Search results for "NVO"

Systems Approach to Eastern Baltic Coastal Zone Management

2020

Relying on the results of multivariate analysis of the re-analysis case studies from the BaltCoast project, specific features of integrated coastal management (ICM) approaches in Estonia, Latvia, Lithuania, and the Kaliningrad Oblast of the Russian Federation are highlighted in this paper. Eleven Eastern Baltic ICM case studies have been re-analyzed in-depth, which was the main focus of the present paper, covering a wide range of coastal landscapes, themes, policy issues, and ICM approaches. Five principal components explaining 84.86% of the total variance of ICM factor scores have been elicited by calculating rotation sums of squared loadings: (1) Stakeholder Involvement

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

2019

Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…

Benchmark database for fine-grained image classification of benthic macroinvertebrates

2018

Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categori…

Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art.

2019

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendium on DSSs in view of supporting researchers and engineers on the selection of the most suitable method to address their management/operation/design problems. Results showed that DSSs were mostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective in order to provide more reliable results. Only …

Temperate Fish Detection and Classification: a Deep Learning based Approach

2021

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

Do Australopithecus aos ciborgues. Estamos diante do fim da evolução humana?.

2020

Abstract Social implementation of post-humanism could affect the biological evolution of living beings and especially that of humans. This paper addresses the issue from the biological and anthropological-philosophical perspectives. From the biological perspective, reference is made first to the evolution of hominids until the emergence of Homo sapiens, and secondly, to the theories of evolution with special reference to their scientific foundation and the theory of extended heredity. In the anthropological-philosophical part, the paradigm is presented according to which human consciousness, in its emancipatory zeal against biological nature, must “appropriate” the roots of its physis to tr…

FeatherCNN: Fast Inference Computation with TensorGEMM on ARM Architectures

2020

Deep Learning is ubiquitous in a wide field of applications ranging from research to industry. In comparison to time-consuming iterative training of convolutional neural networks (CNNs), inference is a relatively lightweight operation making it amenable to execution on mobile devices. Nevertheless, lower latency and higher computation efficiency are crucial to allow for complex models and prolonged battery life. Addressing the aforementioned challenges, we propose FeatherCNN – a fast inference library for ARM CPUs – targeting the performance ceiling of mobile devices. FeatherCNN employs three key techniques: 1) A highly efficient TensorGEMM (generalized matrix multiplication) routine is app…

Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals

2020

Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…

Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks

2018

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is propos…

2021

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and w…