A novel facial expression recognition method using bi-dimensional EMD based edge detection

Facial expressions provide an important channel of nonverbal communication. Facial recognition techniques detect people’s emotions using their facial expressions and have found applications in technical fields such as Human-Computer-Interaction (HCI) and security monitoring. Technical applications generally require fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Traditionally, human facial expressions are recognized using standard images. Existing methods of recognition require subjective expertise and high computational costs. This thesis proposes a novel method for facial expression recognition using image edge detection based on Bi-dimensional Empirical Mode Decomposition (BEMD). In this research, a BEMD based edge detection algorithm was developed, a facial expression measurement metric was created, and an intensive database testing was conducted. The success rates of recognition suggest that the proposed method could be a potential alternative to traditional methods for human facial expression recognition with substantially lower computational costs. Furthermore, a possible blind-detection technique was proposed as a result of this research. Initial detection results suggest great potential of the proposed method for blind-detection that may lead to even more efficient techniques for facial expression recognition

Factor Graph Neural Networks

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs. By aiming to provide expressive methods for representation learning instead of computing marginals or most likely configurations, GNNs provide flexibility in the choice of information flowing rules while maintaining good performance. Despite their success and inspirations, they lack efficient ways to represent and learn higher-order relations among variables/nodes. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs. We then neuralize the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules; this facilitates both efficient inference and powerful end-to-end learning. We further show that with a suitable choice of message aggregation operators, our FGNN is also able to represent Max-Product belief propagation, providing a single family of architecture that can represent both Max and Sum-Product loopy belief propagation. Our extensive experimental evaluation on synthetic as well as real datasets demonstrates the potential of the proposed model.Comment: Accepted by JML

Image-Guided Robotic Dental Implantation With Natural-Root-Formed Implants

Dental implantation is now recognized as the standard of the care for tooth replacement. Although many studies show high short term survival rates greater than 95%, long term studies (\u3e 5 years) have shown success rates as low as 41.9%. Reasons affecting the long term success rates might include surgical factors such as limited accuracy of implant placement, lack of spacing controls, and overheating during the placement. In this dissertation, a comprehensive solution for improving the outcome of current dental implantation is presented, which includes computer-aided preoperative planning for better visualization of patient-specific information and automated robotic site-preparation for superior placement and orientation accuracy. Surgical planning is generated using patient-specific three-dimensional (3D) models which are reconstructed from Cone-beam CT images. An innovative image-guided robotic site-preparation system for implants insertion is designed and implemented. The preoperative plan of the implant insertion is transferred into intra-operative operations of the robot using a two-step registration procedure with the help of a Coordinate Measurement Machine (CMM). The natural-root implants mimic the root structure of natural teeth and were proved by Finite Element Method (FEM) to provide superior stress distribution than current cylinder-shape implants. However, due to their complicated geometry, manual site-preparation for these implants cannot be accomplished. Our innovative image-guided robotic implantation system provides the possibility of using this advanced type of implant. Phantom experiments with patient-specific jaw models were performed to evaluate the accuracy of positioning and orientation. Fiducial Registration Error (FRE) values less than 0.20 mm and final Target Registration Error (TRE) values after the two-step registration of 0.36±0.13 mm (N=5) were achieved. Orientation error was 1.99±1.27° (N=14). Robotic milling of the natural-root implant shape with single- and double-root was also tested, and the results proved that their complicated volumes can be removed as designed by the robot. The milling time for single- and double-root shape was 177 s and 1522 s, respectively

Molecular aggregation of thiols and alcohols: study of non-covalent interactions by microwave spectroscopy

El estudio y comprensión de las interacciones no covalentes a nivel molecular es un campo que está en continuo desarrollo y cobra vital importancia para determinar el comportamiento estructural de muchas moléculas de interés químico, tecnológico o biológico. En esta tésis doctoral se han analizado las interacciones intermoleculares implicadas en la formación de agregados moleculares neutros, tanto dímeros como productos de microsolvatación, en fase gas. Los complejos intermoleculares se han generado mediante expansiones supersónicas pulsadas, caracterizándose posteriormente mediante espectroscopía de rotación. Este trabajo ha utilizado dos técnicas espectroscópicas, incluyendo un espectrómetro de microondas con transformada de Fourier (FTMW) de tipo Balle-Flygare en el rango de frecuencias 8-20 GHz, y un espectrómetro de transformada de Fourier de banda ancha con excitación multifrecuencia (CP-FTMW) cubriendo el rango espectral de 2-8 GHz. Los complejos intermoleculares estudiados han incluido moléculas con grupos alcohol y/o tiol, con objeto de analizar las diferencias entre las interacciones intermoleculares que implican átomos de oxígeno o azufre, en especial el enlace de hidrógeno. Se han estudiado moléculas incluyendo tanto sistemas cíclicos alifáticos (ciclohexanol, ciclohexanotiol) como aromáticos (furfuril alcohol, furfuril mercaptano, tienil alcohol, tienil mercaptano). Los enlaces de hidrógeno analizados han comprendido especialmente interacciones de tipo O-H···O, O-H···S y S-H···S. La formación de los complejos intermoleculares ha revelado en algunos de ellos una gran variedad conformacional, como la observación de seis isómeros del dímero de ciclohexanol. En el caso de los monohidratos se han observado en algunos casos desdoblamientos asociados a movimientos internos de gran amplitud, como la rotación de la molécula de agua en los monohidratos de ciclohexanol y tienil mercaptano. En los casos de moléculas quirales la dimerización ha permitido observar la estabilidad relativa de los diastereoisómeros homo o heteroquirales. El estudio experimental se ha completado con diferentes cálculos teóricos de orbitales moleculares, en especial teoría del funcional de la densidad, a fin de caracterizar las interacciones estructuralmente, energéticamente y mediante análisis topológico de la densidad electrónica. El conjunto de datos experimentales y teóricos permite aumentar la información existente sobre enlaces de hidrógeno con átomos de azufre, generalmente poco estudiados, y su comparación con los análogos oxigenados.The study and understanding of non-covalent interactions at molecular level is a field in continuous development and essential to determine the structural behavior of many molecules of chemical, technological or biological interest. In this PhD thesis, the intermolecular interactions involved in the formation of neutral molecular aggregates, both dimers and microsolvation products, have been analyzed in the gas phase. The intermolecular complexes were generated by pulsed supersonic expansions, and later characterized by rotational spectroscopy. This work has used two spectroscopic techniques, including a Balle-Flygare Fourier-Transform Microwave (FTMW) spectrometer in the 8-20 GHz frequency range, and a broadband Chirped-Pulse Fourier Transform Microwave (CP-FTMW) spectrometer covering the 2-8GHz spectral range. The intermolecular complexes studied have included molecules with alcohol and / or thiol groups, in order to analyze the differences between the intermolecular interactions involving oxygen or sulfur atoms, especially hydrogen bonds. Molecules that comprise both aliphatic (cyclohexanol) and aromatic (furfuryl alcohol, furfuryl mercaptan, thenyl alcohol, thenyl mercaptan) ring systems have been studied. The analyzed hydrogen bonds included especially O-H···O, O-H···S and S-H···S interactions. The formation of intermolecular complexes has revealed a great conformational diversity in some of them, such as the observation of six isomers of the cyclohexanol dimer. With regard to the monohydrates, tunnelling splittings associated with internal large amplitude motions have been observed in some cases, such as the rotation of the water molecule in the monohydrates of cyclohexanol, thenyl alcohol and thenyl mercaptan. In the case of chiral molecules, dimerization has made it possible to observe the relative stability of homo- or heterochiral diastereoisomers. The experimental study has been supported by different theoretical molecular orbital calculations, in particular Density Functional Theory (DFT) calculations, in order to characterize the interactions structurally, energetically and by a topological analysis of electron density. The set of experimental and theoretical data will advance the existing information on hydrogen bonds involving sulfur atoms, generally scarcely studied, and their comparison with the oxygenated analogues.Departamento de Química Física y Química InorgánicaDoctorado en Físic

Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

AI enabled RF sensing of Diversified Human-Centric Monitoring

This thesis delves into the application of various RF signaling techniques in HumanCentric Monitoring (HCM), specifically focusing on WiFi, LoRa, Ultra-wideband (UWB) radars, and Frequency Modulated Continuous Wave (FMCW) radars. Each of these technologies has unique properties suitable for different aspects of HCM. For instance, 77GHz FMCW radar signals demonstrate high sensitivity in detecting subtle human movements, such as heartbeat, contrasting with the capabilities of 2.4GHz/5GHz WiFi signals. The research extends to both large-scale and small-scale Human Activity Recognition (HAR), examining how ubiquitous communication signals like WiFi and LoRa can be utilized for large-scale HAR, while radar signals with higher central frequencies are more effective for small-scale motions, including heartbeat and mouth movements. The thesis also identifies several unresolved challenges in the field. These include the underutilization of spatial spectral information in existing WiFi sensing technologies, the untapped potential of LoRa technology in identity recognition, the sensitivity of millimeterwave radar in detecting breathing and heartbeat against minor movements, and the lack of comprehensive datasets for mouth motion detection in silent speech recognition. Addressing these challenges, the paper proposes several innovative solutions: • A comprehensive analysis of methodologies for RF-based HCM applications, discussing challenges and proposing potential solutions for broader healthcare applications using wireless sensing. • Exploration of communication signals in HCM systems, especially focusing on WiFi and LoRa sensing. It introduces the continuous AoA-ToF maps method to enhance HCM system performance and the LoGait system, which uses LoRa signals for human gait identification, extending the sensing range to 20 meters. • Development of a FMCW radar-based structure for respiration detection, incorporating an ellipse normalization method to adjust distorted IQ signals, reducing the root mean square error by 30% compared to baseline methods. • Collection and analysis of a large-scale multimodal dataset for silent speech recognition and speech enhancement, including designing experiments to validate the dataset’s utility in a multimodal-based speech recognition system