Image Denoising using Attention-Residual Convolutional Neural Networks

During the image acquisition process, noise is usually added to the data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. In that sense, the resultant image needs to be processed to attenuate its noise without losing details. Non-learning-based strategies such as filter-based and noise prior modeling have been adopted to solve the image denoising problem. Nowadays, learning-based denoising techniques showed to be much more effective and flexible approaches, such as Residual Convolutional Neural Networks. Here, we propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN), and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN). The proposed methods try to learn the underlying noise expectation using an Attention-Residual mechanism. Experiments on public datasets corrupted by different levels of Gaussian and Poisson noise support the effectiveness of the proposed approaches against some state-of-the-art image denoising methods. ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.Comment: Published in: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Classification of EEG mental tasks using Multi-Objective Flower Pollination Algorithm for Person Identification

In the modern life, the authentication technique for any system is considered as one of the most important challenges task which must careful consideration. Therefore, many researchers have developed traditional authentication systems to deal with our digital world. Recently, The Biometric techniques have been successfully provided a high level of authentication, such as fingerprint, face recognition, and voice recognition. In this paper, a new authentication system has been proposed which is based on EEG signals with hybridizing wavelet transform and multi-objective flower pollination algorithm (MOFPA-WT). The main task of MOFPA is to find the optimal WT parameters for EEG signal denoising which can extract unique features form the EEG. The proposed method (MOFPA-WT) tested using a standard EEG database which has five different mental tasks, includes baseline, multiplication, rotation, letter composing, and visual counting. To classify the EEG signals using proposed method four classification methods are applied which are, neural network, decision tree, Naive Bayes, and support vector machine. The performance of the (MOFPA-WT) is evaluated using four criteria: (i) accuracy, (ii) sensitivity, (iii) specificity, (v) false acceptance rate. The experimental results show the (MOFPA-WT) can achieve the highest recognition rates up to 85% using neural network classifier based on visual counting task as well as the EEG_Std feature obtained the highest accuracy compared with others EEG features based on visual counting task

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in computer networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k. (C) 2014 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Aquatic weed automatic classification using machine learning techniques

Aquatic weed control through chemical products has attracted much attention in the last years, mainly because of the ecological disorder caused by such plants, and also the consequences to the economical activities. However, this kind of control has been carried out in a non-automatic way by technicians, and may be a not healthy policy, since each species may react differently to the same herbicide. Thus, this work proposes the automatic identification of some species by means of supervised pattern recognition techniques and shape descriptors in order to compose a nearby future expert system for automatic application of the correct herbicide. Experiments using some state-of-the-art techniques have shown the robustness of the employed pattern recognition techniques. (c) 2012 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.FAPESP [2010/12398-0, 2009/16206-1]FAPESPCNPq [303182/2011-3]CNP

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved

A five-year perspective on the situation of haemorrhagic fever with renal syndrome and status of the hantavirus reservoirs in Europe, 2005-2010

Hantavirus infections are reported from many countries in Europe and with highly variable annual case numbers. In 2010, more than 2,000 human cases were reported in Germany, and numbers above the baseline have also been registered in other European countries. Depending on the virus type human infections are characterised by mild to severe forms of haemorrhagic fever with renal syndrome. The member laboratories of the European Network for diagnostics of Imported Viral Diseases present here an overview of the progression of human cases in the period from 2005 to 2010. Further we provide an update on the available diagnostic methods and endemic regions in their countries, with an emphasis on occurring virus types and reservoirs