WTA/TLA: A UAV-captured dataset for semantic segmentation of energy infrastructure

Automated inspection of energy infrastructure with Unmanned Aerial Vehicles (UAVs) is becoming increasingly important, exhibiting significant advantages over manual inspection, including improved scalability, cost/time effectiveness, and risks reduction. Although recent technological advancements enabled the collection of an abundance of vision data from UAVs’ sensors, significant efforts are still required from experts to interpret manually the collected data and assess the condition of energy infrastructure. Thus, semantic understanding of vision data collected from UAVs during inspection is a critical prerequisite for performing autonomous robotic tasks. However, the lack of labeled data introduces challenges and limitations in evaluating the performance of semantic prediction algorithms. To this end, we release two novel semantic datasets (WTA and TLA) of aerial images captured from power transmission networks and wind turbine farms, collected during real inspection scenarios with UAVs. We also propose modifications to existing state-of-the-art semantic segmentation CNNs to achieve improved trade-off between accuracy and computational complexity. Qualitative and quantitative experiments demonstrate both the challenging properties of the provided dataset and the effectiveness of the proposed networks in this domain.The dataset is available at: https://github.com/gzamps/wta_tla_dataset

Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

A Comparative Assessment of Biodiesel Cetane Number Predictive Correlations Based on Fatty Acid Composition

Sixteen biodiesel cetane number (CN) predictive models developed since the early 1980s have been gathered and compared in order to assess their predictive capability, strengths and shortcomings. All are based on the fatty acid (FA) composition and/or the various metrics derived directly from it, namely, the degree of unsaturation, molecular weight, number of double bonds and chain length. The models were evaluated against a broad set of experimental data from the literature comprising 50 series of measured CNs and FA compositions. It was found that models based purely on compositional structure manifest the best predictive capability in the form of coefficient of determination R2. On the other hand, more complex models incorporating the effects of molecular weight, degree of unsaturation and chain length, although reliable in their predictions, exhibit lower accuracy. Average and maximum errors from each model’s predictions were also computed and assessed

Implementation of a Hyperchaotic System with Hidden Attractors into a Microcontroller

In this work, the implementation of a hyperchaotic oscillator by using a microcontroller is proposed. The dynamical system, which is used, belongs to the recently new proposed category of dynamical systems with hidden attractors. By programming the microcontroller, the three most useful tools of nonlinear theory, the phase portrait, the Poincaré map and the bifurcation diagram can be produced. The comparison of these with the respective simulation results, which are produced by solving the continuous dynamical system with Runge-Kutta, verified the feasibility of the proposed method. The algorithms could be easily modified to add or substitute the hyperchaotic system

Implementation of a Hyperchaotic System with Hidden Attractors into a Microcontroller

In this work, the implementation of a hyperchaotic oscillator by using a microcontroller is proposed. The dynamical system, which is used, belongs to the recently new proposed category of dynamical systems with hidden attractors. By programming the microcontroller, the three most useful tools of nonlinear theory, the phase portrait, the Poincaré map and the bifurcation diagram can be produced. The comparison of these with the respective simulation results, which are produced by solving the continuous dynamical system with Runge-Kutta, verified the feasibility of the proposed method. The algorithms could be easily modified to add or substitute the hyperchaotic system

Synthetic ground truth data generation for automatic trajectory-based ADL detection

In-house automatic activity detection is highly important toward the automatic evaluation of the resident's cognitive state. However, current activity detection systems suffer from the demand for on-site acquisition of large amounts of ground truth data for training purposes, which poses a major obstacle to their real-world applicability. In this paper, focusing on resident location trajectory-based activity recognition through limited amount of low-cost cameras, we introduce a novel scheme for automatic ground truth data generation, via simulation of resident trajectories based on formal descriptions of activities. Additionally, we present an activity detection scheme capable of learning activity patterns from such synthetic ground truth data. Experimental results show that our methodology achieves activity detection performance that is comparable to state-of-art methods, while suppressing the need for any actual ground truth recordings, thus boosting the real-world applicability of practical activity detection systems. © 2014 IEEE

A tool to monitor and support physical exercise interventions for MCI and AD patients

This paper presents a tool to monitor and support the execution of common physical exercise interventions targeting people with Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD) and elderly in general. Our tool aims (a) to stimulate and guide patients within physical exercise programs, (b) to monitor patient capacity to perform exercises suggested by clinicians and provide objective feedback and (c) to enable early diagnosis of significant changes in the physical capacity of users over time. Our tool incorporates a virtual 3D trainer, demonstrating prescribed exercises; currently, arms lifting, arms stretching, torso bending and torso twisting are supported. Utilizing a low-cost depth camera and markerless skeletal joint estimation, our tool monitors movement during exercise execution, evaluating patient performance with a set of metrics introduced herein. Through preliminary experimental analysis, our metrics were found of significant potential to discriminate among good and bad executions of the currently supported exercises. Copyright © 2014 ICST