bluuebunny/crossref_metadata_2025 huggingface dataset:
A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object
Authors: Lv Xiaodong , Dai Chuankai , Chen Luyao , Lang Yiran , Tang Rongyu , Huang Qiang , He Jiping
Published: December 2019

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn’t work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.
Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis
Authors: de Santiago Luis , Ortiz del Castillo M. , Garcia-Martin Elena , Rodrigo María Jesús , Sánchez Morla Eva M. , Cavaliere Carlo , Cordón Beatriz , Miguel Juan Manuel , López Almudena , Boquete Luciano
Published: December 2019

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients.
Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study
Authors: Feenstra Brandon , Papapostolou Vasileios , Der Boghossian Berj , Cocker David , Polidori Andrea
Published: December 2019

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site.
A New Positioning Method Based on Multiple Ultrasonic Sensors for Autonomous Mobile Robot
Authors: Shen Mingqi , Wang Yuying , Jiang Yandan , Ji Haifeng , Wang Baoliang , Huang Zhiyao
Published: December 2019

This work proposes a new positioning method based on multiple ultrasonic sensors for the autonomous mobile robot. Unlike the conventional ultrasonic positioning methods, this new method can realize higher accuracy ultrasonic positioning without additional temperature information. Three ultrasonic sensors are used for positioning. A generalized measurement model is established for general sensor configuration. A simplified measurement model, which considers the computational complexity, is also established for linear/simplified sensor configuration. Three time-of-flight signals are obtained from the three ultrasonic sensors. The coordinates of the target are calculated by the ratios of time-of-flights. Positioning experiments were carried out to verify the feasibility and effectiveness of the proposed method. Experimental results show that the new ultrasonic positioning method is effective, both the two established models can implement positioning successfully, and the positioning accuracy is satisfactory. Compared with the conventional ultrasonic positioning method with the default ultrasonic velocity, the positioning accuracy is greatly improved by the proposed method. Compared with the ultrasonic positioning method with additional temperature compensation, the results obtained by the proposed method are comparable.
A Secure Partial RFID Ownership Transfer Protocol with Multi-Owners
Authors: Luo Jia-Ning , Yang Ming-Hour
Published: December 2019

Mobile radio frequency identification (RFID) has been extensively applied in a wide range of fields. In supply chain management, RFID is used to more efficiently manage the ownership transfer of cargo. The transfer of a group of tags belonging to multiple owners is often required at the front end of a supply chain. This study, therefore, proposes a secure, high-performance threshold multi-owner partial tag ownership transfer protocol that supports a mobile RFID environment and features the capabilities and security required for supporting existing ownership transfer environments (e.g., application for different authorities, designation of the transfer target, and ownership transfer of a group of tags). Moreover, the proposed protocol can resist against most of the known attacks on RFID.
Ultrasound Intra Body Multi Node Communication System for Bioelectronic Medicine
Authors: Jaafar Banafsaj , Luo Junwen , Firfilionis Dimitrios , Soltan Ahmed , Neasham Jeff , Degenaar Patrick
Published: December 2019

The coming years may see the advent of distributed implantable devices to support bioelectronic medicinal treatments. Communication between implantable components and between deep implants and the outside world can be challenging. Percutaneous wired connectivity is undesirable and both radiofrequency and optical methods are limited by tissue absorption and power safety limits. As such, there is a significant potential niche for ultrasound communications in this domain. In this paper, we present the design and testing of a reliable and efficient ultrasonic communication telemetry scheme using piezoelectric transducers that operate at 320 kHz frequency. A key challenge results from the multi-propagation path effect. Therefore, we present a method, using short pulse sequences with relaxation intervals. To counter an increasing bit, and thus packet, error rate with distance, we have incorporated an error correction encoding scheme. We then demonstrate how the communication scheme can scale to a network of implantable devices. We demonstrate that we can achieve an effective, error-free, data rate of 0.6 kbps, which is sufficient for low data rate bioelectronic medicine applications. Transmission can be achieved at an energy cost of 642 nJ per bit data packet using on/off power cycling in the electronics.
Nano-Biosensing Platforms for Detection of Cow’s Milk Allergens: An Overview
Authors: Nehra Monika , Lettieri Mariagrazia , Dilbaghi Neeraj , Kumar Sandeep , Marrazza Giovanna
Published: December 2019

Among prevalent food allergies, cow milk allergy (CMA) is most common and may persist throughout the life. The allergic individuals are exposed to a constant threat due to milk proteins’ presence in uncounted food products like yogurt, cheese, and bakery items. The problem can be more severe due to cross-reactivity of the milk allergens in the food products due to homologous milk proteins of diverse species. This problem can be overcome by proper and reliable food labeling in order to ensure the life quality of allergic persons. Therefore, highly sensitive and accurate analytical techniques should be developed to detect the food allergens. Here, significant research advances in biosensors (specifically immunosensors and aptasensors) are reviewed for detection of the milk allergens. Different allergic proteins of cow milk are described here along with the analytical standard methods for their detection. Additionally, the commercial status of biosensors is also discussed in comparison to conventional techniques like enzyme-linked immunosorbent assay (ELISA). The development of novel biosensing mechanisms/kits for milk allergens detection is imperative from the perspective of enforcement of labeling regulations and directives keeping in view the sensitive individuals.
Physical Workload Tracking Using Human Activity Recognition with Wearable Devices
Authors: Manjarres Jose , Narvaez Pedro , Gasser Kelly , Percybrooks Winston , Pardo Mauricio
Published: December 2019

In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat’s score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.
Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation
Authors: Ilyas Naveed , Shahzad Ahsan , Kim Kiseon
Published: December 2019

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).
PDMAC: A Priority-Based Enhanced TDMA Protocol for Warning Message Dissemination in VANETs
Authors: Abbas Ghulam , Abbas Ziaul Haq , Haider Shahab , Baker Thar , Boudjit Saadi , Muhammad Fazal
Published: December 2019

Vehicular ad hoc networks (VANETs) are the key enabling technology for intelligent transportation systems. Carrier-sense multiple access with collision avoidance (CSMA/CA) is the de facto media access standard for inter-vehicular communications, but its performance degrades in high-density networks. Time-division multiple access (TDMA)-based protocols fill this gap to a certain extent, but encounter inefficient clock synchronization and lack of prioritized message delivery. Therefore, we propose a priority-based direction-aware media access control (PDMAC) as a novel protocol for intra-cluster and inter-cluster clock synchronization. Furthermore, PDMAC pioneers a three-tier priority assignment technique to enhance warning messages delivery by taking into account the direction component, message type, and severity level on each tier. Analytical and simulation results validate the improved performance of PDMAC in terms of clock synchronization, channel utilization, message loss rate, end-to-end delays, and network throughput, as compared with eminent VANET MAC protocols.
Magnetic Cell Centrifuge Platform Performance Study with Different Microsieve Pore Geometries
Authors: Wu Xinyu , Bai Zhongyang , Wang Lin , Cui Guangchao , Yang Mengzheng , Yang Qing , Ma Bo , Song Qinglin , Tian Dewen , Ceyssens Frederik , Puers Robert , Kraft Michael , Zhao Weisheng , Wen Lianggong
Published: December 2019

The detection and analysis of circulating tumor cells (CTCs) plays a crucial role in clinical practice. However, the heterogeneity and rarity of CTCs make their capture and separation from peripheral blood very difficult while maintaining their structural integrity and viability. We previously demonstrated the effectiveness of the Magnetic Cell Centrifuge Platform (MCCP), which combined the magnetic-labeling cell separation mechanism with the size-based method. In this paper, a comparison of the effectiveness of different microsieve pore geometries toward MCCP is demonstrated to improve the yield of the target cell capture. Firstly, models of a trapped cell with rectangular and circular pore geometries are presented to compare the contact force using finite element numerical simulations. The device performance is then evaluated with both constant pressure and constant flow rate experimental conditions. In addition, the efficient isolation of magnetically labeled Hela cells with red fluorescent proteins (target cells) from Hela cells with green fluorescent protein (background cells) is validated. The experimental results show that the circular sieves yield 97% purity of the target cells from the sample with a throughput of up to 2 μL/s and 66-fold sample enrichment. This finding will pave the way for the design of a higher efficient MCCP systems.
Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery
Authors: Yang Baohua , Qi Lin , Wang Mengxuan , Hussain Saddam , Wang Huabin , Wang Bing , Ning Jingming
Published: December 2019

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.
Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets
Authors: Kim Bong-seok , Kim Sangdong , Jin Youngseok , Lee Jonghun
Published: December 2019

A low-complexity joint range and Doppler frequency-modulated continuous wave (FMCW) radar algorithm based on the number of targets is proposed in this paper. This paper introduces two low-complexity FMCW radar algorithms, that is, region of interest (ROI)-based and partial discrete Fourier transform (DFT)-based algorithms. We find the low-complexity condition of each algorithm by analyzing the complexity of these algorithms. From this analysis, it is found that the number of targets is an important factor in determining complexity. Based on this result, the proposed algorithm selects a low-complexity algorithm between two algorithms depending the estimated number of targets and thus achieves lower complexity compared two low-complexity algorithms introduced. The experimental results using real FMCW radar systems show that the proposed algorithm works well in a real environment. Moreover, central process unit time and count of float pointing are shown as a measure of complexity.
Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes
Authors: Kim Eung-su , Park Soon-Yong
Published: December 2019

This paper proposes a simple extrinsic calibration method for a multi-sensor system which consists of six image cameras and a 16-channel 3D LiDAR sensor using a planar chessboard. The six cameras are mounted on a specially designed hexagonal plate to capture omnidirectional images and the LiDAR sensor is mounted on the top of the plates to capture 3D points in 360 degrees. Considering each camera–LiDAR combination as an independent multi-sensor unit, the rotation and translation between the two sensor coordinates are calibrated. The 2D chessboard corners in the camera image are reprojected into 3D space to fit to a 3D plane with respect to the camera coordinate system. The corresponding 3D point data that scan the chessboard are used to fit to another 3D plane with respect to the LiDAR coordinate system. The rotation matrix is calculated by aligning normal vectors of the corresponding planes. In addition, an arbitrary point on the 3D camera plane is projected to a 3D point on the LiDAR plane, and the distance between the two points are iteratively minimized to estimate the translation matrix. At least three or more planes are used to find accurate external parameters between the coordinate systems. Finally, the estimated transformation is refined using the distance between all chessboard 3D points and the LiDAR plane. In the experiments, quantitative error analysis is done using a simulation tool and real test sequences are also used for calibration consistency analysis.
Inductive Position and Speed Sensors
Authors: Ripka Pavel , Blažek Josef , Mirzaei Mehran , Lipovský Pavol , Šmelko Miroslav , Draganová Katarína
Published: December 2019

Magnetic position and speed sensors are rugged and durable. While DC magnetic sensors use permanent magnets as a field source and usually have only mm or cm range, inductive sensors use electromagnetic induction and they may work up to a distance of 20 m. Eddy current inductive sensors equipped with magnetoresistive sensors instead of inductive coils can operate at low frequencies, allowing detection through a conductive wall. In this paper, we make an overview of existing systems and we present new results in eddy current velocity and position measurements. We also present several types of inductive position sensors developed in our laboratories for industrial applications in pneumatic and hydraulic cylinders, underground drilling, large mining machines, and for detecting ferromagnetic objects on conveyors. While the most precise inductive position sensors have a resolution of 10 nm and linearity of 0.2%, precision requirements on the industrial sensors which we develop are less demanding, but they should have large working distance and large resistance to environmental conditions and interference.
IEEE 802.11-Enabled Wake-Up Radio: Use Cases and Applications
Authors: Lopez-Aguilera Elena , Demirkol Ilker , Garcia-Villegas Eduard , Paradells Josep
Published: December 2019

IEEE 802.11 is one of the most commonly used radio access technologies, being present in almost all handheld devices with networking capabilities. However, its energy-hungry communication modes are a challenge for the increased battery lifetime of such devices and are an obstacle for its use in battery-constrained devices such as the ones defined by many Internet of Things applications. Wake-up Radio (WuR) systems have appeared as a solution for increasing the energy efficiency of communication technologies by employing a secondary low-power radio interface, which is always in the active state and switches the primary transceiver (used for main data communication) from the energy-saving to the active operation mode. The high market penetration of IEEE 802.11 technology, together with the benefits that WuR systems can bring to this widespread technology, motivates this article’s focus on IEEE 802.11-based WuR solutions. More specifically, we elaborate on the feasibility of such IEEE 802.11-based WuR solutions, and introduce the latest standardization efforts in this IEEE 802.11-based WuR domain, IEEE 802.11ba, which is a forthcoming IEEE 802.11 amendment, discussing its main features and potential use cases. As a use case consisting of green Wi-Fi application, we provide a proof-of-concept smart plug system implemented by a WuR that is activated remotely using IEEE 802.11 devices, evaluate its monetary and energy savings, and compare it with commercially available smart plug solutions. Finally, we discuss novel applications beyond the wake-up functionality that IEEE 802.11-enabled WuR devices can offer using a secondary radio, as well as applications that have not yet been considered by IEEE 802.11ba. As a result, we argue that the IEEE 802.11-based WuR solution will support a wide range of devices and deployments, for both low-rate and low-power communications, as well as high-rate transmissions.
Non-Contact Measurement of Blade Vibration in an Axial Compressor
Authors: Przysowa Radoslaw , Russhard Peter
Published: December 2019

Complex blade responses such as a rotating stall or simultaneous resonances are common in modern engines and their observation can be a challenge even for state-of-the-art tip-timing systems and trained operators. This paper analyses forced vibrations of axial compressor blades, measured during the bench tests of the SO-3 turbojet. In relation to earlier studies conducted in Poland with a small number of sensors, a multichannel tip-timing system let us observe simultaneous responses or higher-order modes. To find possible symptoms of a failure, blade responses in a healthy and unhealthy engine configuration with an inlet blocker were studied. The used analysis methods covered all-blade spectrum and the circumferential fitting of blade deflections to the harmonic oscillator model. The Pearson coefficient of correlation between the measured and predicted tip deflection is calculated to evaluate fitting results. It helps to avoid common operator mistakes and misinterpreting the results. The proposed modal solver can track the vibration frequency and adjust the engine order on the fly. That way, synchronous and asynchronous vibrations are observed and analysed together with an extended variant of least squares. This approach saves a lot of work related to configuring the conventional tip-timing solver.
A Non-Enzymatic Sensor Based on Trimetallic Nanoalloy with Poly (Diallyldimethylammonium Chloride)-Capped Reduced Graphene Oxide for Dynamic Monitoring Hydrogen Peroxide Production by Cancerous Cells
Authors: Jiao Jun , Pan Meixin , Liu Xinran , Li Binshuai , Liu Jian , Chen Qiang
Published: December 2019

Catching cancer at an early stage is necessary to make it easier to treat and to save people’s lives rather than just extending them. Reactive oxygen species (ROS) have sparked a huge interest owing to their vital role in various biological processes, especially in tumorigenesis, thus leading to the potential of ROS as prognostic biomarkers for cancer. Herein, a non-enzymatic biosensor for the dynamic monitoring of intracellular hydrogen peroxide (H2O2), the most important ROS, via an effective electrode composed of poly (diallyldimethylammonium chloride) (PDDA)-capped reduced graphene oxide (RGO) nanosheets with high loading trimetallic AuPtAg nanoalloy, is proposed. The designed biosensor was able to measure H2O2 released from different cancerous cells promptly and precisely owing to the impressive conductivity of RGO and PDDA and the excellent synergistic effect of the ternary alloy in boosting the electrocatalytic activity. Built upon the peroxidase-like activity of the nanoalloy, the developed sensor exhibited distinguished electrochemical performance, resulting in a low detection limit of 1.2 nM and a wide linear range from 0.05 μM to 5.5 mM. Our approach offers a significant contribution toward the further elucidation of the role of ROS in carcinogenesis and the effective screening of cancer at an early stage.
LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting
Authors: Sakr Mostafa , Masiero Andrea , El-Sheimy Naser
Published: December 2019

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.
A Multidimensional Hyperjerk Oscillator: Dynamics Analysis, Analogue and Embedded Systems Implementation, and Its Application as a Cryptosystem
Authors: Nestor Tsafack , De Dieu Nkapkop , Jacques Kengne , Yves Effa , Iliyasu Abdullah , Abd El-Latif Ahmed
Published: December 2019

A lightweight image encryption algorithm is presented based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) network. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend exclusively on the system’s initial values. Further, a simple implementation of the circuit was used to validate its ability to exhibit chaotic dynamical properties. Second, an Arduino UNO platform is used to confirm the usability of our oscillator in embedded system implementation. Finally, an efficient image encryption application is executed using the proposed chaotic networks based on the use of permutation-substitution sequences. The superior qualities of the proposed strategy are traced to the dynamic set of keys used in the substitution process which heralds the generation of the final ciphered image. Based on the average results obtained from the entropy analysis (7.9976), NPCR values (99.62), UACI tests (33.69) and encryption execution time for 512 × 512 images (0.1141 s), the proposed algorithm is adjudged to be fast and robust to differential and statistical attacks relative to similar approaches.
Charge Sharing in (CdZn)Te Pixel Detector Characterized by Laser-Induced Transient Currents
Authors: Vasylchenko Igor , Grill Roman , Belas Eduard , Praus Petr , Musiienko Artem
Published: December 2019

Performance of the (CdZn)Te pixelated detectors heavily relies on the quality of the underlying material. Modern laser-induced transient current technique addresses this problem as a convenient tool for characterizing the associated charge distribution. In this paper, we investigated the charge sharing phenomenon in (CdZn)Te pixel detector as a function of the charge collected on adjacent pixels. The current transients were generated in the defined 4 mm2 spots using 660 nm laser illumination. Waveforms measured on the pixel of interest and its surroundings were used to build the maps of the collected charge at different biases. The detailed study of the maps allowed us to distinguish the charge sharing region, the region with a defect, and the finest part in terms of the performance part of the pixelated anode. We observed the principal inhomogeneity complicating the assignment of the illuminated spot to the nearest pixel.
Flexible Ultrasonic Transducer Array with Bulk PZT for Adjuvant Treatment of Bone Injury
Authors: Liu Huicong , Geng Jiangjun , Zhu Qifeng , Zhang Lue , Wang Fengxia , Chen Tao , Sun Lining
Published: December 2019

Flexible electronic devices are developing rapidly, especially in medical applications. This paper reports an arrayed flexible piezoelectric micromachined ultrasonic transducer (FPMUT) with a sandwich structure for adjuvant treatment of bone injury. To make the device conformable and stretchable for attaching to the skin surface, the flexible substrate of polydimethylsiloxane (PDMS) was combined with the flexible metal line interconnection between the bulk lead zirconate titanate (PZT) arrays. Simulations and experiments were carried out to verify the resonant frequency and tensile property of the reported FPMUT device. The device had a resonant frequency of 321.15 KHz and a maximum sound pressure level (SPL) of 180.19 dB at the distance of 5 cm in water. In addition, detailed experiments were carried out to test its acoustic performance with different pork tissues, and the results indicated good ultrasound penetration. These findings confirm that the FPMUT shows unique advantages for adjuvant treatment of bone injury.
Detection of the Crystallization Process of Paracetamol with a Multi-Mode Optical Fiber in a Reflective Configuration
Authors: Soares Liliana , Novais Susana , Ferreira António , Frazão Orlando , Silva Susana
Published: December 2019

A configuration of a refractometer sensor is described with the aim of optically detecting the crystallization process of paracetamol. The developed sensing head is based on a conventional cleaved multi-mode fiber. The fiber tip sensor structure was submitted to contact with the liquid of interest (paracetamol fully dissolved in 40% v/v of ethanol/water) and the crystallization process of paracetamol, induced with continued exposure to air, was monitored in real time.
Development of a Smartphone-Based Balance Assessment System for Subjects with Stroke
Authors: Hou You-Ruei , Chiu Ya-Lan , Chiang Shang-Lin , Chen Hui-Ya , Sung Wen-Hsu
Published: December 2019

Stroke is a cerebral artery disease that negatively affects activities of daily living (ADLs) and quality of life (QoL). Smartphones have demonstrated strong potential in assessing balance performance. However, such smartphone-based tools have thus far not been applied to stroke survivors. The purpose of this study was to develop a smartphone-based balance assessment system for subjects who have experienced strokes and evaluate the system feasibility. The smartphone-based balance assessment application was developed with Android Studio, and reliability and validity tests were conducted. The smartphone was used to record data using a built-in accelerometer and gyroscope, and increased changes represented greater instability. Six postures were tested for 30 s each. Ten healthy adults were recruited in the reliability test, and the intraclass correlation coefficient (ICC) was used to analyze the within-day and between-day reliabilities. Eight subjects with chronic stroke and eight healthy adults were recruited for the validity test, in which balance performance was compared to represent the application validity. The ICC values of the reliability tests were at least 0.76 (p = 0.00). The acceleration data exhibited no difference between individuals who have experienced stroke and healthy subjects; however, all six postures were found to differ significantly between the two groups in the gyroscope data. The study demonstrates that the smartphone application provides a convenient, reliable, and valid tool for the balance assessments of subjects who have experienced chronic stroke.
Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment
Authors: Gao Jinxia , Liu Longjun , Gao Peng , Zheng Yihuan , Hou Wenxuan , Wang Junhui
Published: December 2019

Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.
A Straightforward Approach for Synthesizing Electromechanical Sigma-Delta MEMS Accelerometers
Authors: Chen Dongliang , Yin Liang , Fu Qiang , Zhang Wenbo , Wang Yihang , Zhang Guorui , Zhang Yufeng , Liu Xiaowei
Published: December 2019

The EM- Σ Δ (electromechanical sigma-delta) approach is a concise and efficient way to realize the digital interface for micro-electromechanical systems (MEMS) accelerometers. However, including a fixed MEMS element makes the synthesizing of the EM- Σ Δ loop an intricate problem. The loop parameters of EM- Σ Δ can not be directly mapped from existing electrical Σ Δ modulator, and the synthesizing problem relies an experience-dependent trail-and-error procedure. In this paper, we provide a new point of view to consider the EM- Σ Δ loop. The EM- Σ Δ loop is analyzed in detail from aspects of the signal loop, displacement modulation path and digital quantization loop. By taking a separate consideration of the signal loop and quantization noise loop, the design strategy is made clear and straightforward. On this basis, a discrete-time PID (proportional integral differential) loop compensator is introduced which enhances the in-band loop gain and suppresses the displacement modulation path, and hence, achieves better performance in system linearity and stability. A fifth-order EM- Σ Δ accelerometer system was designed and fabricated using 0.35 μ m CMOS-BCD technology. Based on proposed architecture and synthesizing procedure, the design effort was saved, and the in-band performance, linearity and stability were improved. A noise floor of 1 μ g / Hz , with a bandwidth 1 kHz and a dynamic range of 140 dB was achieved.
Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments
Authors: Park Keon-woo , Shim Yoo-Jeong , Lee Myeong-jin , Ahn Heejune
Published: December 2019

In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.
Radioisotope Identification and Nonintrusive Depth Estimation of Localized Low-Level Radioactive Contaminants Using Bayesian Inference
Authors: Kim Jinhwan , Lim Kyung Taek , Ko Kilyoung , Ko Eunbie , Cho Gyuseong
Published: December 2019

Obtaining the in-depth information of radioactive contaminants is crucial for determining the most cost-effective decommissioning strategy. The main limitations of a burial depth analysis lie in the assumptions that foreknowledge of buried radioisotopes present at the site is always available and that only a single radioisotope is present. We present an advanced depth estimation method using Bayesian inference, which does not rely on those assumptions. Thus, we identified low-level radioactive contaminants buried in a substance and then estimated their depths and activities. To evaluate the performance of the proposed method, several spectra were obtained using a 3 × 3 inch hand-held NaI (Tl) detector exposed to Cs-137, Co-60, Na-22, Am-241, Eu-152, and Eu-154 sources (less than 1μCi) that were buried in a sandbox at depths of up to 15 cm. The experimental results showed that this method is capable of correctly detecting not only a single but also multiple radioisotopes that are buried in sand. Furthermore, it can provide a good approximation of the burial depth and activity of the identified sources in terms of the mean and 95% credible interval in a single measurement. Lastly, we demonstrate that the proposed technique is rarely susceptible to short acquisition time and gain-shift effects.
Who else Needs Protection? Reflecting on Researcher Vulnerability in Sensitive Research
Authors: Sikic Micanovic Lynette , Stelko Stephanie , Sakic Suzana
Published: December 2019

Ethnographic research characterised by immersion, reflexivity, and rapport can be unpredictable and uncontrollable, producing a wide range of emotional responses. Much of the literature on sensitive research focuses on ethical requirements and strategies for protecting participants while less attention has been given to the need for researcher protection. In this paper, we share some of the concealed and/or overlooked aspects of researcher vulnerability that are commonly disregarded or under-explored. Based on our fieldwork experiences with a vulnerable population, it considers some of the different ways doing sensitive research with people experiencing homelessness has had an impact on our research team and wider. Specifically, we analyze the emotional impact of distressing and painful research experiences on those directly and not directly involved with the collection of research data (i.e., transcribers and coders). The themes that are discussed include: i) blurring of roles in the field; ii) dealing with heart-rending life stories; and iii) handling emotionally charged experiences. By reflecting on our fieldwork experiences and emotions, we also explore the ways in which emotional impacts can be managed in practice. Strategies for emotion management that have helped us deal with the unique challenges of this research are outlined.
Factors Influencing Teachers’ Implementation of a Reformed Instructional Model in China from the Theory of Planned Behavior Perspective: A Multiple Case Study
Authors: Zhao Wenjun , Mok Ida Ah Chee , Cao Yiming
Published: December 2019

It is difficult for curriculum reform to achieve sustainable success without support from teachers in the front line. The authors of this paper investigated teacher factors behind the successful implementation of a reformed instructional model, the Dao Jiang Ping (DJP) model, in China. This study is part of a longitudinal project (2012–2017) that reported the level of DJP implementation and explored factors that influence teachers’ implementation. This paper mainly focuses on the latter. Six teachers were examined to look for reasons behind their different levels of DJP implementation. The theory of planned behavior (TPB), which has been widely used to explain and predict people’s behavior or intention to perform a behavior, is used as the theoretical framework for the study. Referring to the TPB framework, this study summarized factors into individual factors, perceived social factors and perceived contextual factors. Furthermore, the authors suggest extending the TPB framework by including teachers’ understanding of the reform ideas to facilitate its application in order to explore factors in reform contexts. Lastly, this study discusses several important factors that help teachers to implement new teaching methodologies continually, thus promoting the sustainable implementation of reforms.
Integrating Sustainability into Major Infrastructure Projects: Four Perspectives on Sustainable Tunnel Development
Authors: Gijzel Darinde , Bosch-Rekveldt Marian , Schraven Daan , Hertogh Marcel
Published: December 2019

Although sustainability is on the agenda of many countries and agencies, it is still a great challenge to properly integrate sustainability into major infrastructure projects, like tunnels. This is remarkable, as it is commonly accepted that the construction and operation of these civil infrastructures have a heavy impact on environmental, economic and social sustainability. In academia, the foregone conclusion seems to point to a lack of vision about sustainability with respect to tunnels. This article presents an empirical study that investigates sustainability ideas amongst tunnel practitioners in the Netherlands. The case was used as a practical environment to get access to tunnel practitioners, to explicitly define the meaning of a sustainable tunnel in its development phase. Perceptions of sustainable tunnels were extracted by means of a commonly accepted research methodology called Q-methodology. By applying this method, four perspectives were obtained: perspectives with a focus on energy, resilience, social or a transitional focus. Each perspective highlights distinct focal points on how to operationalize sustainability for tunnel projects. Each perspective is also accompanied by an anti-focus; how sustainability should not be approached, sometimes contrary to other perspectives. These insights help project practitioners in creating awareness for the existence of different perspectives, and subsequently help to focus project management efforts to implement sustainability in specific projects.