. The Automatic Plant Watering System uses the latest IoT technology which is helpful and leads to easy farming for the farmers.

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29

Ishida, Megumi. "Automatic thresholding for digital hemispherical photography". Canadian Journal of Forest Research 34, n.º 11 (1 de noviembre de 2004): 2208–16. http://dx.doi.org/10.1139/x04-103.

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This paper proposes an automatic thresholding method for the discrimination of sky and canopy elements in color hemispherical photographs taken with a digital camera (Nikon Coolpix 950). The exposures for photography were principally determined on the basis of zenith luminance. DIFphoto, which is diffuse transmittance calculated from the hemispherical photographs, was related to DIFsensor, which is diffuse transmittance measured directly with a photosynthetic photon flux density sensor. First, the thresholds for calculation of DIFphoto were manually assessed in the photographs to obtain the best match with DIFsensor. At the lower pixel-value level in the pixel histograms from the photographs, L-shaped curves were always recognized, and the threshold occurred at the point with the maximum curvature. Second, an automatic thresholding algorithm, taking into account the position of the thresholds, was computerized. Third, the relationships between DIFphoto and DIFsensor were field-tested across a wide range of light conditions. The method was effective in a planted coniferous forest and a natural broad-leaved forest and under overcast, twilit, and sunny sky conditions. The coefficients of determination between DIFsensor and DIFphoto were greater than 0.99. However, DIFphoto taken with Auto-Exposure was overestimated under dense canopy.

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30

Kim, Jonghoek. "Automatic Thread Defect Examination System". Applied Sciences 12, n.º 12 (16 de junio de 2022): 6109. http://dx.doi.org/10.3390/app12126109.

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This article addresses a Thread Hole (TH) examination system capable of automatically examining a TH. We introduce a contact-based examination system with simple hardware. Our examination system examines TH defects by observing the torque generated when tightening the Inspection Device (ID) through the TH. A buffer with springs is utilized to overcome instances where the TH is not accurately aligned with the axis of the ID. The examination system observes the TH size utilizing a camera, then automatically performs an examination utilizing the optimal torque threshold associated with the TH size; in this way, our examination system is able to examine a TH regardless of its size. In this paper, the performance of the proposed system utilizing the optimal torque threshold is verified through experiments.

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31

Ma, Xing, Jun Li Han y Chang Shun Liu. "Research on CCD Infrared Image Threshold Segmentation". Applied Mechanics and Materials 220-223 (noviembre de 2012): 1292–97. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1292.

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In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

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32

Hegazi, Aya, Ahmed Taha y Mazen Mohamed Selim. "Copy-Move Forgery Detection Based on Automatic Threshold Estimation". International Journal of Sociotechnology and Knowledge Development 12, n.º 1 (enero de 2020): 1–23. http://dx.doi.org/10.4018/ijskd.2020010101.

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Recently, users and news followers across websites face many fabricated images. Moreover, it goes far beyond that to the point of defaming or imprisoning a person. Hence, image authentication has become a significant issue. One of the most common tampering techniques is copy-move. Keypoint-based methods are considered as an effective method for detecting copy-move forgeries. In such methods, the feature extraction process is followed by applying a clustering technique to group spatially close keypoints. Most clustering techniques highly depend on the existence of a specific threshold to terminate the clustering. Determination of the most suitable threshold requires a huge amount of experiments. In this article, a copy-move forgery detection method is proposed. The proposed method is based on automatic estimation of the clustering threshold. The cutoff threshold of hierarchical clustering is estimated automatically based on clustering evaluation measures. Experimental results tested on various datasets show that the proposed method outperforms other relevant state-of-the-art methods.

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33

Cheng, Ho-Chih, Min-Chie Chiu, Che-Min Chiu y Cheng-Ying Yang. "Design and development of an automatic gas poisoning prevention and ventilation system". MATEC Web of Conferences 185 (2018): 00010. http://dx.doi.org/10.1051/matecconf/201818500010.

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As both carbon monoxide (CO) and carbon dioxide (CO2) exit during a fire, they are obvious indicators for the need to be alert to the possibility of fire. Another problem concerning poisoning by carbon monoxide (CO) is asphyxiation that occurs in houses during winter. The development of an automatic gas poisoning prevention system in conjunction with a ventilation function using carbon monoxide/carbon dioxide sensors would prove beneficial, necessary. As presented here, this system includes two gas sensors, an alarm, a ventilation device, a motor, and a rain-protection louver. The louver is manipulated by a motor. Two thresholds of gas concentration are preset inside the microcontroller via a PC. The louver is opened by the motor as the first threshold of gas is reached. Additionally, an alarm system is triggered and the ventilation fan starts up if the second threshold of gas concentration is reached. Consequently, image-monitoring via the PC is established using an IPCAM.

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34

Kang, Inshik, Hyunok Song y Hoekyung Jung. "User command acquisition based IoT automatic control system". Indonesian Journal of Electrical Engineering and Computer Science 13, n.º 1 (1 de enero de 2019): 307. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp307-312.

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<p>Recently, researches are being conducted to build a smart environment using various sensors and devices and to provide customized services to users through IoT (Internet of Things). The existing IoT system operates the device only by using the threshold value. So, there are problems that device operates when a service that does not consider a user characteristic is provided or when user not necessary. In this paper, to solve these problems, the system collects the remote control commands and compares it with the value of device operation threshold and sets the changed threshold value. In addition, devices not registered in the server constitute the environment by linking with existing task. Therefore, it is possible to provide a customized service to the users.</p>

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35

Kim, Sang Kon, Seung Ho Lee y Seung Woo Seo. "An automatic portscan detection system with adaptive threshold setting". Journal of Communications and Networks 12, n.º 1 (febrero de 2010): 74–85. http://dx.doi.org/10.1109/jcn.2010.6388436.

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36

BIFFI, MAURO, GIROLAMO SPITALI, MASSIMO STEFANO SILVETTI, SELINA ARGNANI, IDA RUBINO, PIERLUIGI FONTANA, GAETANO BARBATO et al. "Atrial Threshold Variability: Implications for Automatic Atrial Stimulation Algorithms". Pacing and Clinical Electrophysiology 30, n.º 12 (6 de diciembre de 2007): 1445–54. http://dx.doi.org/10.1111/j.1540-8159.2007.00890.x.

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37

HIIPPALA, ANITA, GERALD A. SERWER, EVA CLAUSSON, LYNN DAVENPORT, TRINA BRAND y JUHA-MATTI HAPPONEN. "Automatic Atrial Threshold Measurement and Adjustment in Pediatric Patients". Pacing and Clinical Electrophysiology 33, n.º 3 (marzo de 2010): 309–13. http://dx.doi.org/10.1111/j.1540-8159.2009.02619.x.

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38

Liu, Yu. "Study on Automatic Threshold Selection Algorithm of Sensor Images". Physics Procedia 25 (2012): 1769–75. http://dx.doi.org/10.1016/j.phpro.2012.03.309.

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39

Kiwanuka, Fred N. y Michael H. F. Wilkinson. "Automatic attribute threshold selection for morphological connected attribute filters". Pattern Recognition 53 (mayo de 2016): 59–72. http://dx.doi.org/10.1016/j.patcog.2015.11.012.

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40

Kishihara, Jun, Shinichi Niwano, Hidehira Fukaya, Ryo Nishinarita, Ai Horiguchi, Hironori Nakamura, Tazuru Igarashi et al. "Pacing failure caused by automatic pacing threshold adjustment system". Journal of Arrhythmia 33, n.º 6 (diciembre de 2017): 637–39. http://dx.doi.org/10.1016/j.joa.2017.05.005.

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41

CHEN, Ruo-han, Ke-ping CHEN, Fang-zheng WANG, Wei HUA y Shu ZHANG. "Impact of automatic threshold capture on pulse generator longevity". Chinese Medical Journal 119, n.º 11 (junio de 2006): 925–29. http://dx.doi.org/10.1097/00029330-200606010-00008.

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42

KENNERGREN, CHARLES, BERIT LARSSON, ÅSA UHRENIUS y FREDRIK GADLER. "Clinical Experience with an Automatic Threshold Tracking Algorithm Study". Pacing and Clinical Electrophysiology 26, n.º 12 (diciembre de 2003): 2219–24. http://dx.doi.org/10.1111/j.1540-8159.2003.00350.x.

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43

Cristina Bisi, Maria, Rita Stagni y Gianni Gnudi. "Automatic detection of maximal oxygen uptake and ventilatory threshold". Computers in Biology and Medicine 41, n.º 1 (enero de 2011): 18–23. http://dx.doi.org/10.1016/j.compbiomed.2010.11.001.

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44

Bach, Michael, Christina Schmitt, Miriam Kromeier y Guntram Kommerell. "The Freiburg Stereoacuity Test: automatic measurement of stereo threshold". Graefe's Archive for Clinical and Experimental Ophthalmology 239, n.º 8 (agosto de 2001): 562–66. http://dx.doi.org/10.1007/s004170100317.

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45

Ed-dhahraouy, Mohammed, Hicham Riri, Manal Ezzahmouly, Abdelmajid Elmoutaouakkil, Farid Bourzgui y Hamid El Byad. "Threshold-Based Segmentation for Landmark Detection Using CBCT Images". International Journal of Online and Biomedical Engineering (iJOE) 19, n.º 10 (1 de agosto de 2023): 169–76. http://dx.doi.org/10.3991/ijoe.v19i10.39489.

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The aim of this study is to examine the influence of threshold-based segmentation on the mean error of automatic landmark detection in 3D CBCT images. A GUI was developed for radiologists, allowing manual landmark identification and visualization of CBCT images. After a threshold-based segmentation, a semi-automatic algorithm for landmark detection was designed using the anatomic definition of each landmark. A step of 50 Hounsfield units was used for threshold variation to assess the detection error. 5 CBCT images were used to validate the proposed approach. The measurement of error detection for one patient was influenced by the threshold variation. For this patient, the error changed from 1.49 mm to 10.32 mm at a low threshold value, while for another patient, the error changed from 1.96 mm to 12.28 mm at high a threshold value. In a CBCT scanner, the choice of threshold value for segmentation can be an important factor in causing error in measurements.

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46

Ouadfel, Salima y Souham Meshoul. "Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding". International Journal of Applied Metaheuristic Computing 5, n.º 4 (octubre de 2014): 47–69. http://dx.doi.org/10.4018/ijamc.2014100103.

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Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding (AMT) problem. The goal is to determine the correct number of thresholds as well as their optimal values. For this purpose, the article adopts—for each metaheuristic—a new hybrid coding scheme such that each individual solution is represented by two parts: a real part which represents the thresholds values and a binary part which indicates if a given threshold will be used or not during the thresholding process. Experiments have been conducted on six real test images and the results have been compared with two automatic multilevel thresholding based PSO methods and the exhaustive search method for fair comparison. Empirical results reveal that AMT-HABC and AMT-HCS algorithms performed equally to the solution provided by the exhaustive search and are better than the other comparison algorithms. In addition, the results indicate that the ATM-HABC algorithm has a higher success rate and a speed convergence than the other metaheuristics.

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Balkys, Gediminas y Gintautas Dzemyda. "SEGMENTING THE EYE FUNDUS IMAGES FOR IDENTIFICATION OF BLOOD VESSELS". Mathematical Modelling and Analysis 17, n.º 1 (1 de febrero de 2012): 21–30. http://dx.doi.org/10.3846/13926292.2012.644046.

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Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.

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48

Tian, Shansi, Leon Bowen, Bo Liu, Fang Zeng, Haitao Xue, Valentina Erastova, H. Chris Greenwell, Zhentao Dong, Rixin Zhao y Jinzhong Liu. "A method for automatic shale porosity quantification using an Edge-Threshold Automatic Processing (ETAP) technique". Fuel 304 (noviembre de 2021): 121319. http://dx.doi.org/10.1016/j.fuel.2021.121319.

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49

Piyush, Nitin Dhiman, Bishan Kumar, Sanjay Yadav y P. K. Dubey. "Measurement of ultrasonic pulse velocity with improved accuracy using automatic threshold error correction". Review of Scientific Instruments 94, n.º 4 (1 de abril de 2023): 045101. http://dx.doi.org/10.1063/5.0142739.

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Ultrasonic Pulse Velocity (UPV) measurement is extensively used to monitor the strength and health of concrete structures as per American Society for Testing and Materials C 597 – 09. The commercially available UPV measurement systems work on the basis of single threshold detection of the received signal. Therefore, measurement accuracy is affected due to threshold error. The effect is sensitive to the signal amplitude reaching the threshold comparator and, hence, receiver gain. It is observed that a UPV tester operating at 50 kHz to test concrete might generate an error of up to 10% in the ultrasonic transit time measurement of 50 µs. Hence, it is of great concern and needs to be improved. In this article, the UPV measurement circuit capable of detecting and compensating the threshold error is described. The threshold error correction is achieved with the help of two threshold comparators and two hybrid counters. The circuit developed minimizes the threshold error for wide receiver gain. The measurement carried out with the developed system shows significant improvement, having deviations within 100 ns.

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50

Long, Shengchun, Xiaoxiao Huang, Zhiqing Chen, Shahina Pardhan y Dingchang Zheng. "Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation". BioMed Research International 2019 (23 de enero de 2019): 1–13. http://dx.doi.org/10.1155/2019/3926930.

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Diabetic retinopathy (DR) is one of the most common causes of visual impairment. Automatic detection of hard exudates (HE) from retinal photographs is an important step for detection of DR. However, most of existing algorithms for HE detection are complex and inefficient. We have developed and evaluated an automatic retinal image processing algorithm for HE detection using dynamic threshold and fuzzy C-means clustering (FCM) followed by support vector machine (SVM) for classification. The proposed algorithm consisted of four main stages: (i) imaging preprocessing; (ii) localization of optic disc (OD); (iii) determination of candidate HE using dynamic threshold in combination with global threshold based on FCM; and (iv) extraction of eight texture features from the candidate HE region, which were then fed into an SVM classifier for automatic HE classification. The proposed algorithm was trained and cross-validated (10 fold) on a publicly available e-ophtha EX database (47 images) on pixel-level, achieving the overall average sensitivity, PPV, and F-score of 76.5%, 82.7%, and 76.7%. It was tested on another independent DIARETDB1 database (89 images) with the overall average sensitivity, specificity, and accuracy of 97.5%, 97.8%, and 97.7%, respectively. In summary, the satisfactory evaluation results on both retinal imaging databases demonstrated the effectiveness of our proposed algorithm for automatic HE detection, by using dynamic threshold and FCM followed by an SVM for classification.

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