Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Utility of real-time decision-making in commercial data stream mining domains

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.

Kernel performance in meeting hard real-time system characteristics (the ARTOS real-time kernel)

Describes the design and implementation of an operating system kernel specifically designed to support real-time applications. It emphasises portability and aims to support state-of-the-art concepts in real-time programming. Discusses architectural aspects of the ARTOS kernel, and introduces new concepts on the areas of interrupt processing, scheduling, mutual exclusion and inter-task communication. Also explains the programming environment of ARTOS kernal and its task model, defines the real-time task states and system data structures and discusses exception handling mechanisms which are used to detect missed deadlines and take corrective action.

Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

An online platform for real-time sensor data collection, visualization, and sharing

Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.

An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.

Real time monitoring of DNA hybridization and replication using optical and acoustic biosensors

Während in den letzten Jahren zahlreiche Biosensoren zum spezifischen Nachweis von DNA entwickelt wurden, ist die Anwendung oberflächen-sensitiver Methoden auf enzymatische Reaktionen ein vergleichsweise neues Forschungsgebiet. Trotz der hohen Empfindlichkeit und der Möglichkeit zur Echtzeit-Beobachtung molekularer Prozesse, ist die Anwendung dieser Methoden nicht etabliert, da die Enzymaktivität durch die Nähe zur Oberfläche beeinträchtigt sein kann. Im Rahmen dieser Arbeit wurde die enzymatische Verlängerung immobilisierter DNA durch eine DNA Polymerase mit Hilfe von Oberflächenplasmonen-Fluoreszenzspektroskopie (SPFS) und einer Quarzkristall-Mikrowaage (QCM) untersucht. Die Synthese von DNA wurde im Fall der QCM als Massenzuwachs detektiert, der sich im Abfall der Resonanzfrequenz des Schwingquarzes und einem Anstieg seiner Dissipationsenergie ausdrückte. Die viskoelastischen Eigenschaften der DNA-Schichten wurden bestimmt, indem die erhaltenen Daten mit einem auf Voigt basierenden Modell ausgewertet wurden. SPFS nutzt das evaneszente elektromagnetische Feld, das mit Oberflächenplasmonen einhergeht, zur oberflächen-sensitiven Anregung von Chromophoren. Auf diese Weise wurde der Einbau von Farbstoff-markierten Nukleotiden in die entstehende DNA-Sequenz als Indikator für das Voranschreiten der Reaktion ausgenutzt. Beide Meßtechniken konnten erfolgreich zum Nachweis der DNA-Synthese herangezogen werden, wobei die katalytische Aktivität des Enzyms vergleichbar zu der in Lösung gemessenen war.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Optimized scheduling in real-time environments with column generation

Im Bereich sicherheitsrelevanter eingebetteter Systeme stellt sich der Designprozess von Anwendungen als sehr komplex dar. Entsprechend einer gegebenen Hardwarearchitektur lassen sich Steuergeräte aufrüsten, um alle bestehenden Prozesse und Signale pünktlich auszuführen. Die zeitlichen Anforderungen sind strikt und müssen in jeder periodischen Wiederkehr der Prozesse erfüllt sein, da die Sicherstellung der parallelen Ausführung von größter Bedeutung ist. Existierende Ansätze können schnell Designalternativen berechnen, aber sie gewährleisten nicht, dass die Kosten für die nötigen Hardwareänderungen minimal sind. Wir stellen einen Ansatz vor, der kostenminimale Lösungen für das Problem berechnet, die alle zeitlichen Bedingungen erfüllen. Unser Algorithmus verwendet Lineare Programmierung mit Spaltengenerierung, eingebettet in eine Baumstruktur, um untere und obere Schranken während des Optimierungsprozesses bereitzustellen. Die komplexen Randbedingungen zur Gewährleistung der periodischen Ausführung verlagern sich durch eine Zerlegung des Hauptproblems in unabhängige Unterprobleme, die als ganzzahlige lineare Programme formuliert sind. Sowohl die Analysen zur Prozessausführung als auch die Methoden zur Signalübertragung werden untersucht und linearisierte Darstellungen angegeben. Des Weiteren präsentieren wir eine neue Formulierung für die Ausführung mit fixierten Prioritäten, die zusätzlich Prozessantwortzeiten im schlimmsten anzunehmenden Fall berechnet, welche für Szenarien nötig sind, in denen zeitliche Bedingungen an Teilmengen von Prozessen und Signalen gegeben sind. Wir weisen die Anwendbarkeit unserer Methoden durch die Analyse von Instanzen nach, welche Prozessstrukturen aus realen Anwendungen enthalten. Unsere Ergebnisse zeigen, dass untere Schranken schnell berechnet werden können, um die Optimalität von heuristischen Lösungen zu beweisen. Wenn wir optimale Lösungen mit Antwortzeiten liefern, stellt sich unsere neue Formulierung in der Laufzeitanalyse vorteilhaft gegenüber anderen Ansätzen dar. Die besten Resultate werden mit einem hybriden Ansatz erzielt, der heuristische Startlösungen, eine Vorverarbeitung und eine heuristische mit einer kurzen nachfolgenden exakten Berechnungsphase verbindet.

Data integration, real-time synchronization and RESTful Web Service

Analisi e sviluppo di procedure di importazione dati per un integratore di annunci immobiliari dedicato alla vendita di soggiorni turistici in case vacanza. Il documento tratta inoltre l'implementazione di un Web Service conforme all'architettura RESTful per l'accesso e l'esportazione dei dati a soggetti terzi autorizzati tramite Digest Authentication.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time. Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Adattamento di un algoritmo di apprendimento automatico per la caratterizzazione e la ricerca di frequent patterns su macchine automatiche

La tesi da me svolta durante questi ultimi sei mesi è stata sviluppata presso i laboratori di ricerca di IMA S.p.a.. IMA (Industria Macchine Automatiche) è una azienda italiana che naque nel 1961 a Bologna ed oggi riveste il ruolo di leader mondiale nella produzione di macchine automatiche per il packaging di medicinali. Vorrei subito mettere in luce che in tale contesto applicativo l’utilizzo di algoritmi di data-mining risulta essere ostico a causa dei due ambienti in cui mi trovo. Il primo è quello delle macchine automatiche che operano con sistemi in tempo reale dato che non presentano a pieno le risorse di cui necessitano tali algoritmi. Il secondo è relativo alla produzione di farmaci in quanto vige una normativa internazionale molto restrittiva che impone il tracciamento di tutti gli eventi trascorsi durante l’impacchettamento ma che non permette la visione al mondo esterno di questi dati sensibili. Emerge immediatamente l’interesse nell’utilizzo di tali informazioni che potrebbero far affiorare degli eventi riconducibili a un problema della macchina o a un qualche tipo di errore al fine di migliorare l’efficacia e l’efficienza dei prodotti IMA. Lo sforzo maggiore per riuscire ad ideare una strategia applicativa è stata nella comprensione ed interpretazione dei messaggi relativi agli aspetti software. Essendo i dati molti, chiusi, e le macchine con scarse risorse per poter applicare a dovere gli algoritmi di data mining ho provveduto ad adottare diversi approcci in diversi contesti applicativi: • Sistema di identificazione automatica di errore al fine di aumentare di diminuire i tempi di correzione di essi. • Modifica di un algoritmo di letteratura per la caratterizzazione della macchina. La trattazione è così strutturata: • Capitolo 1: descrive la macchina automatica IMA Adapta della quale ci sono stati forniti i vari file di log. Essendo lei l’oggetto di analisi per questo lavoro verranno anche riportati quali sono i flussi di informazioni che essa genera. • Capitolo 2: verranno riportati degli screenshoot dei dati in mio possesso al fine di, tramite un’analisi esplorativa, interpretarli e produrre una formulazione di idee/proposte applicabili agli algoritmi di Machine Learning noti in letteratura. • Capitolo 3 (identificazione di errore): in questo capitolo vengono riportati i contesti applicativi da me progettati al fine di implementare una infrastruttura che possa soddisfare il requisito, titolo di questo capitolo. • Capitolo 4 (caratterizzazione della macchina): definirò l’algoritmo utilizzato, FP-Growth, e mostrerò le modifiche effettuate al fine di poterlo impiegare all’interno di macchine automatiche rispettando i limiti stringenti di: tempo di cpu, memoria, operazioni di I/O e soprattutto la non possibilità di aver a disposizione l’intero dataset ma solamente delle sottoporzioni. Inoltre verranno generati dei DataSet per il testing di dell’algoritmo FP-Growth modificato.