Vehicle Combustion Quality Monitoring:A scene visibility-level based non-invasive approach

Pollutants interfere with light, restrict its reflection and so impair visibility. Scene visibility level is therefore used as a measure of air quality and pollution. Treating emission efflux as "some additional noise causing visibility impairment," this work examines if the extracted visibility index from a thermal infrared (TIR) image can help in qualitative assessment of combustion efficiency. The thin-film regime like two dimensional TIR images of unleaded-petroleum run vehicles' exhaust-plumes were first accommodated for time and space related compositional effects. The estimated ratios of visibility indices obtained from two sequential TIR images of the same exhaust plume were compared with their respective electrochemically sensed levels of oxides of nitrogen and combustibles. Initial results suggest that visibility indices extracted from TIR images of emission efflux would help in distinguishing low from high levels of emissions. TIR images can therefore assist in qualitative assessment of engine combustion efficiency

Portable Tongue-Supported Human Computer Interaction System Design and Implementation

Tongue supported human-computer interaction (TSHCI) systems can help critically ill patients interact with both computers and people. These systems can be particularly useful for patients suffering injuries above C7 on their spinal vertebrae. Despite recent successes in their application, several limitations restrict performance of existing TSHCI systems and discourage their use in real life situations. This paper proposes a low-cost, less-intrusive, portable and easy to use design for implementing a TSHCI system. Two applications of the proposed system are reported. Design considerations and performance of the proposed system are also presented

Efficacy of Biophysiological Measurements at FTFPs for Facial Expression Classification: A Validation

Recent works suggest that thermal intensity values (TIVs) measured around the facial thermal feature points (FTFPs) can help in distinguishing between the facial expression of affective states. This work investigates if the average pixel grey-levels, instead of TIVs, measured in sub-image masks around the FTFPs allow classifying facial expressions. Thermal infrared images from the IEEE OTCBVS database were used to distinguish between facial expressions. The pixel grey-levels measured in sub-image masks were used to measure, for each individual, the Euclidean distance between images of different facial expressions. Linear discriminant analysis was performed to obtain hyper-planes for separating the clusters of sample images. Significant pixel grey-level differences were observed at FTFPs between three facial expressions; neutral, happy, and angry. More than 96 of the original images in a three-expression Gaussian mixture model were separable and clustered around distant centroids in a discriminant space

Exhaust Plume Flow Visualization for Qualitative Analysis of Engine Combustion Performance

This work explores use of Thermal Infrared Image based Flow Visualization (TIIFV) for qualitative analysis of gasoline engine combustion performance. It proposes determining engine combustion performance through analysis of the exhaust plume turbulence and radiation extinction patterns. The employed methodology requires estimating the point spread function (PSF) prevailing in a LWIR image and using the PSF estimates for enhancing the engine exhaust plume LWIR images. Influence of exhaust plume composition on the plume flow characteristics, made evident by the turbulence and radiation extinction patterns, is then ascertained. The observed plume flow characteristics and underlying flow patterns are used to qualitatively determine the engine combustion performance. Results suggest that engine exhaust flow visualization can help in qualitative analysis of combustion performance from a distance and our reliance on photochemical-based analysis of gasoline engine combustion efficiency can be reduced. Thus a time consuming and untidy process, difficult to be carried out in real life situations, may be replaced with a swift and cleaner one

A Tongue-Activated Emergency Beacon for Immobile Patients

This paper reports the design and implementation of an infrared signal transmitting tongue activated emergency beacon. This low-cost, simple and reliable device can help immobile patients communicate with the medical staff in the event of an emergency without interfering with other equipment. The physical dimensions of this device were minimized to provide flexibility and suit the most vulnerable and impaired patients. The presented sensor-microcontroller configuration results in a robust and intelligent functionality that would allow this device to outperform many of the commercially available systems used in similar environments

Cluster-analytic classification of facial expressions using infrared measurements of facial thermal features

In previous research, scientists were able to use transient facial thermal features extracted from Thermal Infra-Red Images (TIRIs) for making binary distinction between the affective states. For example, thermal asymmetries localised in facial TIRIs have been used to distinguish anxiety and deceit. Since affective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states, computers’ able to make such binary distinctions would not suffice a robust human-computer interaction. This work, for the first time, uses affective-state-specific transient facial thermal features extracted from TIRIs to recognise a much wider range of facial expressions under a much wider range of conditions. Using infrared thermal imaging within the 8-14 μm, a database of 324 discrete, time-sequential, visible-spectrum and thermal facial images was acquired, representing different facial expressions from 23 participants in different situations. A facial thermal feature extraction and pattern classification approach was developed, refined and tested on various Gaussian mixture models constructed using the image database. Attempts were made to classify: neutral and pretended happy and sad faces; multiple positive and negative facial expressions; six (pretended) basic facial expressions; partially covered or occluded faces; and faces with evoked happiness, sadness, disgust and anger. The cluster-analytic classification in this work began by segmentation and detection of thermal faces in the acquired TIRIs. The affective-state-specific temperature distributions on the facial skin surface were realised through the pixel grey-level analysis. Examining the affectivestate- specific temperature variations within the selected regions of interest in the TIRIs led to the discovery of some significant Facial Thermal Feature Points (FTFPs) along the major facial muscles. Following a multivariate analysis of the Thermal Intensity values (TIVs) measured at the FTFPs, the TIRIs were represented along the Principal Components (PCs) of a covariance matrix. The resulting PCs were ranked in the order of their effectiveness in the between-cluster separation. Only the most effective PCs were retained to construct an optimised eigenspace. A supervised learning algorithm was invoked for linear subdivision of the optimised eigenspace. The statistical significance levels of the classification results were estimated for validating the discriminant functions. The main contribution of this research has been to show that: the infrared imaging of facial thermal features within the 8-14 μm bandwidth may be used to observe affective-state-specific thermal variations on the face; the pixel-grey level analysis of TIRIs can help localise FTFPs along the major facial muscles of the face; cluster-analytic classification of transient thermal features may help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The Gaussian mixture model with one cluster per affect worked better for some facial expressions than others. This made the influence of the Gaussian mixture model structure on the accuracy of the classification results obvious. However, the linear discrimination and confusion patterns observed in this work were consistent with the ones reported in several earlier studies. This investigation also unveiled some important dimensions of the future research on use of facial thermal features in affective human-computer interaction.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Thermographic Investigation of Osseous Stress Pathology

The debilitating pathology of stress fracture accounts for 10% of all athletic injuries[2], with prevalence as high as 20% in modern military basic training cohorts [3]. Increasing concerns surrounding adverse effects of radiology [5],combined with the 12.5% contribution of diagnostic imaging to Australian Medicare benefits paid in 2009-10 [6], have prompted the search for alternative/adjunct electronic decision support systems[7]. Within conducive physioanatomic milieu, thermal infrared imaging (TIRI) may feasibly be used to remotely detect and topographically map diagnostically useful signs of suprathreshold thermodynamic pathophysiology. This paper details a three month clinical pilot study into TIRI-based detection of osseous stress pathology in the lower legs of Australian Army basic trainees. A dataset of over 500 TIRI’s was amassed. The apparent ‘normal’ thermal profile of the anterior aspect of the asymptomatic lower leg is topographically defined and validated against current thermophysiological theory [8] via cadaveric dissection

Image Processing for Pathological Visualization in Multitemporal Convoluted TIRI

The convoluted nature of thermal infrared radiation and poor understanding of the physical mechanismsof human emittance, make objective image acquisition and processing protocols prerequisite for meaningful diagnostic specificity. A longitudinal dataset of clinical thermal infrared images was objectively processed to facilitate visualization of osseous stress pathology in the lower limbs.. This paper details processing of 500+ thermal infrared images acquired during a recent three month clinical study into osseous stress pathology in the lower limbs of Australian Army basic trainees. The use ofthermal chroma-keying in segmentation and multitemporal image calibration is demonstrated. The ‘OpenSURF’ implementation of the scale and rotation-invariant interest point detector and escriptor are shown to be performant in registration of multitemporal clinical thermal infrared image data. Thermal ‘signs’ observed in longitudinal images appear to be revealing detectable changes in osseous stress pathophysiology

Uma abordagem baseada em seleçao pelas conseqüencias para aprendizagem de redes neurais multi-camadas voltadas r concepçao de sistemas autômos inteligentes

Orientador : Maurício F. FigueiredoDissertaçao (mestrado) - Universidade Federal do ParanáResumo: Um modelo de rede neural artificial é proposto. A rede neural possui múltiplas camadas. Cada camada da rede neural é formada por uma grade quadrangular de neurônios (em um espaço toroidal). As conexões sinápticas de cada neurônio abrangem três tipos: excitatórias inter-camadas, laterais inibitórias intra-camada e laterais excitatórias intra-camada. A disposição espacial das conexões é do tipo Gaussiana e específica para cada tipo de sinapse. Cada neurônio estabelece um número restrito de conexões. O modelo de arquitetura contribui para eliminar restrições apresentadas por arquiteturas em que entradas e conexões são distribuídas a todos os neurônios de cada camada. O modelo do neurônio apresenta dinâmica interna, proporcionando uma memória da atividade recente e assumindo papel importante na aprendizagem. A aprendizagem ê baseada na seleção pelas conseqüências, conforme princípios de aprendizagem por reforço. Em particular, a de aprendizagem por reforço utilizada é do tipo clássico. Os experimentos definidos para investigação e confirmação das capacidades da rede neural consideram um ambiente simulado, condizente com o modelo de Seleção pelas Conseqüências. Os resultados obtidos em simulações mostram que o modelo é capaz de reproduzir diversos fenômenos comportamentais, que são: aquisição de comportamento respondente, extinção de comportamento respondente, aquisição de comportamento operante, extinção de comportamento operante, capacidade de generalização de estímulos, habilidade no controle da intensidade das respostas, capacidade de controle de múltiplas respostas e fusão de sensores. Experimentos também ilustram o importante papel das conexões laterais inibitórias e das conexões laterais excitatórias na modelagem da formação de grupos neurais em nível operante. Entende-se que a capacidade de aprendizagem alcançada pela rede neural proposta torna-a viável para a concepção de sistemas autônomos inteligentes com potencialidades superiores àqueles divulgados na literatura especializada.Abstract: A model of an artificial neural network is proposed. The neural network has multiple layers. Each network layer is formed by a quadrangular grid of neurons (on a toroidal space). The synaptic connections that every neuron has are defined between tree types: inter-layer excitatory, lateral intra-layer inhibitory and lateral intra-layer excitatory. The spatial disposition of connections is of a Gaussian type and specific for each type of synapse. Each neuron has a limited number of connections. The model contributes to eliminate restrictions presented by other architectures in witch connections are distributed to all of the neurons of each layer. The neuron model presents an internal dynamic, working as a memory of its recent activity and having important role in the learning process. The learning procedure is based on the selection by consequences according to reinforcement learning principles. Particularly the reinforcement learning approach used is of the classical type. The experiments defined for the investigation and confirmation of the capacities of the neural network consider a simulated environment that works according to the Selection by Consequences model. The simulation results show that the model is capable of reproducing several behavioral phenomena that are: acquisition of respondent behavior, extinction of respondent behavior, acquisition of operant behavior, extinction of operant behavior, stimulus generalization capacity, ability to control the response intensity, capacity to control multiple responses and sensor fusion. Besides that, the experiments also illustrate the important role of the lateral inhibitory and lateral excitatory connections for a correct shaping of operant level responses and neural groups. It is understood that the learning capacities that the proposed neural network exhibits make it viable for the conception of intelligent autonomous systems with potentialities superior to those already presented in the specialized literature

Abstraction-Based Outlier Detection for Image Data

© 2021, Springer Nature Switzerland AG. Data plays an important role in all stages of training, and usage of machine learning algorithms. Outliers are the samples in data that are generated by a “different mechanism” and belong to unexpected patterns that do not conform to normal behaviour. Outlier detection techniques try to deal with such undesirable events. There have been exceptional success of deep learning over classical methods in computer vision. In recent years a number of works employed the representation learning ability of deep autoencoders or Generative Adversarial Networks for outlier detection. Basically, methods are based on plugging representation techniques to outlier detection methods or directly reported employing reconstruction error as an outlier score. The error distributions of inliers and outliers may be still significantly overlapped. This could be associated with variation of samples inside the class, or cases with high outliers ratios, etc. In these cases, simply thresholding reconstruction errors may lead to misclassification. Although the produced representation is perhaps effective in representing the common features of the normal data, it is not necessarily effective in distinguishing outliers from inliers. We present a method that is based on constructing new features using convolutional variational autoencoder (VAE) and generate abstraction based on these features. To identify anomaly detection we tested two scenarios: utilizing VAE itself as well as using abstractions to train an additional architecture. Results are presented in the form of AUC-ROC using four benchmark datasets