bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from SCIAMACHY
Authors: Hochstaffl Philipp , Schreier Franz
Published: April 2020

High-quality observations have indicated the need for improved molecular spectroscopy for accurate atmospheric characterization. Line data provided by the new SEOM-IAS (Scientific Exploitation of Operational Missions—Improved Atmospheric Spectroscopy) database in the shortwave infrared (SWIR) region were used to retrieve CO total vertical columns from a selected set of nadir SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) observations. In order to assess the quality of the retrieval results, differences in the spectral fitting residuals with respect to the HITRAN 2016 (High-resolution TRANsmission molecular absorption) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques) line lists were quantified and column-averaged dry-air CO mole fractions were compared to NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) ground-based measurements. In general, it was found that using SEOM-IAS line data with corresponding line models improve the spectral quality of the retrieval (smaller residuals) and increase the fitted CO columns, thereby reducing the bias to both ground-based networks.
A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover
Authors: Tsoeleng Lesiba Thomas , Odindi John , Mhangara Paidamwoyo
Published: April 2020

Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques
Study on Geospatial Distribution of the Efficiency and Sustainability of Different Energy-Driven Heat Pumps Included in Low Enthalpy Geothermal Systems in Europe
Authors: Nieto Ignacio Martín , Borge-Diez David , Sáez Blázquez Cristina , Martín Arturo Farfán , González-Aguilera Diego
Published: April 2020

This research work aims at a multinational study in Europe of the emissions and energy costs generated by the operation of low enthalpy geothermal systems, with heat pumps fed by different energy sources. From an economic point of view, natural gas and biogas prices are, usually, lower than electricity ones. So it may be advantageous to use these energy sources to feed the heat pumps instead of electricity. From the environmental point of view, it is intended to highlight the fact that under certain conditions of electricity production (electricity mix), more CO2 emissions are produced by electricity consumption than using other a priori less “clean” energy sources such as natural gas. To establish the countries where each of the different heat pumps may be more cost-efficient and environmentally friendly, data from multi-source geospatial databases have been collected and analyzed. The results show that in the majority of cases, the electric heat pump is the most recommendable solution. However, there are some geographic locations (such as Poland and Estonia), where the gas engine heat pump may be a better alternative.
GHS-POP Accuracy Assessment: Poland and Portugal Case Study
Authors: Calka Beata , Bielecka Elzbieta
Published: April 2020

The Global Human Settlement Population Grid (GHS-POP) the latest released global gridded population dataset based on remotely sensed data and developed by the EU Joint Research Centre, depicts the distribution and density of the total population as the number of people per grid cell. This study aims to assess the GHS-POP data accuracy based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and the correlation coefficient. The study was conducted for Poland and Portugal, countries characterized by different population distribution as well as two spatial resolutions of 250 m and 1 km on the GHS-POP. The main findings show that as the size of administrative zones decreases (from NUTS (Nomenclature of Territorial Units for Statistics) to LAU (local administrative unit)) and the size of the GHS-POP increases, the difference between the population counts reported by the European Statistical Office and estimated by the GHS-POP algorithm becomes larger. At the national level, MAPE ranges from 1.8% to 4.5% for the 250 m and 1 km resolutions of GHS-POP data in Portugal and 1.5% to 1.6%, respectively in Poland. At the local level, however, the error rates range from 4.5% to 5.8% in Poland, for 250 m and 1 km, and 5.7% to 11.6% in Portugal, respectively. Moreover, the results show that for densely populated regions the GHS-POP underestimates the population number, while for thinly populated regions it overestimates. The conclusions of this study are expected to serve as a quality reference for potential users and producers of population density datasets.
Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015
Authors: Naeem Shahid , Zhang Yongqiang , Tian Jing , Qamer Faisal Mueen , Latif Aamir , Paul Pranesh Kumar
Published: April 2020

Accurate assessment of vegetation dynamics provides important information for ecosystem management. Anthropogenic activities and climate variations are the major factors that primarily influence vegetation ecosystems. This study investigates the spatiotemporal impacts of climate factors and human activities on vegetation productivity changes in China from 1985 to 2015. Actual net primary productivity (ANPP) is used to reflect vegetation dynamics quantitatively. Climate-induced potential net primary productivity (PNPP) is used as an indicator of climate change, whereas the difference between PNPP and ANPP is considered as an indicator of human activities (HNPP). Overall, 91% of the total vegetation cover area shows declining trends for net primary productivity (NPP), while only 9% shows increasing trends before 2000 (base period). However, after 2000 (restoration period), 78.7% of the total vegetation cover area shows increasing trends, whereas 21.3% of the area shows decreasing trends. Moreover, during the base period, the quantitative contribution of climate change to NPP restoration is 0.21 grams carbon per meter square per year (gC m−2 yr−1) and to degradation is 2.41 gC m−2 yr−1, while during the restoration period, climate change contributes 0.56 and 0.29 gC m−2 yr−1 to NPP restoration and degradation, respectively. Human activities contribute 0.36 and 0.72 gC m−2 yr−1 during the base period, and 0.63 and 0.31 gC m−2 yr−1 during the restoration period to NPP restoration and degradation, respectively. The combined effects of climate and human activities restore 0.65 and 1.11 gC m−2 yr−1, and degrade 2.01 and 0.67 gC m−2 yr−1 during the base and restoration periods, respectively. Climate factors affect vegetation cover more than human activities, while precipitation is found to be more sensitive to NPP change than temperature. Unlike the base period, NPP per unit area increases with an increase in the human footprint pressure during the restoration period. Grassland has more variability than other vegetation classes, and the grassland changes are mainly observed in Tibet, Xinjiang, and Inner Mongolia regions. The results may help policy-makers by providing necessary guidelines for the management of forest, grassland, and agricultural activities.
Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron
Authors: Zhu Fei , Huang Yuchun , Tian Zizhu , Ma Yaowei
Published: April 2020

Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.
Screen-Printed Electrodes for the Voltammetric Sensing of Benzotriazoles in Water
Authors: Muschietti Alessandra , Serrano Núria , Ariño Cristina , Díaz-Cruz M. Silvia , Díaz-Cruz José Manuel
Published: April 2020

Benzotriazoles (BZTs) are high production volume industrial chemicals that are used in various applications such as corrosion inhibitors, antifreeze agents, and UV radiation stabilizers. Given their potential ecotoxicological implications for different ecosystems and in human health, as well as their poor biodegradability, they are of increasing concern. In this study, a new voltammetric method using commercial screen-printed electrodes (SPEs) has been developed for the sensing of BZTs in water samples to help in their environmental monitoring. To this end, different types of SPEs based on carbon nanoallotropes and copper were tested under several experimental conditions to determine the two BZTs most frequently detected in the environment: 1H-benzotriazole (BZT) and 5-methyl-1H-benzotriazole (Me-BZT, tolyltriazole) as model compounds for BZTs. Carbon nanofibers electrodes exhibited the best performance, allowing detection limits as low as 0.4 mg L−1 for both BZTs, with repeatability and reproducibility of ca. 5%. The applicability of the method was tested through the determination of BZT in spiked drinking water samples, suggesting its suitability for the sensing of samples heavily polluted with BZTs.
Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
Authors: Sun Junren , Niu Zun , Zhu Bocheng
Published: April 2020

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.
A Quantum Cascade Laser-Based Multi-Gas Sensor for Ambient Air Monitoring
Authors: Genner Andreas , Martín-Mateos Pedro , Moser Harald , Lendl Bernhard
Published: April 2020

A quantum cascade laser-based sensor for ambient air monitoring is presented and five gases, affecting the air quality, can be quantified. The light sources are selected to measure CO, NO, NO2, N2O and SO2. The footprint of the measurement setup is designed to fit in two standard 19” rack (48 cm × 65 cm) with 4 height units (18 cm) whereas one is holding the optical components and the other one contains the electronics and data processing unit. The concentrations of the individual analytes are measured using 2f-Wavelength Modulation Spectroscopy (2f-WMS) and a commercially available multipass gas cell defines the optical path. In addition, CO can also be measured with a dispersion-based technique, which allows one to cover a wider concentration range than 2f-WMS. The performance of this prototype has been evaluated in the lab and detection limits in the range of 1ppbv have been achieved. Finally, the applicability of this prototype for ambient air monitoring is shown in a five-week measurement campaign in cooperation with the Municipal Department for Environmental Protection (MA 22) of Vienna, Austria.
Real-Time Monitoring System for Shelf Life Estimation of Fruit and Vegetables
Authors: Torres-Sánchez Roque , Martínez-Zafra María Teresa , Castillejo Noelia , Guillamón-Frutos Antonio , Artés-Hernández Francisco
Published: April 2020

The control of the main environmental factors that influence the quality of perishable products is one of the main challenges of the food industry. Temperature is the main factor affecting quality, but other factors like relative humidity and gas concentrations (mainly C2H4, O2 and CO2) also play an important role in maintaining the postharvest quality of horticultural products. For this reason, monitoring such environmental factors is a key procedure to assure quality throughout shelf life and evaluate losses. Therefore, in order to estimate the quality losses that a perishable product can suffer during storage and transportation, a real-time monitoring system has been developed. This system can be used in all post-harvest steps thanks to its Wi-Fi wireless communication architecture. Several laboratory trials were conducted, using lettuce as a model, to determine quality-rating scales during shelf life under different storage temperature conditions. As a result, a multiple non-linear regression (MNLR) model is proposed relating the temperature and the maximum shelf life. This proposed model would allow to predict the days the commodities will reduce their theoretical shelf-life when an improper temperature during storage or in-transit occurs. The system, developed as a sensor-based tool, has been tested during several land transportation trips around Europe.
Microfluidics and Nanomaterial-based Technologies for Circulating Tumor Cell Isolation and Detection
Authors: Cheng Sheng-Jen , Hsieh Kuan Yu , Chen Shiue-Luen , Chen Chong-You , Huang Chien-Yu , Tsou Hung-I , Kumar Priyank V. , Hsieh Jason Chia-Hsun , Chen Guan-Yu
Published: April 2020

Cancer has been one of the leading causes of death globally, with metastases and recurrences contributing to this result. The detection of circulating tumor cells (CTCs), which have been implicated as a major population of cells that is responsible for seeding and migration of tumor sites, could contribute to early detection of metastasis and recurrences, consequently increasing the chances of cure. This review article focuses on the current progress in microfluidics technology in CTCs diagnostics, extending to the use of nanomaterials and surface modification techniques for diagnostic applications, with an emphasis on the importance of integrating microchannels, nanomaterials, and surface modification techniques in the isolating and detecting of CTCs.
A Novel Smart Assistance System for Blood Vessel Approaching: A Technical Report Based on Oximetry
Authors: Lee Chien-Ching , Chuang Chia-Chun , Lai Bo-Cheng , Huang Yi-Chia , Chen Jen-Yin , Lin Bor-Shyh
Published: April 2020

In clinical practice, the catheter has to be placed at an accurate position during anesthesia administration. However, effectively guiding the catheter to the accurate position in deeper tissues can be difficult for an inexperienced practitioner. We aimed to address the current issues associated with catheter placement using a novel smart assistance system for blood vessel catheter placement. We used a hollow introducer needle embedded with dual wavelength (690 and 850 nm) optical fibers to advance the tip into the subclavian vessels in anesthetized piglets. The results showed average optical density changes, and the difference between the absorption spectra and hemoglobin concentrations of different tissue components effectively identified different tissues (p < 0.05). The radial basis function neural network (RBFNN) technique was applied to distinguish tissue components (the F-measure value and accuracy were 93.02% and 94%, respectively). Finally, animal experiments were designed to validate the performance of the proposed system. Using this system based on oximetry, we easily navigated the needle tip to the target vessel. Based on the experimental results, the proposed system could effectively distinguish different tissue layers of the animals.
GRAFT: A Model for Evaluating Actuator Systems in Terms of Force Production
Authors: Baniata Hamza , Sharieh Ahmad , Mahmood Sami , Kertesz Attila
Published: April 2020

In the scope of evaluation methodologies for Internet of Things (IoT) systems, some approaches concern security, while others latency. However, some methodologies evaluate systems that contain active entities, so-called actuators. In this paper, we propose a novel methodology for evaluating such systems with actuator components using Graph Representation of the Angle of the Force and Time (GRAFT). GRAFT facilitates easy computation of the net force produced by physical or mechanical acts occurring on a daily basis on Earth. We use laws and definitions of physics describing the relations between Speed, Distance, and Time (SDT), apply them in a heliocentric system, and model the considered systems with a graph. The continuous movement of the Earth was shown to be weakening the total produced net force in some systems. We considered this weakening issue a problem, and we propose two possible solutions to overcome it by using restoration values, or reordering actuator sessions, in GRAFT to arrive to a more force-efficient system. We compared our default GRAFT algorithm to a special implementation using the Clock-Angle-Problem (CAP) for sessions. We also study and discuss an IoT-focused case for validating our approach, and we present a detailed explanation of the proposed GRAFT algorithm. The experimental results show the ability of GRAFT to provide highly accurate results, which also exemplifies that our GRAFT approach is programmable, hence deployable in real life scenarios.
Privacy-Preserving in Healthcare Blockchain Systems Based on Lightweight Message Sharing
Authors: Fu Junsong , Wang Na , Cai Yuanyuan
Published: April 2020

Electronic medical records (EMRs) are extremely important for patients’ treatment, doctors’ diagnoses, and medical technology development. In recent years, the distributed healthcare blockchain system has been researched for solving the information isolated island problem in centralized healthcare service systems. However, there still exists a series of important problems such as the patients’ sensitive information security, cross-institutional data sharing, medical quality, and efficiency. In this paper, we establish a lightweight privacy-preserving mechanism for a healthcare blockchain system. First, we apply an interleaving encoder to encrypt the original EMRs. This can hide the sensitive information of EMRs to protect the patient’s privacy security. Second, a ( t , n )-threshold lightweight message sharing scheme is presented. The EMRs are mapped to n different short shares, and it can be reconstructed by at least t shares. The EMR shares rather than the original EMRs are stored in the blockchain nodes. This can guarantee high security for EMR sharing and improve the data reconstruction efficiency. Third, the indexes of the stored EMR shares are employed to generate blocks that are chained together and finally form a blockchain. The authorized data users or institutions can recover an EMR by requesting at least t shares of the EMR from the blockchain nodes. In this way, the healthcare blockchain system can not only facilitate the cross-institution sharing process, but also provide proper protections for the EMRs. The security proof and analysis indicate that the proposed scheme can protect the privacy and security of patients’ medical information. The simulation results show that our proposed scheme is more efficient than similar literature in terms of energy consumption and storage space, and the healthcare blockchain system is more stable with the proposed message sharing scheme.
Real-Time Humidity Measurement during Sports Activity using Optical Fibre Sensing
Authors: He Chenyang , Korposh Serhiy , Hernandez Francisco Ulises , Liu Liangliang , Correia Ricardo , Hayes-Gill Barrie R. , Morgan Stephen P.
Published: April 2020

An optical fibre sensor for monitoring relative humidity (RH) changes during exercise is demonstrated. The humidity sensor comprises a tip coating of poly (allylamine hydrochloride) (PAH)/silica nanoparticles (SiO2 NPs) deposited using the layer-by-layer technique. An uncoated fibre is employed to compensate for bending losses that are likely to occur during movement. A linear fit to the response of the sensing system to RH demonstrates a sensitivity of 3.02 mV/% (R2 = 0.96), hysteresis ± 1.17% RH when 11 bilayers of PAH/SiO2 NPs are coated on the tip of the fibre. The performance of two different textiles (100% cotton and 100% polyester) were tested in real-time relative humidity measurement for 10 healthy volunteers. The results demonstrate the moisture wicking properties of polyester in that the relative humidity dropped more rapidly after cessation of exercise compared to cotton. The approach has the potential to be used to monitor sports performance and by clothing developers for characterising different garment designs.
Compact Open-Path Sensor for Fast Measurements of CO2 and H2O using Scanned-Wavelength Modulation Spectroscopy with 1f-Phase Method
Authors: Li Xiang , Yuan Feng , Hu Mai , Chen Bin , He Yabai , Yang Chenguang , Shi Lifang , Kan Ruifeng
Published: April 2020

We report here the development of a compact, open-path CO2 and H2O sensor based on the newly introduced scanned-wavelength modulation spectroscopy with the first harmonic phase angle (scanned-WMS-θ1f) method for high-sensitivity, high temporal resolution, ground-based measurements. The considerable advantage of the sensor, compared with existing commercial ones, lies in its fast response of 500 Hz that makes this instrument ideal for resolving details of high-frequency turbulent motion in exceptionally dynamic coastal regions. The good agreement with a commercial nondispersive infrared analyzer supports the utility and accuracy of the sensor. Allan variance analysis shows that the concentration measurement sensitivities can reach 62 ppb CO2 in 0.06 s and 0.89 ppm H2O vapor in 0.26 s averaging time. Autonomous field operation for 15-day continuous measurements of greenhouse gases (CO2/H2O) was performed on a shore-based monitoring tower in Daya Bay, demonstrating the sensor’s long-term performance. The capability for high-quality fast turbulent atmospheric gas observations allow the potential for better characterization of oceanographic processes.
6TiSCH on SCμM: Running a Synchronized Protocol Stack without Crystals
Authors: Chang Tengfei , Watteyne Thomas , Wheeler Brad , Maksimovic Filip , Khan Osama , Mesri Sahar , Lee Lydia , Suciu Ioana , Burnett David , Vilajosana Xavier , Pister Kris
Published: April 2020

We report the first time-synchronized protocol stack running on a crystal-free device. We use an early prototype of the Single-Chip micro Mote, SCμM, a single-chip 2 × 3 mm2 mote-on-a-chip, which features an ARM Cortex-M0 micro-controller and an IEEE802.15.4 radio. This prototype consists of an FPGA version of the micro-controller, connected to the SCμM chip which implements the radio front-end. We port OpenWSN, a reference implementation of a synchronized protocol stack, onto SCμM. The challenge is that SCμM has only on-chip oscillators, with no absolute time reference such as a crystal. We use two calibration steps – receiving packets via the on-chip optical receiver and RF transceiver – to initially calibrate the oscillators on SCμM so that it can send frames to an off-the-shelf IEEE802.15.4 radio. We then use a digital trimming compensation algorithm based on tick skipping to turn a 567 ppm apparent drift into a 10 ppm drift. This allows us to run a full-featured standards-compliant 6TiSCH network between one SCμM and one OpenMote. This is a step towards realizing the smart dust vision of ultra-small and cheap ubiquitous wireless devices.
A Suppression Method of Concentration Background Noise by Transductive Transfer Learning for a Metal Oxide Semiconductor-Based Electronic Nose
Authors: Liu Huixiang , Li Qing , Li Zhiyong , Gu Yu
Published: April 2020

Signal drift caused by sensors or environmental changes, which can be regarded as data distribution changes over time, is related to transductive transfer learning, and the data in the target domain is not labeled. We propose a method that learns a subspace with maximum independence of the concentration features (MICF) according to the Hilbert-Schmidt Independence Criterion (HSIC), which reduces the inter-concentration discrepancy of distributions. Then, we use Iterative Fisher Linear Discriminant (IFLD) to extract the signal features by reducing the divergence within classes and increasing the divergence among classes, which helps to prevent inconsistent ratios of different types of samples among the domains. The effectiveness of MICF and IFLD was verified by three sets of experiments using sensors in real world conditions, along with experiments conducted in the authors’ laboratory. The proposed method achieved an accuracy of 76.17%, which was better than any of the existing methods that publish their data on a publicly available dataset (the Gas Sensor Drift Dataset). It was found that the MICF-IFLD was simple and effective, reduced interferences, and deftly managed tasks of transfer classification.
Gaze Tracking and Point Estimation Using Low-Cost Head-Mounted Devices
Authors: Lee Ko-Feng , Chen Yen-Lin , Yu Chao-Wei , Chin Kai-Yi , Wu Chen-Han
Published: April 2020

In this study, a head-mounted device was developed to track the gaze of the eyes and estimate the gaze point on the user’s visual plane. To provide a cost-effective vision tracking solution, this head-mounted device is combined with a sized endoscope camera, infrared light, and mobile phone; the devices are also implemented via 3D printing to reduce costs. Based on the proposed image pre-processing techniques, the system can efficiently extract and estimate the pupil ellipse from the camera module. A 3D eye model was also developed to effectively locate eye gaze points from extracted eye images. In the experimental results, average accuracy, precision, and recall rates of the proposed system can achieve an average of over 97%, which can demonstrate the efficiency of the proposed system. This study can be widely used in the Internet of Things, virtual reality, assistive devices, and human-computer interaction applications.
The Influence of Laser Modification on a Composite Substrate and the Resistance of Thin Layers Created Using the PVD Process
Authors: Korzeniewska Ewa , Tomczyk Mariusz , Walczak Maria
Published: April 2020

For physical vapor deposition (PVD) technology, cleaning a substrate is one of the key preliminary processes before depositing the metal layer. In this article, we present the results of research on the modification of a textile composite substrate using laser technology and its influence on the surface resistance of silver structures intended for use in wearable electronics. As a result of the substrate modification, the resistance of the layers increased as compared with the structures produced on an unmodified substrate. An experimental planning technique was used to optimize the laser modification process.
Environment-Friendly, Self-Sensing Concrete Blended with Byproduct Wastes
Authors: Konkanov Marat , Salem Talal , Jiao Pengcheng , Niyazbekova Rimma , Lajnef Nizar
Published: April 2020

Smart structures have attracted significant research attention in the last decade, mainly due to the capabilities of advanced concrete in electrical resistance-enabled self-sensing. In this study, we present a type of environment-friendly, self-sensing concrete enabled by electrical resistance. Environment-friendly, self-sensing concrete was casted with the additions of byproduct wastes (i.e., coal fly ash (FA), blast furnace slag (BOF) and red mud (RM)) at various volume fractions and cured using the conditions of 3, 7 and 28 days. The self-sensing concrete samples were experimentally tested to investigate the effects of the byproduct wastes on the mechanical and electrical properties (i.e., compressive strength and electrical resistance). In the end, parametric studies were experimentally conducted to investigate the influences of the byproduct wastes on the mechanical and electrical properties of the reported environment-friendly, self-sensing concrete.
Legodroid: A Type-Driven Library for Android and LEGO Mindstorms Interoperability
Authors: Spanò Alvise , Cortesi Agostino
Published: April 2020

LEGO Mindstorms robots are widely used as educational tools to acquire skills in programming complex systems involving the interaction of sensors and actuators, and they offer a flexible and modular workbench to design and evaluate user–machine interaction prototypes in the robotic area. However, there is still a lack of support to interoperability features and the need of high-level tools to program the interaction of a robot with other devices. In this paper, we introduce Legodroid, a new Java library enabling cross-programming LEGO Mindstorms robots through Android smartphones that exploits their combined computational and sensorial capabilities in a seamless way. The library provides a number of type-driven coding patterns for interacting with sensors and motors. In this way, the robustness of the software managing robot’s sensors dramatically improves.
Research on a Simulation Method of the Millimeter Wave Radar Virtual Test Environment for Intelligent Driving
Authors: Li Xin , Tao Xiaowen , Zhu Bing , Deng Weiwen
Published: April 2020

This study addresses the virtual testing of intelligent driving, examines the key problems in modeling and simulating millimeter wave radar environmental clutter, and proposes a modeling and simulation method for the environmental clutter of millimeter wave radar in intelligent driving. First, based on the attributes of intelligent vehicle millimeter wave radar, the classification characteristics of the traffic environment of an intelligent vehicle and the generation mechanism of radar environmental clutter are analyzed. Next, the statistical distribution characteristics of the clutter amplitude, the distribution characteristics of the power spectrum, and the electromagnetic dielectric characteristics are analyzed. The simulation method of radar clutter under environmental conditions such as road surface, rainfall, snowfall, and fog are deduced and designed. Finally, experimental comparison results are utilized to validate the model and simulation method.
Large Responsivity of Graphene Radiation Detectors with Thermoelectric Readout: Results of Simulations
Authors: Yurgens August
Published: April 2020

Simple estimations show that the thermoelectric readout in graphene radiation detectors can be extremely effective even for graphene with modest charge-carrier mobility ∼1000 cm 2 /(Vs). The detector responsivity depends mostly on the residual charge-carrier density and split-gate spacing and can reach competitive values of ∼ 10 3 – 10 4 V/W at room temperature. The optimum characteristics depend on a trade-off between the responsivity and the total device resistance. Finding out the key parameters and their roles allows for simple detectors and their arrays, with high responsivity and sufficiently low resistance matching that of the radiation-receiving antenna structures.
Complex Event Processing for Self-Optimizing Cellular Networks
Authors: de-la-Bandera Isabel , Toril Matías , Luna-Ramírez Salvador , Buenestado Víctor , Ruiz-Avilés José María
Published: April 2020

In a cellular network, signaling and data messages exchanged between network elements are an extremely valuable information for network optimization. The consideration of different types of information allows to improve the optimization results. However, the huge amount of information has made it very difficult for operators to process all the available information. To cope with this issue, in this paper, a methodology for processing cell and user connection traces to optimize a live cellular network is presented. The aim is to generate new performance indicators different from those supplied by manufacturers, taking advantage of the ability of complex event processing tools to correlate events of different nature. For illustrative purposes, an example of how a new performance indicator is created from real traces by complex event processing is given.
The Design and Implementation of a Custom Platform for the Experimental Tuning of a Quadcopter Controller
Authors: Waliszkiewicz Michał , Wojtowicz Konrad , Rochala Zdzisław , Balestrieri Eulalia
Published: April 2020

This paper describes the development process of the quadcopter-based unmanned flying platform, designed for testing and experimentation purposes. The project features custom-made hardware, which includes the prototype quadcopter frame and the flight controller, and software solutions, such as control loop setup. The article specifies the controller tuning used for the initialization of the flight stabilization system and presents the final results of the quadcopter performance evaluation.
Frequency Response Stabilization and Comparative Studies of MET Hydrophone at Marine Seismic Exploration Systems
Authors: Egorov Egor , Shabalina Anna , Zaitsev Dmitry , Kurkov Sergey , Gueorguiev Nikolay
Published: April 2020

Low frequency hydrophone with a frequency range of 1−300 Hz for marine seismic exploration systems has been developed. The operation principle of the hydrophone bases on the molecular electronic transfer that allows high sensitivity and low level self-noise at low frequencies (<10 Hz) to be achieved. The paper presents a stabilization method of the frequency response within the frequency range at a depth up to 30 m. Laboratory and marine tests confirmed the stated characteristics as well as the possibility of using this sensor in bottom marine seismic systems. An experimental sample of the hydrophone successfully passed a comparative marine test at Gelendzhik Bay (Black Sea) with the technical support of Joint-Stock Company (JSC) “Yuzhmorgeologiya”. One of the main results is the possibility of obtaining high-quality information in the field of low frequencies, which was demonstrated in the course of field tests.
Motion Control of a Two-Degree-of-Freedom Linear Resonant Actuator without a Mechanical Spring
Authors: Kim Gyunam , Hirata Katsuhiro
Published: April 2020

This study aims to present a new two-degree-of-freedom (DOF) linear resonant actuator (LRA) and its motion control method without a position sensor. The design method of 2-DOF LRA which resonates with only detent force without a mechanical spring is proposed. Since the information of displacement and direction is required to control 2-DOF LRA, a sensor or an estimator is needed. Therefore, we proposed a position estimator and a motion controller for 2-DOF LRA. This paper proved that reciprocating motion, elliptical motion, and scrolling motion can be controlled without a position sensor. Finite element analysis (FEA) and dynamic simulation results validated the proposed method as well.
An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution
Authors: Kabir Sami , Islam Raihan Ul , Hossain Mohammad Shahadat , Andersson Karl
Published: April 2020

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.
Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT
Authors: Al-Aqrabi Hussain , Johnson Anju P. , Hill Richard , Lane Phil , Alsboui Tariq
Published: April 2020

The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.
Design and Realization of an Efficient Large-Area Event-Driven E-Skin
Authors: Bergner Florian , Dean-Leon Emmanuel , Cheng Gordon
Published: April 2020

The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge—the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.
How a Participatory Budget Can Support Sustainable Rural Development—Lessons From Poland
Authors: Bednarska-Olejniczak Dorota , Olejniczak Jarosław , Svobodová Libuše
Published: April 2020

Since the announcement of the Sustainable Development Goals (SDGs), the need for localization of SDGs has been emphasized. In this context, sustainable rural development is still a relatively undescribed area in the context of using the participatory budget as a tool to implement SDGs. Few countries have introduced legal regulations in practice, enabling the creation of participatory budgets (especially in rural areas), so a multifaceted analysis of a decade of Poland's experience may provide important guidelines for countries considering introducing such solutions, which we consider to be the main purpose of this study. This is the first study covering all communes where participatory budgets (Solecki Fund—FS) were created in Poland during the 2010–2018 period (up to 60% of all), covering both the analysis of the process of creating FSs, the directions of spending and the scale of spending (including regional differentiation), as well as legal regulations and the consequences of including central government support in this mechanism. On the basis of the research, it can be observed that, despite the small scale of FS spending, the number of municipalities using this form of citizen participation is increasing. At the same time, there is significant variation between regions, which indicates the flexibility of the FSs in adapting to the needs reported by residents. The analysed directions of expenditure indicate that the FSs are in line with the SDG objectives related to the improvement of residents' quality of life. It can be concluded that, despite the existing legal regulations, the introduction of the Solecki Fund undoubtedly depends on the political will of the local government's legislative authorities and the willingness of residents to participate in decisions on spending directions.
Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America
Authors: Kachba Yslene , Chiroli Daiane Maria de Genaro , T. Belotti Jônatas , Antonini Alves Thiago , de Souza Tadano Yara , Siqueira Hugo
Published: April 2020

The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.