Dissertations / Theses on the topic 'Multiple sensors'
To see the other types of publications on this topic, follow the link: Multiple sensors.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Multiple sensors.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Ribadeneira, M. Xavier. "Ball bearing diagnostics with multiple sensors." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/18963.
Full text
2
Patsikas, Dimitrios. "Track score processing of multiple dissimilar sensors." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FPatsikas.pdf.
Full textAbstract:
Thesis (M.S. in Applied Physics and M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.Thesis Advisor(s): Phillip E. Pace, Murali Tummala, Gamani Karunasiri. "June 2007." Includes bibliographical references (p. 57-58). Also available in print.
3
De, Villiers Hendrik Barney. "Correlation and tracking using multiple radar sensors /." Link to the online version, 2006. http://hdl.handle.net/10019/1006.
Full text
4
De, Villiers Hendrik Barney. "Correlation and tracking using multiple radar sensors." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/2265.
Full textAbstract:
Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2005.Tracking manoeuvring military airborne targets with radar is problematic due to the low scan rates and the high levels of measurement noise. Surveillance systems using multiple radars have the benefit of an increased rate of observation and noise reduction but also have the problem of correlating observations from multiple sensors. Mehtods are discussed to correlate single observations from multiple radar sensors as well as assigning observations to existing tracks. Filtering methods to reduce measurement noise of the target tracks and methods to extrapolate the predicted position of targets are also explored.
5
He, Shaojun. "Integration of Multiple Sensors for Astronaut Navigation on The Lunar Surface." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1324496686.
Full text
6
Boratynski, B., A. Stafiniak, A. Szyszka, M. Ramiaczek-Krasowska, R. Paszkiewicz, M. Tlaczala, A. Baranowska-Korczyc, K. Fronc, and D. Elbaum. "New fabrication approach to ZnO multiple nanofiber sensors." Thesis, Sumy State University, 2011. http://essuir.sumdu.edu.ua/handle/123456789/20587.
Full textAbstract:
In the presented work, ZnO nanofiber sensor structures designed and fabricated
using a standard microelectronic device technology were studied. The structures in the
configuration of a resistor with chemically active ZnO multiple nanofibers deposited by
electrospinning method were prepared. Investigation of inclusion in the process reactive-
ly sputtered AlN insulating film to improve the robustness of the nanofibres on the
substrate was undertaken. Selective wet chemical etching of AlN film using photoresist
developers and a photoresist mask to define the sensor active area was studied. The
Ti/Au ohmic contacts were fabricated using the lift-off photolithography process. To-
pography of the sensor structure details was investigated using AFM. Electrical charac-
terization by means of I-V measurements was made. Sensitivity to the physiologically
relevant concentration of Bovine Serum Albumin in water solution was shown.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/20587
7
Xiao, Xiangyu. "A Multiple Sensors Approach to Wood Defect Detection." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/11145.
Full textAbstract:
In the forest products manufacturing industry, recent price increases in the cost of high-quality lumber together with the reduced availability of this resource have forced manufacturers to utilize lower grade hardwood lumber in their manufacturing operations. This use of low quality lumber means that the labor involved in converting this lumber to usable parts is also increased because it takes more time to remove the additional defects that occur in the lower grade material. Simultaneously, labor costs have gone up and availability of skilled workers capable of getting a high yield of usable parts has markedly decreased. To face this increasingly complex and competitive environment, the industry has a critical need for efficient and cost-effective new processing equipment that can replace human operators who locate and identify defects that need to be removed in lumber and then remove these defects when cutting the lumber into rough parts. This human inspection process is laborious, inconsistent and subjective in nature due to the demands of making decisions very rapidly in a noisy and tiring environment. Hence, an automatic sawing system that could remove defects in lumber while creating maximum yield, offers significant opportunities for increasing profits of this industry. The difficult part in designing an automatic sawing system is creating an automatic inspection system that can detect critical features in wood that affect the quality of the rough parts. Many automatic inspection systems have been proposed and studied for the inspection of wood or wood products. But, most of these systems utilize a single sensing modality, e.g., a single optical sensor or an X-ray imaging system. These systems cannot detect all critical defects in wood.
This research work reported in this dissertation is the first aimed at creating a vision system utilizes three imaging modalities: a color imaging system, a laser range profiling system and an X-ray imaging system. The objective of in designing this vision system is to detect and identify: 1) surface features such as knots, splits, stains; 2) geometry features such as wane, thin board; and 3) internal features such as voids, knots. The laser range profiling system is used to locate and identify geometry features. The X-ray imaging system is primarily used to detect features such as knots, splits and interior voids. The color imaging system is mainly employed to identify surface features.
In this vision system a number of methodologies are used to improve processing speed and identification accuracy. The images from different sensing modalities are analyzed in a special order to offset the larger amount of image data that comes from the multiple sensors and that must be analyzed. The analysis of laser image is performed first. It is used to find defects that have insufficient thickness. These defects are then removed from consideration in the subsequent analysis of the X-ray image. Removing these defects from consideration in the analysis of the X-ray image not only improves the accuracy of detecting and identifying defects but also reduces the amount of time needed to analyze the X-ray image. Similarly, defect areas such as knot and mineral streak that are found in the analysis of the X-ray image are removed from consideration in the analysis of the color image. A fuzzy logic algorithm -- the approaching degree method-- is used to assign defect labels. The fuzzy logic approach is used to mimic human behavior in identifying defects in hardwood lumber.
The initial results obtained from this vision system demonstrate the feasibility of locating and identifying all the major defects that occur in hardwood lumber. This was even true during the initial hardware development phase when only images of unsatisfactory quality from a limited lumber of samples were available. The vision system is capable of locating and identifying defects at the production speed of two linear feet per second that is typical in most hardwood secondary manufacturing plants. This vision system software was designed to run on a relative slow computer (200 MHz Pentium processor) with aid of special image processing hardware, i.e., the MORRPH board that was also designed at Virginia Tech.Ph. D.
8
Tran, Dung T. "An approach to activity recognition using multiple sensors /." Full text available, 2006. http://adt.curtin.edu.au/ETD-db/ETD-maint/view_etd?URN=adt-WCU20071219.140320.
Full text
9
Tran, Tien Dung. "An approach to activity recognition using multiple sensors." Thesis, Curtin University, 2006. http://hdl.handle.net/20.500.11937/1702.
Full textAbstract:
Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training.We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.
10
Tran, Tien Dung. "An approach to activity recognition using multiple sensors." Curtin University of Technology, School of Computing, 2006. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17568.
Full textAbstract:
Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training.We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.
11
Liu, Kaibo. "Data fusion for system modeling, performance assessment and improvement." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52937.
Full textAbstract:
Due to rapid advancements in sensing and computation technology, multiple types of sensors have been embedded in various applications, on-line automatically collecting massive production information. Although this data-rich environment provides great opportunity for more effective process control, it also raises new research challenges on data analysis and decision making due to the complex data structures, such as heterogeneous data dependency, and large-volume and high-dimensional characteristics.
This thesis contributes to the area of System Informatics and Control (SIAC) to develop systematic data fusion methodologies for effective quality control and performance improvement in complex systems. These advanced methodologies enable (1) a better handling of the rich data environment communicated by complex engineering systems, (2) a closer monitoring of the system status, and (3) a more accurate forecasting of future trends and behaviors. The research bridges the gaps in methodologies among advanced statistics, engineering domain knowledge and operation research. It also forms close linkage to various application areas such as manufacturing, health care, energy and service systems.
This thesis started from investigating the optimal sensor system design and conducting multiple sensor data fusion analysis for process monitoring and diagnosis in different applications. In Chapter 2, we first studied the couplings or interactions between the optimal design of a sensor system in a Bayesian Network and quality management of a manufacturing system, which can improve cost-effectiveness and production yield by considering sensor cost, process change detection speed, and fault diagnosis accuracy in an integrated manner. An algorithm named “Best Allocation Subsets by Intelligent Search” (BASIS) with optimality proof is developed to obtain the optimal sensor allocation design at minimum cost under different user specified detection requirements.
Chapter 3 extended this line of research by proposing a novel adaptive sensor allocation framework, which can greatly improve the monitoring and diagnosis capabilities of the previous method. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. The methodology was tested and validated based on a hot forming process and a cap alignment process.
Next in Chapter 4, we proposed a Scalable-Robust-Efficient Adaptive (SERA) sensor allocation strategy for online high-dimensional process monitoring in a general network. A monitoring scheme of using the sum of top-r local detection statistics is developed, which is scalable, effective and robust in detecting a wide range of possible shifts in all directions. This research provides a generic guideline for practitioners on determining (1) the appropriate sensor layout; (2) the “ON” and “OFF” states of different sensors; and (3) which part of the acquired data should be transmitted to and analyzed at the fusion center, when only limited resources are available.
To improve the accuracy of remaining lifetime prediction, Chapter 5 proposed a data-level fusion methodology for degradation modeling and prognostics. When multiple sensors are available to measure the degradation mechanism of a same system, it becomes a high dimensional and challenging problem to determine which sensors to use and how to combine them together for better data analysis. To address this issue, we first defined two essential properties if present in a degradation signal, can enhance the effectiveness for prognostics. Then, we proposed a generic data-level fusion algorithm to construct a composite health index to achieve those two identified properties. The methodology was tested using the degradation signals of aircraft gas turbine engine, which demonstrated a much better prognostic result compared to relying solely on the data from an individual sensor.
In summary, this thesis is the research drawing attention to the area of data fusion for effective employment of the underlying data gathering capabilities for system modeling, performance assessment and improvement. The fundamental data fusion methodologies are developed and further applied to various applications, which can facilitate resources planning, real-time monitoring, diagnosis and prognostics.
12
Kawaguchi, Nobuo, and Yuya Negishi. "Real-world Event Recognition using Multiple Instant Learning Sensors." IEEE, 2008. http://hdl.handle.net/2237/15457.
Full text
13
Kabeya, Kazuhisa III. "Structural Health Monitoring Using Multiple Piezoelectric Sensors and Actuators." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36709.
Full textAbstract:
A piezoelectric impedance-based structural health monitoring technique was developed at the Center for Intelligent Material Systems and Structures. It has been successfully implemented on several complex structures to detect incipient-type damage such as small cracks or loose connections. However, there are still some problems to be solved before full scale development and commercialization can take place. These include: i) the damage assessment is influenced by ambient temperature change; ii) the sensing area is small; and iii) the ability to identify the damage location is poor. The objective of this research is to solve these problems in order to apply the impedance-based structural health monitoring technique to real structures.
First, an empirical compensation technique to minimize the temperature effect on the damage assessment has been developed. The compensation technique utilizes the fact that the temperature change causes vertical and horizontal shifts of the signature pattern in the impedance versus frequency plot, while damage causes somewhat irregular changes.
Second, a new impedance-based technique that uses multiple piezoelectric sensor-actuators has been developed which extends the sensing area. The new technique relies on the measurement of electrical transfer admittance, which gives us mutual information between multiple piezoelectric sensor-actuators. We found that this technique increases the sensing region by at least an order of magnitude.
Third, a time domain technique to identify the damage location has been proposed. This technique also uses multiple piezoelectric sensors and actuators. The basic idea utilizes the pulse-echo method often used in ultrasonic testing, together with wavelet decomposition to extract traveling pulses from a noisy signal. The results for a one-dimensional structure show that we can determine the damage location to within a spatial resolution determined by the temporal resolution of the data acquisition.
The validity of all these techniques has been verified by proof-of-concept experiments. These techniques help bring conventional impedance-based structural health monitoring closer to full scale development and commercialization.Master of Science
14
Marin, Giulio. "3D data fusion from multiple sensors and its applications." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425367.
Full textAbstract:
The introduction of depth cameras in the mass market contributed to make computer vision applicable to many real world applications, such as human interaction in virtual environments, autonomous driving, robotics and 3D reconstruction. All these problems were originally tackled by means of standard cameras, but the intrinsic ambiguity in the bidimensional images led to the development of depth cameras technologies. Stereo vision was first introduced to provide an estimate of the 3D geometry of the scene. Structured light depth cameras were developed to use the same concepts of stereo vision but overcome some of the problems of passive technologies. Finally, Time-of-Flight (ToF) depth cameras solve the same depth estimation problem by using a different technology.
This thesis focuses on the acquisition of depth data from multiple sensors and presents techniques to efficiently combine the information of different acquisition systems. The three main technologies developed to provide depth estimation are first reviewed, presenting operating principles and practical issues of each family of sensors. The use of multiple sensors then is investigated, providing practical solutions to the problem of 3D reconstruction and gesture recognition. Data from stereo vision systems and ToF depth cameras are combined together to provide a higher quality depth map. A confidence measure of depth data from the two systems is used to guide the depth data fusion. The lack of datasets with data from multiple sensors is addressed by proposing a system for the collection of data and ground truth depth, and a tool to generate synthetic data from standard cameras and ToF depth cameras. For gesture recognition, a depth camera is paired with a Leap Motion device to boost the performance of the recognition task. A set of features from the two devices is used in a classification framework based on Support Vector Machines and Random Forests.L'introduzione di sensori di profondità nel mercato di massa ha contribuito a rendere la visione artificiale applicabile in molte applicazioni reali, come l'interazione dell'uomo in ambienti virtuali, la guida autonoma, la robotica e la ricostruzione 3D. Tutti questi problemi sono stati originariamente affrontati con l'utilizzo di normali telecamere ma l'ambiguità intrinseca delle immagini bidimensionali ha portato allo sviluppo di tecnologie per sensori di profondità. La visione stereoscopica è stata la prima tecnologia a permettere di stimare la geometria tridimensionale della scena. Sensori a luce strutturata sono stati sviluppati per sfruttare gli stessi principi della visione stereoscopica ma risolvere alcuni problemi dei dispositivi passivi. Infine i sensori a tempo di volo cercano di risolvere lo stesso problema di stima della distanza utilizzando una differente tecnologia. Questa tesi si focalizza nell'acquisizione di dati di profondità da diversi sensori e presenta tecniche per combinare efficacemente le informazioni dei diversi sistemi di acquisizione. Per prima cosa le tre principali tecnologie sviluppate per fornire una stima di profondità sono esaminate in dettaglio, presentando i principi di funzionamento e i problemi dei diversi sistemi. Successivamente è stato studiato l'utilizzo congiunto di sensori, fornendo delle soluzioni pratiche al problema della ricostruzione 3D e del riconoscimento dei gesti. I dati di un sistema stereoscopico e di un sensore a tempo di volo sono stati combinati per fornire una mappa di profondità più precisa. Per ognuno dei due sensori sono state sviluppate delle mappe di confidenza utilizzate per controllare la fusione delle mappe di profondità. La mancanza di collezioni con dati di diversi sensori è stato affrontato proponendo un sistema per la collezione di dati da diversi sensori e la generazione di mappe di profondità molto precise, oltre ad un sistema per la generazioni di dati sintetici per sistemi stereoscopici e sensori a tempo di volo. Per il problema del riconoscimento dei gesti è stato sviluppato un sistema per l'utilizzo congiunto di un sensore di profondità e un sensore Leap Motion, per migliorare le prestazioni dell'attività riconoscimento. Un insieme di descrittori ricavato dai due sistemi è stato utilizzato per la classificazione dei gesti con un sistema basato su Support Vector Machines e Random Forests.
15
Krumins, Armands. "Gearbox fault detection, based on Machine Learning of multiple sensors." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301603.
Full textAbstract:
The increasing demand for higher efficiency and lower environmental impact of transmissions, used in automotive and wind energy industries has created a need for more advanced technical solutions to fulfil those requirements. Condition monitoring plays an important role in the transmission life cycle, saving resources and time. Recently condition monitoring, using machine learning has shifted from reactive to proactive action, predicting minor faults before they become significant. This thesis intends to develop a methodology that can be used to predict faults like pitting initiation, before propagating in FZG test rig, available at KTH Machine Design department. Standard sensor measurements already available like temperature, rotation speed and torque are used in this project. Four kinds of gears were used, two made of wrought, and two – of powder metal steel, each with ground or superfinish surface. After a literature review about pitting fatigue, condition indicators for these failures and machine learning were done, a statistical analysis was done, to see how the transmission behaves during testing and to have comparison material, helpful when having machine learning results. Two machine learning models, Decision Tree and Support Vector Machine were selected and trained in two combinations, either with Root Mean Square only, or with Crest Factor, Standard Deviation and Kurtosis in addition. As a result, 64 models were trained, 32 for all tests and another 32 to investigate two particular tests due to a longer pitting propagation period. New condition indicators like Standard Deviation and Signal – to – noise ratio was calculated to get more nuanced trends than just using one measurement to monitor the gearbox behavior. After comparing with the results from statistical analysis and previously done tooth profile measurements, it was concluded that the new indicators could indicate the change in gearbox operation before the first pitting initiation is detected, using tooth profile measurement.
16
Tönnes, Simon, and Joakim Storfeldt. "Effects of using multiple sensors to guide an autonomous vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226648.
Full textAbstract:
This thesis aims to discover how well a combination of a mo- tion camera and an ultrasonic sensor performs compared to a solely motion camera guided motor-vehicle-system. The main purpose is to prove that a smart vehicle with sensors can follow a radio controlled vehicle, while maintaining ba- sic functionalities such as steering and throttle, with the constraint of rear-end collision avoidance. The thesis will show through small scale tests, if a vehicle in a highway situation can follow another vehicle in a safe and controlled fashion, using proposed sensors. We assume the work pre- sented in this thesis can also be a proof of concept for pla- tooning. The functionality of the proposed system is valid under regulations provided by Trafikverket. These conditions where replicated in a small scale test en- vironment and resulted in that using another sensor made the system able to follow closer and avoid bumping into the car in front.Avhandlingen syftar till att upptäcka hur ett självstyrande system beroende av en rörelsekamera mäter sig jämfört med ett system som kombinerar en rörelsekamera med en ult- raljudssensor. Det primära syftet är att bevisa att ett smart fordon med sensorer kan följa ett radiostyrt fordon, kontrol- lera grundläggande funktionaliteter som styrning och gas, samt undvika kollision med fordonet framför. Avhandling- en undersöker genom småskaliga tester om ett fordon i en motorvägssituation kan följa ett annat fordon på ett säkert och kontrollerat sätt. Vi antar det arbete som presente- ras i denna avhandling också kan vara bevis för konceptet platooning. Funktionaliteten av det föreslagna systemet är giltigt enligt reglerna från Trafikverket. Dessa förhållanden replikerades i en testmiljö av liten ska- la och resulterade i att en kombination med en extra av- ståndsmätare gjorde att systemet kunde följa närmare och fortfarande undvika att krocka in i bilen framför.
17
Hosseinyalamdary, Saivash Hosseinyalamdary. "Traffic Scene Perception using Multiple Sensors for Vehicular Safety Purposes." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462803166.
Full text
18
Shekaramiz, Mohammad. "Sparse Signal Recovery Based on Compressive Sensing and Exploration Using Multiple Mobile Sensors." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7384.
Full textAbstract:
The work in this dissertation is focused on two areas within the general discipline of statistical signal processing. First, several new algorithms are developed and exhaustively tested for solving the inverse problem of compressive sensing (CS). CS is a recently developed sub-sampling technique for signal acquisition and reconstruction which is more efficient than the traditional Nyquist sampling method. It provides the possibility of compressed data acquisition approaches to directly acquire just the important information of the signal of interest. Many natural signals are sparse or compressible in some domain such as pixel domain of images, time, frequency and so forth. The notion of compressibility or sparsity here means that many coefficients of the signal of interest are either zero or of low amplitude, in some domain, whereas some are dominating coefficients. Therefore, we may not need to take many direct or indirect samples from the signal or phenomenon to be able to capture the important information of the signal. As a simple example, one can think of a system of linear equations with N unknowns. Traditional methods suggest solving N linearly independent equations to solve for the unknowns. However, if many of the variables are known to be zero or of low amplitude, then intuitively speaking, there will be no need to have N equations. Unfortunately, in many real-world problems, the number of non-zero (effective) variables are unknown. In these cases, CS is capable of solving for the unknowns in an efficient way. In other words, it enables us to collect the important information of the sparse signal with low number of measurements. Then, considering the fact that the signal is sparse, extracting the important information of the signal is the challenge that needs to be addressed. Since most of the existing recovery algorithms in this area need some prior knowledge or parameter tuning, their application to real-world problems to achieve a good performance is difficult. In this dissertation, several new CS algorithms are proposed for the recovery of sparse signals. The proposed algorithms mostly do not require any prior knowledge on the signal or its structure. In fact, these algorithms can learn the underlying structure of the signal based on the collected measurements and successfully reconstruct the signal, with high probability. The other merit of the proposed algorithms is that they are generally flexible in incorporating any prior knowledge on the noise, sparisty level, and so on.
The second part of this study is devoted to deployment of mobile sensors in circumstances that the number of sensors to sample the entire region is inadequate. Therefore, where to deploy the sensors, to both explore new regions while refining knowledge in aleady visited areas is of high importance. Here, a new framework is proposed to decide on the trajectories of sensors as they collect the measurements. The proposed framework has two main stages. The first stage performs interpolation/extrapolation to estimate the phenomenon of interest at unseen loactions, and the second stage decides on the informative trajectory based on the collected and estimated data. This framework can be applied to various problems such as tuning the constellation of sensor-bearing satellites, robotics, or any type of adaptive sensor placement/configuration problem. Depending on the problem, some modifications on the constraints in the framework may be needed. As an application side of this work, the proposed framework is applied to a surrogate problem related to the constellation adjustment of sensor-bearing satellites.
19
Georgiev, Roumen H. "Reconstruction of three dimensional coordinates of multiple targets using linear sensors." Doctoral thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/3236.
Full text
20
Vickery, Kathryn J. "Southern African dust sources as identified by multiple space borne sensors." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/4814.
Full textAbstract:
Includes abstract.Includes bibliographical references (leaves 132-145).
Mineral aerosols emitted from arid and semi-arid regions effect global radiation, contribute to regional nutrient dynamics and impact local soil and water quality. Satellite imagery has been central to the identification and determining the distribution of source areas and the trajectories of dust around the globe. This study focuses on the dryland regions of Botswana, Namibia and South Africa. It uses the capabilities of the ultraviolet channels provided by the older Total Ozone Mapping Spectrometer (TOMS), the Ozone Monitoring Instrument (OMI) (a TOMS follow up), the visible bands of Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). This study compares various dust detection products but also focuses on the application of thermal infrared bands from MSG through the usage of the new "Pink Dust" visua lisation technique using channels 7 (8.7 ~m), 9 (lO.8 ~m), and 10 (12.0 ~m). This multisensor approach resulted in a regional maps highlighting the distribution of source points and establishing some of the prevalent transport pathways and likely deposition zones. Southern African dust sources include a few large and many small pans, subtle inland depressions and ephemeral river systems, which are subject to a range of climatic conditions as part of the Kalahari and Namib region. This work in particular examines if source points are productive due to favourable climatic conditions. The debate around transport limit verses supply limit can only be solved at the local scale which requires observation at higher spatial and temporal resolution as provided by the latest dust detection products. MSG and MODIS in particular have shown distinct source point clusters in Etosha and the Makgadikgadi Pans which based on the courser resolution of older TOMS, have so far been treated as homogeneous sources. Data analyses reveal 327 individual dust plumes over the 2005-2008 study period, some of which are more than 300 km in length. These are integrated into existing climate and weather records provided by National Centers for Environmental Prediction (NCEP) data. The results identified a set dust drivers such as the Continental High Pressure, Bergwinds, Tropical Temperate and West Coast Troughs, and Westerly and Easterly Wave lows. This enhances our ability to predict such events, in particular, if transport acts as the limiting driver. Some of these find ings also have the potential to enhance our knowledge of the aerosol generation process elsewhere. The quality of findings are still limited by problems associated with dust plume substrates and clearly require significant surface validation relating to hydrological and climatic controls at the micro-scale. It is furthermore evident that no current instrument fully meets the requirements of the mineral aerosol research community.
21
Krumbein, Marc. "Heading Estimation of a Mobile Robot Using Multiple UWB Position Sensors." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1555001007552678.
Full text
22
Martin, Joseph S. "Aerosol optical depth model assessment with high resolution multiple angle sensors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FMartin.pdf.
Full textAbstract:
Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, Dec. 2004.Thesis Advisor(s): Philip A. Durkee. Includes bibliographical references (p. 35-36). Also available online.
23
Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Full textAbstract:
Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
24
VANKAMAMIDI, SRIHARSHA. "Fusing Joint Information from Multiple Kinect Sensors to Detect Errors in Exercises." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1477921964956213.
Full text
25
Hans, Akshat C. "Continuous Human Activity Tracking over a Large Area with Multiple Kinect Sensors." Cleveland State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=csu1535683935364003.
Full text
26
Javed, Omar. "SCENE MONITORING WITH A FOREST OF COOPERATIVE SENSORS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3448.
Full textAbstract:
In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios.Ph.D.
School of Computer Science
Engineering and Computer Science
Computer Science
27
Sciarini, Lee. "NONINVASIVE PHYSIOLOGICAL MEASURES AND WORKLOAD TRANSITIONS:AN INVESTIGATION OF THRESHOLDS USING MULTIPLE SYNCHRONIZED SENSORS." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4084.
Full textAbstract:
The purpose of this study is to determine under what conditions multiple minimally intrusive physiological sensors can be used together and validly applied for use in areas which rely on adaptive systems including adaptive automation and augmented cognition. Specifically, this dissertation investigated the physiological transitions of operator state caused by changes in the level of taskload. Three questions were evaluated including (1) Do differences exist between physiological indicators when examined between levels of difficulty? (2) Are differences of physiological indicators (which may exist) between difficulty levels affected by spatial ability? (3) Which physiological indicators (if any) account for variation in performance on a spatial task with varying difficulty levels? The Modular Cognitive State Gauge model was presented and used to determine which basic physiological sensors (EEG, ECG, EDR and eye-tracking) could validly assess changes in the utilization of two-dimensional spatial resources required to perform a spatial ability dependent task. Thirty-six volunteers (20 female, 16 male) wore minimally invasive physiological sensing devices while executing a challenging computer based puzzle task. Specifically, participants were tested with two measures of spatial ability, received training, a practice session, an experimental trial and completed a subjective workload survey. The results of this experiment confirmed that participants with low spatial ability reported higher subjective workload and performed poorer when compared to those with high spatial ability. Additionally, there were significant changes for a majority of the physiological indicators between two difficulty levels and most importantly three measures (EEG, ECG and eye-tracking) were shown to account for variability in performance on the spatial task.Ph.D.
Other
Sciences
Modeling and Simulation PhD
28
Propst, Adam Christopher. "Damage Monitoring in Woven Composites Using Fiber-Bragg Grating Sensors on Multiple Time Scales." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-04252009-090648/.
Full textAbstract:
This study investigates the application of Fiber Bragg Grating (FBG) optical sensors interrogation techniques over several time scales to monitor damage in composite structures due to low velocity impacts events. Optical fiber sensors are embedded into carbon fiber/epoxy resin woven composites using a single-step cure process. The composite specimens are subjected to multiple low energy impacts until failure. Impact events are characterized by acceleration and position sensors integral to the impactor head. The embedded FBG sensors are interrogated using three different interrogation techniques. Low speed, full spectrum measurements are recorded using a tunable laser source. High speed, peak wavelength detection data is taken using a commercial peak wavelength interrogation system. Finally, high speed full spectrum measurements are recorded using new instrumentation developed at Brigham Young University. By qualitatively examining the responses of these three techniques and comparing the FBG data with impact characterization data, a more complete picture of the composite health is available.
29
KATO, CARLA CARVALHO. "INTERROGATION SYSTEM FOR MULTIPLE BRAGG GRATING SENSORS USING TIME DOMAIN REFLECTOMETRY AND FIXED FILTERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5965@1.
Full textAbstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOREste trabalho apresenta um sistema de interrogação de sensores a rede de Bragg em fibras ópticas, baseado em reflectometria no domínio do tempo e filtros fixos a rede de Bragg. Utilizando uma fonte de luz pulsada, a posição espectral do sensor é relacionada à razão da intensidade dos pulsos, tornando a detecção independente de variações de intensidade. São abordados aspectos teóricos e experimentais referentes aos princípios de funcionamento desta técnica. Uma vez que a filtragem é feita com redes de Bragg, apenas um circuito de fotodetecção é utilizado e um número reduzido de acopladores/circuladores ópticos é necessário, o sistema possibilita reduzir consideravelmente o custo para a interrogação de um conjunto de sensores. A utilização de apenas um circuito de fotodetecção apresenta a vantagem de manter as mesmas características para todos os pulsos, minimizando influências externas neste circuito como, por exemplo, variações da temperatura ambiente. Foi montada uma bancada de testes para a interrogação de seis sensores. Comparações entre os resultados experimentais e simulados mostram boa concordância. Extrapolações indicam que seria possível interrogar sensores com uma variação espectral de 2 nanômetros, com incertezas menores que 10 picometros, o que é adequado para sensores de temperatura. Análises de interferência entre dois canais adjacentes mostram pouca influência entre eles e são apresentadas opções para diminuir essa interferência.
This work presents a system for the interrogation of fiber- optic Bragg grating sensors based on time domain reflectometry and Bragg grating fixed filters. Using a pulsed light source, the spectral position of the sensor is related to the ratio of two pulses intensities, making detection independent of intensity variations. Theoretical and experimental aspects regarding the working principles of this technique are discussed. Since filtering is accomplished with Bragg grating so that only one photodetection circuit is used and a reduced number of optic couplers/circulators are needed, the system provides a considerable reduction in the cost of interrogation for a set of sensors. Using only one photodetection circuit also has the advantage of maintaining the same characteristics for all pulses, thus minimizing external influences in this circuit, such as variations in the environment temperature. A test stand was assembled for the interrogation of six sensors. Comparisons between experimental and simulated results show a good agreement. Extrapolations indicate that it would be possible to interrogate sensors with a spectral variation of 2 nanometers, with uncertainties lower than 10 pm, which is adequate for temperature sensors. Cross talk analyses between two adjacent channels show small influence between them, and approaches to reduce this interference are presented.
30
Sciarini, Lee William. "Noninvasive physiological measures and workload transitions an investigation of thresholds using multiple synchronized sensors /." Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002781.
Full text
31
Ochoa, Benjamin L. "Precise estimation of the geoposition and orientation of ground-level video cameras from multiple sensors." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3249662.
Full textAbstract:
Thesis (Ph. D.)--University of California, San Diego, 2007.Title from first page of PDF file (viewed April 4, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 82-88).
32
Caccamise, Lauren M. "Regulation of a Differentiation MAPK Pathway by a Novel Integrated Signaling Network and Multiple Sensors." Thesis, State University of New York at Buffalo, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3725898.
Full textAbstract:
Filamentous growth is a cell differentiation program utilized by Saccharomyces cerevisiae to respond to nutrient limitation in the environment. This process is principally controlled by a mitogen-activated protein kinase (MAPK) pathway but is also impacted by a number of other pathways including Ras2p-cAMP-PKA, Target of Rapamycin, Rim101, and mitochondrial retrograde. Using a high-throughput genetic screening approach in conjunction with directed gene-deletion analysis, I have identified 97 new regulators of the filamentous growth MAPK pathway. These new regulators created new connections to the filamentous growth MAPK pathway as well as extended previously known connections. I have linked several of the pathways governing filamentous growth together as part of an integrated signaling network by showing that these pathways regulate each other’s transcriptional targets. This network indicates an intricate level of communication and coordination among these pathways that has not been previously appreciated. I show that proper coordination of the filamentous growth MAPK pathway is essential for proper morphogenesis and this is a potential reason for the many inputs used to control this response. The filamentous growth MAPK pathway is also regulated by three transmembrane proteins – Msb2p, Sho1p, and Opy2p. Here these three proteins are compared to determine that they have specific functions in regulating filamentous growth. The three proteins exhibit different localization patterns and rates of turnover from the plasma membrane. I show that the Rim101 pathway affects the filamentous growth MAPK pathway independently of the ESCRT pathway which shares components with the Rim101 pathway. Additionally, I have shown that overexpression of the arrestin protein Aly1p results in mislocalized Msb2p and diminished pathway activity.
33
Thomas, Mikkel Andrey. "Integrated optical interferometric sensors on silicon and silicon cmos." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26674.
Full textAbstract:
The main objective of this research is to fabricate and characterize an optically integrated interferometric sensor on standard silicon and silicon CMOS circuitry. An optical sensor system of this nature would provide the high sensitivity and immunity to electromagnetic interference found in interferometric based sensors in a lightweight, compact package capable of being deployed in a multitude of situations inappropriate for standard sensor configurations. There are several challenges involved in implementing this system. These include the development of a suitable optical emitter for the sensor system, the interface between the various optically embedded components, and the compatibility of the Si CMOS with heterogeneous integration techniques. The research reported outlines a process for integrating an integrated sensor on Si CMOS circuitry using CMOS compatible materials, integration techniques, and emitter components.
34
Wei, Mei Y., Donald Billings, Joseph G. Leung, and Michio Aoyagi. "Tracking Multiple Airborne 802.11b Wireless Local Area Networks to Extend the Internet to Aircrafts in Flight." International Foundation for Telemetering, 2003. http://hdl.handle.net/10150/605383.
Full textAbstract:
International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, NevadaWireless local area networks (WLANs) enable the extension of the Internet to aircrafts in flight. To establish this wireless network segment, commercial-of-the-shelve (COTS) 802.11b wireless Ethernet bridges were used. Wireless Ethernet bridges were chosen over optical wireless technology and Internet protocol (IP) satellite modems mainly because of their lower costs, ease and flexibility of implementation. Additionally, 802.11b wireless networks allow a wide range of mobile data devices such as laptop computers and personal digital assistance high-speed wireless access to critical information and applications resided on the aircrafts networks. Since 802.11b WLAN media is shared and traffic generated by other users will degrade the overall performance of the network. With the continual wide spread use of 802.11b WLAN, an aircraft in flight will experience network congestions and poor performance across all the frequency channels. The congestion and poor performance issues can be minimized by tracking the airborne wireless LAN using highly directional antenna and RF filtering. The method of tracking multiple 802.11 wirelesses LAN and the RF subsystem will be described. The applications of 802.11b wireless networks to man and unmanned aircrafts flight research will be discussed.
35
Shiroishi, Jason William. "Bearing condition diagnostics via multiple sensors using the high frequency resonance technique with adaptive line enhancer." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/17779.
Full text
36
Acosta, Serafini Pablo M. (Pablo Manuel) 1971. "Predictive multiple sampling algorithm with overlapping integration intervals for linear wide dynamic range integrating image sensors." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/16612.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 163-170).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Machine vision systems are used in a wide range of applications such as security, automated quality control and intelligent transportation systems. Several of these systems need to extract information from natural scenes in the section of the electromagnetic spectrum visible to humans. These scenes can easily have intra-frame illumination ratios in excess of 10⁶ : 1. Solid-state image sensors that can correctly process wide illumination dynamic range scenes are therefore required to ensure correct reliability and performance. This thesis describes a new algorithm to linearly increase the illumination dynamic range of integrating-type image sensors. A user-defined integration time is taken as a reference to create a potentially large set of integration intervals of different duration (the selected integration time being the longest) but with a common end. The light intensity received by each pixel in the sensing array is used to choose the optimal integration interval from the set, while a pixel saturation predictive decision is used to overlap the integration intervals within the given integration time such that only one frame using the optimal integration interval for each pixel is produced. The total integration time is never exceeded. Benefits from this approach are motion minimization, real-time operation, reduced memory requirements, programmable light intensity dynamic range increase and access to incremental light intensity information during the integration time.
(cont.) The algorithm is fully described with special attention to the resulting sensor transfer function, the signal-to-noise ratio, characterization of types and effects of errors in the predictive decision, calculation of the optimal integration intervals set given a certain set size, calculation of the optimal number of integration intervals, and impact of the new algorithm to image data compression. An efficient mapping of this algorithm to a CMOS process was done by designing a proof-of-concept integrated circuit in a 0.18[mu]m 1.8V 5-metal layer process. The major components of the chip are a 1/3" VGA (640 x 480) pixel array, a 4bit per pixel memory array, an integration controller array and an analog-to-digital converter/correlated double sampled (ADC/CDS) array. Supporting components include pixel and memory row decoders, memory and converter output digital multiplexers, pixel-to-ADC/CDS analog multiplexer and test structures. The pixels have a fill factor of nearly 50%, as most of the needed system additions and complexity were taken off-pixel. The prototype is fully functional and linearly expands the dynamic range by more than 60dB.
by Pablo M. Acosta-Serafini.
Ph.D.
37
Newland, Jonathan C. "The fabrication and application of diamond sensors for electrochemical analysis in single and multiple phase systems." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/67483/.
Full textAbstract:
Polycrystalline boron-doped diamond (pBDD) has acquired great interest as a electrode material exhibiting low background currents, wide potential windows and a host of extreme physical properties such as mechanical hardness, chemical inertness and a high resistance to harsh environments. pBDD’s exceptional electrochemical characteristics have made its application as a material for high performance electrochemical sensors the basis of a hugh amount of research over the last decade. Work in this thesis describes the fabrication and application of pBDD sensors in both stationary and fluid flow environments where conventional electrode materials would be unsuitable or problematic. pBDD electrodes functionalised with catalytic metal nano-particles are demonstrated as a means of detecting hydrazine, a genotoxic impurity of interest in pharmaceutical analysis, even in the presence of potentially interfering pharmaceutical matrix. This same sensor is then employed as a means of detecting the presence of non-polar oils on an electrode surface in dual-phase, aqueous/oil systems. An investigation of electrochemical techniques for detecting and characterising phase changes in the form of microdroplets moving under flow in microfluidic systems is detailed. Limitations to the use of conventional materials used to fabricate such microfluidic devices are discussed. In an effort to address these issues as well as those expected in extreme environments, with aggressive media, a fabrication route for realising all-diamond microfluidic devices with integrated, high-quality pBDD electrodes is outlined.
38
Wang, Tingting. "The Electrochemical and Spectroscopic Characterization of Carbon Nanotube Materials and The Development of Multiple Electrochemical Sensors." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308985.
Full text
39
Wu, Weiliang. "The detection of incipient faults in small multi-cylinder diesel engines using multiple acoustic emission sensors." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65649/1/Weiliang_Wu_Thesis.pdf.
Full textAbstract:
This thesis investigates condition monitoring (CM) of diesel engines using acoustic emission (AE) techniques. The AE signals recorded from a small size diesel engine are mixtures of multiple sources from multiple cylinders. Thus, it is difficult to interpret the information conveyed in the signals for CM purposes. This thesis develops a series of practical signal processing techniques to overcome this problem. Various experimental studies conducted to assess the CM capabilities of AE analysis for diesel engines. A series of modified signal processing techniques were proposed. These techniques showed promising results of capability for CM of multiple cylinders diesel engine using multiple AE sensors.
40
BLANCO, SACRISTAN JAVIER. "Investigation of terrain control on dryland functioning and composition using multiple remote sensing sensors and platforms." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/294894.
Full textAbstract:
Le zone aride sono tra le aree più sensibili al cambiamento globale e i modelli prevedono un incremento della loro superficie nei prossimi decenni. La morfologia del terreno ha un ruolo chiave nella distribuzione dell'acqua e delle sostanze nutritive nelle zone aride e nella determinazione della loro composizione. Questi ambienti sono composti da vegetazione e suolo nudo, molte volte colonizzato da biocroste, che si prevede subiranno cambiamenti nella composizione. Il telerilevamento è stato evidenziato come uno strumento importante per il monitoraggio delle zone aride. Si tratta di un approccio molto efficace in termini di costi per identificare gli hotspot di biodiversità, prevedere i cambiamenti nella loro composizione e valutare le relazioni che tali cambiamenti hanno con la morfologia del terreno. Utilizzando specifiche tecniche di analisi delle immagini a seconda del caso di studio, il telerilevamento si è dimostrato utile per il monitoraggio di zone aride ben differenziate, ma non in caso di composizione mista. Pertanto, l’obiettivo principale di questa tesi di dottorato è stato quello di studiare come la composizione eterogenea e il funzionamento delle zone aride sono influenzati dalla morfologia del terreno integrando l’utilizzo di diversi sensori di telerilevamento e piattaforme. Sono stati utilizzati dati provenienti da immagini RGB, termiche ad infrarosso (TIR), multi- e iperspettrali ad altissima risoluzione spaziale acquisite in laboratorio e in campo utilizzando piattaforme aeree, UAV e stazionarie. Sono stati definiti i seguenti obiettivi specifici:
- Valutare se le tecniche Structure from Motion (SfM) possono essere utilizzate in zone aride dalla superficie complessa per ricavare la morfologia del terreno da immagini UAV;
- Sviluppare una tecnica riproducibile per mettere in relazione le azioni antropiche con i cambiamenti nello stato di salute delle comunità vegetali in ecosistemi aridi utilizzando tecniche di analisi object-based;
- Valutare se l'eterogeneità spettrale dei licheni può essere utilizzata per stimare la loro α-diversità utilizzando immagini iperspettrali;
- Sviluppare una metodologia per valutare l’influenza della morfologia del terreno sulla distribuzione delle biocroste in zone aride utilizzando informazioni acquisite esclusivamente mediante UAV;
- Valutare se le immagini TIR possono essere usate per stimare l'umidità del suolo in zone aride eterogenee.
Questa tesi di dottorato comprende una valutazione delle tecniche SfM a diverse scale e della loro applicabilità a diversi livelli. Affronta lo sviluppo di una nuova metodologia per monitorare la vegetazione in un ecosistema dipendente dalle acque sotterranee, dove la loro salute è fondamentale per il funzionamento dell'ecosistema. Inoltre, l'utilizzo di immagini iperspettrali acquisite a distanza ravvicinata ha permesso di stimare la α-diversità dei licheni che formano le biocroste utilizzando la loro diversità spettrale. Questo ha portato ad una migliore comprensione del comportamento spettrale delle biocroste a seconda della loro composizione, permettendo di sviluppare una metodologia per produrre mappe accurate della copertura del suolo in un ecosistema eterogeneo e di relazionare l'effetto della morfologia del terreno sulla composizione degli ambienti aridi.Drylands are among the most sensitive areas to actual global change and their cover will increase in the next decades. Terrain has a key role in the distribution of water and nutrients in drylands and shaping their composition. These environments are composed by vegetation and bare soil, many times colonized by biocrusts, which are expected to suffer compositional changes. Remote sensing has been highlighted as an important tool for dryland monitoring. It is a very cost-effective approach to identify biodiversity hotspots, predict changes in their composition, and to evaluate the relationships these changes have with the terrain. Using the proper image analysis according to the study case, remote sensing has proved to be useful for monitoring well differentiated drylands, but not when dryland components are mixed. Thus, the main aim of this dissertation was to study how heterogeneous dryland composition and functioning is affected by the terrain using different multiple remote sensing sensors and platforms. Data from very high spatial resolution RGB, thermal infrared, multi- and hyperspectral imagery, retrieved in the laboratory and in the field using airborne, UAV and stationary platforms were used. The next specific objectives were set: - Evaluating whether SfM techniques can be used in drylands with complex surfaces to derive their terrain from UAV imagery; - Developing a reproducible technique to relate human actions to changes in the health of dryland scarce vegetation communities by using object-based image analysis; - Testing whether the spectral heterogeneity of lichens can be used to estimate their α-diversity using hyperspectral imagery; - Developing a methodology to evaluate the control that terrain has on dryland biocrusts’ distribution using information solely retrieved from UAV; - Testing if TIR imagery can estimate soil moisture in heterogeneous drylands. This PhD thesis comprises an evaluation of SfM techniques at different scales and their applicability at different levels. It also comprises a novel methodology to monitor vegetation in a ground-water dependent ecosystem, where their health is key for the ecosystem’s functioning. Moreover, the application of close-range hyperspectral imagery allowed to estimate the α-diversity of biocrust-forming lichens using their spectral diversity. This led to a better understanding of the spectral behaviour of biocrusts depending on their composition and allowed to develop a methodology to produce accurate maps of land cover in a dryland ecosystem of heterogeneous composition and to relate the effect of terrain atrributes on dryland composition.
41
Tandon, Prateek. "Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.
Full textAbstract:
Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation (BA) algorithmic framework can leverage data fusion from multiple low Signal-To-Noise Ratio (SNR) sensor observations to boost the capability to detect and characterize the properties of a signal generating source or process of interest. We illustrate our research with application to the nuclear threat detection domain. Developed algorithms are applied to the problem of processing the large amounts of gamma ray spectroscopy data that can be produced in real-time by mobile radiation sensors. The thesis experimentally shows BA’s capability to boost sensor performance in detecting radiation sources of interest, even if the source is faint, partiallyoccluded, or enveloped in the noisy and variable radiation background characteristic of urban scenes. In addition, BA provides simultaneous inference of source parameters such as the source intensity or source type while detecting it. The thesis demonstrates this capability and also develops techniques to efficiently optimize these parameters over large possible setting spaces. Methods developed in this thesis are demonstrated both in simulation and in a radiation-sensing backpack that applies robotic localization techniques to enable indoor surveillance of radiation sources. The thesis further improves the BA algorithm’s capability to be robust under various detection scenarios. First, we augment BA with appropriate statistical models to improve estimation of signal components in low photon count detection, where the sensor may receive limited photon counts from either source and/or background. Second, we develop methods for online sensor reliability monitoring to create algorithms that are resilient to possible sensor faults in a data pipeline containing one or multiple sensors. Finally, we develop Retrospective BA, a variant of BA that allows reinterpretation of past sensor data in light of new information about percepts. These Retrospective capabilities include the use of Hidden Markov Models in BA to allow automatic correction of a sensor pipeline when sensor malfunction may be occur, an Anomaly- Match search strategy to efficiently optimize source hypotheses, and prototyping of a Multi-Modal Augmented PCA to more flexibly model background and nuisance source fluctuations in a dynamic environment.
42
Baerveldt, Albert-Jan. "Contribution to the bin-picking problem : robust singulation of parcels with a robot system using multiple sensors /." [S.l.] : [s.n.], 1993. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10348.
Full text
43
Wilcox, Steven John. "Cutting tool condition monitoring using multiple sensors and artificialintelligence techniques on a computer numerical controlled milling machine." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1446.
Full textAbstract:
This work documents an investigation of the degradation of a variety of different tools whilst conducting milling operations on a computer numerical controlled (CNC) milling machine. The potential of a range of sensors to detect tool degradation has been investigated and the outputs have been incorporated into a monitoring system. Progressive degradation under nominal rough and finish face milling and rough groove milling has been investigated using a two point grooving tool and four and eight point face milling tools on En8, En24 and En24T workpiece materials. Rapid degradation of the cutting tool has also been observed under rough milling conditions using four and eight point face milling tools, whilst machining n8 and En24T materials in a variety of simulated and actual tool breakage situations. A limited investigation of the effect of the individual wear geometries associated with both progressive and instantaneous tool degradation has been conducted by simulating these geometries and carrying out rough miffing tests using a four point face milling tool on a workpiece of En8 material. Similarly, a limited investigation of the effect of machining on different machines has also been undertaken. A number of different sensing technologies have been used, including conventional sensors such as spindle current and cutting force but also novel sensing techniques such as Acoustic Emission. These have been combined using artificial intelligence techniques to provide automatic recognition of the tool wear state. Similarly, the feasibility of breakage detection/prediction has also been demonstrated.
44
Karimi, Majid. "Master ’s Programme in Information Technology: Using multiple Leap Motion sensors in Assembly workplace in Smart Factory." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-32392.
Full textAbstract:
The new industry revolution creates a vast transformation in the manufacturing methods. Embedded Intelligence and communication technologies facilitate the execution of the smart factory. It can provide lots of features for strong customization of products. Assembly system is a critical segment of the smart factory. However, the complexity of production planning and the variety of products being manufactured, persuade the factories to use different methods to guide the workers for unfamiliar tasks in the assembly section. Motion tracking is the process of capturing the movement of human body or objects which has been used in different industrial systems. It can be integrated to a wide range of applications such as interacting with computers, games and entertainment, industry, etc. Motion tracking can be integrated to assembly systems and it has the potential to create an improvement in this industry as well. But the integration of motion tracking in industrial processes is still not widespread. This thesis work provides a fully automatic tracking solution for future systems in manufacturing industry and other fields. In general a configurable, flexible, and scalable motion tracking system is created in this thesis work to amend the tracking process. According to our environment, we have done a research between different motion tracking methods and technologies including Kinect and Leap Motion sensor, and finally the leap motion sensor is selected as the most appropriate method, because it fulfils our demands in this project. Multiple Leap motion sensors are used in this work to cover areas with different size. Data fusion between multiple leap motion sensors can be considered as another novel contribution of this thesis work. To achieve this goal data from multiple sensors are combined. This system can improve the lack of accuracy in order to creating a practical industrial application. By fusion of several sensors in order to achieve accuracies that allow implementation in practice, a motion tracking system with higher accuracy is created.
45
Morison, Alexander M. "Perspective Control: Technology to Solve the Multiple Feeds Problem in Sensor Systems." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281931069.
Full text
46
Khavarian, Nehzak Hassan. "Inter-comparison of multiple angle remotely sensed data across different spatial resolutions and sensors for determination of albedo." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/339986/.
Full textAbstract:
Surface albedo is one of the critical parameters required by studies of surface energy balance and climate models. Albedo is defined as the ratio of outgoing radiances to incoming irradiances over hemispherical view-illumination geometry. Remotely sensed albedo is usually based on multiple view angle observations and a Bidirectional Reflectance Distribution Function (BRDF) model. The accuracy of remotely sensed albedo depends on a variety of factors of which the main ones are the accuracy of atmospherically corrected observations, the reliability of observations, and the validity of the applied BRDF model. Fine spatial resolution airborne and satellite data are valuable for the validation of coarse spatial resolution satellite albedos as they may be validated using field measurements with higher reliability. In this study, a variety of remote sensing data and field measurements were used to estimate, validate and analyse albedo at different spatial resolutions. The main aim was to validate the MODIS albedo product under UK conditions using the methods of direct and indirect comparisons with other available data. The source of the fine spatial resolution data used was the NCAVEO Field Campaign 2006 that took place at the Chilbolton calibration test site in southern England. The CHRIS/PROBA albedo was used as a fine spatial resolution (34 m) albedo map to investigate the spatial variation of albedo. The results of this investigation provided valuable information about the possibility of the extension of the obtained albedo map from CHRIS/PROBA data. The MODIS albedo product with a coarser spatial resolution (500 m), relative to the NCAVEO datasets, was compared with the CHRIS/PROBA albedo map to examine the effect of spatial scale on the accuracy of albedo (direct comparison). The uncertainties in the obtained albedo maps, from both MODIS and CHRIS/PROBA, were mainly examined by testing the accuracy of the input reflectance data and the applied BRDF model (indirect comparison). The results showed the accuracy of the MODIS albedo product inferior to that claimed by the MODIS team.
47
Dippold, Amanda. "Vision-Based Obstacle Avoidance for Multiple Vehicles Performing Time-Critical Missions." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/27830.
Full textAbstract:
This dissertation discusses vision-based static obstacle avoidance for a fleet of
nonholonomic robots tasked to arrive at a final destination simultaneously. Path generation
for each vehicle is computed using a single polynomial function that incorporates the vehicle
constraints on velocity and acceleration and satisfies boundary conditions by construction.
Furthermore, the arrival criterion and a preliminary obstacle avoidance scheme is incorporated
into the path generation. Each robot is equipped with an inertial measurement unit
that provides measurements of the vehicleâ s position and velocity, and a monocular camera
that detects obstacles. The obstacle avoidance algorithm deforms the vehicleâ s original path
around at most one obstacle per vehicle in a direction that minimizes an obstacle avoidance
potential function. Deconfliction of the vehicles during obstacle avoidance is achieved by
imposing a separation condition at the path generation level. Two estimation schemes are
applied to estimate the unknown obstacle parameters. The first is an existing method known
in the literature as Identifier-Based Observer and the second is a recently-developed fast estimator.
It is shown that the performance of the fast estimator and its effect on the obstacle
avoidance algorithm can be arbitrarily improved by the appropriate choice of parameters as
compared to the Identifier-Based Observer method. Coordination in time of all vehicles is
completed in an outer loop which adjusts the desired velocity profile of each vehicle in order
to meet the simultaneous arrival constraints. Simulation results illustrate the theoretical
findings.Ph. D.
48
LIANG, CHE KANG. "Spectrum Sensing of Multiple Channels Using Multiple Sensors." Thesis, 2011. http://hdl.handle.net/1974/6883.
Full textAbstract:
Cognitive radio (CR) is a class of wireless communication technologies that have the ability to learn from the surrounding radio environment and the intelligence to adapt communication resources to enhance quality of service. The problem of acquiring information from a CR's radio environment is called spectrum sensing, which can take on many forms. In particular, this thesis concerns the determination of whether a spectrum band (or channel) is in a busy or idle state. The binary nature of a channels availability means that spectrum sensing can be cast as a hypothesis testing problem. While an abundant literature exists on spectrum sensing as a signal detection problem, this thesis treats spectrum sensing differently, and features the following elements: 1) the system is equipped with an arbitrary number of sensors; 2) sensing is performed over multiple channels; 3) each channels availability is modelled by random periods of busy and idle times corresponding to packet transmission; and 4) the optimization criteria minimizes detection delay subject to a reliability constraint.
A related spectrum sensing problem formulation based on the use of a single sensor has been proposed in the recent literature. The previous research employs an optimization framework based on modeling channel uses as an on-off process via partially observable Markov decision processes (POMDP). This thesis generalizes previous results from single-sensor to multiple-sensor spectrum sensing, i.e., detecting idle periods with multiple sensors. In addition, an alternative reduced-complexity algorithm is proposed. For both proposed detectors, the performances are evaluated based on Monte Carlo simulation with calculated confidence intervals, and the results show that 1) adding sensors generally improves the system performance by reducing detection delay (improved agility); 2) the application of previously existing quickest detection methods result in error floors complicating test design. Finally, performance assessment using a channel model derived experimentally from the wireless local area network (WLAN) traffic is conducted and compared to that obtained using a geometrically-distributed channel traffic model.Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-11-17 10:35:56.751
49
Wagner, Martin [Verfasser]. "Tracking with multiple sensors / Martin Wagner." 2005. http://d-nb.info/974915424/34.
Full text
50
"Pervasive Quantied-Self using Multiple Sensors." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54901.
Full textAbstract:
abstract: The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.Dissertation/Thesis
Doctoral Dissertation Computer Engineering 2019