Unsupervised clustering of IoT signals through feature extraction and self organizing maps

This thesis scope is to build a clustering model to inspect the structural properties of a dataset composed of IoT signals and to classify these through unsupervised clustering algorithms. To this end, a feature-based representation of the signals is used. Different feature selection algorithms are then used to obtain reduced feature spaces, so as to decrease the computational cost and the memory demand. Thus, the IoT signals are clustered using Self-Organizing Maps (SOM) and then evaluatedope

Biometric signals compression with time- and subject-adaptive dictionary for wearable devices

This thesis work is dedicated to the design of a lightweight compression technique for the real-time processing of biomedical signals in wearable devices. The proposed approach exploits the unsupervised learning algorithm of the time-adaptive self-organizing map (TASOM) to create a subject-adaptive codebook applied to the vector quantization of a signal. The codebook is obtained and then dynamically refined in an online fashion, without requiring any prior information on the signal itsel

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks

Spatial Analysis for Landscape Changes

Recent increasing trends of the occurrence of natural and anthropic processes have a strong impact on landscape modification, and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage landscape changes. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. One of the most effective ways of investigating natural landscape changes is the geomorphological one, which benefits from recent advances in the development of digital elevation model (DEM) comparison software and algorithms, image change detection, and landscape evolution models. This Special Issue collects six papers concerning the application of traditional and innovative multidisciplinary methods in several application fields, such as geomorphology, urban and territorial systems, vegetation restoration, and soil science. The papers include multidisciplinary studies that highlight the usefulness of quantitative analyses of satellite images and UAV-based DEMs, the application of Landscape Evolution Models (LEMs) and automatic landform classification algorithms to solve multidisciplinary issues of landscape changes. A review article is also presented, dealing with the bibliometric analysis of the research topic

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

APPLICATIONS OF INNOVATIVE BUILDING MATERIAL AND COMPUTER VISION METHODS IN GEOTECHNICAL ENGINEERING

Ph.D