Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency

Computational intelligence is often used in smart environment applications in order to determine a user’s context. Many computational intelligence algorithms are complex and resource-consuming which can be problematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. These types of devices are, however, highly useful in pervasive and mobile computing due to their small size, energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classifier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers. CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing. The classifier was evaluated on eight different datasets of various types. Our results show that CORPSE, despite its simplistic design, has comparable performance to some common machine learning algorithms. This makes the classifier a viable choice for use in pervasive systems that have limited resources, requires energy-efficiency, or have the need for fast real-time responses.publishedVersionnivå

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Detecção de novidade para sistemas de sonar passivo

Sound is a mechanical wave that propagates over great distances in the oceans and it can, therefore, be used for vessel detection and classification in underwater environments, which are basic sonar system tasks. The development of such systems is directly linked to the country defense, especially, in countries with continental dimensions, such as Brazil. Recently, the Brazilian Navy defined underwater acoustics as a strategic priority area. Passive sonar systems can be installed to monitor the Brazilian coast in a stealthy and efficient way. In addition, these are used in military submarines for different applications. As in this operating environment, each ship has a unique acoustic signature, and ships whose data have not been acquired can be observed, it is necessary to develop a novelty detector operating in conjunction with the contact classifiers implemented in Brazilian Navy systems. Because classification systems operate competing for computing resources with novelty detectors, they can impact in classification efficiency. The number of classes in this environment is very large, and because of this, specific performance indices were created to evaluate the developed model efficiency. In addition, different data compressors were developed to access relevant ship information of, among them can be cited PCD, kPCA, NLPCA and SAE. The novelty detection development was based on the operating environment of the Brazilian Navy and since it can have its operating conditions changed over time, a stationarity monitoring system based on higher order statistics was proposed. Both the novelty detector and the stationarity monitoring system were developed with experimental data provided by the Brazilian Navy.O som é uma onda mecânica que se propaga por grandes distâncias nos oceanos e, por essa razão, pode ser utilizado para a detecção e classificação de contatos em meios submarinos, tarefas básicas de um sistema sonar. O desenvolvimento de tais sistemas está diretamente ligado a defesa de um país com dimensões continentais, como o Brasil. Recentemente, a Marinha do Brasil definiu como prioridade estratégica a área de acústica submarina. Sistemas de sonar passivo podem ser instalados para monitorar a costa brasileira de maneira furtiva e eficiente. Ademais, estes são utilizados em submarinos militares para diferentes aplicações. Como neste ambiente de operação, cada navio possui uma assinatura acústica única, e navios cujos dados não foram adquiridos podem ser observados, faz-se necessário o desenvolvimento de um detector de novidade operando em conjunto com os classificadores de contatos implementados em sistemas da Marinha do Brasil. Como os classificadores operam competindo por recursos computacionais com os detectores de novidade, estes podem impactar na eficiência de classificação. A quantidade de classes, neste ambiente, ´e muito grande e, devido a isso, índices de desempenho específicos foram criados para avaliar a eficiência dos modelos desenvolvidos. Além disso, diferentes extratores de informação foram desenvolvidos para acessar informações relevantes dos navios em questão, dentre eles podem ser citados PCD, kPCA, NLPCA e SAE. O desenvolvimento deste modelo de detecção foi baseado no ambiente de operação da Marinha do Brasil e, como este pode ter suas condições operativas alteradas ao longo do tempo, um sistema de monitoramento da estacionaridade baseado em estatística de ordem superior foi proposto. Tanto o detector de novidade quanto o sistema de monitoramento de estacionaridade foram desenvolvidos com dados experimentais disponibilizados pela Marinha do Brasil

Anomaly Detection Using Hierarchical Temporal Memory in Smart Homes

This work focuses on unsupervised biologically-inspired machine learning techniques and algorithms that can detect anomalies. Specifically, the aim is to investigate the applicability of the Hierarchical Temporal Memory (HTM) theory in detecting anomalies in the smart home domain. The HTM theory proposes a model for the neurons that is more faithful to the actual neurons than their usual counterparts in Artificial Neural Networks (ANN) based on the current Neuroscience understanding. The HTM theory has several algorithmic implementations, the most prominent one is the Cortical Learning Algorithm (CLA). The CLA model typically consists of three main regions: the encoder, the spatial pooler and the temporal memory. Studying the performance of the CLA in the smart home domain revealed an issue with the standard encoders and high-dimensional datasets. In this domain, it is typical to have high-dimensional feature space representing the collection of smart devices. The standard CLA encoders are more suitable for low-dimensional datasets and there are encoders for categorical and scalar data types. A novel Hash Indexed Sparse Distributed Representation (HI-SDR) encoder was proposed and developed, to overcome the high-dimensionality issue. The HI-SDR encoder creates unique representation of the data which allows the rest of the CLA regions to learn from. The standard approach when creating HTM models to work with datasets with many features is to concatenate the output of each encoder. This work concludes that the standard encoders produced representations for the input during every timestep that were similar and less distinguishable for the HTM model. This output similarity confuses the HTM model and makes it hard to discern meaningful representations. The proposed novel encoder manages to capture the required properties in terms of sparsity and representations. To investigate and validate the performance of a proposed machine learning technique, there has to be a representative dataset. In the smart home literature, there exists many real-world smart home datasets that allow the researchers to validate their models. However, most of the existing datasets are created for classification and recognition of Activities of Daily Living (ADL). The lack of datasets for anomaly detection applications in the domain of smart homes required the development of a simulation tool. OpenSHS (Open Smart Home Simulator) was developed as an open-source, 3D and cross-platform smart home simulator that offers a novel hybrid approach to dataset generation. The tool allows the researchers to design a smart home and populate it with the needed smart devices. Then, the participants can use the designed smart home and simulate their habits and patterns. Anomaly detection in the smart home domain is highly contextual and dependent on the inhabitant’s activities. One inhabitant’s anomaly could be the norm for another, therefore the definition of anomalies is a complex consideration. Using OpenSHS, seven participants were invited to generated forty-two datasets of their activities. Moreover, each participant defined his/her own anomalous pattern that he/she would like the model to detect. Thus, the resulting datasets are annotated with contextual anomalies specific to each participant. The proposed encoder has been evaluated and compared against the standard CLA encoders and several state-of-the-art unsupervised anomaly detection algorithms, using Numenta Anomaly Benchmark (NAB). The HI-SDR encoder scored 81.9% accuracy, on the forty-two datasets, with 17.8% increase in accuracy compared to the k-NN algorithm and 47.5% increase over the standard CLA encoders. Using the Principal Component Analysis (PCA) algorithm as a preprocessing step proved to be beneficial to some of the tested algorithms. The k-NN algorithm scored 39.9% accuracy without PCA and scored 64.1% accuracy with PCA. Similarly, the Histogram Based Outlier Score (HBOS) algorithm scored 28.5% accuracy without PCA and 61.9% with PCA. The HTM-based models empirically showed good potential and exceeded in performance several algorithms, even without the HI-SDR encoder. However, the HTM-based models still lack an optimisation algorithm for its parameters when performing anomaly detection

Towards a multipurpose neural network approach to novelty detection

Novelty detection, the identification of data that is unusual or different in some way, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. However, utilising novelty detection approaches in a particular scenario presents significant challenges to the non-expert user. They must first select an appropriate approach from the novelty detection literature for their scenario. Then, suitable values must be determined for any parameters of the chosen approach. These challenges are at best time consuming and at worst prohibitively difficult for the user. Worse still, if no suitable approach can be found from the literature, then the user is left with the impossible task of designing a novelty detector themselves. In order to make novelty detection more accessible, an approach is required which does not pose the above challenges. This thesis presents such an approach, which aims to automatically construct novelty detectors for specific applications. The approach combines a neural network model, recently proposed to explain a phenomenon observed in the neural pathways of the retina, with an evolutionary algorithm that is capable of simultaneously evolving the structure and weights of a neural network in order to optimise its performance in a particular task. The proposed approach was evaluated over a number of very different novelty detection tasks. It was found that, in each task, the approach successfully evolved novelty detectors which outperformed a number of existing techniques from the literature. A number of drawbacks with the approach were also identified, and suggestions were given on ways in which these may potentially be overcome.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Tale of Two Filters -- On-line Novelty Detection

For mobile robots, as well as other learning systems, the ability to highlight unexpected features of their environment -- novelty detection -- is very useful. One particularly important application for a robot equipped with novelty detection is inspection, highlighting potential problems in an environment. In this paper two novelty filters, both of which are capable of on-line and off-line novelty detection, are compared for two robot inspection tasks, one using sonar and the other camera images. The benefits and problems of using each of the filters are discussed and demonstrated