Local data fusion algorithm for fire detection through mobile robot

Multisensor data fusion is a technique that combines the readings of multiple sensors to detect some phenomenon. Data fusion applications are numerous and they can be used in smart buildings, environment monitoring, industry and defense applications. The main goal of multisensor data fusion is to minimize false alarms and maximize the probability of detection based on the detection of multiple sensors. In this paper a local data fusion algorithm based on luminosity, temperature and flame for fire detection is presented. The data fusion approach was embedded in a low cost mobile robot. The prototype test validation has indicated that our approach can detect fire occurrence. Moreover, the low cost project allow the development of robots that could be discarded in their fire detection missions. © 2013 IEEE.

Comprehensive two-dimensional gas chromatography combined to multivariate data analysis for detection of disease-resistant clones of Eucalyptus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Statistical detection of spurious variations in daily raingauge data caused by changes in observation practices, as applied to records from various parts of the world

In the instrumental records of daily precipitation, we often encounter one or more periods in which values below some threshold were not registered. Such periods, besides lacking small values, also have a large number of dry days. Their cumulative distribution function is shifted to the right in relation to that for other portions of the record having more reliable observations. Such problems are examined in this work, based mostly on the two-sample Kolmogorov–Smirnov (KS) test, where the portion of the series with more number of dry days is compared with the portion with less number of dry days. Another relatively common problem in daily rainfall data is the prevalence of integers either throughout the period of record or in some part of it, likely resulting from truncation during data compilation prior to archiving or by coarse rounding of daily readings by observers. This problem is identified by simple calculation of the proportion of integers in the series, taking the expected proportion as 10%. The above two procedures were applied to the daily rainfall data sets from the European Climate Assessment (ECA), Southeast Asian Climate Assessment (SACA), and Brazilian Water Resources Agency (BRA). Taking the statistic D of the KS test >0.15 and the corresponding p-value <0.001 as the condition to classify a given series as suspicious, the proportions of the ECA, SACA, and BRA series falling into this category are, respectively, 34.5%, 54.3%, and 62.5%. With relation to coarse rounding problem, the proportions of series exceeding twice the 10% reference level are 3%, 60%, and 43% for the ECA, SACA, and BRA data sets, respectively. A simple way to visualize the two problems addressed here is by plotting the time series of daily rainfall for a limited range, for instance, 0–10 mm day−1.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. (C) 2012 Elsevier B.V. All rights reserved.

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Water-soluble organic compounds in air particulate matter analyzed by HPLC-DAD-MS and HPLC-MS/MS: abundance, sources and transformation of carboxylic acids, nitrophenols and nitrated proteins

Wasserlösliche organische Verbindungen (WSOCs) sind Hauptbestandteile atmosphärischer Aerosole, die bis zu ~ 50% und mehr der organischen Aerosolfraktion ausmachen. Sie können die optischen Eigenschaften sowie die Hygroskopizität von Aerosolpartikeln und damit deren Auswirkungen auf das Klima beeinflussen. Darüber hinaus können sie zur Toxizität und Allergenität atmosphärischer Aerosole beitragen.In dieser Studie wurde Hochleistungsflüssigchromatographie gekoppelt mit optischen Diodenarraydetektion und Massenspektrometrie (HPLC-DAD-MS und HPLC-MS/MS) angewandt, um WSOCs zu analysieren, die für verschiedene Aerosolquellen und -prozesse charakteristisch sind. Niedermolekulare Carbonsäuren und Nitrophenole wurden als Indikatoren für die Verbrennung fossiler Brennstoffe und die Entstehung sowie Alterung sekundärer organischer Aerosole (SOA) aus biogenen Vorläufern untersucht. Protein-Makromoleküle wurden mit Blick auf den Einfluss von Luftverschmutzung und Nitrierungsreaktionen auf die Allergenität primärer biologischer Aerosolpartikel – wie Pollen und Pilzsporen – untersucht.rnFilterproben von Grob- und Feinstaubwurden über ein Jahr hinweg gesammelt und auf folgende WSOCs untersucht: die Pinen-Oxidationsprodukte Pinsäure, Pinonsäure und 3-Methyl-1,2,3-Butantricarbonsäure (3-MBTCA) sowie eine Vielzahl anderer Dicarbonsäuren und Nitrophenole. Saisonale Schwankungen und andere charakteristische Merkmale werden mit Blick auf Aerosolquellen und -senken im Vergleich zu Daten anderen Studien und Regionen diskutiert. Die Verhätlnisse von Adipinsäure und Phthalsäure zu Azelainsäure deuten darauf hin, dass die untersuchten Aerosolproben hauptsächlich durch biogene Quellen beeinflusst werden. Eine ausgeprägte Arrhenius-artige Korrelation wurde zwischen der 3-MBTCA-¬Konzentration und der inversen Temperatur beobachtet (R2 = 0.79, Ea = 126±10 kJ mol-1, Temperaturbereich 275–300 K). Modellrechnungen zeigen, dass die Temperaturabhängigkeit auf eine Steigerung der photochemischen Produktionsraten von 3-MBTCA durch erhöhte OH-Radikal-Konzentrationen bei erhöhten Temperaturen zurückgeführt werden kann. Im Vergleich zur chemischen Reaktionskinetik scheint der Einfluss von Gas-Partikel-Partitionierungseffekten nur eine untergeordnete Rolle zu spielen. Die Ergebnisse zeigen, dass die OH-initiierte Oxidation von Pinosäure der geschwindigkeitsbestimmende Schritt der Bildung von 3-MBTCA ist. 3-MBTCA erscheint somit als Indikator für die chemische Alterung von biogener sekundärer organischer Aerosole (SOA) durch OH-Radikale geeignet. Eine Arrhenius-artige Temperaturabhängigkeit wurde auch für Pinäure beobachtet und kann durch die Temperaturabhängigkeit der biogenen Pinen-Emissionen als geschwindigkeitsbestimmender Schritt der Pinsäure-Bildung erklärt werden (R2 = 0.60, Ea = 84±9 kJ mol-1).rn rnFür die Untersuchung von Proteinnitrierungreaktionen wurde nitrierte Protein¬standards durch Flüssigphasenreaktion von Rinderserumalbumin (BSA) und Ovalbumin (OVA) mit Tetranitromethan (TNM) synthetisiert.Proteinnitrierung erfolgt vorrangig an den Resten der aromatischen Aminosäure Tyrosin auf, und mittels UV-Vis-Photometrie wurde der Proteinnnitrierungsgrad (ND) bestimmt. Dieser ist definiert als Verhältnis der mittleren Anzahl von Nitrotyrosinresten zur Tyrosinrest-Gesamtzahl in den Proteinmolekülen. BSA und OVA zeigten verschiedene Relationen zwischen ND und TNM/Tyrosin-Verhältnis im Reaktionsgemisch, was vermutlich auf Unterschiede in den Löslichkeiten und den molekularen Strukturen der beiden Proteine zurück zu führen ist.rnDie Nitrierung von BSA und OVA durch Exposition mit einem Gasgemisch aus Stickstoffdioxid (NO2) und Ozon (O3) wurde mit einer neu entwickelten HPLC-DAD-¬Analysemethode untersucht. Diese einfache und robuste Methode erlaubt die Bestimmung des ND ohne Hydrolyse oder Verdau der untersuchten Proteine und ernöglicht somit eine effiziente Untersuchung der Kinetik von Protein¬nitrierungs-Reaktionen. Für eine detaillierte Produktstudien wurden die nitrierten Proteine enzymatisch verdaut, und die erhaltenen Oligopeptide wurden mittels HPLC-MS/MS und Datenbankabgleich mit hoher Sequenzübereinstimmung analysiert. Die Nitrierungsgrade individueller Nitrotyrosin-Reste (NDY) korrelierten gut mit dem Gesamt-Proteinnitrierungsgrad (ND), und unterschiedliche Verhältnisse von NDY zu ND geben Aufschluss über die Regioselektivität der Reaktion. Die Nitrierungmuster von BSA und OVA nach Beahndlung mit TNM deuten darauf hin, dass die Nachbarschaft eines negativ geladenen Aminosäurerestes die Tyrosinnitrierung fördert. Die Behandlung von BSA durch NO2 und O3 führte zu anderend Nitrierungemustern als die Behandlung mit TNM, was darauf hindeutet, dass die Regioselektivität der Nitrierung vom Nitrierungsmittel abhängt. Es zeigt sich jedoch, dass Tyrosinreste in Loop-Strukturen bevorzugt und unabhängig vom Reagens nitriert werden.Die Methoden und Ergebnisse dieser Studie bilden eine Grundlage für weitere, detaillierte Untersuchungen der Reaktionskinetik sowie der Produkte und Mechanismen von Proteinnitrierungreaktionen. Sie sollen helfen, die Zusammenhänge zwischen verkehrsbedingten Luftschadstoffen wie Stickoxiden und Ozon und der Allergenität von Luftstaub aufzuklären.rn

Data breach - services for threat detection, analysis and response

Nel mondo della sicurezza informatica, le tecnologie si evolvono per far fronte alle minacce. Non è possibile prescindere dalla prevenzione, ma occorre accettare il fatto che nessuna barriera risulterà impenetrabile e che la rilevazione, unitamente ad una pronta risposta, rappresenta una linea estremamente critica di difesa, ma l’unica veramente attuabile per poter guadagnare più tempo possibile o per limitare i danni. Introdurremo quindi un nuovo modello operativo composto da procedure capaci di affrontare le nuove sfide che il malware costantemente offre e allo stesso tempo di sollevare i comparti IT da attività onerose e sempre più complesse, ottimizzandone il processo di comunicazione e di risposta.

Automated detection of fiducial screws from CT/DVT volume data for image-guided ENT surgery

This paper presents an automated solution for precise detection of fiducial screws from three-dimensional (3D) Computerized Tomography (CT)/Digital Volume Tomography (DVT) data for image-guided ENT surgery. Unlike previously published solutions, we regard the detection of the fiducial screws from the CT/DVT volume data as a pose estimation problem. We thus developed a model-based solution. Starting from a user-supplied initialization, our solution detects the fiducial screws by iteratively matching a computer aided design (CAD) model of the fiducial screw to features extracted from the CT/DVT data. We validated our solution on one conventional CT dataset and on five DVT volume datasets, resulting in a total detection of 24 fiducial screws. Our experimental results indicate that the proposed solution achieves much higher reproducibility and precision than the manual detection. Further comparison shows that the proposed solution produces better results on the DVT dataset than on the conventional CT dataset.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.