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Journal articles on the topic 'Counting-set automata'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Turoňová, Lenka, Lukáš Holík, Ondřej Lengál, Olli Saarikivi, Margus Veanes, and Tomáš Vojnar. "Regex matching with counting-set automata." Proceedings of the ACM on Programming Languages 4, OOPSLA (2020): 1–30. http://dx.doi.org/10.1145/3428286.

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SCHEICHER, KLAUS, and JÖRG M. THUSWALDNER. "Canonical number systems, counting automata and fractals." Mathematical Proceedings of the Cambridge Philosophical Society 133, no. 1 (2002): 163–82. http://dx.doi.org/10.1017/s0305004102005856.

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In this paper we study properties of the fundamental domain [Fscr ]β of number systems, which are defined in rings of integers of number fields. First we construct addition automata for these number systems. Since [Fscr ]β defines a tiling of the n-dimensional vector space, we ask, which tiles of this tiling ‘touch’ [Fscr ]β. It turns out that the set of these tiles can be described with help of an automaton, which can be constructed via an easy algorithm which starts with the above-mentioned addition automaton. The addition automaton is also useful in order to determine the box counting dimen

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HOLZER, MARKUS, and SEBASTIAN JAKOBI. "FROM EQUIVALENCE TO ALMOST-EQUIVALENCE, AND BEYOND: MINIMIZING AUTOMATA WITH ERRORS." International Journal of Foundations of Computer Science 24, no. 07 (2013): 1083–97. http://dx.doi.org/10.1142/s0129054113400327.

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We introduce E-equivalence, which is a straightforward generalization of almost-equivalence. While almost-equivalence asks for ordinary equivalence up to a finite number of exceptions, in E-equivalence these exceptions or errors must belong to a (regular) set E. The computational complexity of deterministic finite automata (DFAs) minimization problems and their variants w.r.t. almost- and E-equivalence are studied. We show that there is a significant difference in the complexity of problems related to almost-equivalence, and those related to E-equivalence. Moreover, since hyper-minimal and E-m

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Calautti, Marco, Ester Livshits, Andreas Pieris, and Markus Schneider. "Combined Approximations for Uniform Operational Consistent Query Answering." Proceedings of the ACM on Management of Data 2, no. 2 (2024): 1–16. http://dx.doi.org/10.1145/3651600.

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Operational consistent query answering (CQA) is a recent framework for CQA based on revised definitions of repairs, which are built by applying a sequence of operations (e.g., fact deletions) starting from an inconsistent database until we reach a database that is consistent w.r.t. the given set of constraints. It has been recently shown that there is an efficient approximation for computing the percentage of repairs that entail a given query when we focus on primary keys, conjunctive queries, and assuming the query is fixed (i.e., in data complexity). However, it has been left open whether su

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Marchant, Ross, Martin Tetard, Adnya Pratiwi, Michael Adebayo, and Thibault de Garidel-Thoron. "Automated analysis of foraminifera fossil records by image classification using a convolutional neural network." Journal of Micropalaeontology 39, no. 2 (2020): 183–202. http://dx.doi.org/10.5194/jm-39-183-2020.

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Abstract. Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier

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Stanojcic, Nick, Eduardo Mangieri, Nathan Little, and David O’Brart. "A new software for automated counting of glistenings in intraocular lenses in vivo." International Journal of Ophthalmology 16, no. 8 (2023): 1237–42. http://dx.doi.org/10.18240/ijo.2023.08.08.

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AIM: To assess the performance of a bespoke software for automated counting of intraocular lens (IOL) glistenings in slit-lamp images. METHODS: IOL glistenings from slit-lamp-derived digital images were counted manually and automatically by the bespoke software. The images of one randomly selected eye from each of 34 participants were used as a training set to determine the threshold setting that gave the best agreement between manual and automatic grading. A second set of 63 images, selected using randomised stratified sampling from 290 images, were used for software validation. The images we

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Casas, Gianmarco Goycochea, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Antonilmar Araújo Lopes da Silva, and Helio Garcia Leite. "Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model." Forests 14, no. 12 (2023): 2369. http://dx.doi.org/10.3390/f14122369.

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The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an

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Ying, Yu Ming, and Xiao Hong Yang. "Automatic Counting System Based on MCU." Applied Mechanics and Materials 273 (January 2013): 547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.273.547.

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For the low accuracy and low efficiency of artificial counting problem, we introduced a set of automatic counting system which can be applied in the particulate workpiece counting. With infrared transmitting and receiving module as sensor, when a workpiece passes through the surveyed area, the light will be blocked by the workpiece and the receiving module will emit a pulse signal, the counting for the passing workpiece just is the workpiece number. This text make detailed introduction to the hardware circuit and software design and An on-line type automatic control photoelectric counter syste

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Khan, Ahad Mahmud, Salahuddin Ahmed, Nabidul Haque Chowdhury, et al. "Developing a video expert panel as a reference standard to evaluate respiratory rate counting in paediatric pneumonia diagnosis: protocol for a cross-sectional study." BMJ Open 12, no. 11 (2022): e067389. http://dx.doi.org/10.1136/bmjopen-2022-067389.

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IntroductionManual counting of respiratory rate (RR) in children is challenging for health workers and can result in misdiagnosis of pneumonia. Some novel RR counting devices automate the counting of RR and classification of fast breathing. The absence of an appropriate reference standard to evaluate the performance of these devices is a challenge. If good quality videos could be captured, with RR interpretation from these videos systematically conducted by an expert panel, it could act as a reference standard. This study is designed to develop a video expert panel (VEP) as a reference standar

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Cao, Libo, Liping Zeng, Yaoxuan Wang, et al. "U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting." Microorganisms 12, no. 1 (2024): 201. http://dx.doi.org/10.3390/microorganisms12010201.

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In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation betwe

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Fernandez-Gallego, Jose, Ma Buchaillot, Nieves Aparicio Gutiérrez, María Nieto-Taladriz, José Araus, and Shawn Kefauver. "Automatic Wheat Ear Counting Using Thermal Imagery." Remote Sensing 11, no. 7 (2019): 751. http://dx.doi.org/10.3390/rs11070751.

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Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from

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Albuquerque, Carina, Leonardo Vanneschi, Roberto Henriques, et al. "Object detection for automatic cancer cell counting in zebrafish xenografts." PLOS ONE 16, no. 11 (2021): e0260609. http://dx.doi.org/10.1371/journal.pone.0260609.

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Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we impleme

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Gallardo-Caballero, Ramón, Carlos J. García-Orellana, Antonio García-Manso, Horacio M. González-Velasco, Rafael Tormo-Molina, and Miguel Macías-Macías. "Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques." Sensors 19, no. 16 (2019): 3583. http://dx.doi.org/10.3390/s19163583.

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The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains is a handicap due to the high complexity of the images to be processed, with polymorphic and clumped pollen grains, dust, or debris. The purpose of this study is to analyze the feasibility of implementing a reliable pollen grain detection system based on a convolutional neural network architecture, which will be used later as a critical part of an

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Becker, D. E. "Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes." Proceedings, annual meeting, Electron Microscopy Society of America 53 (August 13, 1995): 650–51. http://dx.doi.org/10.1017/s0424820100139627.

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An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational appr

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Phanomchoeng, Gridsada, Chayatorn Kukiattikoon, Suphanut Plengkham, et al. "Machine-learning-based automated quantification machine for virus plaque assay counting." PeerJ Computer Science 8 (March 4, 2022): e878. http://dx.doi.org/10.7717/peerj-cs.878.

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The plaque assay is a standard quantification system in virology for verifying infectious particles. One of the complex steps of plaque assay is the counting of the number of viral plaques in multiwell plates to study and evaluate viruses. Manual counting plaques are time-consuming and subjective. There is a need to reduce the workload in plaque counting and for a machine to read virus plaque assay; thus, herein, we developed a machine-learning (ML)-based automated quantification machine for viral plaque counting. The machine consists of two major systems: hardware for image acquisition and ML

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Bélisle, François, Nicolas Saunier, Guillaume-Alexandre Bilodeau, and Sebastien le Digabel. "Optimized Video Tracking for Automated Vehicle Turning Movement Counts." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (2017): 104–12. http://dx.doi.org/10.3141/2645-12.

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This paper proposes a new method for automatically counting vehicle turning movements based on video tracking, expanding on previous work on optimization of parameters for road user trajectory extraction and on automated trajectory clustering. The counting method is composed of three main steps: an automated tracker that extracts vehicle trajectories from video data, an automated trajectory clustering algorithm, and an optimization algorithm. The proposed method was applied to obtain turning movement counts in three typical traffic engineering case studies in Canada representing industry-type

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Marin, Ambroise, Emmanuel Denimal, Stéphane Guyot, Ludovic Journaux, and Paul Molin. "A Robust Generic Method for Grid Detection in White Light Microscopy Malassez Blade Images in the Context of Cell Counting." Microscopy and Microanalysis 21, no. 1 (2014): 239–48. http://dx.doi.org/10.1017/s1431927614013671.

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AbstractIn biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.

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Bespalov, A. L., and I. G. Svidrak. "Increasing the accuracy of the process of automated counting of thin long products." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 25, no. 99 (2023): 32–36. http://dx.doi.org/10.32718/nvlvet-f9906.

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Vibrating hopper feeders with an electromagnetic drive for automating the process of counting parts have become widely used. This process is necessary when transferring them to another workshop for the next technological operation. Also, it is necessary to count artificial products on the lot when packing them for packaging. Vibrating feeders are used to feed miniature, small, or medium-sized parts to the counting position of counting or packing machines. In this case, the parts supplied to these positions should be arranged in one layer. In addition, they should have a certain gap between the

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Kuske, Dietrich, та Markus Lohrey. "First-order and counting theories of ω-automatic structures". Journal of Symbolic Logic 73, № 1 (2008): 129–50. http://dx.doi.org/10.2178/jsl/1208358745.

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AbstractThe logic extends first-order logic by a generalized form of counting quantifiers (“the number of elements satisfying … belongs to the set C”). This logic is investigated for structures with an injectively ω-automatic presentation. If first-order logic is extended by an infinity-quantifier, the resulting theory of any such structure is known to be decidable [6]. It is shown that, as in the case of automatic structures [21], also modulo-counting quantifiers as well as infinite cardinality quantifiers (“there are many elements satisfying …”) lead to decidable theories. For a structure of

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Ranefall, Petter, Kenneth Wester, Christer Busch, Per-Uno Malmström, and Ewert Bengtsson. "Automatic Quantification of Microvessels Using Unsupervised Image Analysis." Analytical Cellular Pathology 17, no. 2 (1998): 83–92. http://dx.doi.org/10.1155/1998/490585.

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An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram.To be able to count the objects, fragmented objects are connected, all objects are filled, and touching objects are separated using a watershed segmentation algorithm.The method is fully automatic and robust with respect to illumination and focus settings.A test set consisting of images grabbed with different focus and illumination for each field of view

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Okunev, Alexey G., Mikhail Yu Mashukov, Anna V. Nartova, and Andrey V. Matveev. "Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning." Nanomaterials 10, no. 7 (2020): 1285. http://dx.doi.org/10.3390/nano10071285.

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling

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Zhou, Wenjing, Xueyan Zhu, Mengmeng Gu, and Fengjun Chen. "Spruce Counting Based on Lightweight Mask R-CNN with UAV Images." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 634–42. http://dx.doi.org/10.46300/9106.2021.15.70.

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To achieve rapid and accurate counting of seedlings on mobile terminals such as Unmanned Aerial Vehicle (UAV), we propose a lightweight spruce counting model. Given the difficulties of spruce adhesion and complex environment interference, we adopt the Mask R-CNN as the basic model, which performs instance-level segmentation of the target. To successfully apply the basic model to the mobile terminal applications, we modify the Mask R-CNN model in terms of the light-weighted as follows: the feature extraction network is changed to MobileNetV1 network; NMS is changed to Fast NMS. At the implement

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Yu, Ta-Chuan, Wen-Chien Chou, Chao-Yuan Yeh, et al. "Automatic Bone Marrow Cell Identification and Classification By Deep Neural Network." Blood 134, Supplement_1 (2019): 2084. http://dx.doi.org/10.1182/blood-2019-125322.

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Purpose Differential counting of blood cells is the basis of diagnostic hematology. In many circumstances, identification of cells in bone marrow smears is the golden standard for diagnosis. Presently, methods for automatic differential counting of peripheral blood are readily available commercially. However, morphological assessment and differential counting of bone marrow smears are still performed manually. This procedure is tedious, time-consuming and laden with high inter-operator variation. In recent years, deep neural networks have proven useful in many medical image recognition tasks,

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Prabhu, Ghanashyama, Noel E. O’Connor, and Kieran Moran. "Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models." Sensors 20, no. 17 (2020): 4791. http://dx.doi.org/10.3390/s20174791.

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Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g., their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutiona

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Дубровский, В. А., И. В. Забенков, Е. П. Карпочева та С. О. Торбин. "Идентификация и счет эритроцитов нативной донорской крови человека методом цифровой оптической микроскопии с использованием спектрально фильтрованного освещения". Оптика и спектроскопия 129, № 3 (2021): 327. http://dx.doi.org/10.21883/os.2021.03.50660.208-20.

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The possibility of identification and counting of native donor blood erythrocytes based by static, non-flow digital optical microscopy was studied. The object of study was the whole donor blood diluted by saline and placed into Goryaev's counting chamber. The sample was examined in transmitted light by a Lumam P-8 digital optical microscope equipped by a Basler acA920-40um camera. In order to identify erythrocytes by spectral characteristics two sets of micrographs of 20 pieces in each were obtained. In the first set there was no optical filter in the illumination channel of the microscope and

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Fedorenko, V. F., V. V. Kirsanov, and N. P. Mishurov. "Analysis of different options of use of milking robots in dairy livestock." Machinery and Equipment for Rural Area, no. 7 (July 26, 2021): 33–37. http://dx.doi.org/10.33267/2072-9642-2021-7-33-37.

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It has been established that currently there are about 700 milking robots in dairy farming in Russia, with a predominance of mono-box models. In flowconveyor robotic milking, it is possible to significantly reduce the number of automatic handlers and reduce the cost of a set of equipment due to the separate performing process steps for connecting the teatcups and the milking process itself compared to mono-boxes (one robot – one cow). It is noted that the latter are more expedient to use on small farms counting for up to 200-250 heads. For those farms that have 400 cows and more, it is more ra

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Kabir, Mohimenul, Supratik Chakraborty, and Kuldeep S. Meel. "Exact ASP Counting with Compact Encodings." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10571–80. http://dx.doi.org/10.1609/aaai.v38i9.28927.

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Answer Set Programming (ASP) has emerged as a promising paradigm in knowledge representation and automated reason- ing owing to its ability to model hard combinatorial problems from diverse domains in a natural way. Building on advances in propositional SAT solving, the past two decades have wit- nessed the emergence of well-engineered systems for solv- ing the answer set satisfiability problem, i.e., finding mod- els or answer sets for a given answer set program. In re- cent years, there has been growing interest in problems be- yond satisfiability, such as model counting, in the context of A

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Kurzin, Nikolay N., Dmitriy V. Lebedev, Evgeniy A. Rozhkov, and Vadim A. Bezverkhiy. "The Optoelectronic Installation for Counting the Seeds of Agricultural Crops." Elektrotekhnologii i elektrooborudovanie v APK, no. 3 (September 20, 2020): 115–19. http://dx.doi.org/10.22314/2658-4859-2020-67-3-115-119.

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Modern laboratory devices for counting the seeds are widely used in grain-cleaning enterprises and in research and agricultural centers, but they have disadvantages. (Research purpose) The research purpose is in design an innovative compact automatic optical-electronic plant for counting the seeds, which will have a minimum size and function according to a pre-set algorithm. (Materials and methods) The article describes the determined parameters of an optoelectronic device for counting the number of grains, taking into account the requirements for laboratory devices for counting the seeds of v

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Sobolevskii, Vladislav, Leonid Kolpaschikov, Sophia Rosenfeld, and Vladimir Mikhailov. "Automated counting of large vertebrate species using AutoML technology." BIO Web of Conferences 139 (2024): 14010. http://dx.doi.org/10.1051/bioconf/202413914010.

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The purpose of the presented work is to develop an automation system for synthesizing models of automatic recognition of different animal species in photo and video images. The paper presents a system for recognizing and counting two large vertebrate species - reindeer (Rangifer tarandus) and white-cheeked goose (Branta bernicla) on aerial images. The AutoGenNet recognition system is based on a convolutional neural network (CNN) of Mask R-CNN architecture using the concept of automatic machine learning (AutoML). The created system is able to automate a number of stages of model creation for re

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G. Kilima, Frank, Shubi Kaijage, Edith Luhanga, and Colin Torney. "A YOLOV4 METHOD FOR WILD ANIMAL COUNTING AND BEHAVIOUR DETECTION USING SMALL-SIZED CAMERA-TRAP IMAGE DATASET." ICTACT Journal on Soft Computing 15, no. 4 (2025): 3722–28. https://doi.org/10.21917/ijsc.2025.0516.

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Many deep learning-based solutions proposed to automate the analysis of camera-trap images for animal counting and behaviour detection deployed image classifiers that produce image-level labels, tackle animal counting as a classification task, and use images with one animal or attribute. They also used large image datasets which are costly, time-consuming and laborious to collect and annotate, not feasible for rare/elusive species and resource-constrained projects and did not explain the generalization of the models on images with untrained image backgrounds. This study developed animal counti

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Egi, Yunus, Mortaza Hajyzadeh, and Engin Eyceyurt. "Drone-Computer Communication Based Tomato Generative Organ Counting Model Using YOLO V5 and Deep-Sort." Agriculture 12, no. 9 (2022): 1290. http://dx.doi.org/10.3390/agriculture12091290.

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The growth and development of generative organs of the tomato plant are essential for yield estimation and higher productivity. Since the time-consuming manual counting methods are inaccurate and costly in a challenging environment, including leaf and branch obstruction and duplicate tomato counts, a fast and automated method is required. This research introduces a computer vision and AI-based drone system to detect and count tomato flowers and fruits, which is a crucial step for developing automated harvesting, which improves time efficiency for farmers and decreases the required workforce. T

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Beitel, David, Spencer McNee, Fraser McLaughlin, and Luis F. Miranda-Moreno. "Automated Validation and Interpolation of Long-Duration Bicycle Counting Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 43 (2018): 75–86. http://dx.doi.org/10.1177/0361198118783123.

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Bicycle flow data is crucial for transportation agencies to evaluate and improve cycling infrastructure. Average annual daily bicyclists (AADB) is commonly used in research and practice as a metric for cycling studies such as ridership analysis, infrastructure planning, and injury risk. AADB is estimated by averaging the daily cyclist totals measured throughout the year using a long-term automated bicycle counter, or by using long-term bicycle counting data to extrapolate data from a short-term counting site. Extrapolation of a short-term bicycle counting site requires an accurate and complete

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Groshev, Vasiliy. "On Location of Axle Counters at Station Necks." Automation on transport 8, no. 2 (2022): 162–77. http://dx.doi.org/10.20295/2412-9186-2022-8-2-162-177.

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The use of axle counters makes it possible to automate the processes of station car model formation and renewal. The existing approaches to define installation sites for axle counters at station necks, fixed by regulatory documentation, are in the location of counting points next to all insulating joints of track chains that leads to equipment redundancy. At the same time, the application of axle counters qua the only control device of track freedom faces a number of obstacles among which we can outline the lack of technical means of rail thread integrity control, the need to apply additional

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Ojieabu, Clement E. "Developed Automated Vehicle Traffic Light Controller System for Cities in Nigeria." Journal of Advances in Science and Engineering 1, no. 1 (2018): 19–25. http://dx.doi.org/10.37121/jase.v1i1.6.

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This paper presents a research work that was carried out to resolve challenges of traffic light system. This work employs the use of a microcontroller, an inductive loop which acts as the vehicle detector and LED’s (light emitting diodes) for simulating the red, yellow and green light in a traffic light. The inductive loop is embedded in pavement along the road which senses the presence of vehicle. When a vehicle passes across the loop, the magnetic field changes and the inductance of the coil is decreased resulting in a frequency change of the oscillator which is detected by the controller. T

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Kermanidis, Katia Lida, Nikos Fakotakis, and George Kokkinakis. "Automatic acquisition of verb subcategorization information by exploiting mininal linguistic resources." International Journal of Corpus Linguistics 9, no. 1 (2004): 1–28. http://dx.doi.org/10.1075/ijcl.9.1.01ker.

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A set of well known statistical filtering methods (binomial hypothesis testing, log-likelihood ratio, t-test, thresholds on relative frequencies) is used on Modern Greek and English corpora in order to automatically acquire verb subcategorization frames that are not limited in number and are not known beforehand. As sophisticated linguistic resources and tools are not available for most languages (including Modern Greek), pre-processing of our corpora reaches merely the stage of elementary, intrasentential, non-embedded phrase chunking. By forming, permutating and counting subsets of the verb'

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Friel, J. J., and E. B. Prestridge. "Image Analysis—Turning Images Into Data." Microscopy and Microanalysis 4, S2 (1998): 58–59. http://dx.doi.org/10.1017/s1431927600020419.

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Image analysis is the process of quantifying some aspect of an image—its particle size distribution, for example. Manual methods were in use long before computers made image analysis much faster and more reproducible. Linear measurements of diameter, point counting to measure volume fraction, and intercept counting to determine grain size have been used for over 100 years. Automatic image analysis (AIA), however, can make more measurements, and even calculate derived measurements, such as aspect ratio or circularity. AIA of a specimen or micrograph, of course, is only as good as the contrast m

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Zhou, Chengquan, Hongbao Ye, Jun Hu, et al. "Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform." Sensors 19, no. 14 (2019): 3106. http://dx.doi.org/10.3390/s19143106.

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The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were condu

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Petra, Hlaváčková, Slováčková Hana, Březina David, and Michal Jakub. "Comparison of results of visitor arrival monitoring using regression analysis." Journal of Forest Science 64, No. 7 (2018): 303–12. http://dx.doi.org/10.17221/20/2018-jfs.

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Monitoring of visitor arrivals is one of the tools which help to ensure good-quality and suitable management of the respective area. This paper is aimed at the monitoring of visitor arrivals when the visitors are counted automatically using a field counting device, namely a pyroelectric sensor. In 2015, visitor arrival monitoring using a pyro sensor was conducted on the selected forest roads in the Křtiny Training Forest Enterprise of Masaryk Forest. Since this method should be employed in another project, it was necessary to find out whether the pyroelectric sensor is a reliable tool and whet

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JIJI, G. WISELIN, HENRY SELVARAJ, and G. EVELIN SUJI. "SUPERVISED CLASSIFICATION OF WHITE BLOOD CELLS BY FUSION OF COLOR TEXTURE FEATURES AND NEURAL NETWORK." International Journal of Computational Intelligence and Applications 10, no. 04 (2011): 471–80. http://dx.doi.org/10.1142/s1469026811003197.

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Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test

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Giovannini, N., D. Sacchero, C. Quispe Bonilla, M. Quispe Bonilla, and E. Quispe Peña. "Human counting versus artificial intelligence for assessing medullation in mohair fibres." South African Journal of Animal Science 55, no. 5 (2025): 237–47. https://doi.org/10.4314/sajas.v55i5.02.

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The fleeces of mammals with dense coats, such as the mohair fleeces of Angora goats, usually include medullated fibres. These fibres constitute a problem for the textile industry because of their structural characteristics. Three experiments were conducted in this study, with the aim of comparing human image analysis to digital image analysis and artificial intelligence (AI), in terms of their ability to determine the incidence of medullation in mohair samples. The experiments entailed determining the incidences of industry non-objectionable medullated (NOB) fibres and objectionable medullated

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Beitel, David, Spencer McNee, and Luis F. Miranda-Moreno. "Quality Measure of Short-Duration Bicycle Counts." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (2017): 64–71. http://dx.doi.org/10.3141/2644-08.

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The average annual daily bicyclists (AADB) measure is commonly used in research and practice as a metric for cycling studies, such as bike ridership analysis, infrastructure planning, and injury risk. It is estimated in one of two ways: by averaging the daily cyclist totals measured throughout the year with a long-term automated bicycle counter, or by using a long-term bicycle counter to extrapolate data from a short-term counting site. Unfortunately, extrapolation of a short-term bicycle counting site can produce inaccurate AADB estimates as a result of different error sources; the range of p

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Grosjean, Philippe, Marc Picheral, Caroline Warembourg, and Gabriel Gorsky. "Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system." ICES Journal of Marine Science 61, no. 4 (2004): 518–25. http://dx.doi.org/10.1016/j.icesjms.2004.03.012.

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Abstract Identifying and counting zooplankton are labour-intensive and time-consuming processes that are still performed manually. However, a new system, known as ZOOSCAN, has been designed for counting zooplankton net samples. We describe image-processing and the results of (semi)-automatic identification of taxa with various machine-learning methods. Each scan contains between 1500 and 2000 individuals <0.5 mm. We used two training sets of about 1000 objects each divided into 8 (simplified) and 29 groups (detailed), respectively. The new discriminant vector forest algorithm, which is one

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Bae, Jung Ho, Hyun Wook Han, Sun Young Yang, et al. "Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study." JMIR Medical Informatics 10, no. 4 (2022): e35257. http://dx.doi.org/10.2196/35257.

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Background Manual data extraction of colonoscopy quality indicators is time and labor intensive. Natural language processing (NLP), a computer-based linguistics technique, can automate the extraction of important clinical information, such as adverse events, from unstructured free-text reports. NLP information extraction can facilitate the optimization of clinical work by helping to improve quality control and patient management. Objective We developed an NLP pipeline to analyze free-text colonoscopy and pathology reports and evaluated its ability to automatically assess adenoma detection rate

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Lewis, Joshua, Conrad Shebelut, Bradley Drumheller, et al. "An Automated Pipeline for Cell Differentials on Whole-Slide Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 158, Supplement_1 (2022): S12. http://dx.doi.org/10.1093/ajcp/aqac126.020.

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Abstract Current pathologic diagnosis of benign and neoplastic bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of nucleated cell populations to obtain a cell differential. This manual process has significant limitations, including the limited sample of cells analyzed by a conventional 500-cell differential compared to the thousands of nucleated cells present, as well as the inter-observer variability seen between differentials on single samples due to differences in cell selection and classification. To address these sho

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Xu, Qiaoji, Lingling Jin, James H. Leebens-Mack, and David Sankoff. "Validation of Automated Chromosome Recovery in the Reconstruction of Ancestral Gene Order." Algorithms 14, no. 6 (2021): 160. http://dx.doi.org/10.3390/a14060160.

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The RACCROCHE pipeline reconstructs ancestral gene orders and chromosomal contents of the ancestral genomes at all internal vertices of a phylogenetic tree. The strategy is to accumulate a very large number of generalized adjacencies, phylogenetically justified for each ancestor, to produce long ancestral contigs through maximum weight matching. It constructs chromosomes by counting the frequencies of ancestral contig co-occurrences on the extant genomes, clustering these for each ancestor and ordering them. The main objective of this paper is to closely simulate the evolutionary process givin

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Lewis, Joshua, Xuebao Zhang, Nithya Shanmugam, et al. "Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 156, Supplement_1 (2021): S1—S2. http://dx.doi.org/10.1093/ajcp/aqab189.001.

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Abstract Manual microscopic examination of bone marrow aspirate (BMA) smears and counting of cell populations remains the standard of practice for accurate assessment of benign and neoplastic bone marrow disorders. While automated cell classification software using machine learning models has been developed and applied to BMAs, current systems nonetheless require manual identification of optimal regions within the slide that are rich in marrow hematopoietic cells. To address this issue, we have developed a machine learning-based platform for automated identification of optimal regions in whole

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Kruchak, Liudmyla, and Volodymyr Muravskyi. "Automation of receivables accounting based on an integrated database of counterparties." Herald of Ternopil National Economic University, no. 1(83) (February 22, 2017): 109–18. http://dx.doi.org/10.35774/visnyk2017.01.109.

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The development of information and communication technology has led to the introduction of electronic communication channels to accounting processes. Electronic communications provide information interaction of all debtors and creditors of a company, through which collec- tion, processing and storage of data on payments to counterparties take place in a consoli- dated database. The received primary information can be used to automate receivables ac- counting. Theoretical and practical issues related to automation of receivables accounting are identified. The purpose of the article is to theore

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Gervasi, Alain, Pierre Cardol, and Patrick E. Meyer. "Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation." Micromachines 13, no. 6 (2022): 833. http://dx.doi.org/10.3390/mi13060833.

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Bright field microscopes are particularly useful tools for biologists for cell and tissue observation, phenotyping, cell counting, and so on. Direct cell observation provides a wealth of information on cells’ nature and physiological condition. Microscopic analyses are, however, time-consuming and usually not easy to parallelize. We describe the fabrication of a stand-alone microscope able to automatically collect samples with 3D printed pumps, and capture images at up to 50× optical magnification with a digital camera at a good throughput (up to 24 different samples can be collected and scann

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Becker, D. E., H. Ancin, B. Roysam, and J. N. Turner. "Fast automated mosaic synthesis method for 2-D/3-D image analysis of specimens much wider than the field of view." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 224–25. http://dx.doi.org/10.1017/s0424820100168852.

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We present an efficient, robust, and widely-applicable technique for computational synthesis of wide-area images from a series of overlapping partial views. The synthesized image is the set union of the areas covered by the partial views, and is called the “mosaic”. One application is the laser-scanning confocal microscopy of specimens that are much wider than the field of view of the microscope. Another is imaging of the retinal periphery using a standard fundus imager. This technique can also be used to combine the results of various forms of image analysis, such as cell counting and neuron

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Humaira, Fitrah Maharani, Tengku Musri, Sarimuddin Sarimuddin, Dwi Samsuifin Alham, and Aurista Miftahatul Ilmah. "Analisis Robustness Teks Captcha Paypal HIP Menggunakan Template Matching." JOURNAL OF APPLIED INFORMATICS AND COMPUTING 2, no. 2 (2018): 29–33. http://dx.doi.org/10.30871/jaic.v2i2.1039.

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CAPTCHA refer to Completely Automated Public Turing test to tell Computers and Humans Apart. CAPTCHA are used to ensure that the operators are human not robots. The basic idea of using CAPTCHA is segmentation and recognition. Random characters, graphic images, or CAPTCHA audio become possible solutions to improve security and resilience for protection systems. In this paper used CAPTCHA random characters. However the CAPTCHA text needs to be analyzed again whether it is still solved by the computer or not it needs to be analyzed, improved, and developed to avoid automatic interference. Data se

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