26 March 2012
To positively identify marine mammals using passive acoustics, large volumes of data are often collected that need to be processed by a trained analyst. To reduce acoustic analyst workload, an automatic detector can be implemented that produces many detections, which feed into an automatic classifier to significantly reduce the number of false detections. This requires the development of a robust classifier capable of performing inter-species classification as well as discriminating cetacean vocalizations from anthropogenic noise sources. A prototype aural classifier was developed at Defence Research and Development Canada that uses perceptual signal features which model the features employed by the human auditory system. The dataset included anthropogenic passive transients and vocalizations from five cetacean species: bowhead, humpback, North Atlantic right, minke and sperm whales. Discriminant analysis was implemented to replace principal component analysis; the projection obtained using discriminant analysis improved between-species discrimination during multiclass cetacean classification, compared to principal component analysis. The aural classifier was able to successfully identify the vocalizing cetacean species. The area under the receiver operating characteristic curve (AUC) is used to quantify the two-class classifier performance and the M-measure is used when there are three or more classes; the maximum possible value of both AUC and M is 1.00 – which is indicative of an ideal classifier model. Accurate classification results were obtained for multiclass classification of all species in the dataset (M = 0.99), and the challenging bowhead/ humpback (AUC = 0.97) and sperm whale click/anthropogenic transient (AUC = 1.00) two-class classifications.
12 July 2007
In recent years, many researches focused on the supervised classification, one of the machine learning methods, using Lidar and remotely sensed image to provide the four buildings, trees, roads, and grass categories of the ground features. However, buildings and trees are usually much closed or overlapped and this problem will lead buildings and nearby trees not easy to classify by single classification approach. The derived building outlines have many cracks which are not satisfactory for the requirement of GIS building vector map or building 3D modeling. To provide complete building outlines, this study develops an ¡§automatic detection of the overlapped areas of buildings and trees (ADOABT)¡¨ algorithm and an ¡§automatic linear feature recovery (ALFR)¡¨ approach to connect building outlines consequently. First, this research integrates Maximum Likelihood Classification (MLC) and Knowledge-Based Correction (KBC) to derive buildings and trees classification resultant images. Next, the ADOABT based on ¡§divide and conquer¡¨ principle was used to detect the overlapped areas of buildings and trees. Meanwhile, the building and tree edge images were detected using the Canny edge detector based on Lidar height image. Then, the intersection operator was applied to the detected areas and edge images to detect the crack of the building images. Afterward, vectorization and generalization of the intersection resultant images are applied to extract the straight line of the buildings. Finally, the automatic linear feature recovery procedure was performed to compensate the damage straight line effectively. According to the experiment results, the classification accuracy derived from integrated MLC and KBC classification method and the object-based classification (OBC) are similar. However, when applying the classification results to detect the overlapped areas of building and trees, because MLC and KBC has the procedure for handling temporal inconsistencies, the success rate of automatic detection is totally the same by artificial interpretation; the detection rate for the results of MLC and KBC is 100% whereas the one for the OBC only 67.7%. It can be concluded that the MLC and KBC approach is more suitable for the automatic detection for the overlapped areas of building and trees. Moreover, the ADOABT algorithm simplifies the workflow of the overlapped area detection. According to the result of edge detection and line detection, the Canny detector presents the clearest edge image. The lines extracted by Vectorized and generalization method are superior to the ones derived from Hough transform. The ALFR algorithm offers a way to connect building outline completely.
Detecção automática e análise temporal de slope streaks na superfície de Marte / Automatic detection and temporal fading quantification of slope streaks from Mars surfaceCarvalho, Fernanda Puga Santos [UNESP] 31 March 2016 (has links)
Submitted by Fernanda Puga Santos null (firstname.lastname@example.org) on 2016-04-05T19:55:10Z No. of bitstreams: 1 Tese_FINAL.pdf: 3778155 bytes, checksum: 12218385281bca2455fe3172e45e514c (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (email@example.com) on 2016-04-07T16:09:03Z (GMT) No. of bitstreams: 1 carvalho_fps_dr_prud.pdf: 3778155 bytes, checksum: 12218385281bca2455fe3172e45e514c (MD5) / Made available in DSpace on 2016-04-07T16:09:03Z (GMT). No. of bitstreams: 1 carvalho_fps_dr_prud.pdf: 3778155 bytes, checksum: 12218385281bca2455fe3172e45e514c (MD5) Previous issue date: 2016-03-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Slope streaks são rastros escuros que se estendem por declives íngremes na superfície de Marte. Estes rastros representam um dos poucos processos geológicos ativos na superfície deste planeta. Atualmente, muitos pesquisadores os têm estudado com a finalidade de descobrir sua natureza, a qual permanece controversa. Além disso, os slope streaks clareiam com o tempo, fornecendo pistas sobre a deposição de poeira e também sobre a natureza do material da superfície. Embora exista um número considerável de pesquisadores que estudam esses rastros, a identificação destes ainda é realizada por especialistas manualmente, através de amostras de pequena dimensão. A existência de um número elevado destas estruturas na superfície de Marte, a necessidade de caracterizá-las e também de quantificar a sua evolução temporal, não pode continuar a ser efetuada simplesmente por amostragem e de uma forma manual. É neste contexto que esta pesquisa se enquadra. A proposta consiste em contribuir para a automação do processo de extração de informações em imagens da superfície de Marte, especificamente, extração de informações sobre slope streaks. Através do desenvolvimento de um método de detecção automática de slope streaks em imagens orbitais e, também, de um método automático para análise temporal da taxa de esmaecimento, este objetivo foi alcançado neste trabalho. O método de detecção desenvolvido baseia-se principalmente em Morfologia Matemática e faz uso de operadores morfológicos conectados para o pré-processamento das imagens, transformada top-hat, binarização pelo método de Otsu, afinamento e reconstrução geodésica, seguido por um filtro de fator de forma. O método para a análise temporal desenvolvido consiste em um algoritmo que calcula a taxa de contraste entre o interior e a área de vizinhança de um mesmo rastro, em imagens multi-temporais registradas. Os resultados obtidos com ambos os métodos foram bastante satisfatórios e possibilitaram extrair informações inovadoras a respeito do comportamento destes rastros na superfície de Marte. As duas ferramentas desenvolvidas mostraram-se robustas para serem aplicadas a grande escala e a um grande conjunto de imagens. / Slope streaks are typically dark, narrow and fan-shaped features that extend down slope on Mars surface. They are one of the most active and dynamic process observed on the planet’s surface. Dry and wet processes have been suggested for causing their formation but their origin is still unclear. Moreover, the streaks tend to fade with time, providing clues about dust settling and material properties. Studies that quantify some characteristics of these streaks are based on manual interactive procedures to delineate only a small portion of the available slope streaks and to measure their morphometric characteristics. The availability of a methodology to segment the streaks and to extract meaningful information would naturally increase the regional knowledge and the statistical significance, as a much larger amount of images from different locations could be analyzed, together with a more complete monitoring of the fading/appearance of the dark streaks. Thus, the purpose of this research is to contribute to the information extraction process from surface images of Mars. Hence, a method to automatically detect slope streaks and an algorithm to quantify the temporal fading of each streak over the years were developed. The detection method was based on morphological operators and it was used in a part of the methodology to quantify the fading of the streaks. The results of the detection method and the temporal fading algorithm were very satisfactory. Both methods are able to extract information about the behaviour of the slope streaks on the Martian surface. Finally, the two tools are robust enough to be applied on a large scale and a large set of images. / CAPES: 12791-13-0
26 July 2013
Οι διάφορες δραστηριότητες του εγκεφάλου συχνά χαρακτηρίζονται από ειδικούς ρυθμούς στο ηλεκτροεγκεφαλογράφημα (ΗΕΓ). Το 2ο στάδιο του ύπνου χωρίς ταχείες οφθαλμικές κινήσεις (στάδιο NREM) χαρακτηρίζεται από τις ατράκτους που σηματοδοτούν την ουσιαστική έναρξη του ύπνου. Αποτελούν 0.5-1 δευτερόλεπτα ρυθμικής διαδοχής κύρια αρνητικών κυμάτων γενικευμένα στο ΗΕΓ τα οποία παρουσιάζουν προϊούσα αύξηση και μετά μείωση του πλάτους τους. Οι άτρακτοι συμμετέχουν σε διάφορες σημαντικές λειτουργίες του εγκεφάλου. Η κατανόηση του πολύπλευρου και πολύ σημαντικού ρόλου των ατράκτων έχει αποτελέσει αφορμή ώστε να γίνουν προσπάθειες εντοπισμού των υπεύθυνων για τη γένεσή τους εγκεφαλικών κυκλωμάτων. Σκοπός της προτεινόμενης μεθόδου είναι η εφαρμογή της μεθόδου εντοπισμού σημάτων σε πολυκαναλικές καταγραφές χρησιμοποιώντας περιορισμούς που στηρίζονται στο πεδίο του χώρου (spatial constraints) και το πεδίο της συχνότητας (frequency constraints) χρησιμοποιώντας την τεχνική ανάλυσης σε ανεξάρτητες συνιστώσες (ICA). Η μέθοδος εφαρμόστηκε για την ανάλυση βραδέων και ταχέων ατράκτων σε εγκεφαλογραφήματα ύπνου. Στο εργαστήριο Φυσιολογίας έχουν γίνει ΗΕΓ καταγραφές ολονύκτιου ύπνου με καταμέτρηση πολλών εκατοντάδων ατράκτων οι οποίες χρησιμοποιήθηκαν για την ανάπτυξη της παραπάνω μεθόδου αυτομάτου ανίχνευσης και εντοπισμού των ατράκτων. / Several brain activities are characterized by specific rhythms in electroencephalogram (EEG). The non rapid eye movement (NREM) stage of sleep is characterized by sleep spindles signaling the beginning of sleep. Spindles are rhythmic generalized negative waves in EEG with progressively increasing and gradually decreasing amplitude lasting 0.5-1 sec. Spindles are involved in several brain functions. The comprehension of the significance and multilateral role of spindles has driven efforts to detect the brain circuits involved in their generation. The purpose of this study is the introduction of a signal detection method in multichannel records, using Independent Component Analysis with spatial and frequency constraints. This automatic detection method was applied to the analysis of fast and slow spindles in sleep EEG, obtained from whole-night sleep recording in the laboratory of Physiology Department at University of Patras.
Due to the large increase in the proliferation of "fake news" in recent years, it has become a widely discussed menace in the online world. In conjunction with this popularity, research of ways to limit the spread has also increased. This paper aims to look at the current research of this area in order to see what automatic fake news detection methods exist and are being developed, which can help online users in protecting themselves against fake news. A systematic literature review is conducted in order to answer this question, with different detection methods discussed in the literature being divided into categories. The consensus which appears from the collective research between categories is also used to identify common elements between categories which are important to fake news detection; notably the relation of headlines and article content, the importance of high-quality datasets, the use of emotional words, and the circulation of fake news in social media groups.
Bampovits, Stefanos, Löwe, Amelie
Code smells are defined as poor implementation and coding practices, and as a result decrease the overall quality of a source code. A number of code smell detection tools are available to automatically detect poor implementation choices, i.e., code smells. The detection of code smells is essential in order to improve the quality of the source code. This report aims to evaluate the accuracy and quality of seven different open-source code smell detection tools, with the purpose of establishing their level of trustworthiness.To assess the trustworthiness of a tool, we utilize a controlled experiment in which several versions of each tool are scrutinized using the most recent version of the same tool. In particular, we wanted to verify to what extent the code smell detection tools that reveal code smells in other systems, contain smells themselves. We further study the evolution of code smells in the tools in terms of number, types of code smells and code smell density.
31 January 2022
Some fish produce sounds for a variety of reasons, such as to find mates, defend their territory, or maintain cohesion within their group. These sounds could be used to non-intrusively detect the presence of fish and potentially to estimate their number (or density) over large areas and long time periods. However, many fish sounds have not yet been associated to specific species, which limits the usefulness of this approach. While recording fish sounds in tanks is reasonably straightforward, it presents several problems: many fish do not produce sounds in captivity or their behavior and sound production is altered significantly, and the complex acoustic propagation conditions in tanks often leads to distorted measurements. The work presented in this thesis aims to address these issues by providing methodologies to record, detect, and identify species-specific fish sounds in the wild. A set of hardware and software solutions are developed to simultaneously record fish sounds, acoustically localize the fish in three-dimensions, and record video to identify the fish and observe their behavior. Three platforms have been developed and tested in the field. The first platform, referred to as the large array, is composed of six hydrophones connected to an AMAR acoustic recorder and two open-source autonomous video cameras (FishCams) that were developed during this thesis. These instruments are secured to a PVC frame of dimension 2 m x 2 m x 3 m that can be transported and assembled in the field. The hydrophone configuration for this array was defined using a simulated annealing optimization approach that minimized localization uncertainties. This array provides the largest field of view and most accurate acoustic localization, and is well suited to long-term deployments (weeks). The second platform, referred to as the mini array, uses a single FishCam and four hydrophones connected to a SoundTrap acoustic recorder on a one cubic meter PVC frame; this array can be deployed more easily in constrained locations or on rough/uneven seabeds. The third platform, referred to as the mobile array, consists of four hydrophones connected to a SoundTrap recorder and mounted on a tethered Trident underwater drone with built-in video, allowing remote control and real-time positioning in response to observed fish presence, rather than long-term deployments as for the large and mini arrays. For each array, acoustic localization is performed by measuring time-difference of arrivals between hydrophones and estimating the sound-source location using linearized (for the large array) or non-linear (for the mini and mobile arrays) inversion. Fish sounds are automatically detected and localized in three dimensions, and sounds localized within the field of view of the camera(s) are assigned to a fish species by manually reviewing the video recordings. The three platforms were deployed at four locations off the East coast of Vancouver Island, British Columbia, Canada, and allowed the identification of sounds from quillback rockfish (Sebastes maliger), copper rockfish (Sebastes caurinus), and lingcod (Ophiodon elongatus), species that had not been documented previously to produce sounds. While each platform developed during this thesis has its own set of advantages and limitations, using them in coordination helps identify fish sounds over different habitats and with various budget and logistical constraints. In an effort to make passive acoustics a more viable way to monitor fish in the wild, this thesis also investigates the use of automatic detection and classification algorithms to efficiently find fish sounds in large passive acoustic datasets. The proposed approach detects acoustic transients using a measure of spectrogram variance and classifies them as “noise” or “fish sounds” using a binary classifier. Five different classification algorithms were trained and evaluated on a dataset of more than 96,000 manually annotated examples of fish sounds and noise from five locations off Vancouver Island. The classification algorithm that performed best (random forest) has an Fscore of 0.84 (Precision = 0.82,Recall = 0.86) on the test dataset. The analysis of 2.5 months of acoustic data collected in a rockfish conservation area off Vancouver Island shows that the proposed detector can be used to efficiently explore large datasets, formulate hypotheses, and help answer practical conservation questions. / Graduate
Detection of histological features in liver biopsy images to help identify Non-Alcoholic Fatty Liver DiseaseSethunath, Deepak 26 April 2018 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / This thesis explores a minimally invasive approach of diagnosing Non-Alcoholic Fatty Liver disease (NAFLD) on mice and humans which can be useful for pathologists while performing their diagnosis. NAFLD is a spectrum of diseases going from least severe to most severe – steatosis, steatohepatitis, fibrosis and finally cirrhosis. This disease primarily results from fat deposition in the liver which is unrelated to alcohol or viral causes. In general, it affects individuals having a combination of at least three of the five metabolic syndromes namely, obesity, hypertension, diabetes, hypertriglyceridemia, and hyperlipidemia. Given how common these metabolic syndromes have become, the rate of NAFLD has increased dramatically over the years affecting about three-quarters of all obese individuals including many children, making it one of the most common diseases in United States. Our study focuses on building various computational models which help identify different histological features in a liver biopsy image, thereby analyzing if a person is affected by NAFLD or not. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis, lobular and portal inflammation and also categorize different types fibrosis in murine and human fatty liver disease and study the correlation of automated quantification of macrosteatosis, lobular and portal inflammation, and fibrosis (amount of collagen) with expert pathologist’s semi-quantitative grades. Our research for macrosteatosis and microsteatosis prediction shows the model’s precision and sensitivity as 94.2%, 95% for macrosteatosis and 79.2%, 77% for microsteatosis. Our models detect lobular and portal inflammation(s) with a precision, sensitivity of 79.6%, 77.1% for lobular inflammation and 86%, 90.4% for portal inflammation. We also present the first study on identification of the six different types of fibrosis having a precision of 85.6% for normal fibrosis and >70% for portal fibrosis, periportal fibrosis, pericellular fibrosis, bridging fibrosis and cirrhosis. We have also quantified the amount of collagen in a liver biopsy and compared it to the pathologist semi-quantitative fibrosis grade.
Landa Borges, José Manuel
Approved for public release; distribution in unlimited. / This research develops a detection rate model to analyze the effectiveness of the RDR 1500B search radar installed in the CASA 212 S43 aircraft belonging to Venezuelan Naval Aviation. The model is based on a search and detection mission to find a diesel submarine executing an incursion inside the Venezuelan Caribbean Sea area, assumed to be intermittently operating with periscopes or masts exposed above the sea surface. The analysis obtains cumulative probability of detection vs. time based on the radar manufacturer's performance data, user inputs for aircraft search area size, search speed, and search altitude, and submarine periscope or mast exposure profile. The model can use given periscope radar cross section data, or roughly calculate radar cross section given assumptions about exposed periscope height above the sea-surface and sea-state conditions. Submarine evasion due to radar counterdetection is also modeled. / Lieutenant Commander, Venezuelan Navy
Σχεδίαση και υλοποίηση εργαλείου ανίχνευσης ρυθμών και κυμάτων σε ηλεκτροεγκεφαλογράφημα / Design and develop an EEG rythm and wave detection toolΑλεξόπουλος, Άγγελος 10 August 2011 (has links)
Ο ύπνος αποτελεί ένα από τα πιο μυστήρια φαινόμενα της ανθρώπινης ζωής. Η επεξεργασία και ανάλυση του εγκεφαλογραφήματος με τη χρήση υπολογιστικών μεθόδων και αλγορίθμων μπορεί να δώσει μεγάλη ώθηση στην διερεύνηση της εγκεφαλικής δραστηριότητας. Στην παρούσα εργασία υλοποιήθηκε ένα γραφικό εργαλείο για την ανίχνευση ρυθμών και κυμάτων που εμφανίζονται στο εγκεφαλογράφημα ύπνου. Το εργαλείο συνδέεται με το πρόγραμμα καταγραφής Neuroscan του εργαστηρίου Νευροφυσιολογίας της Ιατρικής Σχολής του Πανεπιστημίου Πατρών. Το περιβάλλον περιλαμβάνει αλγορίθμους για την αυτόματη ανάλυση του σήματος και την ανίχνευση επιλεγμένων κυμάτων και ρυθμών. Σκοπός του εργαλείου είναι η αποστολή ακουστικού ερεθισμού στην περίπτωση ανίχνευσης του επιλεγμένου κύματος ή ρυθμού. Το εργαλείο περιλαμβάνει γραφικό περιβάλλον για την εύκολη χρήση και παραμετροποίηση των διαθέσιμων επιλογών. Το πρόγραμμα αναπτύχθηκε εξ ολοκλήρου πρωτότυπα με γνώμονα την ταχύτητα ανίχνευσης και επεξεργασίας του ΗΕΓ. Τελικός στόχος του προγράμματος είναι η χρήση του σε πειράματα διερεύνησης της απαντητικότητας του εγκεφάλου σε ερεθισμούς που συμβαίνουν σε συγκεκριμένες χρονικές στιγμές μετά από την στιγμή ανίχνευσης επιλεγμένου κύματος ή ρυθμού. Με αυτό τον τρόπο μπορεί να εξερευνηθεί ο ρόλος διαφόρων καταστάσεων του εγκεφάλου (π.χ. αφυπνιστικός ή υπναγωγικός κατά τον ύπνο) χαρακτηριζόμενων από τα επιλεγόμενα ΗΕΓ κύματα και ρυθμούς. / One of the greatest human mysteries is the phenomenon of sleep. The use of computing methods and algorithms in the analysis and processing of electroencephalogram can boost the research of brain activity. The present work presents the graphical program that was developed and used at the Neurophysiology Unit of the University of Patras’ Medical School for the support of EEG studies. The program detects specific rythms and waves during the sleep EEG (online). The tool connects with the Neuroscan Systems that the lab uses for the sleep experiments. The program supports several algorithms for the automatic signal analysis and the specific rythms’ and waves’ detection. The target of the tool is to send sound stimulus in the case of rhythm or wave detection. The user-friendly graphical interface of the tool includes all the parameters for the experiments. The program was developed originally from scratch, aiming to make signal processing as fast as possible. The final goal of the program is to explore the nature of specific brain states i.e. in sleep, by probing brain reactivity at precise times after EEG signs characterizing this brain state.
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