447 research outputs found

    Applications of Machine Learning Strategy for Wireless Power Transfer and Identification

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    The objective of my research is to propose and demonstrate Machine Learning (ML) applications of wireless power transfer and identification technology. Several works describe the implementation of a ML strategy based on 1) the use of Neural Networks (NN) for real-time range-adaptive automatic impedance matching of Wireless Power Transfer (WPT) applications, 2) the Naive Bayes algorithm for the prediction of the drone’s position, thus enhancing the WPT efficiency, and 3) the Support Vector Machine (SVM) classification strategy for read/interrogation enhancement in chipless RFID applications. The ML approach for the effective prediction of the optimal parameters of the tunable matching network, and classification range-adaptive transmitter coils (Tx) is introduced, aiming to achieve an effective automatic impedance matching over a wide range of relative distances. A novel WPT system consisting of a tunable matching circuit and 3 Tx coils which have different radius controlled by trained NN models is characterized. A proof-ofconcept WPT platform which allows the accurate prediction of the drone’s position based on the flight data utilizing ML classification using the Naive Bayes algorithm is also given. A ML-based approach for classification and of detection tag IDs has been presented, which can perform effective transponder readings for a wide variety of ranges and contexts, while providing high tag-ID detection accuracy. A SVM algorithm was trained using measurement data, and its accuracy was tested and characterized as a function of the included training data. In summary, this research sets a precedent, opening the door to a rich and wide area of research for the implementation of ML methods for the enhancement of WPT and chipless RFID applications.Ph.D

    Machine Learning and Neutron Sensing in Mobile Nuclear Threat Detection

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    A proof of concept (PoC) neutron/gamma-ray mobile threat detection system was constructed at Oak Ridge National Laboratory. This device, the Dual Detection Localization and Identification (DDLI) system, was designed to detect threat sources at standoff distance using neutron and gamma ray coded aperture imaging. A major research goal of the project was to understand the benefit of neutron sensing in the mobile threat search scenario. To this end, a series of mobile measurements were conducted with the completed DDLI PoC. These measurements indicated that high detection rates would be possible using neutron counting alone in a fully instrumented system. For a 280,000 neutrons per second Cf-252 source placed 15.9 meters away, a 4σ [sigma] detection rate of 99.3% was expected at 5 m/s. These results support the conclusion that neutron sensing enhances the detection capabilities of systems like the DDLI when compared to gamma-only platforms. Advanced algorithms were also investigated to fuse neutron and gamma coded aperture images and suppress background. In a simulated 1-D coded aperture imaging study, machine learning algorithms using both neutron and gamma ray data outperformed gamma-only threshold methods for alarming on weapons grade plutonium. In a separate study, a Random Forest classifier was trained on a source injection dataset from the Large Area Imager, a mobile gamma ray coded aperture system. Geant4 simulations of weapons-grade plutonium (WGPu) were combined with background data measured by the Large Area Imager to create nearly 4000 coded aperture images. At 30 meter standoff and 10 m/s, the Random Forest classifier was able to detect WGPu with error rates as low as 0.65% without spectroscopic information. A background subtracting filter further reduced this error rate to 0.2%. Finally, a background subtraction method based on principal component analysis was shown to improve detection by over 150% in figure of merit

    Statistical classification of soft solder alloys by laser-induced breakdown spectroscopy: review of methods

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    This paper reviews machine-learning methods that are nowadays the most frequently used for the supervised classification of spectral signals in laser-induced breakdown spectroscopy (LIBS). We analyze and compare various statistical classification methods, such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), naive Bayes method, probabilistic neural networks (PNN), and K-nearest neighbor (KNN) method. The theoretical considerations are supported with experiments conducted for real soft-solder-alloy spectra obtained using LIBS. We consider two decision problems: binary and multiclass classification. The former is used to distinguish overheated soft solders from their normal versions. The latter aims to assign a testing sample to a given group of materials. The measurements are obtained for several laser-energy values, projection masks, and numbers of laser shots. Using cross-validation, we evaluate the above classification methods in terms of their usefulness in solving both classification problems

    A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

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    The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

    Retainer-Free Optopalatographic Device Design and Evaluation as a Feedback Tool in Post-Stroke Speech and Swallowing Therapy

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    Stroke is one of the leading causes of long-term motor disability, including oro-facial impairments which affect speech and swallowing. Over the last decades, rehabilitation programs have evolved from utilizing mainly compensatory measures to focusing on recovering lost function. In the continuing effort to improve recovery, the concept of biofeedback has increasingly been leveraged to enhance self-efficacy, motivation and engagement during training. Although both speech and swallowing disturbances resulting from oro-facial impairments are frequent sequelae of stroke, efforts to develop sensing technologies that provide comprehensive and quantitative feedback on articulator kinematics and kinetics, especially those of the tongue, and specifically during post-stroke speech and swallowing therapy have been sparse. To that end, such a sensing device needs to accurately capture intraoral tongue motion and contact with the hard palate, which can then be translated into an appropriate form of feedback, without affecting tongue motion itself and while still being light-weight and portable. This dissertation proposes the use of an intraoral sensing principle known as optopalatography to provide such feedback while also exploring the design of optopalatographic devices itself for use in dysphagia and dysarthria therapy. Additionally, it presents an alternative means of holding the device in place inside the oral cavity with a newly developed palatal adhesive instead of relying on dental retainers, which previously limited device usage to a single person. The evaluation was performed on the task of automatically classifying different functional tongue exercises from one another with application in dysphagia therapy, whereas a phoneme recognition task was conducted with application in dysarthria therapy. Results on the palatal adhesive suggest that it is indeed a valid alternative to dental retainers when device residence time inside the oral cavity is limited to several tens of minutes per session, which is the case for dysphagia and dysarthria therapy. Functional tongue exercises were classified with approximately 61 % accuracy across subjects, whereas for the phoneme recognition task, tense vowels had the highest recognition rate, followed by lax vowels and consonants. In summary, retainer-free optopalatography has the potential to become a viable method for providing real-time feedback on tongue movements inside the oral cavity, but still requires further improvements as outlined in the remarks on future development.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7.2.1 Optode placement and circuit board layout . . . . . . . . . . . . . . . . 87 7.2.2 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.3 Device characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3.1 Photodiode transient response . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.2 Current source and rise time . . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.3 Multiplexer switching speed . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.4 Measurement cycle and firmware implementation . . . . . . . . . . . . . 93 7.3.5 In vitro measurement accuracy . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.6 Optode measurement stability . . . . . . . . . . . . . . . . . . . . . . . 96 7.4 Evaluation on the phoneme recognition task . . . . . . . . . . . . . . . . . . . . 98 7.4.1 Corpus selection and recording setup . . . . . . . . . . . . . . . . . . . . 98 7.4.2 Annotation and sensor data post-processing . . . . . . . . . . . . . . . . 98 7.4.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 99 7.4.4 Classifier and feature selection . . . . . . . . . . . . . . . . . . . . . . . 100 7.4.5 Evaluation paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.5.1 Tongue distance curve prediction . . . . . . . . . . . . . . . . . . . . . . 105 7.5.2 Tongue contact patterns and contours . . . . . . . . . . . . . . . . . . . 105 7.5.3 Phoneme recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 7.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8 Conclusion and future work 115 9 Appendix 9.1 Analytical light transport models . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9.2 Meshed Monte Carlo method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 9.3 Laser safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.4 Current source modulation voltage . . . . . . . . . . . . . . . . . . . . . . . . . 123 9.5 Transimpedance amplifier’s frequency responses . . . . . . . . . . . . . . . . . . 123 9.6 Initial OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . . . 127 9.7 Improved OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . 129 9.8 Test station layout drawing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Bibliography 152Der Schlaganfall ist eine der häufigsten Ursachen für motorische Langzeitbehinderungen, einschließlich solcher im Mund- und Gesichtsbereich, deren Folgen u.a. Sprech- und Schluckprobleme beinhalten, welche sich in den beiden Symptomen Dysarthrie und Dysphagie äußern. In den letzten Jahrzehnten haben sich Rehabilitationsprogramme für die Behandlung von motorisch ausgeprägten Schlaganfallsymptomatiken substantiell weiterentwickelt. So liegt nicht mehr die reine Kompensation von verlorengegangener motorischer Funktionalität im Vordergrund, sondern deren aktive Wiederherstellung. Dabei hat u.a. die Verwendung von sogenanntem Biofeedback vermehrt Einzug in die Therapie erhalten, um Motivation, Engagement und Selbstwahrnehmung von ansonsten unbewussten Bewegungsabläufen seitens der Patienten zu fördern. Obwohl jedoch Sprech- und Schluckstörungen eine der häufigsten Folgen eines Schlaganfalls darstellen, wird diese Tatsache nicht von der aktuellen Entwicklung neuer Geräte und Messmethoden für quantitatives und umfassendes Biofeedback reflektiert, insbesondere nicht für die explizite Erfassung intraoraler Zungenkinematik und -kinetik und für den Anwendungsfall in der Schlaganfalltherapie. Ein möglicher Grund dafür liegt in den sehr strikten Anforderungen an ein solche Messmethode: Sie muss neben Portabilität idealerweise sowohl den Kontakt zwischen der Zunge und dem Gaumen, als auch die dreidimensionale Bewegung der Zunge in der Mundhöhle erfassen, ohne dabei die Artikulation selbst zu beeinflussen. Um diesen Anforderungen gerecht zu werden, wird in dieser Dissertation das Messprinzip der Optopalatographie untersucht, mit dem Schwerpunkt auf der Anwendung in der Dysarthrie- und Dysphagietherapie. Dies beinhaltet auch die Entwicklung eines entsprechenden Gerätes sowie dessen Befestigungsmethode in der Mundhöhle über ein dediziertes Mundschleimhautadhäsiv. Letzteres umgeht das bisherige Problem der notwendigen Anpassung eines solchen intraoralen Gerätes an einen einzelnen Nutzer. Für die Anwendung in der Dysphagietherapie erfolgte die Evaluation anhand einer automatischen Erkennung von Mobilisationsübungen der Zunge, welche routinemäßig in der funktionalen Dysphagietherapie durchgeführt werden. Für die Anwendung in der Dysarthrietherapie wurde eine Lauterkennung durchgeführt. Die Resultate bezüglich der Verwendung des Mundschleimhautadhäsives suggerieren, dass dieses tatsächlich eine valide Alternative zu den bisher verwendeten Techniken zur Befestigung intraoraler Geräte in der Mundhöhle darstellt. Zungenmobilisationsübungen wurden über Probanden hinweg mit einer Rate von 61 % erkannt, wogegen in der Lauterkennung Langvokale die höchste Erkennungsrate erzielten, gefolgt von Kurzvokalen und Konsonanten. Zusammenfassend lässt sich konstatieren, dass das Prinzip der Optopalatographie eine ernstzunehmende Option für die intraorale Erfassung von Zungenbewegungen darstellt, wobei weitere Entwicklungsschritte notwendig sind, welche im Ausblick zusammengefasst sind.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . .

    Battery-less near field communications (nfc) sensors for internet of things (iot) applications

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    L’ implementació de la tecnologia de comunicació de camp proper (NFC) en els telèfons intel·ligents no para de créixer degut a l’ús d’aquesta per fer pagaments, això, junt amb el fet de poder aprofitar l’energia generada pel mòbil no només per la comunicació, sinó també per transmetre energia, el baix cost dels xips NFC, i el fet de que els telèfons tinguin connectivitat amb internet, possibilita i fa molt interesant el disseny d’etiquetes sense bateria incorporant-hi sensors i poder enviar la informació al núvol, dins del creixent escenari de l’internet de les coses (IoT). La present Tesi estudia la viabilitat d’aquests sensors, analitzant la màxima distància entre lector i sensor per proveir la potència necessària, presenta tècniques per augmentar el rang d’operació, i analitza els efectes de certs materials quan aquests estan propers a les antenes. Diversos sensors han estat dissenyats i analitzats i son presentats en aquest treball. Aquests son: Una etiqueta que mesura la humitat de la terra, la temperatura i la humitat relativa de l’aire per controlar les condicions de plantes. Un sensor per detectar la humitat en bolquers, imprès en material flexible que s’adapta a la forma del bolquer. Dues aplicacions, una per estimació de pH i una altre per avaluar el grau de maduració de fruites, basats en un sensor de color. I, per últim, s’estudia la viabilitat de sensors en implants per aplicacions mèdiques, analitzant l’efecte del cos i proposant un sistema per augmentar la profunditat a la que aquests es poden llegir utilitzant un telèfon mòbil. Tots aquests sensors poden ser alimentats i llegits per qualsevol dispositiu que disposin de connexió NFC.La implementación de la tecnología de comunicaciones de campo cercano (NFC) en los teléfonos inteligentes no para de crecer debido al uso de esta para llevar a cabo pagos, esto, junto con el hecho de poder aprovechar la energía generada por el móvil no sólo para la comunicación, sino también para transmitir energía, el bajo coste de los chips NFC, i el hecho que los teléfonos tengan conectividad a internet, posibilita y hace muy interesante el diseño de etiquetas sin batería que incorporen sensores i poder enviar la información a la nube, enmarcado en el creciente escenario del internet de las cosas (IoT). La presente Tesis estudia la viabilidad de estos sensores, analizando la máxima distancia entre lector i sensor para proveer la potencia necesaria, presenta técnicas para aumentar el rango de operación, y analiza los efectos de ciertos materiales cuando estos están cerca de las antenas. Varios sensores han sido diseñados y analizados y son presentados en este trabajo. Estos son: Una etiqueta que mide la humedad de la tierra, la temperatura y la humedad relativa del aire para controlar las condiciones de plantas. Un sensor para detectar la humedad en pañales, impreso en material flexible que se adapta a la forma del pañal. Dos aplicaciones, una para estimación de pH y otra para evaluar el grado de maduración de frutas, basados en un sensor de color. Y, por último, se estudia la viabilidad de sensores en implantes para aplicaciones médicas, analizando el efecto del cuerpo y proponiendo un sistema para aumentar la profundidad a la que estos se pueden leer usando un teléfono móvil. Todos estos sensores pueden ser alimentados y leídos por cualquier dispositivo que disponga de conexión NFC.The implementation of near field communication (NFC) technology into smartphones grows rapidly due the use of this technology as a payment system. This, altogether with the fact that the energy generated by the phone can be used not only to communicate but for power transfer as well, the low-cost of the NFC chips, and the fact that the smartphones have connectivity to internet, makes possible and very interesting the design of battery-less sensing tags which information can be sent to the cloud, within the growing internet of things (IoT) scenario. This Thesis studies the feasibility of these sensors, analysing the maximum distance between reader and sensor to provide the necessary power, presents techniques to increase the range of operation, and analyses the effects of certain materials when they are near to the antennas. Several sensors have been designed and analysed and are presented in this work. These are: a tag that measures the soil moisture, the temperature and the relative humidity of the air to control the conditions of plants. A moisture sensor for diapers, printed on flexible material that adapts to the diaper shape. Two applications, one for pH estimation and another for assessing the degree of fruit ripening, based on a colour sensor. And finally, the feasibility of sensors in implants for medical applications is studied, analysing the effect of the body and proposing a system to increase the depth at which they can be read using a mobile phone. All of these sensors can be powered and read by any NFC enabled device

    Building a boundaryless manufacturing organisation through HITOP method

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    There is little empirical research to support the allegation that ‘leagile’ manufacturing organisations thrive in hostile environments, nor has it been demonstrated that organisation processes (referred to as enablers) actually support ‘leagile’ performance. This study tests the statistical significance of five selected HITOP (highly integrated technology, organisation and people) ‘leagile’ enablers. This was accomplished by using a mail survey instrument to measure the presence of ‘leagile enablers’ in a sample of companies taken from best factory award winners in UK, US and Japan. [Continues.

    Feature Selection and Classifier Development for Radio Frequency Device Identification

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    The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

    Face recognition by means of advanced contributions in machine learning

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    Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

    Fused mechanomyography and inertial measurement for human-robot interface

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    Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces
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