bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility of Low Fermentable Oligosaccharide, Disaccharide, Monosaccharide, and Polyol Diet and Its Effects on Quality of Life in an Italian Cohort
Authors: Cingolani Arianna , Paduano Danilo , Vecchiarelli Valentina , Demelas Manuela , Corrias Paola Teresa , Casula Laura , Usai Paolo
Published: March 2020

The low Fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) diet has demonstrated excellent results in terms of symptom control and health-related quality of life (HRQoL) in irritable bowel syndrome (IBS) sufferers, but patients have complained about unsatisfying taste, difficulty in following the diet, and time consumption. To investigate the feasibility of the low FODMAP diet in an Italian (Sardinian) cohort, sixty consecutive eligible outpatients (11 men and 49 women) with IBS were enrolled and followed a low FODMAP diet (gluten allowed, restriction phase of four weeks, reintroduction phase of four weeks). Food habits were assessed using 24-hour dietary recall, Bristol Stool Scale for stool consistency, Visual Analogue Scale for abdominal bloating, VAS (Visual Analogue Scale) for abdominal pain, IBS Severity Scoring System for perceived disease severity, and a 12-item Short Form Survey for HRQoL (psychological component summary + mental component summary) were applied at baseline (T0) and at the end of each phase (T1-four weeks and T2-eight weeks). Statistical analysis was performed by dividing the cohort into diarrhoea-dominant IBS (IBS-D) and other IBS subtypes (selected IBS-others). Comparisons between T1 and T2 vs. T0 and T2 vs. T1 were performed. The low FODMAP diet lowered VASp (VAS pain), VASb (VAS bloating), and IBS SSS (IBS Severity Scoring System), and increased PCS (Physical Component Summary) and MCS (Mental Component Summary) in both subgroups. Bristol Stool Scale (BSS) only improved in the IBS-D subgroup. The dropout mean values for MCS were higher than treated subjects and the percentage of unemployment was lower in the dropouts. According to the dropout features, the low FODMAP diet seems to show greater feasibility for patients with more time to dedicate to the diet (unemployed, homemakers, housewives, or students), more motivation, and more severe clinical features, independent of their place of residence.
Advances in Medical Nutrition Therapy: Parenteral Nutrition
Authors: Hellerman Itzhaki Moran , Singer Pierre
Published: March 2020

Parenteral nutrition has evolved tremendously, with parenteral formulas now safer and more accessible than ever. “All-in-one” admixtures are now available, which simplify parenteral nutrition usage and decrease line infection rates alongside other methods of infectious control. Recently published data on the benefits of parenteral nutrition versus enteral nutrition together with the widespread use of indirect calorimetry solve many safety issues that have emerged over the years. All these advances, alongside a better understanding of glycemic control and lipid and protein formulation improvements, make parenteral nutrition a safe alternative to enteral nutrition.
Determinants of Sweetness Preference: A Scoping Review of Human Studies
Authors: Venditti Carolina , Musa-Veloso Kathy , Lee Han Youl , Poon Theresa , Mak Alastair , Darch Maryse , Juana Justine , Fronda Dylan , Noori Daniel , Pateman Erika , Jack Maia
Published: March 2020

Factors associated with sweetness preference are multi-faceted and incredibly complex. A scoping review was undertaken to identify determinants of sweetness preference in humans. Using an online search tool, ProQuest ™, a total of 99 publications were identified and subsequently grouped into the following categories of determinants: Age, dietary factors, reproductive hormonal factors, body weight status, heritable, weight loss, sound, personality, ethnicity and lifestyle, previous exposure, disease, and ‘other’ determinants. Methodologies amongst studies were heterogenous in nature (e.g., there was variability across studies in the sweetness concentrations tested, the number of different sweetness concentrations used to assess sweetness preference, and the methods utilized to measure sweetness preference), rendering interpretation of overall findings challenging; however, for certain determinants, the evidence appeared to support predictive capacity of greater sweetness preference, such as age during certain life-stages (i.e., young and old), being in a hungry versus satiated state, and heritable factors (e.g., similar sweetness preferences amongst family members). Recommendations for the design of future studies on sweetness preference determinants are provided herein, including an “investigator checklist” of criteria to consider.
A Rational Design of a Biphasic Dissolution Setup—Modelling of Biorelevant Kinetics for a Ritonavir Hot-Melt Extruded Amorphous Solid Dispersion
Authors: Denninger Alexander , Westedt Ulrich , Rosenberg Jörg , Wagner Karl G.
Published: March 2020

Biphasic dissolution systems achieved good predictability for the in vivo performance of several formulations of poorly water-soluble drugs by characterizing dissolution, precipitation, re-dissolution, and absorption. To achieve a high degree of predictive performance, acceptor media, aqueous phase composition, and the apparatus type have to be carefully selected. Hence, a combination of 1-decanol and an optimized buffer system are proposed as a new, one-vessel biphasic dissolution method (BiPHa+). The BiPHa+ was developed to combine the advantages of the well-described biorelevance of the United States Pharmacopeia (USP) apparatus II coupled with USP apparatus IV and a small-scale, one-vessel method. The BiPHa+ was designed for automated medium addition and pH control of the aqueous phase. In combination with the diode array UV-spectrophotometer, the system was able to determine the aqueous and the organic medium simultaneously, even if scattering or overlapping of spectra occurred. At controlled hydrodynamic conditions, the relative absorption area, the ratio between the organic and aqueous phase, and the selected drug concentrations were identified to be the discriminating factors. The performance of a hot-melt extruded ritonavir-containing amorphous solid dispersion (ritonavir-ASD) was compared in fasted-state dissolution media leading to different dissolution-partitioning profiles depending on the content of bile salts. An advanced kinetic model for ASD-based well described all phenomena from dispersing of the ASD to the partitioning of the dissolved ritonavir into the organic phase.
Development of an Orodispersible Film Containing Stabilized Influenza Vaccine
Authors: Tian Yu , Bhide Yoshita C. , Woerdenbag Herman J. , Huckriede Anke L. W. , Frijlink Henderik W. , Hinrichs Wouter L. J. , Visser J. Carolina
Published: March 2020

Most influenza vaccines are administered via injection, which is considered as user-unfriendly. Vaccination via oral cavity using an orodispersible film (ODF) might be a promising alternative. To maintain the antigenicity of the vaccine during preparation and subsequent storage of these ODFs, sugars such as trehalose and pullulan can be employed as stabilizing excipients for the antigens. In this study, first, β-galactosidase was used as a model antigen. Solutions containing β-galactosidase and sugar (trehalose or trehalose/pullulan blends) were pipetted onto plain ODFs and then either air- or vacuum-dried. Subsequently, sugar ratios yielding the highest β-galactosidase stability were used to prepare ODFs containing H5N1 whole inactivated influenza virus vaccine (WIV). The stability of the H5N1 hemagglutinin was assessed by measuring its hemagglutination activity. Overall, various compositions of trehalose and pullulan successfully stabilized β-galactosidase and WIV in ODFs. WIV incorporated in ODFs showed excellent stability even at challenging storage conditions (60 °C/0% relative humidity or 30 °C/56% relative humidity) for 4 weeks. Except for sugars, the polymeric component of ODFs, i.e., hypromellose, possibly improved stability of WIV as well. In conclusion, ODFs may be suitable for delivering of WIV to the oral cavity and can possibly serve as an alternative for injections.
Solid-State Synthesis of Water-Soluble Chitosan-g-Hydroxyethyl Cellulose Copolymers
Authors: Demina Tatiana S. , Birdibekova Aisylu V. , Svidchenko Eugenia A. , Ivanov Pavel L. , Kuryanova Anastasia S. , Kurkin Tikhon S. , Khaibullin Zulfar I. , Goncharuk Galina P. , Zharikova Tatiana M. , Bhuniya Sankarprasad , Grandfils Christian , Timashev Peter S. , Akopova Tatiana A.
Published: March 2020

Graft copolymers of chitosan with cellulose ether have been obtained by the solid-state reactive mixing of chitin, sodium hydroxide and hydroxyethyl cellulose under shear deformation in a pilot twin-screw extruder. The structure and composition of the products were determined by elemental analysis and IR spectroscopy. The physicochemical properties of aqueous solutions of copolymers were studied as a function of the composition, and were correlated to the mechanical characteristics of the resulting films to assess the performance of new copolymers as coating materials, non-woven fibrous materials or emulsifiers for interface stabilization during the microparticle fabrication process.
Preparation and Characterization of DOPO-Functionalized MWCNT and Its High Flame-Retardant Performance in Epoxy Nanocomposites
Authors: Gu Liqiang , Qiu Chen , Qiu Jianhui , Yao Youwei , Sakai Eiichi , Yang Liting
Published: March 2020

In this work, functionalized multi-walled carbon nanotubes (MWCNT) were synthesized by the reaction between acylated MWCNT and 10-(2,5-dihydroxyphenyl)-9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (ODOPB). The obtained MWCNT-ODOPB was well dispersed into epoxy resins together with aluminum diethylphosphinate (AlPi) to form flame-retardant nanocomposites. The epoxy resin nanocomposite with phosphorus content of 1.00 wt % met UL 94 V-0 rating, exhibited LOI value of 39.5, and had a higher Tg compared to neat epoxy resin, which indicates its excellent flame retardant performance. These experimental results indicated that MWCNT-ODOPB was a compatible and efficient flame retardant for epoxy resins. Moreover, cone calorimeter analysis showed that the peak heat release rate (pHRR), total heat release (THR) values, and CO2 production profiles of the composites decreased with an increase in the additional amount of phosphorus.
Durability of an Epoxy Resin and Its Carbon Fiber- Reinforced Polymer Composite upon Immersion in Water, Acidic, and Alkaline Solutions
Authors: Uthaman Arya , Xian Guijun , Thomas Sabu , Wang Yunjia , Zheng Qiang , Liu Xiaoling
Published: March 2020

The usage of polymer composites in various engineering fields has increased. However, the long-term service performance of such materials under aggressive conditions is still poorly understood, which limits the development of safe and economically effective designs. In this study, the aging of an epoxy resin and its carbon fiber-reinforced polymer (CFRP) composites upon immersion in water, acidic, and alkaline solutions was evaluated at different temperatures. The service life of the CFRP composites under various conditions could be predicted by the Arrhenius theory. The thermal and mechanical analysis results indicated that the CFRP composites were more vulnerable to HCl owing to the higher moisture absorption and diffusion of HCl into their cracks. The scanning electron microscopy results showed that the polymer matrix was damaged and degraded. Therefore, to allow long-term application, CFRP composites must be protected from acidic environments.
Polymer- and Hybrid-Based Biomaterials for Interstitial, Connective, Vascular, Nerve, Visceral and Musculoskeletal Tissue Engineering
Authors: Abalymov Anatolii , Parakhonskiy Bogdan , Skirtach Andre
Published: March 2020

In this review, materials based on polymers and hybrids possessing both organic and inorganic contents for repairing or facilitating cell growth in tissue engineering are discussed. Pure polymer based biomaterials are predominantly used to target soft tissues. Stipulated by possibilities of tuning the composition and concentration of their inorganic content, hybrid materials allow to mimic properties of various types of harder tissues. That leads to the concept of “one-matches-all” referring to materials possessing the same polymeric base, but different inorganic content to enable tissue growth and repair, proliferation of cells, and the formation of the ECM (extra cellular matrix). Furthermore, adding drug delivery carriers to coatings and scaffolds designed with such materials brings additional functionality by encapsulating active molecules, antibacterial agents, and growth factors. We discuss here materials and methods of their assembly from a general perspective together with their applications in various tissue engineering sub-areas: interstitial, connective, vascular, nervous, visceral and musculoskeletal tissues. The overall aims of this review are two-fold: (a) to describe the needs and opportunities in the field of bio-medicine, which should be useful for material scientists, and (b) to present capabilities and resources available in the area of materials, which should be of interest for biologists and medical doctors.
Microstructural Features and Ductile-Brittle Transition Behavior in Hot-Rolled Lean Duplex Stainless Steels
Authors: Takahashi O. , Shibui Y. , Xu P.G. , Harjo S. , Suzuki T. , Tomota Y.
Published: March 2020

The characteristics of texture and microstructure of lean duplex stainless steels with low Ni content produced through hot rolling followed by annealing were investigated locally with electron backscatter diffraction and globally with neutron diffraction. Then, the ductile–brittle transition (DBT) behavior was studied by Charpy impact test. It is found that the DBT temperature (DBTT) is strongly affected by the direction of crack propagation, depending on crystallographic texture and microstructural morphology; the DBTT becomes extremely low in the case of fracture accompanying delamination. A high Ni duplex stainless steel examined for comparison, shows a lower DBTT compared with the lean steel in the same crack propagating direction. The obtained results were also discussed through comparing with those of cast duplex stainless steels reported previously (Takahashi et al., Tetsu-to-Hagané, 100(2014), 1150).
Luminous Bodies, Playful Children, and Abusive Grandmothers: Trauma, Dissociation, and Disorganized Attachment in the Early History of Great Perfection (rDzogs Chen) Buddhism
Authors: Geisshuesler Flavio A.
Published: March 2020

This contribution explores the development of the highest teachings of the “Old School” (rnying ma) of Tibetan Buddhism, known as the Great Perfection (rdzogs chen). Between the tenth and the twelfth centuries, when the “New Schools” (gsar ma) rose to prominence and challenged the legitimacy of the established ones, Dzogchen underwent radical transformations and grew into a complex of contradictory voices. Unlike existing scholarship, which relies exclusively on textual–philological analysis to elucidate the conflictual relationships between sub-traditions like the Mind Series (sems sde), the Seminal Heart (snying thig), and the Crown Pith (spyi ti), this article proffers a transdisciplinary perspective, which complements history with psychological investigations into myth and cognition. Introducing research from cognitive science, trauma studies, attachment theory, and dissociation, it scrutinizes fascinating Dzogchen myths of luminous bodies, playful children, and abusive grandmothers. Ultimately, this transdisciplinary approach results in a new interpretation of the early history of the Great Perfection, as marked by an internal division in the tradition that was the direct result of a historical trauma, which was first processed, then internalized, and finally perpetuated.
A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots
Authors: Fernández-Manso Alfonso , Quintano Carmen
Published: March 2020

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.
Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System
Authors: Dalla Corte Ana Paula , Rex Franciel Eduardo , Almeida Danilo Roberti Alves de , Sanquetta Carlos Roberto , Silva Carlos A. , Moura Marks M. , Wilkinson Ben , Zambrano Angelica Maria Almeyda , Cunha Neto Ernandes M. da , Veras Hudson F. P. , Moraes Anibal de , Klauberg Carine , Mohan Midhun , Cardil Adrián , Broadbent Eben North
Published: March 2020

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.
Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements
Authors: Frey Julian , Asbeck Thomas , Bauhus Jürgen
Published: March 2020

The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R2: 0.31) and abundance (R2: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage.
Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm
Authors: Li Xinyu , Liu Zhaohua , Lin Hui , Wang Guangxing , Sun Hua , Long Jiangping , Zhang Meng
Published: March 2020

Accurately estimating growing stem volume (GSV) is very important for forest resource management. The GSV estimation is affected by remote sensing images, variable selection methods, and estimation algorithms. Optical images have been widely used for modeling key attributes of forest stands, including GSV and aboveground biomass (AGB), because of their easy availability, large coverage and related mature data processing and analysis technologies. However, the low data saturation level and the difficulty of selecting feature variables from optical images often impede the improvement of estimation accuracy. In this research, two GaoFen-2 (GF-2) images, a Landsat 8 image, and fused images created by integrating GF-2 bands with the Landsat multispectral image using the Gram–Schmidt method were first used to derive various feature variables and obtain various datasets or data scenarios. A DC-FSCK approach that integrates feature variable screening and a combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (kNN) algorithm was proposed and compared with the stepwise regression analysis (SRA) and random forest (RF) for feature variable selection. The DC-FSCK considers the self-correlation and combination effect among feature variables so that the selected variables can improve the accuracy and saturation level of GSV estimation. To validate the proposed approach, six estimation algorithms were examined and compared, including Multiple Linear Regression (MLR), kNN, Support Vector Regression (SVR), RF, eXtreme Gradient Boosting (XGBoost) and Stacking. The results showed that compared with GF-2 and Landsat 8 images, overall, the fused image (Red_Landsat) of GF-2 red band with Landsat 8 multispectral image improved the GSV estimation accuracy of Chinese pine and larch plantations. The Red_Landsat image also performed better than other fused images (Pan_Landsat, Blue_Landsat, Green_Landsat and Nir_Landsat). For most of the combinations of the datasets and estimation models, the proposed variable selection method DC-FSCK led to more accurate GSV estimates compared with SRA and RF. In addition, in most of the combinations obtained by the datasets and variable selection methods, the Stacking algorithm performed better than other estimation models. More importantly, the combination of the fused image Red_Landsat with the DC-FSCK and Stacking algorithm led to the best performance of GSV estimation with the greatest adjusted coefficients of determination, 0.8127 and 0.6047, and the smallest relative root mean square errors of 17.1% and 20.7% for Chinese pine and larch, respectively. This study provided new insights on how to choose suitable optical images, variable selection methods and optimal modeling algorithms for the GSV estimation of Chinese pine and larch plantations.
An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN
Authors: Zhang Lili , Wu Jisen , Fan Yu , Gao Hongmin , Shao Yehong
Published: March 2020

In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy.
A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets
Authors: Casilari Eduardo , Lora-Rivera Raúl , García-Lagos Francisco
Published: March 2020

Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.
Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring
Authors: An Xiang , K. Stylios George
Published: March 2020

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.
Wrist Vascular Biometric Recognition Using a Portable Contactless System
Authors: Garcia-Martin Raul , Sanchez-Reillo Raul
Published: March 2020

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.
Connected Bike-smart IoT-based Cycling Training Solution
Authors: Catargiu George , Dulf Eva-H. , Miclea Liviu C.
Published: March 2020

The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, cadence and power generated by the cyclist. Also, the trainer can see at every moment where the rider is with the aid of a GPS module. The system is built out of both hardware and software components. The hardware is in charge of collecting, scaling, converting and sending data from sensors. On the software side, there is the server, which consists of the Back-End and the MQTT (Message Queues Telemetry Transport) Broker, as well as the Front-End of the Web Application that displays and manages data as well as collaboration between cyclists and trainers. Finally, there is the Android Application that acts like a remote command for the hardware module on the bike, giving the rider control over how and when the ride is monitored.
Active Player Detection in Handball Scenes Based on Activity Measures
Authors: Pobar Miran , Ivasic-Kos Marina
Published: March 2020

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.
IoT Smart Parking System Based on the Visual-Aided Smart Vehicle Presence Sensor: SPIN-V
Authors: Luque-Vega Luis F. , Michel-Torres David A. , Lopez-Neri Emmanuel , Carlos-Mancilla Miriam A. , González-Jiménez Luis E.
Published: March 2020

Humanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented.
Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis
Authors: Zamudio-Ramírez Israel , Osornio-Ríos Roque Alfredo , Antonino-Daviu Jose Alfonso , Quijano-Lopez Alfredo
Published: March 2020

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.
Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition
Authors: Ng Wing W.Y. , Xu Shichao , Wang Ting , Zhang Shuai , Nugent Chris
Published: March 2020

Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people’s activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.
Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot
Authors: Vega-Heredia Manuel , Muhammad Ilyas , Ghanta Sriharsha , Ayyalusami Vengadesh , Aisyah Siti , Elara Mohan Rajesh
Published: March 2020

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.
A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles
Authors: Gao Demin , Sun Quan , Hu Bin , Zhang Shuo
Published: March 2020

With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 °C and 16 °C, and high rainfall decreases the spread of wheat powdery mildew.
A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study
Authors: Peralta Federico , Arzamendia Mario , Gregor Derlis , Reina Daniel G. , Toral Sergio
Published: March 2020

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.
Doubling the Accuracy of Indoor Positioning: Frequency Diversity
Authors: Horn Berthold K.P.
Published: March 2020

Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m in distance measurement under favorable circumstances—limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent “noise gain” of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces “frequency diversity,” a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors.
Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths
Authors: Lee Jongshill , Kim Minseong , Park Hoon-Ki , Kim In Young
Published: March 2020

Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.