bluuebunny/crossref_metadata_2025 huggingface dataset:
An Investigation of the Work Hardening Behavior in Interrupted Cutting Inconel 718 under Cryogenic Conditions
Authors: Dai Xing , Zhuang Kejia , Pu Donglin , Zhang Weiwei , Ding Han
Published: May 2020

The severe work hardening phenomenon generated in the machining of Inconel 718 is harmful to continue cutting processes, while being good for the component’s service performance. This paper investigates the performance of cryogenic assisted machining used in the cutting processes, which can reduce the waste of fluids. The influence of dry and cryogenic machining conditions with different cutting speeds on the work hardening layer is investigated based on the interrupted cutting of Inconel 718. Cutting temperature distribution obtained from simulations under different conditions is used to discuss the potential mechanism of work hardening. Then, the depth of work hardening and degree of work hardening (DWH) are investigated to analyze the surface deformation behavior, which strengthens the machined surface during metal cutting processes. From the cutting experiments, the depth of the work hardening layer can reach more than 60 μm under the given cutting conditions. In addition, a deeper zone can be obtained by the cooling of liquid nitrogen, which may potentially enhance the wear performance of the component. The results obtained from this work can be utilized to effectively control the work hardening layer beneath the surface, which can be applied to improve the service performance.
Evolution Inclusions in Banach Spaces under Dissipative Conditions
Authors: Donchev Tzanko , Bilal Shamas , Cârjă Ovidiu , Javaid Nasir , Lazu Alina I.
Published: May 2020

We develop a new concept of a solution, called the limit solution, to fully nonlinear differential inclusions in Banach spaces. That enables us to study such kind of inclusions under relatively weak conditions. Namely we prove the existence of this type of solutions and some qualitative properties, replacing the commonly used compact or Lipschitz conditions by a dissipative one, i.e., one-sided Perron condition. Under some natural assumptions we prove that the set of limit solutions is the closure of the set of integral solutions.
Anti-Inflammatory and Protein Tyrosine Phosphatase 1B Inhibitory Metabolites from the Antarctic Marine-Derived Fungal Strain Penicillium glabrum SF-7123
Authors: Ha Tran Minh , Kim Dong-Cheol , Sohn Jae Hak , Yim Joung Han , Oh Hyuncheol
Published: May 2020

A chemical investigation of the marine-derived fungal strain Penicillium glabrum (SF-7123) revealed a new citromycetin (polyketide) derivative (1) and four known secondary fungal metabolites, i.e, neuchromenin (2), asterric acid (3), myxotrichin C (4), and deoxyfunicone (5). The structures of these metabolites were identified primarily by extensive analysis of their spectroscopic data, including NMR and MS data. Results from the initial screening of anti-inflammatory effects showed that 2, 4, and 5 possessed inhibitory activity against the excessive production of nitric oxide (NO) in lipopolysaccharide (LPS)-stimulated BV2 microglial cells, with IC50 values of 2.7 µM, 28.1 µM, and 10.6 µM, respectively. Compounds 2, 4, and 5 also inhibited the excessive production of NO, with IC50 values of 4.7 µM, 41.5 µM, and 40.1 µM, respectively, in LPS-stimulated RAW264.7 macrophage cells. In addition, these compounds inhibited LPS-induced overproduction of prostaglandin E2 in both cellular models. Further investigation of the most active compound (2) revealed that these anti-inflammatory effects were associated with a suppressive effect on the over-expression of inducible nitric oxide synthase and cyclooxygenase-2. Finally, we showed that the anti-inflammatory effects of compound 2 were mediated via the downregulation of inflammation-related pathways such as those dependent on nuclear factor kappa B and p38 mitogen-activated protein kinase in LPS-stimulated BV2 and RAW264.7 cells. In the evaluation of the inhibitory effects of the isolated compounds on protein tyrosine phosphate 1B (PTP1B) activity, compound 4 was identified as a noncompetitive inhibitor of PTP1B, with an IC50 value of 19.2 µM, and compound 5 was shown to inhibit the activity of PTP1B, with an IC50 value of 24.3 µM, by binding to the active site of the enzyme. Taken together, this study demonstrates the potential value of marine-derived fungal isolates as a bioresource for bioactive compounds.
Where do we Stand after Decades of Studying Human Cytomegalovirus?
Authors: Gugliesi Francesca , Coscia Alessandra , Griffante Gloria , Galitska Ganna , Pasquero Selina , Albano Camilla , Biolatti Matteo
Published: May 2020

Human cytomegalovirus (HCMV), a linear double-stranded DNA betaherpesvirus belonging to the family of Herpesviridae, is characterized by widespread seroprevalence, ranging between 56% and 94%, strictly dependent on the socioeconomic background of the country being considered. Typically, HCMV causes asymptomatic infection in the immunocompetent population, while in immunocompromised individuals or when transmitted vertically from the mother to the fetus it leads to systemic disease with severe complications and high mortality rate. Following primary infection, HCMV establishes a state of latency primarily in myeloid cells, from which it can be reactivated by various inflammatory stimuli. Several studies have shown that HCMV, despite being a DNA virus, is highly prone to genetic variability that strongly influences its replication and dissemination rates as well as cellular tropism. In this scenario, the few currently available drugs for the treatment of HCMV infections are characterized by high toxicity, poor oral bioavailability, and emerging resistance. Here, we review past and current literature that has greatly advanced our understanding of the biology and genetics of HCMV, stressing the urgent need for innovative and safe anti-HCMV therapies and effective vaccines to treat and prevent HCMV infections, particularly in vulnerable populations.
Petrogenesis and Tectonic Implications of the Early Cretaceous Granitic Pluton in the Sulu Orogenic Belt: The Caochang Granitic Pluton as an Example
Authors: Meng Yuanku , Wang Zhongbo , Gan Baoping , Liu Jinqing
Published: May 2020

The Sulu orogenic belt is the source of information on important magmatic events associated with the collision of the Yangtze craton and North China craton (NCC) and the destruction of the NCC during the Mesozoic in eastern China. In this study, we have, for the first time, identified a monzonitic granitic pluton. We hereby present petrological, geochemical, and zircon U-Pb-Hf-O isotopic data, shedding new light on the petrogenesis and tectonic implications for the granitic pluton in the Sulu belt. LA-ICP-MS and SHRIMP II analyses of zircon grains suggest that the monzonitic granitic pluton was crystallized in the Early Cretaceous (ca. 120 Ma). Geochemically, the granitic pluton shows sub-alkaline, high-K calc-alkaline, and metaluminous signatures, and is genetically of I-type granite, excluding the possibility of S-type granite, as evidenced by mantle-like zircon oxygen isotopic features. In addition, the pluton is enriched in light REE and large-ion lithophile elements (LILE) (e.g., La, Cs, Ba, K, and Pb), but depleted in high-field-strength elements (HFSE) (e.g., Nb, Ta, P, and Ti), suggesting an arc-related affinity. Zircon Hf isotopes (εHf(t) = −27.51~−32.35; TDM2 = 2979~3175 Ma) and mantle-like δ18O values (5.12–6.24‰) together indicate that the identified granitic pluton is derived from the partial melting (reworking) of the ancient mafic lower crustal material, with no supra-crustal material participation. Moreover, high Magnesium number (Mg# = 42–49) values and mafic micro-granular enclaves suggest that mantle-derived magma participated in the evolution of the granitic pluton in this study. Integrating the findings of this study and previous work, we propose that the Caochang granitic pluton is derived from the partial melting of the deep Yangtze basaltic lower crust during the Early Cretaceous, and that the large-scale thinning of the lithospheric mantle was the main factor that led to Early Cretaceous magmatic flare-up in the Sulu orogenic belt.
Layer Morphology and Ink Compatibility of Silver Nanoparticle Inkjet Inks for Near-Infrared Sintering
Authors: Reenaers Dieter , Marchal Wouter , Biesmans Ianto , Nivelle Philippe , D’Haen Jan , Deferme Wim
Published: May 2020

The field of printed electronics is rapidly evolving, producing low cost applications with enhanced performances with transparent, stretchable properties and higher reliability. Due to the versatility of printed electronics, industry can consider the implementation of electronics in a way which was never possible before. However, a post-processing step to achieve conductive structures—known as sintering—limits the production ease and speed of printed electronics. This study addresses the issues related to fast sintering without scarifying important properties such as conductivity and surface roughness. A drop-on-demand inkjet printer is employed to deposit silver nanoparticle-based inks. The post-processing time of these inks is reduced by replacing the conventional oven sintering procedure with the state-of-the-art method, named near-infrared sintering. By doing so, the post-processing time shortens from 30–60 min to 6–8 s. Furthermore, the maximum substrate temperature during sintering is reduced from 200 °C to 120 °C. Based on the results of this study, one can conclude that near-infrared sintering is a ready-to-industrialize post-processing method for the production of printed electronics, capable of sintering inks at high speed, low temperature and with low complexity. Furthermore, it becomes clear that ink optimization plays an important role in processing inkjet printable inks, especially after being near-infrared sintered.
Toxoplasma gondii Monitoring in Liver Transplantation Patients: A Single Center Cross-Sectional Study in an Italian Hospital
Authors: Pinto Barbara , Lotti Federica , Petruccelli Stefania , Carrai Paola , De Simone Paolo , Bruschi Fabrizio
Published: May 2020

Toxoplasma gondii (TG) is one of the most widespread intracellular parasites in the world, despite the slight declining trend in industrialized countries. Whilst the infection is often asymptomatic in immunocompetent hosts, in immunocompromised patients such as organ transplant recipients it can have important clinical sequels with even fatal consequences. We retrospectively reviewed 568 primary liver transplants (LT) from deceased donors from 2012 to 2017. Data were analyzed adjusting for year, gender, and age. The study objective was to assess the incidence of post-transplant TG infection and adherence to international guidelines for primary chemoprophylaxis. Prior to transplantation, 42.4% of recipients tested seronegative and 56.5% seropositive, while 36.6% of donors were seropositive and 40.4% showed undetermined serology. Anti-TG antibody titer was higher in patients born abroad (71.4%) versus Italy (54.8%). Among recipients at high risk of post-transplant TG infection, 82.7% of them received chemoprophylaxis, while in 17.3% of cases no prophylaxis was administered. At a mean (SD) follow-up of 21.2 (12.4) months no case of TG infection has been observed. Despite the low rate of adherence to recommendations, prophylaxis of high-risk LT recipients provides control of post-transplant TG infection risk. Review of current guidelines is warranted for low-risk populations.
Differentially-Charged Liposomes Interact with Alphaherpesviruses and Interfere with Virus Entry
Authors: Kolyvushko Oleksandr , Latzke Juliane , Dahmani Ismail , Osterrieder Nikolaus , Chiantia Salvatore , Azab Walid
Published: May 2020

Exposure of phosphatidylserine (PS) in the outer leaflet of the plasma membrane is induced by infection with several members of the Alphaherpesvirinae subfamily. There is evidence that PS is used by the equine herpesvirus type 1 (EHV-1) during entry, but the exact role of PS and other phospholipids in the entry process remains unknown. Here, we investigated the interaction of differently charged phospholipids with virus particles and determined their influence on infection. Our data show that liposomes containing negatively charged PS or positively charged DOTAP (N-[1-(2,3-Dioleoyloxy)propyl]-N,N,N-trimethylammonium) inhibited EHV-1 infection, while neutral phosphatidylcholine (PC) had no effect. Inhibition of infection with PS was transient, decreased with time, and was dose dependent. Our findings indicate that both cationic and anionic phospholipids can interact with the virus and reduce infectivity, while, presumably, acting through different mechanisms. Charged phospholipids were found to have antiviral effects and may be used to inhibit EHV-1 infection.
Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process
Authors: Khaled Alfadhl Yahya , Kabutey Abraham , Selvi Kemal Çağatay , Mizera Čestmír , Hrabe Petr , Herák David
Published: May 2020

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.
Optimization of In Situ Backwashing Frequency for Stable Operation of Anaerobic Ceramic Membrane Bioreactor
Authors: Thejani Nilusha Rathmalgodage , Wang Tuo , Wang Hongyan , Yu Dawei , Zhang Junya , Wei Yuansong
Published: May 2020

The cost-effective and stable operation of an anaerobic ceramic membrane bioreactor (AnCMBR) depends on operational strategies to minimize membrane fouling. A novel strategy for backwashing, filtration and relaxation was optimized for stable operation of a side stream tubular AnCMBR treating domestic wastewater at the ambient temperature. Two in situ backwashing schemes (once a day at 60 s/day, and twice a day at 60 s × 2/day) maintaining 55 min filtration and 5 min relaxation as a constant were compared. A flux level over 70% of the initial membrane flux was stabilized by in situ permeate backwashing irrespective of its frequency. The in situ backwashing by permeate once a day was better for energy saving, stable membrane filtration and less permeate consumption. Ex situ chemical cleaning after 60 days’ operation was carried out using pure water, sodium hypochlorite (NaOCl), and citric acid as the order. The dominant cake layer was effectively reduced by in situ backwashing, and the major organic foulants were fulvic acid-like substances and humic acid-like substances. Proteobacteria, Firmucutes, Epsilonbacteria and Bacteroides were the major microbes attached to the ceramic membrane fouling layer which were effectively removed by NaOCl.
Developing Emotion-Aware Human–Robot Dialogues for Domain-Specific and Goal-Oriented Tasks
Authors: Huang Jhih-Yuan , Lee Wei-Po , Chen Chen-Chia , Dong Bu-Wei
Published: May 2020

Developing dialogue services for robots has been promoted nowadays for providing natural human–robot interactions to enhance user experiences. In this study, we adopted a service-oriented framework to develop emotion-aware dialogues for service robots. Considering the importance of the contexts and contents of dialogues in delivering robot services, our framework employed deep learning methods to develop emotion classifiers and two types of dialogue models of dialogue services. In the first type of dialogue service, the robot works as a consultant, able to provide domain-specific knowledge to users. We trained different neural models for mapping questions and answering sentences, tracking the human emotion during the human–robot dialogue, and using the emotion information to decide the responses. In the second type of dialogue service, the robot continuously asks the user questions related to a task with a specific goal, tracks the user’s intention through the interactions and provides suggestions accordingly. A series of experiments and performance comparisons were conducted to evaluate the major components of the presented framework and the results showed the promise of our approach.
Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method
Authors: Yasmin Jannat , Lohumi Santosh , Ahmed Mohammed Raju , Kandpal Lalit Mohan , Faqeerzada Mohammad Akbar , Kim Moon Sung , Cho Byoung-Kwan
Published: May 2020

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.
Prediction of Visual Memorability with EEG Signals: A Comparative Study
Authors: Jo Sang-Yeong , Jeong Jin-Woo
Published: May 2020

Visual memorability is a method to measure how easily media contents can be memorized. Predicting the visual memorability of media contents has recently become more important because it can affect the design principles of multimedia visualization, advertisement, etc. Previous studies on the prediction of the visual memorability of images generally exploited visual features (e.g., color intensity and contrast) or semantic information (e.g., class labels) that can be extracted from images. Some other works tried to exploit electroencephalography (EEG) signals of human subjects to predict the memorability of text (e.g., word pairs). Compared to previous works, we focus on predicting the visual memorability of images based on human biological feedback (i.e., EEG signals). For this, we design a visual memory task where each subject is asked to answer whether they correctly remember a particular image 30 min after glancing at a set of images sampled from the LaMemdataset. During the visual memory task, EEG signals are recorded from subjects as human biological feedback. The collected EEG signals are then used to train various classification models for prediction of image memorability. Finally, we evaluate and compare the performance of classification models, including deep convolutional neural networks and classical methods, such as support vector machines, decision trees, and k-nearest neighbors. The experimental results validate that the EEG-based prediction of memorability is still challenging, but a promising approach with various opportunities and potentials.
Non-Dimensional Star-Identification
Authors: Leake Carl , Arnas David , Mortari Daniele
Published: May 2020

This study introduces a new “Non-Dimensional” star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit. The algorithm performance is compared in terms of accuracy, speed, and robustness to the Pyramid algorithm. These comparisons highlight the clear advantages that a combined approach of these methodologies provides.
Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature
Authors: Algabri Redhwan , Choi Mun-Taek
Published: May 2020

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.
EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies
Authors: Jiang Yihang , Qi Yuankai , Wang Will Ke , Bent Brinnae , Avram Robert , Olgin Jeffrey , Dunn Jessilyn
Published: May 2020

The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks.
A Panorama of Cloud Platforms for IoT Applications Across Industries
Authors: Yangui Sami
Published: May 2020

Internet of Things (IoT) applications can play a critical role in business and industry. Industrial IoT (IIoT) refers to the use of IoT technologies in manufacturing. Enabling IIoT applications in cloud environments requires the design of appropriate IIoT Platform as-a-Service (IIoT PaaS) to support and ease their provisioning (i.e., development, deployment and management). This paper critically reviews the IIoT PaaS architectures proposed so far in the relevant literature. It only surveys the architectures that are suitable for IIoT applications provisioning and it excludes regular IoT solutions from its scope. The evaluation is based on a set of well-defined architectural requirements. It also introduces and discusses the future challenges and the research directions. The critical review discusses the PaaS solutions that focus on the whole spectrum of IoT verticals and also the ones dealing with specific IoT verticals. Existing limitations are identified and hints are provided on how to tackle them. As critical research directions, the mechanisms that enable the secure provisioning, and IIoT PaaS interaction with virtualized IoT Infrastructure as-a-Service (IaaS) and fog computing layer are discussed.
Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones
Authors: Ashraf Imran , Hur Soojung , Park Yongwan
Published: May 2020

Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.
Space–Time–Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography
Authors: Auboiroux Vincent , Larzabal Christelle , Langar Lilia , Rohu Victor , Mishchenko Ales , Arizumi Nana , Labyt Etienne , Benabid Alim-Louis , Aksenova Tetiana
Published: May 2020

Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space–time–frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time–frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger.
On the Testing of Advanced Automotive Radar Sensors by Means of Target Simulators
Authors: Hudec Premysl , Adler Viktor
Published: May 2020

The rapid development and wide commercial implementation of automotive radar sensors are strengthening the already considerable interest in matching radar target simulators. Such simulators boast promising results when used for both essential functional inspections of active sensors and the high-speed testing of numerous traffic scenarios while examining complex reactions of automobile electronic systems. For these purposes, advanced versions of target simulators enabling a generation of multiple targets moving at different velocities and ranges are required. The design, practical implementation and system programming of advanced sensor simulator setups require a detailed analytical description concerning all important technical aspects. An abundance of detailed information on the behavior and parameters of automotive radar sensors can be found in the references, but similar knowledge on sensor simulator setups is lacking. This article presents detailed analyses of the all-important RF parameters, where special attention is paid to phase noise, and its analytical description takes into account an even greater number of simulated targets. The derived analytical formulas enable both an optimal setup implementation and system programming of a wide range of practical testing procedures.
A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification
Authors: Vununu Caleb , Lee Suk-Hwan , Kwon Ki-Ryong
Published: May 2020

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated task due to the heterogeneity of these cellular images. Hence, an automated classification scheme appears to be necessary. However, the majority of the available methods prefer to utilize the supervised learning approach for this problem. The need for thousands of images labelled manually can represent a difficulty with this approach. The first contribution of this work is to demonstrate that classifying HEp-2 cell images can also be done using the unsupervised learning paradigm. Unlike the majority of the existing methods, we propose here a deep learning scheme that performs both the feature extraction and the cells’ discrimination through an end-to-end unsupervised paradigm. We propose the use of a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding–decoding scheme. At the same time, we embed in the network a clustering layer whose purpose is to automatically discriminate, during the feature learning process, the latent representations produced by the DCAE. Furthermore, we investigate how the quality of the network’s reconstruction can affect the quality of the produced representations. We have investigated the effectiveness of our method on some benchmark datasets and we demonstrate here that the unsupervised learning, when done properly, performs at the same level as the actual supervised learning-based state-of-the-art methods in terms of accuracy.
Experimental Investigation of Optimal Relay Position for Magneto-Inductive Wireless Sensor Networks
Authors: Qiao Gang , Muzzammil Muhammad , Ahmed Niaz , Ullah Irfan
Published: May 2020

Magneto-inductive (MI) waveguide technology is often proposed to increase the MI communication distance without adding significant cost and power consumption to the wireless sensor network. The idea is to add intermediate relaying nodes between transmitter (Tx) and receiver (Rx) to relay the information from Tx to Rx. Our study of MI wave-guides has realized that adding a relay node improves the communication distance, however, the performance is greatly dependent on the position of the relaying node in the network. We therefore, in this work have investigated the effect of placement of a relay node and have determined the optimal relay position. We have performed various sets of experiments to thoroughly understand the behavior and identified three main regions: (a) for region 1, when the distance between Tx and Rx is equal or less than the diameter of the coils ( d ≤ 2 r ), the optimal relay position is close to Tx, (b) for region 2, when the distance between Tx and Rx is greater than diameter of the coils but less than twice the diameter ( 2 r < d < 4 r ), the optimal relay position lies in the center of Tx and Rx, and (c) for region 3, when the distance between the Tx and Rx is equal or greater than twice the diameter of the coils ( d ≥ 4 r ), the optimal relay position is close to Rx.
Effective and Efficient Pretreatment of Polyimide Substrates by Capacitively Coupled Plasma for Coating the Composites of Tetracycline-Imprinted Polymers and Quantum Dots: Comparison with Chemical Pretreatment
Authors: Ke Ching-Bin , Chen Jian-Lian
Published: May 2020

Composites of tetracycline (Tc)-imprinted polymethacrylates and quantum dots have been coated on chemically pretreated polyimide substrates (PIs) as fluorescent sensors. In this study, PIs were pretreated by capacitively coupled plasma (CCP) before coating the same composites on them. For the first time, to fabricate sensors by plasma modification of PIs, the CCP conditions, including plasma gas, flow rate, radio frequency generation power, and duration time, the fabrication details, including coating, baking, and stripping steps, and the sample loading process were optimized to perform a linear decrease in fluorescent intensity with Tc concentrations in the range of 5.0–3000 μM (R2 = 0.9995) with a limit of detection of 0.2 μM (S/N = 3, relative standard deviation (RSD) = 2.2%). The selectivity of the stripped PIs was evaluated by the imprinting factors (IFs) for Tc (IF = 7.2), other Tc analogues (IF = 3.4–5.3), and steroids (IF ≈ 1) and by the recoveries of 5.0 μM Tc from bovine serum albumin at 300 μg∙mL−1 (98%, RSD = 3.2%), fetal bovine serum at 1.5 ppt (98%, RSD = 2.8%), and liquid milk (94.5%, RSD = 5.3%). The superiority of the present plasma-treated-based sensor over the previous chemically-treated one in fabrication efficiency and detection effectiveness was clear.
Radiation Effects on Long Period Fiber Gratings: A Review
Authors: Esposito Flavio , Srivastava Anubhav , Campopiano Stefania , Iadicicco Agostino
Published: May 2020

Over the last years, fiber optic sensors have been increasingly applied for applications in environments with a high level of radiation as an alternative to electrical sensors, due to their: high immunity, high multiplexing and long-distance monitoring capability. In order to assess the feasibility of their use, investigations on optical materials and fiber optic sensors have been focusing on their response depending on radiation type, absorbed dose, dose rate, temperature and so on. In this context, this paper presents a comprehensive review of the results achieved over the last twenty years concerning the irradiation of in-fiber Long Period Gratings (LPGs). The topic is approached from the point of view of the optical engineers engaged in the design, development and testing of these devices, by focusing the attention on the fiber type, grating fabrication technique and properties, irradiation parameters and performed analysis. The aim is to provide a detailed review concerning the state of the art and to outline the future research trends.
Examination of Multi-Receiver GPS/EGNOS Positioning with Kalman Filtering and Validation Based on CORS Stations
Authors: Ciećko Adam , Bakuła Mieczysław , Grunwald Grzegorz , Ćwiklak Janusz
Published: May 2020

This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. Next, the Kalman filter was employed to give the final solution. It was proven that EGNOS positioning allows to obtain an accuracy in the range of about 0.5–1.5 m. The proposed solution involving the use of three mobile receivers and Kalman filtering allowed to reduce the 3D error to a level below 0.3 m. Such an accuracy was achieved using only GPS L1 code observations and EGNOS corrections. Additionally, a reliable monitoring of quality of GPS/EGNOS positioning in the test area based on CORS stations was presented.
A Rapid and Sensitive Salmonella Biosensor Based on Viscoelastic Inertial Microfluidics
Authors: Yao Lan , Zheng Lingyan , Cai Gaozhe , Wang Siyuan , Wang Lei , Lin Jianhan
Published: May 2020

Salmonella is a main cause of foodborne illnesses and rapid screening of Salmonella is the key to prevent Salmonella outbreaks, however available detection methods either require a long time, or need complex pretreatment, or have low sensitivity. In this study, a microfluidic biosensor was developed for Salmonella detection using viscoelastic inertial microfluidics for separating magnetic bacteria from unbound magnetic nanoparticles (MNPs) and enzyme catalytic colorimetry for amplifying biological signals. The polyclonal antibodies and horseradish peroxidase (HRP) modified MNPs were first used to specifically capture Salmonella to form magnetic HRP-bacteria. Both magnetic HRP-bacteria and unbound MNPs were magnetically separated from background and resuspended in viscoelastic polyvinylpyrrolidone solution as sample flow. When sample flow was injected with polyvinylpyrrolidone sheath flow into a T-shaped microchannel, larger-sized magnetic HRP-bacteria could penetrate the sample flow, however smaller-sized MNPs remained in the sample flow due to weaker inertial lift force and elastic lift force, resulting in continuous-flow separation of magnetic HRP-bacteria. Finally, magnetic HRP-bacteria were collected and concentrated to catalyze tetramethyl benzidine, and absorbance was measured to determine the bacteria. This biosensor was able to detect Salmonella as low as 30 CFU/mL in 1 h and featured the advantages of shorter time due to a one-step immunoreaction, easier extension due to only one antibody and one label, and lower cost due to less expensive materials.
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
Authors: Akhtiamov Oleg , Siegert Ingo , Karpov Alexey , Minker Wolfgang
Published: May 2020

Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to themselves while interacting with the system. The SDS is supposed to determine whether it is being addressed or not. All existing studies on acoustic H-M AD were conducted on corpora designed in such a way that a human addressee and a machine played different dialogue roles. This peculiarity influences speakers’ behaviour and increases vocal differences between human- and machine-directed utterances. In the present study, we consider the Restaurant Booking Corpus (RBC) that consists of complexity-identical human- and machine-directed phone calls and allows us to eliminate most of the factors influencing speakers’ behaviour implicitly. The only remaining factor is the speakers’ explicit awareness of their interlocutor (technical system or human being). Although complexity-identical H-M AD is essentially more challenging than the classical one, we managed to achieve significant improvements using data augmentation (unweighted average recall (UAR) = 0.628) over native listeners (UAR = 0.596) and a baseline classifier presented by the RBC developers (UAR = 0.539).
Development of Novel Real-Time Radiation Systems Using 4-Channel Sensors
Authors: Inaba Yohei , Nakamura Masaaki , Zuguchi Masayuki , Chida Koichi
Published: May 2020

Radiation-related tissue injuries after medical radiation procedures, such as fluoroscopically guided intervention (FGI), have been reported in patients. Real-time monitoring of medical radiation exposure administered to patients during FGI is important to avoid such tissue injuries. In our previous study, we reported a novel (prototype) real-time radiation system for FGI. However, the prototype sensor indicated low sensitivity to radiation exposure from the side and back, although it had high-quality fundamental characteristics. Therefore, we developed a novel 4-channel sensor with modified shape and size than the previous sensor, and evaluated the basic performance (i.e., measured the energy, dose linearity, dose rate, and angular dependence) of the novel and previous sensors. Both sensors of our real-time dosimeter system demonstrated the low energy dependence, excellent dose linearity (R2 = 1.0000), and good dose rate dependence (i.e., within 5% statistical difference). Besides, the sensitivity of 0° ± 180° in the horizontal and vertical directions was almost 100% sensitivity for the new sensor, which significantly improved the angular dependence. Moreover, the novel dosimeter exerted less influence on X-ray images (fluoroscopy) than other sensors because of modifying a small shape and size. Therefore, the developed dosimeter system is expected to be useful for measuring the exposure of patients to radiation doses during FGI procedures.
Deep Color Transfer for Color-Plus-Mono Dual Cameras
Authors: Jang Hae Woong , Jung Yong Ju
Published: May 2020

A few approaches have studied image fusion using color-plus-mono dual cameras to improve the image quality in low-light shooting. Among them, the color transfer approach, which transfers the color information of a color image to a mono image, is considered to be promising for obtaining improved images with less noise and more detail. However, the color transfer algorithms rely heavily on appropriate color hints from a given color image. Unreliable color hints caused by errors in stereo matching of a color-plus-mono image pair can generate various visual artifacts in the final fused image. This study proposes a novel color transfer method that seeks reliable color hints from a color image and colorizes a corresponding mono image with reliable color hints that are based on a deep learning model. Specifically, a color-hint-based mask generation algorithm is developed to obtain reliable color hints. It removes unreliable color pixels using a reliability map computed by the binocular just-noticeable-difference model. In addition, a deep colorization network that utilizes structural information is proposed for solving the color bleeding artifact problem. The experimental results demonstrate that the proposed method provides better results than the existing image fusion algorithms for dual cameras.
Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network
Authors: Palczynska Beata , Masnicki Romuald , Mindykowski Janusz
Published: May 2020

The contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed from under-sampled incoherent linear measurements. This paper highlights the use of the discrete Radon transform (DRT) techniques in the CS scheme. In the reconstruction algorithm section, a fast algorithm based on the inverse DRT is presented, in which a few randomly sampled projections of the input signal are used to correctly reconstruct the original signal. However, DRT requires a very large set of measurements that can defeat the purpose of compressive data acquisition. To acquire the wideband data below the Nyquist frequency, the K-rank-order filter is applied in the sparse transform domain to extract the most significant components and accelerate the convergence of the solution. While most CS research efforts focus on random Gaussian measurements, the Bernoulli matrix with different values of the probability of ones is applied in the presented algorithm. Preliminary results of numerical simulation confirm the effectiveness of the algorithm used, but also indicate its limitations. A significant advantage of the proposed approach is the speed of analysis, which uses fast Fourier transform (FFT) and inverse FFT (IFFT) algorithms widely available in programming environments. Moreover, the data processing algorithm is quite simple, and therefore memory usage and burden of the data processing load are relatively low.
High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers
Authors: Popescu Dragos Constantin , Dumitrache Ioan , Caramihai Simona Iuliana , Cernaianu Mihail Octavian
Published: May 2020

The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an adaptive Kalman algorithm which is used for calibrating the setup of cameras as well as for estimating the pose of the marker. Special measures are taken in order to mitigate the effect of the measurement noise. The proposed method is further tested in different scenarios using a Monte Carlo simulation, whose qualitative precision results are determined and compared. The solution is designed for specific positioning and alignment tasks in physics experiments, but also, has a degree of generality that makes it suitable for a wider range of applications.