Relevant bibliographies by topics / Fingerprints Classification Data processing

Academic literature on the topic 'Fingerprints Classification Data processing'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Fingerprints Classification Data processing"

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van Duijvenbode, Jeroen R., Mike W. N. Buxton, and Masoud Soleymani Shishvan. "Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework." Minerals 10, no. 4 (April 18, 2020): 366. http://dx.doi.org/10.3390/min10040366.

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Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance.
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Ibitayo, Faluyi Bamidele, Olowojebutu Akinyemi Olanrewaju, and Makinde Bukola Oyeladun. "A FINGERPRINT BASED GENDER DETECTOR SYSTEM USING FINGERPRINT PATTERN ANALYSIS." international journal of advanced research in computer science 13, no. 4 (August 20, 2022): 35–47. http://dx.doi.org/10.26483/ijarcs.v13i4.6885.

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Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identifies people by measuring some aspect of individual’s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The aim of this research is to analyse humans fingerprint texture in order to determine their gender, and correlation of RTVTR and Ridge Count on gender detection. The study is to analyze the effectiveness of physical biometrics (thumbprint) in order to determine gender in humans. Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual’s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. This work developed a system that determines human gender using fingerprint analysis trained with SVM+CNN (for gender classification). To build an accurate fingerprint based model for gender detection system using fingerprint pattern analysis, there are certain steps that must be taken, which include; Data collection (in conducting research, the first step is collecting data in the form of a set of fingerprint image), Pre-processing Data (before entering the training data, pre-processing data is performed, which is resize the fingerprint image 96x96 pixels). Training Data (in this processing the dataset will be trained using the Convolutional neural network and Support vector machine methodology. This training data processing is a stage where CNN + SVM are trained to obtained high accuracy from the classification conducted). Result Verification (after doing all the above process, at this stage, we will display the results of gender prediction based on fingerprint images in the application that has been making). SOCOFing database is made up of 6,000 fingerprint images from 600 African subjects. It contains unique attributes such as labels for gender, hand and finger name as well as synthetically altered versions with three different levels of alteration for obliteration, central rotation, and z-cut. The values for accuracy, sensitivity and precision using the CNN classifier at threshold 0.25 were 96%, 97.8% and 96.92% respectively. At threshold 0.45 the values were 96.3%, 97.6% and 97.6% respectively. At threshold 0.75 the values were 96.5%, 97.3% and 97.9% respectively. In case of the SVM classifier, at threshold 0.25 were 94.3%, 96.6% and 95.8% respectively. At threshold 0.45 the values were 94.5%, 96.4% and 96.2% respectively. At threshold 0.75 the values were 94.8%, 97.3% and 96.8% respectively. From the 600 fingerprints classified, it was observed that a total of 450 fingerprints were detected for male and 150 for female. Results were obtained for gender accuracy, sensitivity and precision through several thresholds to compare the two classifiers. However, it should be verified that the results obtained showed that the CNN classification yielded better accuracy, sensitivity and precision than SVM.
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Dincă Lăzărescu, Andreea-Monica, Simona Moldovanu, and Luminita Moraru. "A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks." Inventions 7, no. 2 (May 27, 2022): 39. http://dx.doi.org/10.3390/inventions7020039.

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This study presents an algorithm for fingerprint classification using a CNN (convolutional neural network) model and making use of full images belonging to four digital databases. The main challenge that we face in fingerprint classification is dealing with the low quality of fingerprints, which can impede the identification process. To overcome these restrictions, the proposed model consists of the following steps: a preprocessing stage which deals with edge enhancement operations, data resizing, data augmentation, and finally a post-processing stage devoted to classification tasks. Primarily, the fingerprint images are enhanced using Prewitt and Laplacian of Gaussian filters. This investigation used the fingerprint verification competition with four databases (FVC2004, DB1, DB2, DB3, and DB4) which contain 240 real fingerprint images and 80 synthetic fingerprint images. The real images were collected using various sensors. The innovation of the model is in the manner in which the number of epochs is selected, which improves the performance of the classification. The number of epochs is defined as a hyper-parameter which can influence the performance of the deep learning model. The number of epochs was set to 10, 20, 30, and 50 in order to keep the training time at an acceptable value of 1.8 s/epoch, on average. Our results indicate the overfitting of the model starting with the seventh epoch. The accuracy varies from 67.6% to 98.7% for the validation set, and between 70.2% and 75.6% for the test set. The proposed method achieved a very good performance compared to the traditional hand-crafted features despite the fact that it used raw data and it does not perform any handcrafted feature extraction operations.
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Suryadibrata, Alethea, and Suryadi Darmawan Salim. "Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine." Ultimatics : Jurnal Teknik Informatika 11, no. 1 (August 30, 2019): 46–51. http://dx.doi.org/10.31937/ti.v11i1.1076.

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One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F- score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.
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Chhabra, Megha, Manoj Kumar Shukla, and Kiran Kumar Ravulakollu. "Boosting the classification performance of latent fingerprint segmentation using cascade of classifiers." Intelligent Decision Technologies 14, no. 3 (September 29, 2020): 359–71. http://dx.doi.org/10.3233/idt-190105.

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Segmentation and classification of latent fingerprints is a young challenging area of research. Latent fingerprints are unintentional fingermarks. These marks are ridge patterns left at crime scenes, lifted with latent or unclear view of fingermarks, making it difficult to find the guilty party. The segmentation of lifted images of such finger impressions comes with some unique challenges in domain such as poor quality images, incomplete ridge patterns, overlapping prints etc. The classification of poorly acquired data can be improved with image pre-processing, feeding all or optimal set of features extracted to suitable classifiers etc. Our classification system proposes two main steps. First, various effective extracted features are compartmentalised into maximal independent sets with high correlation value, Second, conventional supervised technique based binary classifiers are combined into a cascade/stack of classifiers. These classifiers are fed with all or optimal feature set(s) for binary classification of fingermarks as ridge patterns from non-ridge background. The experimentation shows improvement in accuracy rate on IIIT-D database with supervised algorithms.
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Mohammed Salih, Basna, Adnan Mohsin Abdulazeez, and Omer Mohammed Salih Hassan. "Gender Classification Based on Iris Recognition Using Artificial Neural Networks." Qubahan Academic Journal 1, no. 2 (May 31, 2021): 156–63. http://dx.doi.org/10.48161/qaj.v1n2a63.

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Biometric authentication is one of the most quickly increasing innovations in today's world; this promising technology has seen widespread use in a variety of fields, including surveillance services, safe financial transfers, credit-card authentication. in biometric verification processes such as gender, age, ethnicity is iris recognition technology is considered the most accurate compared to other vital features such as face, hand geometry, and fingerprints. Because the irises in the same person are not similar. In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. The eye image data were collected from the IIT Delhi IRIS Database. All datasets of images were processed using various image processing techniques using the neural network. The results obtained showed high performance in training and got good results in testing. ANN's training and testing process gave a maximum performance at 96.4% and 97% respectively.
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Hausdorf, Lena, Antje Fröhling, Oliver Schlüter, and Michael Klocke. "Analysis of the bacterial community within carrot wash water." Canadian Journal of Microbiology 57, no. 5 (May 2011): 447–52. http://dx.doi.org/10.1139/w11-013.

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Vegetables are washed after harvest to remove unwanted organic and inorganic particles, but wash water contaminated with certain pathogenic microorganisms can potentially contaminate produce. In this study, the microbial diversity of wash water was analyzed in samples taken from a carrot-processing facility. A 16S rRNA gene library with 427 clones was constructed and analyzed by amplified rDNA restriction analysis. For taxonomic classification, the 16S rRNA gene nucleotide sequences of 94 amplified rDNA restriction analysis fingerprints were determined. Each fingerprint indicates a distinct group of microorganisms. The nucleotide sequences were assigned to corresponding reference species. The most prevalent genus was Tolumonas , with 26% of the clones, followed by Acinetobacter and Flacobacterium , with 11% each. The latter two genera contain species that are known to cause nosocomial infections. The fourth most common genus was Arcobacter , comprising 9% of all clones. Some species of Arcobacter are considered to be emerging food pathogens, mainly associated with the contamination of meat products. So far, they have not been considered as contaminants of fresh produce. Based on the sequence data, an Arcobacter-specific PCR assay was developed to facilitate the detection of vegetable-associated Arcobacter strains.
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Zhu, Qingchao, Qiqi Guo, Xiaoou Song, and Yue Zhang. "Research on combined radio frequency fingerprint identification model with limited samples." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2284/1/012014.

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Abstract Aiming to solve the real problem of civilian aircraft identification, a novel combined radio frequency fingerprint (RFF) identification model is proposed, consisting of data analyzing processing, standard characteristic parameter database establishment, classification and optimization. In data analyzing processing step, discrimination was realized for wavelet coefficients, instantaneous phase, Hilbert huang transform energy spectrum, coefficients, time field envelope, probability density function, on basis of which, characteristic parameters were confirmed. In standard characteristic parameter database establishment step, a standard database was found through direct measurement method to avoid losing the RFF feature. In classification step, single character assortment rule and combined classifying rule were defined, with correlative concept and threshold concept. Finally, optimization for the model was realized by modifying parameters manually. Results show that, though hardware was limited and amount of samples were fewer, average identification rate is near to 69.75 percent, providing a theoretical reference for the real problem of identifying different aircrafts.
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Jeon, Woosung, and Dongsup Kim. "FP2VEC: a new molecular featurizer for learning molecular properties." Bioinformatics 35, no. 23 (May 9, 2019): 4979–85. http://dx.doi.org/10.1093/bioinformatics/btz307.

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Abstract Motivation One of the most successful methods for predicting the properties of chemical compounds is the quantitative structure–activity relationship (QSAR) methods. The prediction accuracy of QSAR models has recently been greatly improved by employing deep learning technology. Especially, newly developed molecular featurizers based on graph convolution operations on molecular graphs significantly outperform the conventional extended connectivity fingerprints (ECFP) feature in both classification and regression tasks, indicating that it is critical to develop more effective new featurizers to fully realize the power of deep learning techniques. Motivated by the fact that there is a clear analogy between chemical compounds and natural languages, this work develops a new molecular featurizer, FP2VEC, which represents a chemical compound as a set of trainable embedding vectors. Results To implement and test our new featurizer, we build a QSAR model using a simple convolutional neural network (CNN) architecture that has been successfully used for natural language processing tasks such as sentence classification task. By testing our new method on several benchmark datasets, we demonstrate that the combination of FP2VEC and CNN model can achieve competitive results in many QSAR tasks, especially in classification tasks. We also demonstrate that the FP2VEC model is especially effective for multitask learning. Availability and implementation FP2VEC is available from https://github.com/wsjeon92/FP2VEC. Supplementary information Supplementary data are available at Bioinformatics online.
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Beckmann, Manfred, David P. Enot, David P. Overy, Ian M. Scott, Paul G. Jones, David Allaway, and John Draper. "Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs." British Journal of Nutrition 103, no. 8 (December 15, 2009): 1127–38. http://dx.doi.org/10.1017/s000711450999300x.

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Selective breeding of dogs has culminated in a large number of modern breeds distinctive in terms of size, shape and behaviour. Inadvertently, a range of breed-specific genetic disorders have become fixed in some pure-bred populations. Several inherited conditions confer chronic metabolic defects that are influenced strongly by diet, but it is likely that many less obvious breed-specific differences in physiology exist. Using Labrador retrievers and miniature Schnauzers maintained in a simulated domestic setting on a controlled diet, an experimental design was validated in relation to husbandry, sampling and sample processing for metabolomics. Metabolite fingerprints were generated from ‘spot’ urine samples using flow injection electrospray MS (FIE-MS). With class based on breed, urine chemical fingerprints were modelled using Random Forest (a supervised data classification technique), and metabolite features (m/z) explanatory of breed-specific differences were putatively annotated using the ARMeC database (http://www.armec.org). GC-MS profiling to confirm FIE-MS predictions indicated major breed-specific differences centred on the metabolism of diet-related polyphenols. Metabolism of further diet components, including potentially prebiotic oligosaccharides, animal-derived fats and glycerol, appeared significantly different between the two breeds. Analysis of the urinary metabolome of young male dogs representative of a wider range of breeds from animals maintained under domestic conditions on unknown diets provided preliminary evidence that many breeds may indeed have distinctive metabolic differences, with significant differences particularly apparent in comparisons between large and smaller breeds.
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Dissertations / Theses on the topic "Fingerprints Classification Data processing"

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Deng, Huimin, and 鄧惠民. "Robust minutia-based fingerprint verification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37036427.

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Aygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.

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In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.
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Fernandez, Noemi. "Statistical information processing for data classification." FIU Digital Commons, 1996. http://digitalcommons.fiu.edu/etd/3297.

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This thesis introduces new algorithms for analysis and classification of multivariate data. Statistical approaches are devised for the objectives of data clustering, data classification and object recognition. An initial investigation begins with the application of fundamental pattern recognition principles. Where such fundamental principles meet their limitations, statistical and neural algorithms are integrated to augment the overall approach for an enhanced solution. This thesis provides a new dimension to the problem of classification of data as a result of the following developments: (1) application of algorithms for object classification and recognition; (2) integration of a neural network algorithm which determines the decision functions associated with the task of classification; (3) determination and use of the eigensystem using newly developed methods with the objectives of achieving optimized data clustering and data classification, and dynamic monitoring of time-varying data; and (4) use of the principal component transform to exploit the eigensystem in order to perform the important tasks of orientation-independent object recognition, and di mensionality reduction of the data such as to optimize the processing time without compromising accuracy in the analysis of this data.
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Varnavas, Andreas Soteriou. "Signal processing methods for EEG data classification." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/11943.

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Shen, Shan. "MRI brain tumour classification using image processing and data mining." Thesis, University of Strathclyde, 2004. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21543.

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Detecting and diagnosing brain tumour types quickly and accurately is essential to any effective treatment. The general brain tumour diagnosis procedure, biopsy, not only causes a great deal of pain to the patient but also raises operational difficulty to the clinician. In this thesis, a non-invasive brain tumour diagnosis system based on MR images is proposed. The first part is image preprocessing applied to original MR images from the hospital. Non-uniformed intensity scales of MR images are standardized relying on their statistic characteristics without requiring prior or post templates. It is followed by a non-brain region removal process using morphologic operations and a contrast enhancement between white matter and grey matter by means of histogram equalization. The second part is image segmentation applied to preprocessed MR images. A new image segmentation algorithm named IFCM is developed based on the traditional FCM algorithm. Neighbourhood attractions considered in IFCM enable this new algorithm insensitive to noise, while a neural network model is designed to determine optimized degrees of attractions. This extension can also estimate inhomogenities. Brain tissue intensities are acquired from segmentation. The final part of the system is brain tumour classification. It extracts hidden diagnosis information from brain tissue intensities using a fuzzy logic based GP algorithm. This novel method imports a fuzzy membership to implement a multi-class classification directly without converting it into several binary classification problems as with most other methods. Two fitness functions are defined to describe the features of medical data precisely. The superiority of image analysis methods in each part was demonstrated on synthetic images and real MR images. Classification rules of three types and two grades of brain tumours were discovered. The final diagnosis accuracy was very promising. The feasibility and capability of the non-invasive diagnosis system were testified comprehensively.
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Kirkin, S., and K. V. Melnyk. "Intelligent Data Processing in Creating Targeted Advertising." Thesis, National Technical University "Kharkiv Polytechnic Institute", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/44710.

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Pinheiro, Muriel Aline. "Processing, radiometric correction, autofocus and polarimetric classification of circular SAR data." Instituto Tecnológico de Aeronáutica, 2010. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1083.

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The demand for high resolution SAR systems and also for imaging techniques to retrieve scene information on the third dimension have stimulated the development of new acquisition modes and processing approaches. This work studies one of the newest SAR acquisition modes being used, namely the Circular SAR, in which the platform follows a non-linear circular trajectory. A brief introduction of the acquisition geometry is present along with the advantages of this acquisition mode, such as the volumetric reconstruction capability, higher resolutions and the possibility to retrieve target information from a wider range of observation angles. To deal with the non-linearity of trajectory, a processing approach using the time domain back-projection algorithm is suggested to focus and radiometric correct the images, taking into account the antenna patterns and loss due to propagation. An existing autofocus approach to correct motion errors is validated for the circular SAR context and a new frequency domain approach is proposed. Once the images are processed and calibrated, a polarimetric analysis is presented. In this context, a new polarimetric classification methodology is proposed for the particular geometry under consideration. The method uses the H- plane and the information of the first eigenvalue to classify small sub-apertures of the circular trajectory and finally classify the entire 360 circular aperture. Using information of all sub-apertures it is possible to preserve information of directional targets and diminish the effects caused by topography defocusing on the classification. To obtain speckle reduction improving the classification algorithm a Lee adaptive filter is implemented. The processing calibration approaches and the classification methodology are validated with circular SAR real data acquired with the SAR systems from the German Aerospace Center (DLR).
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ALMEIDA, Marcos Antonio Martins de. "Statistical analysis applied to data classification and image filtering." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/25506.

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Statistical analysis is a tool of wide applicability in several areas of scientific knowledge. This thesis makes use of statistical analysis in two different applications: data classification and image processing targeted at document image binarization. In the first case, this thesis presents an analysis of several aspects of the consistency of the classification of the senior researchers in computer science of the Brazilian research council, CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico. The second application of statistical analysis developed in this thesis addresses filtering-out the back to front interference which appears whenever a document is written or typed on both sides of translucent paper. In this topic, an assessment of the most important algorithms found in the literature is made, taking into account a large quantity of parameters such as the strength of the back to front interference, the diffusion of the ink in the paper, and the texture and hue of the paper due to aging. A new binarization algorithm is proposed, which is capable of removing the back-to-front noise in a wide range of documents. Additionally, this thesis proposes a new concept of “intelligent” binarization for complex documents, which besides text encompass several graphical elements such as figures, photos, diagrams, etc.
Análise estatística é uma ferramenta de grande aplicabilidade em diversas áreas do conhecimento científico. Esta tese faz uso de análise estatística em duas aplicações distintas: classificação de dados e processamento de imagens de documentos visando a binarização. No primeiro caso, é aqui feita uma análise de diversos aspectos da consistência da classificação de pesquisadores sêniores do CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico, na área de Ciência da Computação. A segunda aplicação de análise estatística aqui desenvolvida trata da filtragem da interferência frente-verso que surge quando um documento é escrito ou impresso em ambos os lados da folha de um papel translúcido. Neste tópico é inicialmente feita uma análise da qualidade dos mais importantes algoritmos de binarização levando em consideração parâmetros tais como a intensidade da interferência frente-verso, a difusão da tinta no papel e a textura e escurecimento do papel pelo envelhecimento. Um novo algoritmo para a binarização eficiente de documentos com interferência frente-verso é aqui apresentado, tendo se mostrado capaz de remover tal ruído em uma grande gama de documentos. Adicionalmente, é aqui proposta a binarização “inteligente” de documentos complexos que envolvem diversos elementos gráficos (figuras, diagramas, etc).
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Schmidt, Sven. "Quality-of-Service-Aware Data Stream Processing." Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A23955.

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Data stream processing in the industrial as well as in the academic field has gained more and more importance during the last years. Consider the monitoring of industrial processes as an example. There, sensors are mounted to gather lots of data within a short time range. Storing and post-processing these data may occasionally be useless or even impossible. On the one hand, only a small part of the monitored data is relevant. To efficiently use the storage capacity, only a preselection of the data should be considered. On the other hand, it may occur that the volume of incoming data is generally too high to be stored in time or–in other words–the technical efforts for storing the data in time would be out of scale. Processing data streams in the context of this thesis means to apply database operations to the stream in an on-the-fly manner (without explicitly storing the data). The challenges for this task lie in the limited amount of resources while data streams are potentially infinite. Furthermore, data stream processing must be fast and the results have to be disseminated as soon as possible. This thesis focuses on the latter issue. The goal is to provide a so-called Quality-of-Service (QoS) for the data stream processing task. Therefore, adequate QoS metrics like maximum output delay or minimum result data rate are defined. Thereafter, a cost model for obtaining the required processing resources from the specified QoS is presented. On that basis, the stream processing operations are scheduled. Depending on the required QoS and on the available resources, the weight can be shifted among the individual resources and QoS metrics, respectively. Calculating and scheduling resources requires a lot of expert knowledge regarding the characteristics of the stream operations and regarding the incoming data streams. Often, this knowledge is based on experience and thus, a revision of the resource calculation and reservation becomes necessary from time to time. This leads to occasional interruptions of the continuous data stream processing, of the delivery of the result, and thus, of the negotiated Quality-of-Service. The proposed robustness concept supports the user and facilitates a decrease in the number of interruptions by providing more resources.
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Kutzner, Kendy. "Processing MODIS Data for Fire Detection in Australia." Thesis, Universitätsbibliothek Chemnitz, 2002. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200200831.

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The aim of this work was to use remote sensing data from the MODIS instrument of the Terra satellite to detect bush fires in Australia. This included preprocessing the demodulator output, bit synchronization and reassembly of data packets. IMAPP was used to do the geolocation and data calibration. The fire detection used a combination of fixed threshold techniques with difference tests and background comparisons. The results were projected in a rectangular latidue/longitude map to remedy the bow tie effect. Algorithms were implemented in C and Matlab. It proved to be possible to detect fires in the available data. The results were compared with fire detection done done by NASA and fire detections based on other sensors and found to be very similar
Das Ziel dieser Arbeit war die Nutzung von Fernerkundungsdaten des MODIS Instruments an Bord des Satelliten Terra zur Erkennung von Buschfeuern in Australien. Das schloss die Vorverarbeitung der Daten vom Demodulator, die Bitsynchronisation und die Umpacketierung der Daten ein. IMAPP wurde genutzt um die Daten zu kalibrieren und zu geolokalisieren. Die Feuererkennung bedient sich einer Kombination von absoluten Schwellwerttests, Differenztests und Vergleichen mit dem Hintergrund. Die Ergebnisse wurden in eine rechteckige Laengen/Breitengradkarte projiziert um dem BowTie Effekt entgegenzuwirken. Die benutzten Algrorithmen wurden in C und Matlab implementiert. Es zeigte sich, dass es moeglich ist in den verfuegbaren Daten Feuer zu erkennen. Die Ergebnisse wurden mit Feuererkennungen der NASA und Feuererkennung die auf anderen Sensoren basieren verglichen und fuer sehr aehnlich befunden
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Books on the topic "Fingerprints Classification Data processing"

1

Bowen, Jacqueline D. Automatic fingerprint pattern classification using neutral networks. London: Home Office, Science and Technology Group, 1992.

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C, Lee Henry, and Gaensslen R. E, eds. Advances in fingerprint technology. New York: Elsevier, 1991.

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C, Lee Henry, and Gaensslen R. E, eds. Advances in fingerprint technology. 2nd ed. Boca Raton, Fla: CRC Press, 2001.

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C, Lee Henry, and Gaensslen R. E, eds. Advances in fingerprint technology. Boca Raton, Fla: CRC Press, 1994.

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Shaw, Sandy. Overview of watermarks, fingerprints, and digital signature. Manchester: Joint Information Systems Committee, 1999.

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Bolle, Ruud, and Nalini K. Ratha. Automatic fingerprint recognition systems. New York: Springer, 2004.

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Lee and Gaensslen's advances in fingerprint technology. 3rd ed. Boca Raton, FL: CRC Press, 2013.

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García, Florencio Oscar. MARC, OCLC, ISBN: A book's fingerprints. 2nd ed. Albuquerque, NM: FOG Publications, 1987.

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Fox, R. P. The classification and coding of accounting information. London: Institute of Cost and Management Accountants, 1986.

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Fox, R. P. The classification and coding of accounting information. London: Chartered Institute of Management Accountants, 1992.

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Book chapters on the topic "Fingerprints Classification Data processing"

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Omatu, Sigeru. "Odor classification by neural networks *." In Advanced Data Acquisition and Intelligent Data Processing, 159–79. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337027-7.

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Chekanov, Sergei V. "Finding Regularities and Data Classification." In Advanced Information and Knowledge Processing, 505–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28531-3_14.

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Giron-Sierra, Jose Maria. "Data Analysis and Classification." In Digital Signal Processing with Matlab Examples, Volume 2, 647–835. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2537-2_7.

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Lourens, J. G. "Classification of Ships Using Underwater Radiated Noise." In Underwater Acoustic Data Processing, 591–96. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_66.

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Zeng, Zhi-Qiang, and Ji Gao. "Improving SVM Classification with Imbalance Data Set." In Neural Information Processing, 389–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_44.

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Tirumala, Sreenivas Sremath, and A. Narayanan. "Hierarchical Data Classification Using Deep Neural Networks." In Neural Information Processing, 492–500. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26532-2_54.

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Suganya, R., S. Rajaram, and A. Sheik Abdullah. "Image Classification and Retrieval." In Big Data in Medical Image Processing, 125–89. Boca Raton, FL : CRC Press, [2018] | “A science publishers book.”: CRC Press, 2018. http://dx.doi.org/10.1201/b22456-5.

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Muroi, Kota, and Kazuhiro Kondo. "Speech Manipulation Detection Method Using Speech Fingerprints and Timestamp Data." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 1–9. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1053-1_1.

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Lu, Lin, and Michał Woźniak. "Imbalanced Data Classification Using Weighted Voting Ensemble." In Image Processing and Communications, 82–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_11.

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Nelson, Stacy A. C., and Siamak Khorram. "Supervised Classification." In Image Processing and Data Analysis with ERDAS IMAGINE®, 207–29. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b21969-10.

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Conference papers on the topic "Fingerprints Classification Data processing"

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Sahu, Suman, A. Prabhakar Rao, and Saurabh Tarun Mishra. "Fingerprints based gender classification using Adaptive Neuro Fuzzy Inference System." In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2015. http://dx.doi.org/10.1109/iccsp.2015.7322700.

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Novikov, Sergey O., and Valery S. Kot. "Singular feature detection and classification of fingerprints using Hough transform." In Sixth International Workshop on Digital Image Processing and Computer Graphics, edited by Emanuel Wenger and Leonid I. Dimitrov. SPIE, 1998. http://dx.doi.org/10.1117/12.301375.

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Makrushin, Andrey, Venkata Srinath Mannam, B. N. Meghana Rao, and Jana Dittmann. "Data-driven Reconstruction of Fingerprints from Minutiae Maps." In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022. http://dx.doi.org/10.1109/mmsp55362.2022.9949242.

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Shinde, Swapnil R., and Sudeep D. Thepade. "Gender classification with KNN by extraction of Haar wavelet features from canny shape fingerprints." In 2015 International Conference on Information Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/infop.2015.7489473.

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Fan, Hao, Guangping Xu, Yi Zhang, Liming Yuan, and Yanbing Xue. "CSF: An Efficient Parallel Deduplication Algorithm by Clustering Scattered Fingerprints." In 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00091.

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Alharbiy, A. A., and C. Abhayaratne. "Classification of Remotely Compressively Sensed Data." In IET Intelligent Signal Processing Conference 2013 (ISP 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2064.

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Li, Yanling, Guoshe Sun, and Yehang Zhu. "Data Imbalance Problem in Text Classification." In 2010 Third International Symposium on Information Processing (ISIP). IEEE, 2010. http://dx.doi.org/10.1109/isip.2010.47.

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Fawzi, Alhussein, Horst Samulowitz, Deepak Turaga, and Pascal Frossard. "Adaptive data augmentation for image classification." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533048.

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Lu, Yuzhe, Haichun Yang, Zheyu Zhu, Ruining Deng, Agnes B. Fogo, and Yuankai Huo. "Improve global glomerulosclerosis classification with imbalanced data using CircleMix augmentation." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580482.

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Rizzi, Antonello, Nicola Maurizio Buccino, Massimo Panella, and Aurelio Uncini. "Genre classification of compressed audio data." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665157.

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Reports on the topic "Fingerprints Classification Data processing"

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Bell, Thomas, and Ryan Steigerwalt. Tensor Invariant Processing of Multistatic EMI Data for Target Classification. Fort Belvoir, VA: Defense Technical Information Center, May 2015. http://dx.doi.org/10.21236/ada624853.

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Pavlicheva, E. N., V. P. Meshalkin, and N. S. CHikunov. Algorithm for processing text data for an automatic classification problem using the word2vec method. OFERNIO, February 2021. http://dx.doi.org/10.12731/ofernio.2021.24759.

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Davis, Benjamin. Applying Machine Learning to the Classification of DC-DC Converters: Real-world data collection processing & Validation. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670255.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Neeley, Aimee, Stace E. Beaulieu, Chris Proctor, Ivona Cetinić, Joe Futrelle, Inia Soto Ramos, Heidi M. Sosik, et al. Standards and practices for reporting plankton and other particle observations from images. Woods Hole Oceanographic Institution, July 2021. http://dx.doi.org/10.1575/1912/27377.

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This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genus) and scientificNameID, the unique identifier from a reference database such as the World Register of Marine Species or AlgaeBase. The data table described here includes the field names associatedMedia, scientificName/ scientificNameID for both automated and manual identification, biovolume, area_cross_section, length_representation and width_representation. Additional steps that instruct the user on how to format their data for a submission to the Ocean Biodiversity Information System (OBIS) are also included. Examples of documentation and data files are provided for the user to follow. The documentation requirements and data table format are approved by both NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and the National Science Foundation’s Biological and Chemical Oceanography Data Management Office (BCO-DMO).
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