bluuebunny/crossref_metadata_2025 huggingface dataset:
Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor
Authors: Niu Yanmin , Qin Lan , Wang Xuchu
Published: April 2019

Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.
CNT-Based Inkjet-Printed RF Gas Sensor: Modification of Substrate Properties during the Fabrication Process
Authors: George Julien , Abdelghani Aymen , Bahoumina Prince , Tantot Olivier , Baillargeat Dominique , Frigui Kamel , Bila Stéphane , Hallil Hamida , Dejous Corinne
Published: April 2019

This paper presents the feasibility of a fully inkjet-printed, microwave flexible gas sensor based on a resonant electromagnetic transducer in microstrip technology and the impact of the printing process that affects the characteristics of the gas sensor. The sensor is fabricated using silver ink and multi-wall carbon nanotubes (MWCNTs) embedded in poly (3,4-ethylenedioxythiophene) polystyrene (PEDOT: PSS-MWCNTs) as sensitive material for Volatile Organic Compounds (VOCs) detection. Particular attention is paid to the characterization of the printed materials and the paper substrate. The manufacturing process results in a change in relative permittivity of the paper substrate by nearly 20%. Electrical characterization, made in the presence of gas, validates our theoretical approach and the radiofrequency (RF) gas sensor proof of concept.
Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments
Authors: Yang Lingyu , Feng Xiaoke , Zhang Jing , Shu Xiangqian
Published: April 2019

Due to its payload, size and computational limits, localizing a micro air vehicle (MAV) using only its onboard sensors in an indoor environment is a challenging problem in practice. This paper introduces an indoor localization approach that relies on only the inertial measurement unit (IMU) and four ultrasonic sensors. Specifically, a novel multi-ray ultrasonic sensor model is proposed to provide a rapid and accurate approximation of the complex beam pattern of the ultrasonic sensors. A fast algorithm for calculating the Jacobian matrix of the measurement function is presented, and then an extended Kalman filter (EKF) is used to fuse the information from the ultrasonic sensors and the IMU. A test based on a MaxSonar MB1222 sensor demonstrates the accuracy of the model, and a simulation and experiment based on the T h a l e s I I MAV platform are conducted. The results indicate good localization performance and robustness against measurement noises.
Mobile Robot Indoor Positioning Based on a Combination of Visual and Inertial Sensors
Authors: Gao Mingjing , Yu Min , Guo Hang , Xu Yuan
Published: April 2019

Multi-sensor integrated navigation technology has been applied to the indoor navigation and positioning of robots. For the problems of a low navigation accuracy and error accumulation, for mobile robots with a single sensor, an indoor mobile robot positioning method based on a visual and inertial sensor combination is presented in this paper. First, the visual sensor (Kinect) is used to obtain the color image and the depth image, and feature matching is performed by the improved scale-invariant feature transform (SIFT) algorithm. Then, the absolute orientation algorithm is used to calculate the rotation matrix and translation vector of a robot in two consecutive frames of images. An inertial measurement unit (IMU) has the advantages of high frequency updating and rapid, accurate positioning, and can compensate for the Kinect speed and lack of precision. Three-dimensional data, such as acceleration, angular velocity, magnetic field strength, and temperature data, can be obtained in real-time with an IMU. The data obtained by the visual sensor is loosely combined with that obtained by the IMU, that is, the differences in the positions and attitudes of the two sensor outputs are optimally combined by the adaptive fade-out extended Kalman filter to estimate the errors. Finally, several experiments show that this method can significantly improve the accuracy of the indoor positioning of the mobile robots based on the visual and inertial sensors.
Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review
Authors: Amador-Domínguez Elvira , Serrano Emilio , Manrique Daniel , De Paz Juan F.
Published: April 2019

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context.
A 0.0014 mm2 150 nW CMOS Temperature Sensor with Nonlinearity Characterization and Calibration for the −60 to +40 °C Measurement Range
Authors: Yang Wendi , Jiang Hanjun , Wang Zhihua
Published: April 2019

This work presents a complementary metal–oxide–semiconductor (CMOS) ultra-low power temperature sensor chip for cold chain applications with temperatures down to −60 °C. The sensor chip is composed of a temperature-to-current converter to generate a current proportional to the absolute temperature (PTAT), a current controlled oscillator to convert the current to a frequency signal, and a counter as the frequency-to-digital converter. Unlike the conventional linear error calibration method, the nonlinear error of the PTAT current under the low temperature range is fully characterized based on the device model files provided by the foundry. Simulation has been performed, which clearly shows the nonlinear model is much more accurate than the linear model. A nonlinear error calibration method, which requires only two-point calibration, is then proposed. The temperature sensor chip has been designed and fabricated in a 0.13 μm CMOS process, with a total active die area of 0.0014 mm2. The sensor only draws a 140 nA current from a 1.1 V supply, with the key transistors working in the deep subthreshold region. Measurement results show that the proposed nonlinear calibration can decrease the measurement error from −0.9 to +1.1 °C for the measurement range of −60 to +40 °C, in comparison with the error of −1.8 to +5.3 °C using the conventional linear error calibration.
Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches
Authors: Wu Juan , Yang Simon X.
Published: April 2019

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.
Assessment of Shoulder Range of Motion Using a Wireless Inertial Motion Capture Device—A Validation Study
Authors: Rigoni Michael , Gill Stephen , Babazadeh Sina , Elsewaisy Osama , Gillies Hugh , Nguyen Nhan , Pathirana Pubudu N. , Page Richard
Published: April 2019

(1) Background: Measuring joint range of motion has traditionally occurred with a universal goniometer or expensive laboratory based kinematic analysis systems. Technological advances in wearable inertial measurement units (IMU) enables limb motion to be measured with a small portable electronic device. This paper aims to validate an IMU, the ‘Biokin’, for measuring shoulder range of motion in healthy adults; (2) Methods: Thirty participants completed four shoulder movements (forward flexion, abduction, and internal and external rotation) on each shoulder. Each movement was assessed with a goniometer and the IMU by two testers independently. The extent of agreement between each tester’s goniometer and IMU measurements was assessed with intra-class correlation coefficients (ICC) and Bland-Altman 95% limits of agreement (LOA). Secondary analysis compared agreement between tester’s goniometer or IMU measurements (inter-rater reliability) using ICC’s and LOA; (3) Results: Goniometer and IMU measurements for all movements showed high levels of agreement when taken by the same tester; ICCs > 0.90 and LOAs < ±5 degrees. Inter-rater reliability was lower; ICCs ranged between 0.71 to 0.89 and LOAs were outside a prior defined acceptable LOAs (i.e., > ±5 degrees); (4) Conclusions: The current study provides preliminary evidence of the concurrent validity of the Biokin IMU for assessing shoulder movements, but only when a single tester took measurements. Further testing of the Biokin’s psychometric properties is required before it can be confidently used in routine clinical practice and research settings.
Inertial Measurement Unit Based Upper Extremity Motion Characterization for Action Research Arm Test and Activities of Daily Living
Authors: Nam Hyung Seok , Lee Woo Hyung , Seo Han Gil , Kim Yoon Jae , Bang Moon Suk , Kim Sungwan
Published: April 2019

In practical rehabilitation robot development, it is imperative to pre-specify the critical workspace to prevent redundant structure. This study aimed to characterize the upper extremity motion during essential activities in daily living. An IMU-based wearable motion capture system was used to access arm movements. Ten healthy subjects performed the Action Research Arm Test (ARAT) and six pre-selected essential daily activities. The Euler angles of the major joints, and acceleration from wrist and hand sensors were acquired and analyzed. The size of the workspace for the ARAT was 0.53 (left-right) × 0.92 (front-back) × 0.89 (up-down) m for the dominant hand. For the daily activities, the workspace size was 0.71 × 0.70 × 0.86 m for the dominant hand, significantly larger than the non-dominant hand (p ≤ 0.011). The average range of motion (RoM) during ARAT was 109.15 ± 18.82° for elbow flexion/extension, 105.23 ± 5.38° for forearm supination/pronation, 91.99 ± 0.98° for shoulder internal/external rotation, and 82.90 ± 22.52° for wrist dorsiflexion/volarflexion, whereas the corresponding range for daily activities were 120.61 ± 23.64°, 128.09 ± 22.04°, 111.56 ± 31.88°, and 113.70 ± 18.26°. The shoulder joint was more abducted and extended during pinching compared to grasping posture (p < 0.001). Reaching from a grasping posture required approximately 70° elbow extension and 36° forearm supination from the initial position. The study results provide an important database for the workspace and RoM for essential arm movements.
A Calibration Method for a Laser Triangulation Scanner Mounted on a Robot Arm for Surface Mapping
Authors: Idrobo-Pizo Gerardo Antonio , Motta José Maurício S. T. , Sampaio Renato Coral
Published: April 2019

This paper presents and discusses a method to calibrate a specially built laser triangulation sensor to scan and map the surface of hydraulic turbine blades and to assign 3D coordinates to a dedicated robot to repair, by welding in layers, the damage on blades eroded by cavitation pitting and/or cracks produced by cyclic loading. Due to the large nonlinearities present in a camera and laser diodes, large range distances become difficult to measure with high precision. Aiming to improve the precision and accuracy of the range measurement sensor based on laser triangulation, a calibration model is proposed that involves the parameters of the camera, lens, laser positions, and sensor position on the robot arm related to the robot base to find the best accuracy in the distance range of the application. The developed sensor is composed of a CMOS camera and two laser diodes that project light lines onto the blade surface and needs image processing to find the 3D coordinates. The distances vary from 250 to 650 mm and the accuracy obtained within the distance range is below 1 mm. The calibration process needs a previous camera calibration and special calibration boards to calculate the correct distance between the laser diodes and the camera. The sensor position fixed on the robot arm is found by moving the robot to selected positions. The experimental procedures show the success of the calibration scheme.
Sperm-Cultured Gate Ion-Sensitive Field-Effect Transistor for Non-Optical and Live Monitoring of Sperm Capacitation
Authors: Saito Akiko , Sakata Toshiya
Published: April 2019

We have successfully monitored the effect of progesterone and Ca2+ on artificially induced sperm capacitation in a real-time, noninvasive and label-free manner using an ion-sensitive field-effect transistor (ISFET) sensor. The sperm activity can be electrically detected as a change in pH generated by sperm respiration based on the principle of the ISFET sensor. Upon adding mouse sperm to the gate of the ISFET sensor in the culture medium with progesterone, the pH decreases with an increasing concentration of progesterone from 1 to 40 μM. This is because progesterone induces Ca2+ influx into spermatozoa and triggers multiple Ca2+-dependent physiological responses, which subsequently activates sperm respiration. Moreover, this pH response of the ISFET sensor is not observed for a Ca2+-free medium even when progesterone is introduced, which means that Ca2+ influx is necessary for sperm activation that results in sperm capacitation. Thus, a platform based on the ISFET sensor system can provide a simple method of evaluating artificially induced sperm capacitation in the field of male infertility treatment.
Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites
Authors: Robustelli Umberto , Benassai Guido , Pugliano Giovanni
Published: April 2019

In August 2016, Milena (E14) and Doresa (E18) satellites started to broadcast ephemeris in navigation message for testing purposes. If these satellites could be used, an improvement in the position accuracy would be achieved. A small error in the ephemeris would impact the accuracy of positioning up to ±2.5 m, thus orbit error must be assessed. The ephemeris quality was evaluated by calculating the SISEorbit (in orbit Signal In Space Error) using six different ephemeris validity time thresholds (14,400 s, 10,800 s, 7200 s, 3600 s, 1800 s, and 900 s). Two different periods of 2018 were analyzed by using IGS products: DOYs 52–71 and DOYs 172–191. For the first period, two different types of ephemeris were used: those received in IGS YEL2 station and the BRDM ones. Milena (E14) and Doresa (E18) satellites show a higher SISEorbit than the others. If validity time is reduced, the SISEorbit RMS of Milena (E14) and Doresa (E18) greatly decreases differently from the other satellites, for which the improvement, although present, is small. Milena (E14) and Doresa (E18) reach a SISEorbit RMS of about 1 m (comparable to that of the other Galileo satellites reach with the nominal validity time) when validity time of 1800 s is used. Therefore, using this threshold, the two satellites could be used to improve single point positioning accuracy.
Experimental Setup and Measuring System to Study Solitary Wave Interaction with Rigid Emergent Vegetation
Authors: Tognin Davide , Peruzzo Paolo , De Serio Francesca , Ben Meftah Mouldi , Carniello Luca , Defina Andrea , Mossa Michele
Published: April 2019

The aim of this study is to present a peculiar experimental setup, designed to investigate the interaction between solitary waves and rigid emergent vegetation. Flow rate changes due to the opening and closing of a software-controlled electro-valve generate a solitary wave. The complexity of the problem required the combined use of different measurement systems of water level and velocity. Preliminary results of the experimental investigation, which allow us to point out the effect of the vegetation on the propagation of a solitary wave and the effectiveness of the measuring system, are also presented. In particular, water level and velocity field changes due to the interaction of the wave with rigid vegetation are investigated in detail.
The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
Authors: Tariq Noshina , Asim Muhammad , Al-Obeidat Feras , Zubair Farooqi Muhammad , Baker Thar , Hammoudeh Mohammad , Ghafir Ibrahim
Published: April 2019

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.
Collision-Free Advertisement Scheduling for IEEE 802.15.4-TSCH Networks
Authors: Karalis Apostolos , Zorbas Dimitrios , Douligeris Christos
Published: April 2019

IEEE802.15.4-time slotted channel hopping (TSCH) is a medium access control (MAC) protocol designed to support wireless device networking, offering high reliability and low power consumption, two features that are desirable in the industrial internet of things (IIoT). The formation of an IEEE802.15.4-TSCH network relies on the periodic transmissions of network advertising frames called enhanced beacons (EB). The scheduling of EB transmissions plays a crucial role both in the joining time and in the power consumption of the nodes. The existence of collisions between EB is an important factor that negatively affects the performance. In the worst case, all the neighboring EB transmissions of a node may collide, a phenomenon which we call a full collision. Most of the EB scheduling methods that have been proposed in the literature are fully or partially based on randomness in order to create the EB transmission schedule. In this paper, we initially show that the randomness can lead to a considerable probability of collisions, and, especially, of full collisions. Subsequently, we propose a novel autonomous EB scheduling method that eliminates collisions using a simple technique that does not increase the power consumption. To the best of our knowledge, our proposed method is the first non-centralized EB scheduling method that fully eliminates collisions, and this is guaranteed even if there are mobile nodes. To evaluate our method, we compare our proposal with recent and state-of-the-art non-centralized network-advertisement scheduling methods. Our evaluation does not consider only fixed topology networks, but also networks with mobile nodes, a scenario which has not been examined before. The results of our simulations demonstrate the superiority of our method in terms of joining time and energy consumption.
A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization
Authors: Soro Bedionita , Lee Chaewoo
Published: April 2019

The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zeroth and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation.
Mapping Tobacco Fields Using UAV RGB Images
Authors: Zhu Xiufang , Xiao Guofeng , Wen Ping , Zhang Jinshui , Hou Chenyao
Published: April 2019

Tobacco planting information is an important part of tobacco production management. Unmanned aerial vehicle (UAV) remote sensing systems have become a popular topic worldwide because they are mobile, rapid and economic. In this paper, an automatic identification method for tobacco fields based on UAV images is developed by combining supervised classifications with image morphological operations, and this method was used in the Yunnan Province, which is the top province for tobacco planting in China. The results show that the produce accuracy, user accuracy, and overall accuracy of tobacco field identification using the method proposed in this paper are 92.59%, 96.61% and 95.93%, respectively. The method proposed in this paper has the advantages of automation, flow process, high accuracy and easy operation, but the ground sampling distance (GSD) of the UAV image has an effect on the accuracy of the proposed method. When the image GSD was reduced to 1 m, the overall accuracy decreased by approximately 10%. To solve this problem, we further introduced the convolution method into the proposed method, which can ensure the recognition accuracy of tobacco field is above 90% when GSD is less than or equal to 1 m. Some other potential improvements of methods for mapping tobacco fields were also discussed in this paper.
Compressive Behavior of Composite Concrete Columns with Encased FRP Confined Concrete Cores
Authors: Wang Xuxu , Qi Yujun , Sun Yunlou , Xie Zhijin , Liu Weiqing
Published: April 2019

A composite concrete column with encased fiber reinforced polymer (FRP) confined concrete cores (EFCCC) is proposed in this paper. The cross-sectional form of the EFCCC column is composed of several orderly arranged FRP confined concrete cores (FCCCs) surrounding a filled core concrete. This novel composite column has several advantages, such as higher compressive capacity, stronger FRP confinement, and ductile response. The compressive experiment is employed to investigate the compressive behavior of the EFCCC column with deferent parameters, such as outside concrete and stirrups. Test results show that the main failure mode of the EFCCC column with and without an outside concrete or stirrups is tensile fracture of the glass fiber reinforced polymer (GFRP) tubes. Compared to a reinforced concrete (RC) column, the strength and ductility of the EFCCC column was obviously improved by 20% and 500%, respectively. A finite element model (FEM) based on the Drucker–Prager (D-P) was developed that can accurately predict the axial compression behavior of the composite column with FRP confined concrete core. The predicted results obtained by using this FEM have excellent agreement with the experimental results.
Nanopipette/Nanorod-Combined Quartz Tuning Fork–Atomic Force Microscope
Authors: An Sangmin , Jhe Wonho
Published: April 2019

We introduce a nanopipette/quartz tuning fork (QTF)–atomic force microscope (AFM) for nanolithography and a nanorod/QTF–AFM for nanoscratching with in situ detection of shear dynamics during performance. Capillary-condensed nanoscale water meniscus-mediated and electric field-assisted small-volume liquid ejection and nanolithography in ambient conditions are performed at a low bias voltage (~10 V) via a nanopipette/QTF–AFM. We produce and analyze Au nanoparticle-aggregated nanowire by using nanomeniscus-based particle stacking via a nanopipette/QTF–AFM. In addition, we perform a nanoscratching technique using in situ detection of the mechanical interactions of shear dynamics via a nanorod/QTF–AFM with force sensor capability and high sensitivity.
Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network
Authors: Lin Xiao , Sánchez-Escobedo Dalila , Casas Josep R. , Pardàs Montse
Published: April 2019

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.
A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities
Authors: Ho George To Sum , Tsang Yung Po , Wu Chun Ho , Wong Wai Hung , Choy King Lun
Published: April 2019

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.
Simulating and Testing Microvibrations on an Optical Satellite Using Acceleration Sensor-Based Jitter Measurements
Authors: Chen Shan-Bo , Xuan Ming , Zhang Lei , Gu Song , Gong Xiao-Xue , Sun Hong-Yu
Published: April 2019

The present study uses a method to address microvibrations effects on an optical satellite by combining simulations and experiments based on high-precision acceleration sensors. The displacement and angular displacement of each optical component can be obtained by introducing flywheel perturbation data from a six-component test bench to the finite element model of the optical satellite. Combined with an optical amplification factor inferred from the linear optical model, the pixel offset of the whole optical system is calculated. A high accuracy and broad frequency range for a new microvibration measurement experimental system is established to validate the simulation. The pixel offset of the whole optical system can be measured by testing the acceleration signals of each optical component and calculating optical amplification factors. The results are consistent with optical imaging test results, indicating correctness of the experimental scheme and the effectiveness of the simulation. The results suggest that the effect of microvibrations on a camera can be verified by using mechanical simulators instead of a whole optical camera for the experiment scheme, which is demonstrated to be an effective way for increasing efficiency in jitter measurements.
Convolutional Neural Network for Breathing Phase Detection in Lung Sounds
Authors: Jácome Cristina , Ravn Johan , Holsbø Einar , Aviles-Solis Juan , Melbye Hasse , Ailo Bongo Lars
Published: April 2019

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73–0.88) than expiration (0.63–0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings.
A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
Authors: Brini Oussama , Deslandes Dominic , Nabki Frederic
Published: April 2019

Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are arising. Wireless sensor networks (WSNs) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node’s lifetime dictated by the battery’s size. As the form factor of the SN is critical in various use cases, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded and suboptimal decisions about the right hardware components, their configuration, and reliable data communication techniques, such as automatic repeat request (ARQ) and forward error correction (FEC). In this paper, a methodology to design, configure, and deploy a reliable ultra-low power WSNs is proposed. A comprehensive energy model and a realistic path-loss (PL) model of the sensor node are also established. Through estimations and field measurements it is proven that, following the proposed methodology, the designer can thoroughly explore the design space and the make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN.
Characteristics of an Implantable Blood Pressure Sensor Packaged by Ultrafast Laser Microwelding
Authors: Kim Sungil , Park Jaesoon , So Sangkyun , Ahn Sanghoon , Choi Jiyeon , Koo Chiwan , Joung Yeun-Ho
Published: April 2019

We propose a new packaging process for an implantable blood pressure sensor using ultrafast laser micro-welding. The sensor is a membrane type, passive device that uses the change in the capacitance caused by the membrane deformation due to applied pressure. Components of the sensor such as inductors and capacitors were fabricated on two glass (quartz) wafers and the two wafers were bonded into a single package. Conventional bonding methods such as adhesive bonding, thermal bonding, and anodic bonding require considerable effort and cost. Therefore CO2 laser cutting was used due to its fast and easy operation providing melting and bonding of the interface at the same time. However, a severe heat process leading to a large temperature gradient by rapid heating and quenching at the interface causes microcracks in brittle glass and results in low durability and production yield. In this paper, we introduce an ultrafast laser process for glass bonding because it can optimize the heat accumulation inside the glass by a short pulse width within a few picoseconds and a high pulse repetition rate. As a result, the ultrafast laser welding provides microscale bonding for glass pressure sensor packaging. The packaging process was performed with a minimized welding seam width of 100 μm with a minute. The minimized welding seam allows a drastic reduction of the sensor size, which is a significant benefit for implantable sensors. The fabricated pressure sensor was operated with resonance frequencies corresponding to applied pressures and there was no air leakage through the welded interface. In addition, in vitro cytotoxicity tests with the sensor showed that there was no elution of inner components and the ultrafast laser packaged sensor is non-toxic. The ultrafast laser welding provides a fast and robust glass chip packaging, which has advantages in hermeticity, bio-compatibility, and cost-effectiveness in the manufacturing of compact implantable sensors.
Analysis of the Relationship between Turning Signal Detection and Motorcycle Driver’s Characteristics on Urban Roads; A Case Study
Authors: Micucci Alfonso , Mantecchini Luca , Sangermano Maurizio
Published: April 2019

The investigations on the effectiveness of the turn signal in motorcyclists understanding of motorists’ potential intentions in potentially dangerous car–motorcycle interactions and on the relationships among some variables that could influence the perception of rear and front turn signal status are examined in this paper. The investigations have been based on data pooled from the answers of a survey of 136 motorcycle riders, with special regards to the correct detection of turning indicators. Experimental videos have been realized during in-situ simulations, both in urban and suburban areas, recording vehicular interactions in three-leg road intersections, able to potentially generate crash risks, through a 360-camera mounted on a motorcyclist’s helmet. The blinkers detection rate has been combined with other factors related to motorcyclist’s characteristics and test context (e.g., age, gender, location of the test site, presence of a car behind tester vehicles and if the motorcyclist are also habitual car or bicycle drivers) in a stepwise logistic regression that modelled the odds of detecting the turn signal turned on as a function of significant factors. Within the limits of the proposed methodology, the results highlight the low percentage of correct sighting of the turn indicators and confirm the existence of a relation between the detection of the turn indicators aspect and some of the variables considered (e.g., age, being habitual cyclist or car driver and the presence of a car occluding the views), suggesting the opportunity to further investigate the phenomenon through the use of ad-hoc simulations, in order to highlight connections among the factors that can influence the perception of turning indicators in potentially dangerous contexts for cars and motorcycles.
Combined Organic Photovoltaic Cells and Ultra Low Power CMOS Circuit for Indoor Light Energy Harvesting
Authors: Batista Duarte , Oliveira Luis Bica , Paulino Nuno , Carvalho Carlos , Oliveira João P. , Farinhas Joana , Charas Ana , dos Santos Pedro Mendonça
Published: April 2019

This paper describes an energy harvesting system composed of an organic photovoltaic cell (OPV) connected to a DC–DC converter, designed in a 130 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology, with a quasi- maximum power point tracking (MPPT) algorithm to maximize the system efficiency, for indoor applications. OPVs are an emerging technology with potential for low cost indoor light energy harvesting. The OPV current-voltage curves (I-V) under an irradiance of solar simulator Oriel Sol 3A, at room temperature, are obtained and an accurate electrical model is derived. The energy harvesting system is subjected to four different indoor light sources: 35 W halogen, 3.5 W LED, 5 W LED, and 7 W LED, positioned at three different heights (0.45 m, 0.26 m, and 0.11 m), to evaluate the potential of the system for indoor applications. The measurements showed maximum efficiencies of 60% for 35 W halogen and 45% for 7 W LED at the highest distance (0.45 m) and between 60% (5 W LED) and 70% (35 W halogen), at the shorter distance (0.11 m). Under irradiation, the integrated CMOS circuit presented a maximum efficiency of 75.76%, which is, to the best of the authors’ knowledge, the best reported power management unit (PMU) energy system using organic photovoltaic cells.
Examining Relations Between Public Participation and Public Expenditure: Opinions from English and French Users on Environmental Issues in the English Channel
Authors: Barreiro-Gen Maria , Carpenter Angela , Von Haartman Robin , Lozano Rodrigo
Published: April 2019

Governments need to decide how to allocate their public expenditure, which is commonly misconstrued as simply targeting social issues. Most scientific literature highlights that the role of public spending is to enhance social welfare and fight poverty and inequality. Nonetheless, public expenditure also includes spending on environmental issues. This paper analyses relations between public participation, support for public expenditure, and pro-environmental behaviour (PEB) intentions in the English Channel region. An online public survey was developed to investigate public use of the English and French sides and the public’s willingness to change their behaviour to better protect the Channel region. The survey was undertaken in the summer of 2014 and was answered by 2000 respondents. The Channel region public is willing to participate more in behaviour that involves direct changes or switches between buying/purchasing choices. In contrast, there is less willingness to engage in pro-environmental behaviour intentions that involve more active engagement activities. French respondents were slightly less inclined to change their consumer behaviour intentions, while women and older people were slightly more likely to do so. This research shows that pro-environmental behaviour could positively affect support for proposed public expenditure on environmental issues.
Pollution Tradeoffs for Conventional and Natural Gas-Based Marine Fuels
Authors: Winebrake James J. , Corbett James J. , Umar Fatima , Yuska Daniel
Published: April 2019

This paper presents a life-cycle emissions analysis of conventional and natural gas-based marine transportation in the United States. We apply a total fuel cycle—or “well-to-propeller”—analysis that evaluates emissions along the fuel production and delivery pathway, including feedstock extraction, processing, distribution, and use. We compare emissions profiles for methanol, liquefied natural gas, and low sulfur marine fuel in our analysis, with a focus on exploring tradeoffs across the following pollutants: greenhouse gases, particulate matter, sulfur oxides, and nitrogen oxides. For our greenhouse gas analysis, we apply global warming potentials that consider both near-term (20-year) and long-term (100-year) climate forcing impacts. We also conduct uncertainty analysis to evaluate the impacts of methane leakage within the natural gas recovery, processing, and distribution stages of its fuel cycle. Our results indicate that natural-gas based marine fuels can provide significant local environmental benefits compared to distillate fuel; however, these benefits come with a near-term—and possibly long-term—global warming penalty, unless such natural gas-based fuels are derived from renewable feedstock, such as biomass. These results point to the importance of controlling for methane leaks along the natural gas production process and the important role that renewable natural gas can play in the shipping sector. Decision-makers can use these results to inform decisions related to increasing the use of alternative fuels in short sea and coast-wise marine transportation systems.
Sustainable Ethical Leadership and Employee Outcomes in the Hotel Industry in Cameroon
Authors: Tarkang Mary Magdaline Enow Mbi , Ozturen Ali
Published: April 2019

Lack of sustainability in ethical behavior is one of the principal reasons for unsustainable outcomes. The present study observes how sustainability in ethical leadership affects employee outcomes like trust, effective commitments, and organizational deviance. Ethical behavior and decisions of leaders will continue to be a source of concern in organizations where deviant actions are carried out by employees. The perception of bad ethics becomes a threat to the success of the organization. This study examines the impact of ethical leadership (EL) on trust (TR), affective commitment (AC), and deviance behavior of employees in the hotel industry. Using convenience sampling and a cross-sectional research method, the study made use of 150 questionnaires to get the perceptions of the respondents about the constructs. Data were obtained from employees of hotels in Cameroon. Confirmatory factor analysis, path analysis, and correlation analysis were conducted to assess the primary outcomes and to test the causality between each set of variables. The results showed that ethical leadership encourages employees to build trust in their leaders. Employees become effectively committed to their jobs and, thus, find no reason to engage in deviance actions. Furthermore, the results also confirm that trust feelings towards the leader by the employees positively influence affective commitment, hence, prevent deviance actions. Therefore, effectively committed employees do not engage in organizational deviance behaviors (OD). In light of these results, practical implications and recommendations are provided for decision-makers and future researchers.
Speed Optimization vs Speed Reduction: the Choice between Speed Limits and a Bunker Levy
Authors: Psaraftis Harilaos
Published: April 2019

“Speed optimization and speed reduction” are included in the set of candidate short-term measures under discussion at the International Maritime Organization (IMO), in the quest to reduce greenhouse gas (GHG) emissions from ships. However, there is much confusion on what either speed optimization or speed reduction may mean, and some stakeholders have proposed mandatory speed limits as a measure to achieve GHG emissions reduction. The purpose of this paper is to shed some light into this debate, and specifically examine whether reducing speed by imposing a speed limit is better than doing the same by imposing a bunker levy. To that effect, the two options are compared. The main result of the paper is that the speed limit option exhibits a number of deficiencies as an instrument to reduce GHG emissions, at least vis-à-vis the bunker levy option.