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Barua, Shaibal. "Multi-sensor Information Fusion for Classification of Driver's Physiological Sensor Data." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-18880.
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Physiological sensor signals analysis is common practice in medical domain for diagnosis andclassification of various physiological conditions. Clinicians’ frequently use physiologicalsensor signals to diagnose individual’s psychophysiological parameters i.e., stress tiredness,and fatigue etc. However, parameters obtained from physiological sensors could vary becauseof individual’s age, gender, physical conditions etc. and analyzing data from a single sensorcould mislead the diagnosis result. Today, one proposition is that sensor signal fusion canprovide more reliable and efficient outcome than using data from single sensor and it is alsobecoming significant in numerous diagnosis fields including medical diagnosis andclassification. Case-Based Reasoning (CBR) is another well established and recognizedmethod in health sciences. Here, an entropy based algorithm, “Multivariate MultiscaleEntropy analysis” has been selected to fuse multiple sensor signals. Other physiologicalsensor signals measurements are also taken into consideration for system evaluation. A CBRsystem is proposed to classify ‘healthy’ and ‘stressed’ persons using both fused features andother physiological i.e. Heart Rate Variability (HRV), Respiratory Sinus Arrhythmia (RSA),Finger Temperature (FT) features. The evaluation and performance analysis of the system have been done and the results ofthe classification based on data fusion and physiological measurements are presented in thisthesis work.
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Dennis, Jacob Henry. "On Quaternions and Activity Classification Across Sensor Domains." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51196.
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Activity classification based on sensor data is a challenging task. Many studies have focused on two main
methods to enable activity classification; namely sensor level classification and body-model level classification.
This study aims to enable activity classification across sensor domains by considering an e-textile garment
and provide the groundwork for transferring the e-textile garment to a vision-based classifier. The framework
is comprised of three main components that enable the successful transfer of the body-worn system to the
vision-based classifier. The inter-class confusion of the activity space is quantified to allow an ideal prediction
of known class accuracy for varying levels of error within the system. Methods for quantifying sensor and
garment level error are undertaken to identify challenges specific to a body-worn system. These methods
are then used to inform decisions related to the classification accuracy and threshold of the classifier. Using
activities from a vision-based system known to the classifier, a user study was conducted to generate an
observed set of activities from the body-worn system. The results indicate that the vision-based classifier
used is user-independent and can successfully handle classification across sensor domains.Master of Science
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Wang, Beng Wei. "Analysis and classification of traffic in wireless sensor networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FWang.pdf.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2007.Thesis Advisor(s): John C. McEachen. "March 2007." Includes bibliographical references (p. 61-63). Also available in print.
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Sun, Yang. "Intelligent wireless sensor network based vehicle detection and classification /." Full text available from ProQuest UM Digital Dissertations, 2007. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=1&did=1414125751&SrchMode=1&sid=3&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1219779610&clientId=22256.
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Abdelbar, Mahi Othman Helmi Mohamed Helmi Hussein. "Applications of Sensor Fusion to Classification, Localization and Mapping." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82955.
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Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm
for integrating data from multiple sources to synthesize new information that in general would not
have been feasible from the individual parts. Within the wireless communications fields, many
emerging technologies such asWireless Sensor Networks (WSN), the Internet of Things (IoT), and
spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively
and sharing information. In addition, there is a huge proliferation of smartphones in the world with
a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor
a diverse range of human activities and the surrounding environment far beyond the scale of what
was possible before. Wireless communications open up great opportunities for the application of
sensor fusion techniques at multiple levels.
In this dissertation, we identify two key problems in wireless communications that can greatly
benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor
localization and mapping based on smartphone sensors. Automatic Modulation Classification is
a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node
is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion
techniques to the signal classification problem within a network of distributed nodes is presented
as a means to overcome the detrimental channel effects faced by single nodes and provide more
reliable classification performance.
Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed
positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the
room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework.Ph. D.
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Hameed, Tariq, Ahsan Ashfaq, and Rabid Mehmood. "Intelligent Sensor." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310.
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The task is to build an intelligent sensor that can instruct a Lego robot to perform certain tasks. The sensor is mounted on the Lego robot and it contains a digital camera which takes continuous images of the front view of the robot. These images are received by an FPGA which simultaneously saves them in an external storage device (SDRAM). At one time only one image is saved and during the time it is being saved, FPGA processes the image to extract some meaningful information. In front of digital camera there are different objects. The sensor is made to classify various objects on the basis of their color. For the classification, the requirement is to implement color image segmentation based object tracking algorithm on a small Field Programmable Gate array (FPGA). For the color segmentation in the images, we are using RGB values of the pixels and with the comparison of their relative values we get the binary image which is processed to determine the shape of the object. A histogram is used to retrieve object‟s features and saves results inside the memory of FPGA which can be read by an external microcontroller with the help of serial port (RS-232).
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Tyni, Elin, and Johanna Wikberg. "Classification of Wi-Fi Sensor Data for a Smarter City : Probabilistic Classification using Bayesian Statistics." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159797.
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As cities are growing with an increasing number of residents, problems with the traffic such as congestion and larger emission arise. The city planners have challenges with making it as easy as possible for the residents to commute and in as large scale as possible to avoid vehicles. Before any improvements or reconstructions can be made, the traffic situation has to be mapped. The results from a probabilistic classification on Wi-Fi sensor data collected in an area in the southern part of Stockholm showed that some streets are more likely to be trafficked by cyclists than pedestrians while other streets showed the opposite. The goal of this thesis was to classify observations as either pedestrians or as cyclists. To do that, Bayesian statistics was applied to perform a classification. Results from a cluster analysis performed with K-means algorithm were used as prior information to a probabilistic classification model. To be able to validate the results from this unsupervised statistical learning problem, several model diagnostic methods were used. The final model passes all limits of what is considered to be a stable model and shows clear signs of convergence. The data was collected using Wi-Fi sensors which detect a device passing by when the device is searching the area for a network to connect to. This thesis will focus on data from three months. Using Wi-Fi sensors as a data collection method makes it possible to track a device. However, many manufacturers produce network interface controllers that generate randomized addresses when the device is connecting to a network, which makes it difficult to track the majority of the devices. Therefore, Wi-Fi sensor data could be seen as not suitable for this type of study. Hence it is suggested that other methods should be used in the future.I takt med att städer växer med ökat antal invånare uppståar det problem i trafiken såsom trängsel och utsläpp av partiklar. Trafikplanerare ställs inför utmaningar i form av hur de kan underlätta pendling för invånarna och hur de, i så stor utsträckning som möjligt, kan minska fordon i tätorten. Innan potentiella förbättringar och ombyggnationer kan genomföras måste trafiken kartläggas. Resultatet från en sannolikhetsklassificering på Wi-Fi sensordata insamlat i ett område i södra delen av Stockholm visar att vissa gator är mer trafikerade av cyclister än fotgängare medan andra gator visar på motsatt föhållande. Resultatet ger en indikation på hur proportionen mellan de två grupperna kan se ut. Målet var att klassificera varje observation som antingen fotgängare eller cyklist. För att göra det har Bayesiansk statistik applicerats i form av en sannolikhetsklassifikation. Reslutatet från en klusteranalys genomförd med ”K-means clustering algorithm” användes som prior information till klassificeringsmodellen. För att kunna validera resultatet från detta ”unsupervised statistical learning” -problem, användes olika metoder för modelldiagnostik. Den valda modellen uppfyller alla krav för vad som anses vara rimligt f ̈or en stabil modell och visar tydliga tecken på konvergens. Data samlades in med Wi-Fi sensorer som upptäcker förbipasserande enheter som söker efter potentiella nätverk att koppla upp sig mot. Denna metod har visat sig inte vara den mest optimala, eftersom tillverkare idag producerar nätverkskort som genererar en slumpad adress varje gång en enhet försöker ansluta till ett nätverk. De slumpade adresserna gör det svårt att följa majoriteten av enheterna mellan sensorera, vilket gör denna typ av data olämplig för denna typ av studie. Därf ̈or föreslås att andra metoder för att samla in data används i framtiden.
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Finkele, R. "A polarimetric millimetre wave sensor system for road surface classification." Thesis, Cranfield University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284920.
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Svanström, Fredrik. "Drone Detection and Classification using Machine Learning and Sensor Fusion." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42141.
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This thesis explores the process of designing an automatic multisensordrone detection system using machine learning and sensorfusion. Besides the more common video and audio sensors, the systemalso includes a thermal infrared camera. The results show thatutilizing an infrared sensor is a feasible solution to the drone detectiontask, and even with slightly lower resolution, the performance isjust as good as a video sensor. The detector performance as a functionof the sensor-to-target distance is also investigated. Using sensor fusion, the system is made more robust than the individualsensors. It is observed that when using the proposed sensorfusion approach, the output system results are more stable, and thenumber of false detections is mitigated. A video dataset containing 650 annotated infrared and visible videosof drones, birds, airplanes and helicopters is published. Additionally,an audio dataset with the classes drones, helicopters and backgroundsis also published.
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Jonsson, Patrik. "Surface Status Classification, Utilizing Image Sensor Technology and Computer Models." Doctoral thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24828.
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There is a great need to develop systems that can continuously provide correct information about road surface status depending on the prevailing weather conditions. This will minimize accidents and optimize transportation. In this thesis different methods for the determination of the road surface status have been studied and analyzed, and suggestions of new technology are proposed. Information about the road surface status is obtained traditionally from various sensors mounted directly in the road surface. This information must then be analyzed to create automated warning systems for road users and road maintenance personnel. The purpose of this thesis is to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Another purpose is also to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Furthermore, the aim is to develop non-contact technologies able to determine and classify road conditions over a larger area, since there is no system available today that can identify differences in road surface status in the wheel tracks and between the wheel tracks. Literature studies have been carried out to find the latest state of the art research and technology, and the research work is mainly based on empirical studies. A large part of the research has involved planning and setting up laboratory experiments to test and verify hypotheses that have emerged from the literature studies. Initially a few traditional road-mounted sensors were analyzed regarding their ability to determine the road conditions and the impact on their measured values when the sensors were exposed to contamination agents such as glycol and oil. Furthermore, non-contact methods for determining the status of the road surface have been studied. Images from cameras working in the visible range, together data from the Swedish Transportation Administration road weather stations, have been used to develop computerized road status classification models that can distinguish between a dry, wet, icy and snowy surface. Field observations have also been performed to get the ground truth for developing these models. In order to improve the ability to accurately distinguish between different surface statuses, measurement systems involving sensors working in the Near-Infrared (NIR) range have been utilized. In this thesis a new imaging method for determining road conditions with NIR camera technology is developed and described. This method was tested in a field study performed during the winter 2013-2014 with successful results. The results show that some traditional sensors could be used even with future user-friendly de-icing chemicals. The findings from using visual camera systems and meteorological parameters to determine the road status showed that they provide previously unknown information about road conditions. It was discovered that certain road conditions such as black ice is not always detectable using this technology. Therefore, research was performed that utilized the NIR region where it proved to be possible to detect and distinguish different road conditions, such as black ice. NIR camera technology was introduced in the research since the aim of the thesis was to find a method that provides information on the status of the road over a larger area. The results show that if several images taken in different spectral bands are analyzed with the support of advanced computer models, it is possible to distinguish between a dry, wet, icy and snowy surface. This resulted in the development of a NIR camera system that can distinguish between different surface statuses. Finally, two of these prototype systems for road condition classification were evaluated. These systems were installed at E14 on both sides of the border between Sweden and Norway. The results of these field tests show that this new road status classification, based on NIR imaging spectral analysis, provides new information about the status of the road surface, compared to what can be obtained from existing measurement systems, particularly for detecting differences in and between the wheel tracks.
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Peng, Yingqi. "Japanese Black Cattle Behavior Pattern Classification Based on Neural Networks Using Inertial Sensors and Magnetic Direction Sensor." Kyoto University, 2019. http://hdl.handle.net/2433/244558.
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Gok, Sercan. "Fuzzy Decision Fusion For Single Target Classification In Wireless Sensor Networks." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611296/index.pdf.
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Nowadays, low-cost and tiny sensors are started to be commonly used due to developing technology. Wireless sensor networks become the solution for a variety of applications such as military applications. For military applications, classification of a target in a battlefield plays an important role. Target classification can be done effectively by using wireless sensor networks. A wireless sensor node has the ability to sense the raw signal data in battlefield, extract the feature vectors from sensed signal and produce a local classification result using a classifier. Although only one sensor is enough to produce a classification result, decision fusion of the local classification results for the sensor nodes improves classification accuracy and loads lower computational burden on the sensor nodes. Decision fusion performance can also be improved by picking optimum sensor nodes for target classification.
In this thesis, we propose fuzzy decision fusion methods for single target classification in wireless sensor networks. Our proposed fusion algorithms use fuzzy logic for selecting the appropriate sensor nodes to be used for classification. Our solutions provide better classification accuracy over some popular decision fusion algorithms. In addition to fusion algorithms, we present some techniques for feature vector size reduction on sensor nodes, and training set formation for classifiers.
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Olsson, Isak, and André Lindgren. "LiDAR-Equipped Wireless Sensor Network for Speed Detection on Classification Yards." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301053.
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Varje dag kopplas tusentals godsvagnar om på de olika rangerbangårdarna i Sverige. För att kunna automatiskt bromsa vagnarna tillräckligt mycket är det nödvändigt att veta deras hastigheter. En teknik som har blivit populär på sistone är Light Detection and Ranging (LiDAR) som använder ljus för att mäta avstånd till objekt. Den här rapporten diskuterar design- och implementationsprocessen av ett trådlöst sensornätverk bestående av en LiDARutrustad sensornod. Designprocessen gav en insikt i hur LiDAR-sensorer bör placeras för att täcka en så stor yta som möjligt. Sensornoden var programmerad att bestämma avståndet av objekt genom att använda Random Sample Consensus (RANSAC) för att ta bort outliers och sen linjär regression på de inliers som detekterats. Implementationen utvärderades genom att bygga ett litet spår med en låda som kunde glida fram och tillbaka över spåret. LiDAR- sensorn placerades med en vinkel vid sidan om spåret. Resultaten visade att implementationen både kunde detektera objekt på spåret och också hastigheten av objekten. En simulation gjordes också med hjälp av en 3D-modell av en tågvagn för att se hur väl algoritmen hanterade ojämna ytor. LiDAR-sensorn i simuleringen hade en strålavvikelse på 0_. 30% av de simulerade mätvärdena gjordes om till outliers för att replikera dåliga väderförhållanden. Resultaten visade att RANSAC effektivt kunde ta bort outliers men att de ojämna ytorna på tåget ledde till felaktiga hastighetsmätningar. En slutsats var att en sensor med en divergerande stråle möjligtvis skulle leda till bättre resultat. Framtida arbete inkluderar att utvärdera implementationen på en riktig bangård, hitta optimala parametrar för algoritmen samt evaluera algoritmer som kan filtrera data från ojämn geometri.Every day, thousands of train wagons are coupled on the multiple classification yards in Sweden. To be able to automatically brake the wagons a sufficient amount, it is a necessity to determine the speed of the wagons. A technology that has been on the rise recently is Light Detection and Ranging (LiDAR) that emits light to determine the distance to objects. This report discusses the design and implementation of a wireless sensor network consisting of a LiDAR-equipped sensor node. The design process provided insight into how LiDAR sensors may be placed for maximum utilization. The sensor node was programmed to determine the speed of an object by first using Random Sample Consensus (RANSAC) for outlier removal and then linear regression on the inliers. The implementation was evaluated by building a small track with an object sliding over it and placing the sensor node at an angle to the side of the track. The results showed that the implementation could both detect objects on the track and also track the speed of the objects. A simulation was also made using a 3D model of a wagon to see how the algorithm performs on non-smooth surfaces. The simulated LiDAR sensor had a beam divergence of 0_. 30% of the simulated measurements were turned into outliers to replicate bad weather conditions. The results showed that RANSAC was efficient at removing the outliers but that the rough surface of the wagon resulted in some incorrect speed measurements. A conclusion was made that a sensor with some beam divergence could be beneficial. Future work includes testing the implementation in real-world scenarios, finding optimal parameters for the proposed algorithm, and to evaluate algorithms that can filter rough geometry data.
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Petersson, Henrik. "Multivariate Exploration and Processing of Sensor Data-applications with multidimensional sensor systems." Doctoral thesis, Linköpings universitet, Tillämpad Fysik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14879.
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A sensor is a device that transforms a physical, chemical, or biological stimulus into a readable signal. The integral part that sensors make in modern technology is considerable and many are those trying to take the development of sensor technology further. Sensor systems are becoming more and more complex and may contain a wide range of different sensors, where each may deliver a multitude of signals.Although the data generated by modern sensor systems contain lots of information, the information may not be clearly visible. Appropriate handling of data becomes crucial to reveal what is sought, but unfortunately, that process is not always straightforward and there are many aspects to consider. Therefore, analysis of multidimensional sensor data has become a science.The topic of this thesis is signal processing of multidimensional sensordata. Surveys are given on methods to explore data and to use the data to quantify or classify samples. It is also discussed how to avoid the rise of artifacts and how to compensate for sensor deficiencies. Special interest is put on methods being practically applicable to chemical gas sensors. The merits and limitations of chemical sensors are discussed and it is argued that multivariate data analysis plays an important role using such sensors. The contribution made to the public by this thesis is primarily on techniques dealing with difficulties related to the operation of sensors in applications. In the second paper, a method is suggested that aims at suppressing the negative effects caused by unwanted sensor-to-sensor differences. If such differences are not suppressed sufficiently, systems where sensors occasionally must be replaced may degrade and lose performance. The strong-point of the suggested method is its relative ease of use considering large-scale production of sensor components and when integrating sensors into mass-market products. The third paper presents a method that facilitates and speeds up the process of assembling an array of sensors that is optimal for a particular application. The method combines multivariate data analysis with the `Scanning Light Pulse Technique'. In the first and fourth papers, the problem of source separation is studied. In two separate applications, one using gas sensors for combustion control and one using acoustic sensors for ground surveillance, it has been identified that the current sensors outputs mixtures of both interesting- and interfering signals. By different means, the two papers applies and evaluates methods to extract the relevant information under such circumstances.En sensor är en komponent som överför en fysikalisk, kemisk, eller biologisk storhet eller kvalitet till en utläsbar signal. Sensorer utgör idag en viktig del i flertalet högteknologiska produkter och sensorforskning är ett aktivt område. Komplexiteten på sensorbaserade system ökar och det blir möjligt att registrera allt er olika typer av mätsignaler. Mätsignalerna är inte alltid direkt tydbara, varvid signalbehandling blir ett väsentligt verktyg för att vaska fram den viktiga information som sökes. Signalbehandling av sensorsignaler är dessvärre inte en okomplicerad procedur och det finns många aspekter att beakta. Av denna anledning har signalbehandling och analys av sensorsignaler utvecklats till ett eget forskningsområde. Denna avhandling avhandlar metoder för att analysera komplexa multidimensionella sensorsignaler. En introduktion ges till metoder för att, utifrån mätningar, klassificera och kvantifiera egenskaper hos mätobjekt. En överblick ges av de effekter som kan uppstå på grund av imperfektioner hos sensorerna och en diskussion föres kring metoder för att undvika eller lindra de problem som dessa imperfektioner kan ge uppkomst till. Speciell vikt lägges vid sådana metoder som medför en direkt applicerbarhet och nytta för system av kemiska sensorer. I avhandlingen ingår fyra artiklar, som vart och en belyser hur de metoder som beskrivits kan användas i praktiska situationer.
Sensor,
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Roberts, Matthew Simon. "Tracking and classification with wireless sensor networks and the transferable belief model." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55134/.
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The use of small, cheap, networked devices to collaboratively perform a task presents an attractive opportunity for many scenarios. One such scenario is the tracking and classification of an object moving through a region of interest. A single sensor is capable of very little, but a group of sensors can potentially provide a flexible, self-organising system that can carry out tasks in harsh conditions for long periods of time. This thesis presents a new framework for tracking and classification with a wire less sensor network. Existing algorithms have been integrated and extended within this framework to perform tracking and classification whilst managing energy usage in order to balance the quality of information with the cost of obtaining it. Novel improvements are presented to perform tracking and classification in more realistic scenarios where a target is moving in a non-linear fashion over a varying terrain. The framework presented in this thesis can be used not only in algorithm development, but also as a tool to aid sensor deployment planning. All of the algorithms presented in this thesis have a common basis that results from the integration of a wireless sensor network management algorithm and a tracking and classification algorithm both of which are considered state-of-the-art. Tracking is performed with a particle filter, and classification is performed with the Transferable Belief Model. Simulations are used throughout this thesis in order to compare the performance of different algorithms. A large number of simulations are used in each experiment with various parameter combinations in order to provide a detailed analysis of each algorithm and scenario. The work presented in this thesis could be of use to developers of wireless sensor network algorithms, and also to people who plan the deployment of nodes. This thesis focuses on military scenarios, but the research presented is not limited to this.
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Aplin, Paul. "Fine spatial resolution satellite sensor imagery for pre-field land cover classification." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297413.
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Verner, Alexander. "LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1074.
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In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requires considerable effort from domain experts.
An alternative approach is to use deep learning algorithms. Research has shown that modern deep neural networks are very effective in automated extraction of abstract features from raw data in classification tasks. Long short-term memory networks, or LSTMs in short, are a special kind of recurrent neural networks that are capable of learning long-term dependencies. These networks have proved to be especially effective in the classification of raw time-series data in various domains. This dissertation systematically investigates the effectiveness of the LSTM model for anomaly detection and classification in raw time-series sensor data.
As a proof of concept, this work used time-series data of sensors that measure blood glucose levels. A large number of time-series sequences was created based on a genuine medical diabetes dataset. Anomalous series were constructed by six methods that interspersed patterns of common anomaly types in the data. An LSTM network model was trained with k-fold cross-validation on both anomalous and valid series to classify raw time-series sequences into one of seven classes: non-anomalous, and classes corresponding to each of the six anomaly types.
As a control, the accuracy of detection and classification of the LSTM was compared to that of four traditional machine learning classifiers: support vector machines, Random Forests, naive Bayes, and shallow neural networks. The performance of all the classifiers was evaluated based on nine metrics: precision, recall, and the F1-score, each measured in micro, macro and weighted perspective.
While the traditional models were trained on vectors of features, derived from the raw data, that were based on knowledge of common sources of anomaly, the LSTM was trained on raw time-series data. Experimental results indicate that the performance of the LSTM was comparable to the best traditional classifiers by achieving 99% accuracy in all 9 metrics. The model requires no labor-intensive feature engineering, and the fine-tuning of its architecture and hyper-parameters can be made in a fully automated way. This study, therefore, finds LSTM networks an effective solution to anomaly detection and classification in sensor data.
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Ramakrishnan, Naveen. "Distributed Learning Algorithms for Sensor Networks." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632.
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Alirezaei, Gholamreza [Verfasser]. "Optimizing power allocation in sensor networks with application in target classification / Gholamreza Alirezaei." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1059650142/34.
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Li, Sichu. "Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/913.
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There is a significant need to identify approaches for classifying chemical sensor array data with high success rates that would enhance sensor detection capabilities. The present study attempts to fill this need by investigating six machine learning methods to classify a dataset collected using a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 classes containing 136 samples is used to build the classifiers, and a dataset of 4 classes with 56 samples is used for testing. The results generated with the six different methods are compared and discussed. The RF, CART, and KNN are found to have success rates greater than 90%, and to outperform the other methods.
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Sher, Rabnawaz Jan. "Classification of a Sensor Signal Attained By Exposure to a Complex Gas Mixture." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172769.
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This thesis is carried out in collaboration with a private company, DANSiC AB This study is an extension of a research work started by DANSiC AB in 2019 to classify a source. This study is about classifying a source into two classes with the sensitivity of one source higher than the other as one source has greater importance. The data provided for this thesis is based on sensor measurements on different temperature cycles. The data is high-dimensional and is expected to have a drift in measurements. Principal component analysis (PCA) is used for dimensionality reduction. “Differential”, “Relative” and “Fractional” drift compensation techniques are used for compensating the drift in data. A comparative study was performed using three different classification algorithms, which are “Linear Discriminant Analysis (LDA)”, “Naive Bayes classifier (NB)” and “Random forest (RF)”. The highest accuracy achieved is 59%,Random forest is observed to perform better than the other classifiers.This work is done with DANSiC AB in collaboration with Linkoping University.
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Symanzik, Horst-G. "Interface-Elektronik für mikromechanische Sensor- und Aktorarrays." Doctoral thesis, Universitätsbibliothek Chemnitz, 2003. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200301571.
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The dissertation covers circuits in the field of micro-system electronics working as an interface between micro-electromechanical components and digital signal processing. The important problems of signal crosstalk and multiplication of complexity arising in conjunction with sensor- and actuator-arrays are considered. As a foundation for design the modeling of DMOS-transistors and system aspects of sensor-signal-recovery are discussed. The design and implementation of a high-voltage driver-amplifier, a modulator for sensor-signal-recovery, a correlation IC to read out arrays and a micromechanical resonator with monolithically integrated read-out-amplifier is presentedDie Dissertation behandelt Schaltungen der Mikrosystemelektronik in ihrer Funktion als Schnittstelle zwischen mikromechanischen Komponenten und signalverarbeitender Digitalelektronik. Hierbei werden die für Sensor- und Aktorarrays besonderen Probleme Signalübersprechen und Aufwandsvervielfachung berücksichtigt. Als Entwurfsgrundlage werden die Modellierung von DMOS-Transistoren und Systemaspekte der Sensorsignalauswertung besprochen. Vorgestellt wird der Entwurf und die Realisierung eines Hochvolt-Ansteuerverstärkers, eines Modulators zur Sensorauswertung, eines Korrelator-ICs zur Arraysauswertung und eines mikromechanischen Resonators mit monolithisch integriertem Ausleseverstärker
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Feitosa, Allan Eduardo. "Classification techniques for adaptive distributed networks and aeronautical structures." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-05022019-104746/.
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This master thesis is the result of a collaborative work between EMBRAER and the Escola Politécnica da USP for the study of structural health monitoring (SHM) techniques using sensors applied to aircraft structures. The goal was to develop classification techniques to discriminate between different events arising in the aircraft structure during tests; in the short term, improving the current SHM system used by EMBRAER, based on acoustic emission and, in the long term, fostering the development of a fully distributed system. As a result of studying classification methods for immediate use, we developed two techniques: the Spectral Similarity and a Support Vector Machines (SVM) classifier. Both are unsupervised solutions, due to the unlabeled nature of the data provided. The two solutions were delivered as a final product to EMBRAER for prompt use in the existing SHM system. By studying distributed solutions for future implementations, we developed a detection algorithm based on adaptive techniques. The main result was a special initialization for a maximum likelihood (ML) detector that yields an exponential decay rate in the error probability to a nonzero steady state, using adaptive diffusion estimation in a distributed sensor network. The nodes that compose the network must decide, locally, between two concurrent hypotheses concerning the environment state where they are inserted, using local measurements and shared estimates coming from their neighbors. The exponential performance does not depend on the adaptation step size value, provided it is sufficiently small. The results concerning this distributed detector were published in the journal IEEE Signal Processing Letters.Esta dissertação de mestrado é o resultado de um trabalho colaborativo entre a EMBRAER e a Escola Politécnica da USP no estudo de técnicas de monitoramento do estado de saúde de estruturas (Structural Health Monitoring - SHM) utilizando sensores em estruturas aeronáuticas. O objetivo foi desenvolver técnicas de classificação para discriminar entre diferentes eventos que surgem em estruturas aeronáuticas durante testes; para o curto prazo, aperfeiçoando o atual sistema de SHM utilizado pela EMBRAER, baseado em emissão acústica e, no longo prazo, fomentando o desenvolvimento de um sistema completamente distribuído. Como resultado do estudo de métodos de classificação para uso imediato, desenvolvemos duas técnicas: a Similaridade Espectral e um classificador que utiliza Support Vector Machines (SMV). Ambas as técnicas são soluções não-supervisionadas, devido a natureza não rotulada dos dados fornecidos. As duas soluções foram entregues como um produto final para a EMBRAER para pronta utilização em seu atual sistema de SHM. Ao estudar soluções completamente distribuídas para futuras implementações, desenvolvemos um algoritmo de detecção baseado em técnicas adaptativas. O principal resultado foi uma inicialização especial para um detector de máxima verossimilhança (maximum likelihood - ML) que possui uma taxa de decaimento exponencial na probabilidade de erro até um valor não nulo em regime estacionário, utilizando estimação adaptativa em uma rede distribuída. Os nós que compõem a rede devem decidir, localmente, entre duas hipóteses concorrentes com relação ao estado do ambiente onde eles estão inseridos, utilizando medidas locais e estimativas compartilhadas vindas de nós vizinhos. O desempenho exponencial não depende do valor do passo de adaptação, se este for suficientemente pequeno. Os resultas referentes a este detector distribuído foram publicados na revista internacional IEEE Signal Processing Letters.
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Ollander, Simon. "Wearable Sensor Data Fusion for Human Stress Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122348.
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With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression.I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test, Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 % på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.
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Bales, Dustin Bennett. "Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/81183.
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The ability to classify occupants in a building has far-reaching applications in security, monitoring human health, and managing energy resources effectively. In this work, gender and weight of walkers are classified via machine learning or pattern recognition techniques. Accelerometers mounted beneath the floor of Virginia Tech's Goodwin Hall measured walkers' gait. These acceleration measurements serve as the inputs to machine learning techniques allowing for classification. For this work, the gait of fifteen individual walkers was recorded via fourteen accelerometers as they, alone, walked down the instrumented hallway, in multiple trials. These machine learning algorithms produce an 88 % accurate model for gender classification. The machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are Discrete Empirical Interpolation Method (DEIM), Q-DEIM, and Projection Coefficients. A two-part methodology is proposed to implement the approach completed in this thesis work. The first step validates the algorithm design choices, i.e. using bagged or boosted decision trees for classification. The second step reduces the walking data measured to truncate accelerometers which do not aid in increasing characteristic classification.Master of Science
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Subramanian, Chidambaram. "Real-Time Implementation of Road Surface Classification using Intelligent Tires." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/101014.
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The growth of the automobile Industry in the past 50 years is radical. The development
of chassis control systems have grown drastically due to the demand for safer, faster and
more comfortable vehicles. For example, the invention of Anti-lock Braking System (ABS)
has resulted in saving more than a million lives since its adaptation while also allowing
the vehicles to commute faster. As we move into the autonomous vehicles era, demand for
additional information about tire-road interaction to improve the performance of the onboard
chassis control systems, is high. This is due to the fact that the interaction between
the tire and the road surface determines the stability boundary limits of the vehicles. In
this research, a real-time system to classify the road surface into five major categories was
developed. The five surfaces include Dry Asphalt, Wet Asphalt, Snow, and Ice and dry
Concrete. tri-axial accelerometers were placed on the inner liner of the tires.
An advanced signal processing technique was utilized along with a machine learning model
to classify the road surfaces. The instrumented Volkswagen Jetta with intelligent tires was
retrofitted with new instrumentation for collecting data and evaluating the performance of
the developed real-time system. A comprehensive study on road surface classification was
performed in order to determine the features of the classification algorithm. Performance of
the real-time system is discussed in details and compared with offline results.Master of Science
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Kanoun, Olfa. "Scientific Reports on Measurement and Sensor Technology." Universitätsverlag Chemnitz, 2016. https://monarch.qucosa.de/id/qucosa%3A20552.
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Wissenschaftliche Schriftenreihe, die Dissertationen der Professur Mess- und Sensortechnik beinhaltet.Scientific series containing dissertations of the Professorship of Measurement and Sensor Technology.
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Chavez, Garcia Ricardo Omar. "Multiple sensor fusion for detection, classification and tracking of moving objects in driving environments." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM034/document.
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Les systèmes avancés d'assistance au conducteur (ADAS) aident les conducteurs à effectuer des tâches de conduite complexes et à éviter ou atténuer les situations dangereuses. Le véhicule détecte le monde extérieur au moyen de capteurs, et ensuite construit et met à jour un modèle interne de la configuration de l'environnement. La perception de véhicule consiste à établir des relations spatiales et temporelles entre le véhicule et les obstacles statiques et mobiles dans l'environnement. Cette perception se compose de deux tâches principales : la localisation et cartographie simultanées (SLAM) traite de la modélisation de pièces statiques; et la détection et le suivi d'objets en mouvement (DATMO) est responsable de la modélisation des pièces mobiles dans l'environnement. Afin de réaliser un bon raisonnement et contrôle, le système doit modéliser correctement l'environnement. La détection précise et la classification des objets en mouvement est un aspect essentiel d'un système de suivi d'objets. Classification des objets en mouvement est nécessaire pour déterminer le comportement possible des objets entourant le véhicule, et il est généralement réalisée au niveau de suivi des objets. La connaissance de la classe d'objets en mouvement au niveau de la détection peut aider à améliorer leur suivi. La plupart des solutions de perception actuels considèrent informations de classification seulement comme information additional pour la sortie final de la perception. Aussi, la gestion de l'information incomplète est une exigence importante pour les systèmes de perception. Une information incomplète peut être originaire de raisons liées à la détection, tels que les problèmes d calibrage et les dysfonctionnements des capteurs; ou des perturbations de la scène, comme des occlusions, des problèmes de météo et objet déplacement. Les principales contributions de cette thèse se concentrent sur la scène DATMO. Précisément, nous pensons que l'inclusion de la classe de l'objet comme un élément clé de la représentation de l'objet et la gestion de l'incertitude de plusieurs capteurs de détections, peut améliorer les résultats de la tâche de perception. Par conséquent, nous abordons les problèmes de l'association de données, la fusion de capteurs, la classification et le suivi à différents niveaux au sein de la phase de DATMO. Même si nous nous concentrons sur un ensemble de trois capteurs principaux: radar, lidar, et la caméra, nous proposons une architecture modifiables pour inclure un autre type ou nombre de capteurs. Premièrement, nous définissons une représentation composite de l'objet pour inclure des informations de classe et de l'état d'objet deouis le début de la tâche de perception. Deuxièmement, nous proposons, mettre en œuvre, et comparons deux architectures de perception afin de résoudre le problème de DATMO selon le niveau où l'association des objets, la fusion et la classification des informations sont inclus et appliquées. Nos méthodes de fusion de données sont basées sur la théorie de l'evidence, qui est utilisé pour gérer et inclure l'incertitude de la détection du capteur et de la classification des objets. Troisièmement, nous proposons une approche d'association de données bassée en la théorie de l'evidence pour établir une relation entre deux liste des détections d'objets. Quatrièmement, nous intégrons nos approches de fusion dans le cadre d'une application véhicule en temps réel. Cette intégration a été réalisée dans un réelle démonstrateur de véhicule du projet European InteractIVe. Finalement, nous avons analysé et évalué expérimentalement les performances des méthodes proposées. Nous avons comparé notre fusion rapproche les uns contre les autres et contre une méthode state-of-the-art en utilisant des données réelles de scénarios de conduite différents. Ces comparaisons sont concentrés sur la détection, la classification et le suivi des différents objets en mouvement: piétons, vélos, voitures et camionsAdvanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts in the environment. In order to perform a good reasoning and control, the system has to correctly model the surrounding environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system. Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help improve their tracking. Most of the current perception solutions consider classification information only as aggregate information for the final perception output. Also, management of incomplete information is an important requirement for perception systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. It is important to manage these situations by taking them into account in the perception process. The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. First, we define a composite object representation to include class information as a part of the object state from early stages to the final output of the perception task. Second, we propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. Third, we propose an evidential data association approach to establish a relationship between two sources of evidence from object detections. We observe how the class information improves the final result of the DATMO component. Fourth, we integrate the proposed fusion approaches as a part of a real-time vehicle application. This integration has been performed in a real vehicle demonstrator from the interactIVe European project. Finally, we analysed and experimentally evaluated the performance of the proposed methods. We compared our evidential fusion approaches against each other and against a state-of-the-art method using real data from different driving scenarios. These comparisons focused on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck
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Cordova, Torres Rodrigo Fernando. "The application of Laser Induced Breakdown Spectroscopy sensor system for real time ore classification." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60272.
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Laser Induced Breakdown Spectroscopy (LIBS) is a geoanalytical tool capable of identifying elements, and measuring element concentrations and the composition of rock samples. LIBS is a method based on a laser energy pulse that creates an ablation in the surface of a rock sample and the ionization of photons to produce a breakdown of the sample’s elemental composition. The ionization process can be captured to produce a spectrum that contains information about elemental composition. The wavelength is used to identify elements, and its intensity peaks are used to identify the concentrations of the element. The mining production cycle involves such processes as rock support, drilling, blasting, loading, hauling, dumping, reclamation and ventilation, depending on the mining method. Although pre-sorting, pre-concentration and classification techniques have been applied to aspects of mineral processing after the mining cycle, this research proposes the use of LIBS in the mining cycle, and defines the basic capabilities of a sensor with potential applications in the drilling and loading cycle, particularly with respect to shovels, drills and belt conveyors. The purpose of LIBS is not to provide an accurate measurement of the target mineral, which in this research is Copper ore, but responses from different elements that can be mineralogically and statistically related to obtain a predicted concentration of the target mineral. In this paper, the methodologies and the foundations of LIBS have been developed as a sensor and proxy to an ore sorting system for the real-time in situ classification of rock material. The research is based on samples from the Escondida Mine located in the north of Chile. The samples are divided into groups of Oxides and Sulphides. The results reveal the ability to predict Oxides, Sulphides and the discrimination of Oxide and Sulphide ores. The prediction regarding the target ores is obtained by comparing the LIBS data to Certified Analysis with ICP techniques. The results include models for the prediction of Cu content for Oxides and Sulphide ore types by LIBS analysis, as well as the discrimination of Oxide ores from Sulphide ores using this technology.Applied Science, Faculty of
Mining Engineering, Keevil Institute of
Graduate
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Yang, Ying. "Routing protocols for wireless sensor networks: A survey." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-19700.
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Wireless sensor networks(WSNs) are different to traditional networks and are highly dependent on applications, thus traditional routing protocols cannot be applied efficiently to the networks. As the variability of both the application and the network architecture, the majority of the attention, therefore, has been given to the routing protocols. This work surveys and evaluates state-of-the-art routing protocols based on many factors including energy efficiency, delay andcomplexity, and presents several classifications for the various approaches pursued. Additionally, more attention is paid to several routing protocols and their advantages and disadvantages and, indeed, this work implements two of selected protocols, LEACH and THVRG, on the OPNET, and compares them in many aspects based on a large amount of experimental data. The survey also provides a valuable framework for comparing new and existing routing protocols. According to the evaluation for the performance of the routing protocols, this thesis provides assistance in relation to further improving the performance in relation to routing protocols. Finally, future research strategies and trends in relation to routing technology in wireless sensor networks are also provided.
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Auerswald, Christian. "Mikromechanischer Körperschall-Sensor zur Strukturüberwachung." Doctoral thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-205864.
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Strukturüberwachung und Condition Monitoring spielen in vielen Gebieten der Technik eine große Rolle. Zur Überwachung von Leichtbaustrukturen aus faserverstärkten Kunststoffen bietet sich hierfür besonders die Körperschall-Analyse an. Am Markt etabliert sind hierfür piezoelektrische Signalaufnehmer. Diese Arbeit stellt eine kostengünstige Alternative in Form von mikromechanischen Körperschall-Sensoren vor. Eine Besonderheit stellt hierbei das Prinzip des mechanischen Bandpasses dar. Es wird die Elektronik- und Gehäuseentwicklung sowie die experimentelle Untersuchung dargelegtStructural health monitoring is of vital importance in many technical fields. For monitoring of lightweight structures made from fiber reinforced plastics especially acoustic emission testing is used. Commercially available transducers utilize the piezoelectric effect. This thesis introduces a cost efficient alternative in form of micromechanical sensors, in particular sensors using the principle of a mechanical bandpass. The design of electronics and the packaging as well as experimental investigations are provided
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Rensfelt, Olof. "Experimental Challenges in Wireless Sensor Networks — Environment, Mobility, and Interference." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179807.
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Wireless sensor networks are used to collect sensor data in different applications such as environmental monitoring, smart building control, and health care applications. Wireless sensor nodes used are typically small, low-cost, and battery powered. The nodes are often hard to access after deployment, for example when they are in remote locations. Another property of wireless sensor networks is that their operation is dependent on the environment they operate in, both due to the specific sensor readings but also due to the effects on communication by factors such as fading and radio interference. This makes it important to evaluate a wireless sensor network in its intendent target environment before final deployment. To enable experiments with wireless sensor networks in their target environment, we have designed and implemented a testbed called Sensei-UU. It is designed to allow WSN experiments to be repeated in different locations, thus exposing effects caused by the environment. To allow this, the testbed is designed to be easily moved between experimental sites. One type of WSN applications Sensei-UU is aimed to evaluate is protocols where nodes are mobile. Mobile testbed nodes are low-cost robots which follow a tape track on the floor. The localization accuracy of the robot approach is evaluated and is accurate enough to expose a protocol to fading phenoma in a repeatable manner. Sensei-UU has helped us develop a lightweight interference classification approach, SoNIC, which runs on standard motes. The approach only use information from a standard cc2420 chipset available when packets are received. We believe that the classification accuracy is good enough to motivate specific transmission techniques avoiding interference.WISENET
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Abdul, Rahman Hala. "Multi-Sensor Based Activity Recognition˸ Development and Validation in Real-Life context." Thesis, Rennes, École normale supérieure, 2017. http://www.theses.fr/2017ENSR0011/document.
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Yang, Rong. "Vehicle Detection and Classification from a LIDAR equipped probe vehicle." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1253598183.
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Günther, Robert, Martin Abbrent, Thomas Schnicke, and Jan Bumberger. "Vom Sensor zum Forschungsdatensatz: Automatisierte Datenflüsse am UFZ." Helmholtz-Zentrum für Umweltforschung GmbH - UFZ, 2019. https://slub.qucosa.de/id/qucosa%3A39015.
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Hintergrund:
Am Helmholtz-Zentrum für Umweltforschung - UFZ arbeiten Forschende an Fragestellungen, die sich u.a. mit den aktuellen Veränderungen des Klimas und dessen Auswirkungen auf die Landnutzung beschäftigen. Dazu werden an verschiedenen Orten eine Vielzahl unterschiedlichster Umweltparameter mit Sensoren erfasst. Diese Daten werden kontinuierlich erhoben, um die Veränderungen möglichst in Echtzeit zu beobachten (Monitoring). Teilweise kommen pro Beobachtungsort mehrere Hunderte solcher Sensoren zum Einsatz. Die dafür eingesetzten Sensoren erfassen z.B. Bodenfeuchte, Niederschlagsmenge, Strahlungen und andere abiotische Kenngrößen. Damit die Daten (nach)nutzbar sind, müssen sie so aufbereitet und beschrieben werden, dass sie für nachfolgende Prozesse maschinen-lesbar bearbeitet werden können und in einer Form vorliegen, die eine Veröffentlichung nach den FAIR-Prinzipien ermöglicht.
Herausforderung:
Die erhobenen Messdaten müssen nicht nur gesichert werden, sondern auch auf Plausibilität geprüft, prozessiert und mit hinreichender Detailtiefe beschrieben werden, damit sie nachfolgend den Forschenden für die Beantwortung ihrer Forschungsfragen als Grundlage zur Verfügung stehen. Eine Herausforderung dabei ist, dass die Daten kontinuierlich als Datenstrom anfallen. Folglich müssen Prozesse wie die strukturierte Ablage, die Anreicherung mit Metadaten sowie Prüfung auf Fehlmessungen (sog. Qualitätssicherung) automatisiert werden. Aufgrund der Heterogenität der Sensoren (unterschiedliche Hersteller stellen Daten in unterschiedlichen Formaten zur Verfügung) muss bei diesen Prozessen auch eine Formatumwandlung erfolgen. Darüber hinaus sind je nach Messgröße und -verfahren verschiedene Methoden zur Plausibiläts- und Qualitätsprüfung anzuwenden.
Lösungsansatz:
Das Research Data Management Team des UFZ hat gemeinsam mit der IT-Abteilung einen Daten-Workflow entwickelt, der die unterschiedlichen Daten automatisch zusammenführt, sichert und nach einem vordefinierten Schema mit Metadaten anreichert.
Der Einsatz des Workflows wird exemplarisch anhand von aktuellen Forschungsprojekten vorgestellt und die darin enthaltenen Schritte detailliert beschrieben, wobei auch auf die technische Umsetzung eingegangen wird. Insbesondere werden die Komponenten zur Datenstrukturierung und semiautomatischen Qualitätssicherung vorgestellt, bei denen auch Methoden des Machine Mearning zum Einsatz kommen. Innerhalb des Workflows können die prozessierten Daten nach verschieden Verfahren aggregiert und weiterverarbeitet werden. Das geschieht u.a. über definierte Schnittstellen zu internen und externen Services (z.B. durch Bereitstellung als Sensor Observation Service (SOS) oder mittels einer API).
Fazit:
Die im Rahmen des hier vorgestellten Workflows entwickelten Prozesse und Komponenten zum automatisierten Management von Forschungsdaten bilden eine wichtige Grundlage für das Forschungsdatenmanagement am UFZ. Durch die modulare Ausgestaltung können die Komponenten an den Bedarf der Forschenden angepasst werden und sind auch für Szenarien geeignet, in denen die Messdaten nicht als Datenstrom anfallen.
Mit diesem Workflow ist die Voraussetzung geschaffen, die am UFZ erhobene Daten auch als Linked Data der wissenschaftlichen Community und anderen Stakeholdern zur Verfügung zu stellen.
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Cui, Jin. "Data aggregation in wireless sensor networks." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI065/document.
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Depuis plusieurs années, l’agrégation de données sont considérés comme un domaine émergent et prometteur tant dans le milieu universitaire que dans l’industrie. L’énergie et la capacité du réseau seront donc économisées car il y aura moins de transmissions de données. Le travail de cette thèse s’intéresse principalement aux fonctions d’agrégation Nous faisons quatre contributions principales. Tout d’abord, nous proposons deux nouvelles métriques pour évaluer les performances des fonctions d’agrégations vue au niveau réseau : le taux d’agrégation et le facteur d’accroissement de la taille des paquets. Le taux d’agrégation est utilisé pour mesurer le gain de paquets non transmis grâce à l’agrégation tandis que le facteur d’accroissement de la taille des paquets permet d’évaluer la variation de la taille des paquets en fonction des politiques d’agrégation. Ces métriques permettent de quantifier l’apport de l’agrégation dans l’économie d’énergie et de la capacité utilisée en fonction du protocole de routage considéré et de la couche MAC retenue. Deuxièmement, pour réduire l’impact des données brutes collectées par les capteurs, nous proposons une méthode d’agrégation de données indépendante de la mesure physique et basée sur les tendances d’évolution des données. Nous montrons que cette méthode permet de faire une agrégation spatiale efficace tout en améliorant la fidélité des données agrégées. En troisième lieu, et parce que dans la plupart des travaux de la littérature, une hypothèse sur le comportement de l’application et/ou la topologie du réseau est toujours sous-entendue, nous proposons une nouvelle fonction d’agrégation agnostique de l’application et des données devant être collectées. Cette fonction est capable de s’adapter aux données mesurées et à leurs évolutions dynamiques. Enfin, nous nous intéressons aux outils pour proposer une classification des fonctions d’agrégation. Autrement dit, considérant une application donnée et une précision cible, comment choisir les meilleures fonctions d’agrégations en termes de performances. Les métriques, que nous avons proposé, sont utilisées pour mesurer la performance de la fonction, et un processus de décision markovien est utilisé pour les mesurer. Comment caractériser un ensemble de données est également discuté. Une classification est proposée dans un cadre précisWireless Sensor Networks (WSNs) have been regarded as an emerging and promising field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. However, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, processes information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis. We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capacity change due to data aggregation. Using these metrics, we confirm that data aggregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better recovered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a target accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements
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Johnson, Darrell Wesley. "Assessing Resolution Tradeoffs of Remote Sensing Data via Classification Accuracy Cubes for Sensor Selection and Design." MSSTATE, 2006. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04032006-150953/.
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In order to aid federal agencies and private companies in the ever-growing problem of invasive species target detection, an investigation has been done on classification accuracy data cubes for use in the determination of spectral, spatial, and temporal sensor resolution requirements. The data cube is the result of a developed automated target recognition system that begins with ?ideal? hyperspectral data, and then reduces and combines spectral and spatial resolutions. The reduced data is subjected to testing methods using the Best Spectral Bands (BSB) and the All Spectral Bands (ASB) approaches and classification methods using nearest mean (NM), nearest neighbor (NN), and maximum likelihood (ML) classifiers. The effectiveness of the system is tested via two target-nontarget case studies, namely, terrestrial Cogongrass (Imperata cylindrica)-Johnsongrass (Sorghum halepense), and aquatic Water Hyacinth (Eichhornia crassipes)-American Lotus (Nelumbo lutea). Results reveal the effects, or trade-offs, of spectral-spatial-temporal resolution combinations on the ability of an ATR system to accurately detect the target invasive species. For example, in the aquatic vegetation case study, overall classification accuracies of around 90% or higher can be obtained during the month of August for spectral resolutions of 80 ? 1000nm FWHM for target abundances of 70 ? 100% per pixel. Furthermore, the ATR system demonstrates the use of resolution cubes that can be readily used to design or select cost-effective sensors for use in invasive species target detection, since lower resolution combinations may be acceptable in order to gain satisfactory classification accuracy results.
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Hermans, Frederik. "Sensor Networks and Their Radio Environment : On Testbeds, Interference, and Broken Packets." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230769.
Full textAbstract:
Sensor networks consist of small sensing devices that collaboratively fulfill a sensing task, such as monitoring the soil in an agricultural field or measuring vital signs in a marathon runner. To avoid cumbersome and expensive cabling, nodes in a sensor network are powered by batteries and communicate wirelessly. As a consequence of the latter, a sensor network's communication is affected by its radio environment, i.e., the environment's propagation characteristics and the presence of other radio devices. This thesis addresses three issues related to the impact of the radio environment on sensor networks. Firstly, in order to draw conclusions from experimental results, it is necessary to assess how the environment and the experiment infrastructure affect the results. We design a sensor network testbed, dubbed Sensei-UU, to be easily relocatable. By performing an experiment in different environments, a researcher can asses the environments’ impact on results. We further augment Sensei-UU with support for mobile nodes. The implemented mobility approach adds only little variance to results, and therefore enables repeatable experiments with mobility. The repeatability of experiments increases the confidence in conclusions drawn from them. Secondly, sensor networks may experience poor communication performance due to cross-technology radio interference, especially in office and residential environments. We consider the problem of detecting and classifying the type of interference a sensor network is exposed to. We find that different sources of interference each leave a characteristic "fingerprint" on individual, corrupt 802.15.4 packets. We design and implement the SoNIC system that enables sensor nodes to classify interference using these fingerprints. SoNIC supports accurate classification in both a controlled and an uncontrolled environment. Finally, we consider transmission errors in an outdoor sensor network. In such an environment, errors occur despite the absence of interference if the signal-to-noise ratio at a receiver is too low. We study the characteristics of corrupt packets collected from an outdoor sensor network deployment. We find that content transformation in corrupt packets follows a specific pattern, and that most corrupt packets contain only few errors. We propose that the pattern may be useful for applications that can operate on inexact data, because it reduces the uncertainty associated with a corrupt packet.WISENET
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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.
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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method
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Liess, Martin. "Design neuer Sensoren unter Berücksichtigung von Strukturaspekten." Doctoral thesis, Shaker Verlag, 2004. https://monarch.qucosa.de/id/qucosa%3A17283.
Full textAbstract:
This work is a contribution to sensor science and engineering. A
mathematical method is introduced to examine sensor structures and examples
of application of this method are given. One of them is the analysis of the
retinal receptive field structure.
The main focus is chapter 4 that presents 4 novel or significantly improved
sensor principles, which are based on improved structures. They are
- Gas sensors based on the electric field induced migration of chemisorbed
gas ions on a sensitive thin film (patent DE 10041263).
- Gas sensors based on the effect that the Seebeck voltage between
thermocouples with at least one chemical sensitive material depends on the
gas environment of that material.
- Gas detectors based on photo induced ionisation (PID) where the motion of
space charges is controlled by an electric field (patent DE18928903, DE
19838759).
- Multidimensional motion sensors that are based on self-mixing of
scattered Laser light with the light wave in the cavity of the generating
laser diode (patents WO0237124, EP1261877, CN1408064T, US2003016365,
EP1261877, WO0237410, US2003160155, WO03032138, WO0237411A1, CN1416554T,
EP1334464, US2003006367, WO03102717, US6707027, US2002104957, WO2004021158)
In chapter 5 a categorization scheme for sensor structures is presented.
The scheme is used to discuss different structural improvements of sensors,
in particular those presented in chapter 4.Vorwort und Zusammenfassung Im Hauptteil der vorliegenden Arbeit (Kapitel 4) werden vier verschiedene neu entwickelte oder wesentlich verbesserte Sensorprinzipien vorgestellt. Die Stärke dieser Sensorprinzipien ist deren Struktur, die zu einer verbesserten Nachweisgrenze oder Stabilität führt. Die Struktur eines Sensors (Kapitel 2) Um die Wirkung der Sensorstruktur algemeingültig zu diskutieren wird im zweiten Kapitel ein Modell entwickelt, das Eigenschaften von Sensoren auf deren Struktur zurückführt. Dabei werden alle Sensoreigenschaften allgemein von einer einfachen Gleichung generiert und daraus Schlussfolgerungen für die Eigenschaften der Sensorstruktur gezogen. Es zeigt sich, wie sich der Effekt struktureller Maßnahmen in der Nachweisgrenze niederschlägt, und sich mit der verbesserten Nachweisgrenze die Messunsicherheit (als Funktion aller Eingangsgrößen) parallel verschiebt. Strukturanalyse eines Sensors am Beispiel der Retina (Kapitel 3) Im dritten Kapitel wird das Modell beispielhaft auf das Auge höherer Säugetiere angewandt. In der Einleitung werden die bekannten biologischen Fakten für Ingenieure und Physiker verständlich eingeführt. Darauf folgt eine mathematischen Strukturanalyse der Retina (und Leiterstrukturen allgemein), die als Sensorsystem betrachtet wird. Es zeigt sich, wie die Schwächen der Komponenten (Nervenzellen) der Retina durch deren Struktur kompensiert werden. Sensoren mit verbesserter Struktur (Kapitel 4) 1. Gasmessung mit Hilfe gasempfindlicher Thermopaare Bekannt ist der Gebrauch von Thermopaaren zur Messung von Temperaturunterschieden. In dieser Arbeit wird eine bisher unbekannt gewesene Methode vorgestellt, mit der bei einem konstanten Temperaturunterschied eine Gaskonzentration gemessen wird. Dabei spielt die Abhängigkeit der differentiellen Thermospannung von der Ladungsträgerdichte in sensitiven Materialien eine Rolle. 2. Elektromigration von chemisorbierten Ionen auf einem halbleitenden Film Sensoren basierend auf Widerstandsänderungen von gasempfindlichen Filmen sind seit längerem im Gebrauch. Neu ist, deren aufgrund von Migration veränderliches Widerstandsprofil in Ort und Zeit zu messen und damit Sensoren zu bauen, die unempfindlicher gegen Alterung sind. 3. Modulation von Ionenbewegungen mit Hilfe eines zusätzlichen Gitters im Photoionisationdetektor Zwar sind sowohl Photoionisationsdetektoren (PID's) als auch das Modulationsprinzip an sich bekannt, jedoch ist bis dahin noch kein modulierter PID vorgestellt worden. Entscheidend an der hier eingeführten Innovation ist die Methode, den Photoionisationsstrom zu modulieren, jedoch dem Leckstrom und den äußeren Photostrom an der Kathode unmoduliert zu lassen. Das führt zu einer 20-fachen Verbesserung der Nachweisgrenze. 4. Laserdiodeneigenmischung zur mehrdimensionalen Bewegungsmessung Die Rückwirkung von in die Quelle zurückgestreutem Laserlicht war bisher als Störeffekt bekannt. Um diesen Effekt in einem Bewegungssensor nutzen zu können, mussten Probleme wie Richtungserkennung und Miniaturisierung gelöst werden. Kapitel 5 befasst sich mit Strukturverbesserungen der im vorherigen Kapitel genannten und weiteren Sensoren. Dazu wird eine Strukturschreibweise vorgestellt. Kapitel 6 enthält eine Zusammenfassung und einen Ausblick.
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Khadoor, Nadim Kvernes. "Audio classification with Neural Networks for IoT implementation." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-37640.
Full textAbstract:
This project is based upon two previous projects handed to the author by the Norwegian University of Science and Technology in co-operation with Disruptive Technologies. The report discusses sound sensing and Neural Networks, and their application in IoT. The goal was to determine what type of Neural Networks or classification methods was most suited for audio classification. This was done by applying various classification methods and Neural Networks on a data set consisting of 8732 sound samples. These methods where logistic regression, Feed-Forward Neural Network, Convolutional Neural Network, Gated Recurrent Unit, and Long Short-term Memory network. To compare the Neural Networks the accuracy of the training data set and the validation data set were evaluated. Out of these methods the feed-forward network yielded the highest validation accuracy and is the preferable classification method. However, with more work and refinement the Long Short-term memory may prove to be the better solution. Future work with a Vesper V1010 piezoelectric microphone and IoT implementation is discussed, as well as the social and ethical difficulties proposed by what is essentially a data gathering system.
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Kulathumani, Vinodkrishnan. "Network Abstractions for Designing Reliable Applications Using Wireless Sensor Networks." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211560039.
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Zhu, Feng. "A Novel Fault Detection and Classification Approach in Semiconductor Manufacturing Using Time Series Alignment Kernel." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592135306729513.
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Deng, Kangfa, Gerald Gerlach, and Margarita Guenther. "Force-compensated hydrogel-based pH sensor." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35185.
Full textAbstract:
This paper presents the design, simulation, assembly and testing of a force-compensated hydrogel-based pH sensor. In the conventional deflection method, a piezoresistive pressure sensor is used as a chemical-mechanical-electronic transducer to measure the volume change of a pH-sensitive hydrogel. In this compensation method, the pH-sensitive hydrogel keeps its volume constant during the whole measuring process, independent of applied pH value. In order to maintain a balanced state, an additional thermal actuator is integrated into the close-loop sensor system with higher precision and faster dynamic response. Poly (N-isopropylacrylamide) (PNIPAAm) with 5 mol% monomer 3-acrylamido propionic acid (AAmPA) is used as the temperature-sensitive hydrogel, while poly (vinyl alcohol) with poly (acrylic acid) (PAA) serves as the pH-sensitive hydrogel. A thermal simulation is introduced to assess the temperature distribution of the whole microsystem, especially the temperature influence on both hydrogels. Following tests are detailed to verify the working functions of a sensor based on pH-sensitive hydrogel and an actuator based on temperature-sensitive hydrogel. A miniaturized prototype is assembled and investigated in deionized water: the response time amounts to about 25 min, just half of that one of a sensor based on the conventional deflection method. The results confirm the applicability of the compensation method to the hydrogel-based sensors.
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Pitt, Luke. "Monitoring thermal comfort in the built environment using a wired sensor network." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/monitoring-thermal-comfort-in-the-built-environment-using-a-wired-sensor-network(88b0f2e2-e1a4-4d59-ba32-43995a5ed13a).html.
Full textAbstract:
This thesis documents a sensor networking project with an interest in internal environment monitoring in relation to thermal comfort. As part of this project sensor nodes were designed, built and deployed. Data was collected from the nodes via a wired Ethernet network and was stored in a database. The network remains operational several years after its initial deployment. The collected data was analyzed in conjunction with data from a local meteorological station and the building's smart fiscal energy meters. The analysis suggests the possibility of automated thermal comfort classification using data from a sensor network.
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M, Loretz, Pezzagna Sébastien, C. L. Degen, and Jan Berend Meijer. "Nanoscale nuclear magnetic resonance with a 1.9-nm-deep nitrogen-vacancy sensor." AIP Publishing, 2014. https://ul.qucosa.de/id/qucosa%3A31857.
Full textAbstract:
We present nanoscale nuclear magnetic resonance (NMR) measurements performed with
nitrogen-vacancy (NV) centers located down to about 2 nm from the diamond surface. NV centers
were created by shallow ion implantation followed by a slow, nanometer-by-nanometer removal of
diamond material using oxidative etching in air. The close proximity of NV centers to the surface
yielded large 1H NMR signals of up to 3.4 lT-rms, corresponding to ~330 statistically polarized or
~10 fully polarized proton spins in a (1.8 nm)3 detection volume.
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Dieckman, Eric Allen. "Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform." W&M ScholarWorks, 2014. https://scholarworks.wm.edu/etd/1539623643.
Full textAbstract:
In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.
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Al-Tarawneh, Mu'ath. "Traffic Monitoring System Using In-Pavement Fiber Bragg Grating Sensors." Diss., North Dakota State University, 2019. https://hdl.handle.net/10365/31539.
Full textAbstract:
Recently, adding more lanes becomes less and less feasible, which is no longer an applicable solution for the traffic congestion problem due to the increment of vehicles. Using the existing infrastructure more efficiently with better traffic control and management is the realistic solution. An effective traffic management requires the use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles and their movement on the road. A three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced for the traffic monitoring system. The proposed sensor network was installed for validation at the Cold Weather Road Research Facility in Minnesota (MnROAD) facility of Minnesota Department of Transportation (MnDOT) in MN. A vehicle classification system based on the proposed sensor network has been validated. The vehicle classification system uses support vector machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field-testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5 % of accuracy. Also, the proposed sensor network has been validated for low-speed and high-speed WIM measurements in flexible pavement. Field testing validated that the longitudinal component of the sensor has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. A performed parametric study on the stability of the WIM system shows that the loading position is the most significant parameter affecting the WIM measurements accuracy compared to the vehicle speed and pavement temperature. Also the system shows the capability to estimate the location of the loading position to enhance the system accuracy.
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Brown, Ryan Charles. "Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84944.
Full textAbstract:
Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in the image. This hardware has not been studied in the context of autonomy as the sensors are large, expensive, and have non-trivial image capture times.
This work presents three novel contributions: a Labeled Hyperspectral Image Dataset (LHID) of ground-level, outdoor objects based on typical scenes that a vehicle or pedestrian may encounter, an open-source hyperspectral interface software package (HSImage), and a feature selection based sensor design algorithm for object detection sensors (DLSD). These three contributions are novel and useful in the fields of hyperspectral data analysis, visual sensor design, and hyperspectral machine learning. The hyperspectral dataset and hyperspectral interface software were used in the design and testing of the sensor design algorithm.
The LHID is shown to be useful for machine learning tasks through experimentation and provides a unique data source for hyperspectral machine learning. HSImage is shown to be useful for manipulating, labeling and interacting with hyperspectral data, and allows wavelength and classification based data retrieval, storage of labeling information and ambient light data. DLSD is shown to be useful for creating wavelength bands for a sensor design that increase the accuracy of classifiers trained on data from the LHID. DLSD shows accuracy near that of the full spectrum hyperspectral data, with a reduction in features on the order of 100 times. It compared favorably to other state-of-the-art wavelength feature selection techniques and exceeded the accuracy of an RGB sensor by 10%.Ph. D.
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Azevedo, Rui Filipe Cabral de. "Sensor fusion of laser and vision in active pedestrian detection." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/14414.
Full textAbstract:
Mestrado em Engenharia MecânicaThis work explores a technique of sensor fusion that aims to equip vehicles with pedestrian fast detection mechanisms in exterior environments. This method restricts image areas of search based on indicators obtained by another sensor (LIDAR). This technique is based on the idea that when having a registration among the involved sensors, one "fast" sensor, but inaccurate, that can indicate regions where potential pedestrian are located on the image, and another sensor, "slower" but more robust that is used to confirm detection more accurately. So, an algorithm was created to merge two algorithms, a LIDAR-based tracking and a vision-based detection algorithm; The LIDAR indicates the precise location and scale of the potential pedestrian on the image, and crop the image relative to the potential pedestrian, being processed afterwards by one pedestrian detection algorithm to validate the classification. The method is tested in two different cases and the results confirm their validity.
Este trabalho explora uma técnica de fusão sensorial que visa dotar veículos de mecanismos rápidos de detecção de peões em ambiente exterior. O método restringe as zonas de procura numa imagem com base em indicadores obtidos por outro sensor (LIDAR). Esta técnica tem como base a idéia de que havendo um registo entre os sensores envolvidos, um sensor "rápido" mas pouco preciso, pode indicar as regiões onde potencialmente há alvos, e outro sensor, "lento" mas mais robusto, é utilizado para fazer a confirmação da deteção. Com vista a explorar essas propriedades, foi criado um algoritmo que utiliza a informação de dois sensores, para primeiro selecionar, de entre muitos objectos, possíveis peões(fase LIDAR) e dada a informação da localização do possível pedestre, uma imagem já à escala e precisa da localização, é recortada da imagem inicial, sendo a mesma enviada a ser processada por um detetor de peões (sensor mais robusto), permitindo a sua rigorosa classificação. O método é testado em dois conjuntos de dados diferentes e os resultados confirmam a sua validade.