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Abstract: Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi’s entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon’s entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi’s entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks. [Copyright &y& Elsevier]

This paper focuses on the processing of experimentally measured pollution data. Measuring locally both air quality parameters and atmospheric data can show how complex can be their interrelations and how they change spatially. Furthermore, apart from physical and biochemical dependencies, two important aspects need to be incorporated in the model, traffic data and topographic information, like presence and configuration of buildings and roads. Since estimating the evolution of pollutant in the urban air can have significant economic impact already on a short term basis as well as relevant consequences on public health on a medium-long term scale, various interdisciplinary researches are under way on this subject. In this work, we pursue two goals. The first one is to derive a representative model of the multivariate relationships that should be able to reproduce local interactions; the second goal of the paper is to predict, when possible, the short term evolution of pollutants in order to prevent the onset of above threshold levels of pollutants that can be dangerous to humans. The threshold levels of interest are fixed by both EU recommendations and regional regulations. As a by-product of the research, we could derive some directives to be supplied to local authorities to properly organize car traffic in advance based on the estimated parameters. The case study here proposed is that of Villa San Giovanni, a small town at the tip of Italy, located just in front of Sicily, on the Messina Strait. This is a significant case, since the city is affected by the heavy traffic directed (and coming from) Sicily. The main results here reported include the short time prediction of the concentration of hydrocarbons (HC) in the local air, the comparison between different methods based on fuzzy neural systems, and the proposal of local models of non-linear interactions among traffic, atmospheric and pollution data. Additionally, comments on a longer horizon forecast are given. [Copyright &y& Elsevier]

In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dangerous malware attacks that make intrusion recognition a very difficult task. In this context, traditional analytical tools are facing severe challenges to detect and mitigate these threats. In this work, we introduce a novel statistical analysis and autoencoder (AE) driven intelligent intrusion detection system (IDS). Specifically, the proposed IDS combines data analytics and statistical techniques with recent advances in machine learning theory to extract more optimized, strongly correlated features. The proposed IDS is evaluated using the benchmark NSL-KDD database. Comparative experimental results show that the designed statistical analysis and AE based IDS achieves better classification performance compared to conventional deep and shallow machine learning and other recently proposed state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet , is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems. [ABSTRACT FROM AUTHOR]

Abstract: The up-to-date structural designing makes by now widely use of high performance numerical codes, mainly in terms of computational powerful, cost and sizing, only available till some time before to limited groups of users. This allowed the experts to focus their attention on a qualifying aspect of the designing, i.e. an use of the materials very close to their limit behavior. Late innovative approaches in material mechanics gave in addition the opportunity to build models very close to the actual behavior but without introducing heavy computational aspects. In this paper, phenomena which relate mechanical stresses with electromagnetic properties of a defined material have been exploited in order to reconstruct electromagnetic maps starting from mechanical quantities by means of support vector regression machines (SVRMs). Purpose of the proposed study is to reconstruct a stress map in strained metallic plates by using electromagnetic measures. Moreover, an heuristic approach is proposed in order to estimate electromagnetic behavior of a stressed plate starting from easily measurable mechanical quantities. It would be very interesting when electrical or mechanical measurements are very hard to realize. The proposed approach could be very useful in such situations as quality controls of civil buildings, without the necessity of applying expensive and time-consuming destructive or non-destructive testing. In this way, it is possible to have a substantially precise idea of mechanical stresses in metallic materials by estimating the local variation of electromagnetic field into the same material using a SVRM-based interpolator. [Copyright &y& Elsevier]

Creutzfeldt-Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs). In this paper, we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD. Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs, which characterize the presence of PSWCs in an EEG tracing. The new coefficient, called connection coefficient, and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs. Furthermore, network motifs are able to detect what are the most active and/or connected brain areas in the tracing segments with PSWCs. The results obtained show that, analogously to what happens for other neurological diseases, network analysis can be successfully exploited to investigate sCJD. [ABSTRACT FROM AUTHOR]

Highlights: [•] We evaluated permutation entropy (PE) extracted from different electrodes in patients with typical absences (TAs) and healthy subjects. [•] In patients with TAs there was a recurrent behavior of PE topography, with electrodes from anterior regions associated to higher randomness and electrodes from posterior regions associated to lower randomness. [•] PE seems a useful tool to disclose abnormalities of cerebral electric activity not revealed by conventional EEG. [Copyright &y& Elsevier]

Abstract: Real-time non-destructive testing and evaluation (NDT/E) of conducting materials using eddy current techniques (ECTs) has gained significance in the last few years. This paper proposes a real-time application of ECT–NDT system exploiting giant magneto-resistive (GMR) sensors for inspection of printed circuit boards (PCBs). Probe design aims to crack inspection over flat surface, especially suitable for micro-defect detection on high density bare PCB. We propose a system based on a GMR sensor able to detect the magnetic field resulting from the interaction between a planar coil exciter and PCBs. The EC signals, detected by the GMR sensor, have been acquired by a high speed analog-to-digital (A/D) converter, for a subsequent application of signal processing based on digital techniques. The achieved results have highlighted the efficient design of the system. The advantages of the proposed models and some possible improvements of the system are also discussed. [Copyright &y& Elsevier]