bluuebunny/crossref_metadata_2025 huggingface dataset:
A Highly Linear CMOS Image Sensor Design Based on an Adaptive Nonlinear Ramp Generator and Fully Differential Pipeline Sampling Quantization with a Double Auto-Zeroing Technique
Authors: Li Chuangze , Han Benguang , He Jie , Guo Zhongjie , Wu Longsheng
Published: February 2020

For a complementary metal-oxide-semiconductor image sensor with highly linear, low noise and high frame rate, the nonlinear correction and frame rate improvement techniques are becoming very important. The in-pixel source follower transistor and the integration capacitor on the floating diffusion node cause linearity degradation. In order to address this problem, this paper proposes an adaptive nonlinear ramp generator circuit based on dummy pixels used in single-slope analog-to-digital converter topology for a complementary metal-oxide-semiconductor (CMOS) image sensor. In the proposed approach, the traditional linear ramp generator circuit is replaced with the new proposed adaptive nonlinear ramp generator circuit that can mitigate the nonlinearity of the pixel unit circuit, especially the gain nonlinearity of the source follower transistor and the integration capacitor nonlinearity of the floating diffusion node. Moreover, in order to enhance the frame rate and address the issue of high column fixed pattern noise, a new readout scheme of fully differential pipeline sampling quantization with a double auto-zeroing technique is proposed. Compared with the conventional readout structure without a fully differential pipeline sampling quantization technique and double auto-zeroing technique, the proposed readout scheme cannot only enhance the frame rate but can also improve the consistency of the offset and delay information of different column comparators and significantly reduce the column fixed pattern noise. The proposed techniques are simulated and verified with a prototype chip fabricated using typical 180 nm CMOS process technology. The obtained measurement results demonstrate that the overall nonlinearity of the CMOS image sensor is reduced from 1.03% to 0.047%, the efficiency of the comparator is improved from 85.3% to 100%, and the column fixed pattern noise is reduced from 0.43% to 0.019%.
An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory
Authors: Li Chaoqun , Han Wenting , Peng Manman , Zhang Mengfei , Yao Xiaomin , Liu Wenshuai , Wang Tonghua
Published: February 2020

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system’s own original gas using its pump. CO2 and PM2.5/10 above the corn field are used as the test species to validate the accuracy of the CO2 gas and PM concentrations collected by UGSS. Deming regression analyses showed good agreement between the measurements from the UGSS and the ground sampling station (y = 1.027x – 11.239, Pearson’s correlation coefficient of 0.98 for CO2; y = 0.992x + 0.704, Pearson’s correlation coefficient of 0.99 for PM).The UGSS provides a measuring method that actively collects gases and PM for manual analyses in the laboratory.
MEMS-Based Pulse Wave Sensor Utilizing a Piezoresistive Cantilever
Authors: Nguyen Thanh-Vinh , Mizuki Yuya , Tsukagoshi Takuya , Takahata Tomoyuki , Ichiki Masaaki , Shimoyama Isao
Published: February 2020

This paper reports on a microelectromechanical systems (MEMS)-based sensor for pulse wave measurement. The sensor consists of an air chamber with a thin membrane and a 300-nm thick piezoresistive cantilever placed inside the chamber. When the membrane of the chamber is in contact with the skin above a vessel of a subject, the pulse wave of the subject causes the membrane to deform, leading to a change in the chamber pressure. This pressure change results in bending of the cantilever and change in the resistance of the cantilever, hence the pulse wave of the subject can be measured by monitoring the resistance of the cantilever. In this paper, we report the sensor design and fabrication, and demonstrate the measurement of the pulse wave using the fabricated sensor. Finally, measurement of the pulse wave velocity (PWV) is demonstrated by simultaneously measuring pulse waves at two points using the two fabricated sensor devices. Furthermore, the effect of breath holding on PWV is investigated. We showed that the proposed sensor can be used to continuously measure the PWV for each pulse, which indicates the possibility of using the sensor for continuous blood pressure measurement.
Tandem Mach Zehnder Directional Coupler Design and Simulation on Silicon Platform for Optical Coherence Tomography Applications
Authors: Lu Yi-Ting , Widhianto Benedictus Yohanes Bagus , Hsu Shih-Hsiang , Chang Che-Chang
Published: February 2020

We design and compare the splitting ratio wavelength flatness of directional coupler (DC), Mach-Zehnder directional coupler (MZDC), and tandem MZDC. All coupler responses are analyzed, and tandem MZDC performance is the best in the wavelength insensitivity compared with the other two. An MZDC with any coupling ratio could be utilized to match the maximum flatness in a 40-nm wavelength range. To extend a broad flatness range, the tandem MZDC is proposed and still follows the Mach Zehnder structure taking two MZDCs as couplers connected through a decoupled region. Unlike DC, MZDC with the flat wavelength response has a non-linear output phase. Hence, using two wavelength-insensitive MZDCs as the coupling function in a tandem MZDC could demonstrate a more extensive decoupled phase term to maximize the flat wavelength response. The tandem MZDC theoretically demonstrates the splitting ratio with 100-nm flatness in the wavelength range from 1250 nm to 1350 nm. Finally, a point spread function through the tandem MZDC shows a 24-dB signal-to-noise ratio improvement in optical coherence tomography applications.
Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration
Authors: Wu Chuan , Huang He , Li Rui , Fan Chenxing
Published: February 2020

The traditional downhole drilling vibration measurement methods which use cable or battery as power supplies increase the drilling costs and reduce the drilling efficiency. This paper proposes a spherical triboelectric nanogenerator, which shows the potential to collect the downhole vibration energy and measure the vibration frequency in a self-powered model. The power generation tests show that the output signal amplitude of the spherical triboelectric nanogenerator increases as the vibration frequency increases, and it can reach a maximum output voltage of 70 V, a maximum current of 3.3 × 10−5 A, and a maximum power of 10.9 × 10−9 W at 8 Hz when a 10-ohm resistor is connected. Therefore, if the power generation is stored for a certain period of time when numbers of the spherical triboelectric nanogenerators are connected in parallel, it may provide intermittent power for the low-power downhole measurement instruments. In addition, the sensing tests show that the measurement range is 0 to 8 Hz, the test error is less than 2%, the applicable working environment temperature is below 100 degrees Celsius, and the installation distance between the spherical triboelectric nanogenerator and the vibration source should be less than the critical value of 150 cm because the output signal amplitude is inversely proportional to the distance.
A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods
Authors: Chen Weiya , Yu Chenchen , Tu Chenyu , Lyu Zehua , Tang Jing , Ou Shiqi , Fu Yan , Xue Zhidong
Published: February 2020

Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.
Object-Based Shadow Index via Illumination Intensity from High Resolution Satellite Images over Urban Areas
Authors: Fu Haoyang , Zhou Tingting , Sun Chenglin
Published: February 2020

For multi-spectral remote sensing imagery, accurate shadow extraction is of great significance for overcoming the information loss caused by high buildings and the solar incidence angle in urban remote sensing. However, diverse solar illumination conditions, similarities between shadows, and other dark land features bring uncertainties and deviations to shadow extraction processes and results. In this paper, we classify shadows as either strong or weak based on the ratio between ambient light intensity and direct light intensity, and use the fractal net evolution approach (FNEA), which is a multi-scale segmentation method based on spectral and shape heterogeneity, to reduce the interference of salt and pepper noise and relieve the error of misdiagnosing land covers with high reflectivity in shaded regions as unshaded ones. Subsequently, an object-based shadow index (OSI) is presented according to the illumination intensities of different reflectance features, as well as using the normalized difference water index (NDWI) and near infrared (NIR) band to highlight shadows and eliminate water body interference. The data from three high-spatial-resolution satellites—WorldView-2 (WV-2), WorldView-3 (WV-3), and GaoFen-2 (GF-2)—were used to test the methods and verify the robustness of the OSI. The results show that the OSI index performed well regarding both strong and weak shadows with the user accuracy and the producer accuracy both above 90%, while the four other existing indexes that were tested were not effective at diverse solar illumination conditions. In addition, all the disturbances from water body were excluded well when using the OSI, except for the GF-2 data in weak shadows.
UAV Mission Planning with SAR Application
Authors: Stecz Wojciech , Gromada Krzysztof
Published: February 2020

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.
Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories
Authors: Wang Qi , Lu Min , Li Qingquan
Published: February 2020

Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.
eXnet: An Efficient Approach for Emotion Recognition in the Wild
Authors: Riaz Muhammad Naveed , Shen Yao , Sohail Muhammad , Guo Minyi
Published: February 2020

Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for real-time systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.
3D Measurement of Human Chest and Abdomen Surface Based on 3D Fourier Transform and Time Phase Unwrapping
Authors: Wu Haibin , Yu Shuang , Yu Xiaoyang
Published: February 2020

Monitoring respiratory movements is an effective way to improve radiotherapy treatments of thoracic and abdominal tumors, but the current approach is limited to measuring specific points in the chest and abdomen. In this paper, a dynamic three-dimensional (3D) measurement approach of the human chest and abdomen surface is proposed, which can infer tumor movement more accurately, so the radiotherapy damage to the human body can be reduced. Firstly, color stripe patterns in the RGB color model are projected, then after color correction, the collected stripe image sequences are separated into the three RGB primary color stripe image sequences. Secondly, a fringe projection approach is used to extract the folded phase combined 3D Fourier transform with 3D Gaussian filtering. By the relationship between adjacent fringe images in the time sequence, Gaussian filter parameters with individual characteristics are designed and optimized to improve the accuracy of wrapped phase extraction. In addition, based on the difference between the fractional parts of the folded phase error, one remainder equation can be determined, which is used for time-phase unwrapping. The simulation model and human experiments show that the proposed approach can obtain the 3D image sequences of the chest and abdomen surface in respiratory motion effectively and accurately with strong anti-interference ability.
Avoiding 3D Obstacles in Mixed Reality: Does It Differ from Negotiating Real Obstacles?
Authors: Coolen Bert , Beek Peter J. , Geerse Daphne J. , Roerdink Melvyn
Published: February 2020

Mixed-reality technologies are evolving rapidly, allowing for gradually more realistic interaction with digital content while moving freely in real-world environments. In this study, we examined the suitability of the Microsoft HoloLens mixed-reality headset for creating locomotor interactions in real-world environments enriched with 3D holographic obstacles. In Experiment 1, we compared the obstacle-avoidance maneuvers of 12 participants stepping over either real or holographic obstacles of different heights and depths. Participants’ avoidance maneuvers were recorded with three spatially and temporally integrated Kinect v2 sensors. Similar to real obstacles, holographic obstacles elicited obstacle-avoidance maneuvers that scaled with obstacle dimensions. However, with holographic obstacles, some participants showed dissimilar trail or lead foot obstacle-avoidance maneuvers compared to real obstacles: they either consistently failed to raise their trail foot or crossed the obstacle with extreme lead-foot margins. In Experiment 2, we examined the efficacy of mixed-reality video feedback in altering such dissimilar avoidance maneuvers. Participants quickly adjusted their trail-foot crossing height and gradually lowered extreme lead-foot crossing heights in the course of mixed-reality video feedback trials, and these improvements were largely retained in subsequent trials without feedback. Participant-specific differences in real and holographic obstacle avoidance notwithstanding, the present results suggest that 3D holographic obstacles supplemented with mixed-reality video feedback may be used for studying and perhaps also training 3D obstacle avoidance.
Autonomous Dam Surveillance Robot System Based on Multi-Sensor Fusion
Authors: Zhang Chao , Zhan Quanzhong , Wang Qi , Wu Haichao , He Ting , An Yi
Published: February 2020

Dams are important engineering facilities in the water conservancy industry. They have many functions, such as flood control, electric power generation, irrigation, water supply, shipping, etc. Therefore, their long-term safety is crucial to operational stability. Because of the complexity of the dam environment, robots with various kinds of sensors are a good choice to replace humans to perform a surveillance job. In this paper, an autonomous system design is proposed for dam ground surveillance robots, which includes general solution, electromechanical layout, sensors scheme, and navigation method. A strong and agile skid-steered mobile robot body platform is designed and created, which can be controlled accurately based on an MCU and an onboard IMU. A novel low-cost LiDAR is adopted for odometry estimation. To realize more robust localization results, two Kalman filter loops are used with the robot kinematic model to fuse wheel encoder, IMU, LiDAR odometry, and a low-cost GNSS receiver data. Besides, a recognition network based on YOLO v3 is deployed to realize real-time recognition of cracks and people during surveillance. As a system, by connecting the robot, the cloud server and the users with IOT technology, the proposed solution could be more robust and practical.
Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events
Authors: Abo-Tabik Maryam , Costen Nicholas , Darby John , Benn Yael
Published: February 2020

Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.
Transmit BeampattarnOptimization for Automotive MIMO Radar Coexistedwith Communication in V2V Networks
Authors: Jiang Zheng-Ming , Zhang Pei-Chang , Huang Lei , He Xin , Zhang Ji-Hong , Rihan Mohamed
Published: February 2020

Due to the flourishing development of vehicle-to-vehicle (V2V) communications and autonomous driving, interference between radar sensing and communication signals becomes a challenging issue. We propose a transmit beamforming based spectrum sharing scheme to achieve peaceful coexistence between automotive multiple-input multiple-out (MIMO) radar and communication systems. Our objective is to maximize the signal-to-interference-plus-noise ratio (SINR) of the automotive radar receiver subject to the communication capacity and the transmitted power budget constraints to optimize both the communication covariance matrix and the radar transmit precoder. The formulated optimization problem is non-convex, which is converted to convex by introducing a new slack variable and then solving it using the block coordinate descent, also called alternation optimization, approach. Additionally, the ellipsoid sub-gradient method is then employed to reduce the computational complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional schemes.
Advanced Hand Gesture Prediction Robust to Electrode Shift with an Arbitrary Angle
Authors: Xu Zhenjin , Shen Linyong , Qian Jinwu , Zhang Zhen
Published: February 2020

Recent advances in myoelectric controlled techniques have made the surface electromyogram (sEMG)-based sensing armband a promising candidate for acquiring bioelectric signals in a simple and convenient way. However, inevitable electrode shift as a non-negligible defect commonly causes a trained classifier requiring continuous recalibrations. In this study, a novel hand gesture prediction is firstly proposed; it is robust to electrode shift with arbitrary angle. Unlike real-time recognition which outputs target gestures only after the termination of hand motions, our proposed advanced prediction can provide the same results, even before the completion of signal collection. Moreover, by combining interpolated peak location and preset synchronous gesture, the developed simplified rapid electrode shift detection and correction at random rather than previous fixed angles are realized. Experimental results demonstrate that it is possible to achieve both electrode shift detection with high precision and gesture prediction with high accuracy. This study provides a new insight into electrode shift robustness which brings gesture prediction a step closer to practical applications.
An Enhanced Dynamic Simulation Model of a Hybrid Magnetic Bearing Taking Account of the Sensor Noise
Authors: Wajnert Dawid , Sykulski Jan K. , Tomczuk Bronislaw
Published: February 2020

The paper presents simulation and experimental verification of the hybrid magnetic bearing (HMB) performance characteristics. It has been demonstrated that the additional errors from the eddy current sensors have a significant impact on the control signals. An improved mathematical model combines a nonlinear magnetic equivalent circuit of the HMB with the ordinary differential equations of its transients. These equations describe the rotor motion and the electric circuit of the system, as well as the control system required for stable levitation of the rotor. Certain harmonics have been observed in the displacement signals of frequencies equal to the multiples of the cylinder rotations. The calculation model has, therefore, been improved, taking into account the interference of the harmonics. Simulation results were validated by comparing the time responses of the transients obtained from the numerical calculations with those measured on a real object; a satisfactory agreement between the results has been achieved.
High-Precision Acceleration Measurement System Based on Tunnel Magneto-Resistance Effect
Authors: Gao Lu , Chen Fang , Yao Yingfei , Xu Dacheng
Published: February 2020

A high-precision acceleration measurement system based on an ultra-sensitive tunnel magneto-resistance (TMR) sensor is presented in this paper. A “force–magnetic–electric” coupling structure that converts an input acceleration into a change in magnetic field around the TMR sensor is designed. In such a structure, a micro-cantilever is integrated with a magnetic field source on its tip. Under an acceleration, the mechanical displacement of the cantilever causes a change in the spatial magnetic field sensed by the TMR sensor. The TMR sensor is constructed with a Wheatstone bridge structure to achieve an enhanced sensitivity. Meanwhile, a low-noise differential circuit is developed for the proposed system to further improve the precision of the measured acceleration. The experimental results show that the micro-system achieves a measurement resolution of 19 μg/√Hz at 1 Hz, a scale factor of 191 mV/g within a range of ± 2 g, and a bias instability of 38 μg (Allan variance). The noise sources of the proposed system are thoroughly investigated, which shows that low-frequency 1/f noise is the dominant noise source. We propose to use a high-frequency modulation technique to suppress the 1/f noise effectively. Measurement results show that the 1/f noise is suppressed about 8.6-fold at 1 Hz and the proposed system resolution can be improved to 2.2 μg/√Hz theoretically with this high-frequency modulation technique.
Precise Point Positioning Algorithm for Pseudolite Combined with GNSS in a Constrained Observation Environment
Authors: Sheng Chuanzhen , Gan Xingli , Yu Baoguo , Zhang Jingkui
Published: February 2020

In urban canyon environments, Global Navigation Satellite System (GNSS) satellites are heavily obstructed with frequent rise and fall and severe multi-path errors induced by signal reflection, making it difficult to acquire precise, continuous, and reliable positioning information. To meet imperative demands for high-precision positioning of public users in complex environments, like urban canyons, and to solve the problems for GNSS/pseudolite positioning under these circumstances, the Global Navigation Satellite System (GNSS) Precision Point Positioning (PPP) algorithm combined with a pseudolite (PLS) was introduced. The former problems with the pseudolite PPP technique with distributed pseudo-satellites, which relies heavily on known points for initiation and prerequisite for previous high-precision time synchronization, were solved by means of a real-time equivalent clock error estimation algorithm, ambiguity fixing, and validation method. Experiments based on a low-cost receiver were performed, and the results show that in a weak obstructed environment with low-density building where the number of GNSS satellites was greater than seven, the accuracy of pseudolite/GNSS PPP with fixed ambiguity was better than 0.15 m; when there were less than four GNSS satellites in severely obstructed circumstances, it was impossible to obtain position by GNSS alone, but with the support of a pseudolite, the accuracy of PPP was able to be better than 0.3 m. Even without GNSS, the accuracy of PPP could be better than 0.5 m with only four pseudolites. The pseudolite/GNSS PPP algorithm presented in this paper can effectively improve availability with less GNSS or even without GNSS in constrained environments, like urban canyons in cities.
Characterization of Room-Temperature Ionic Liquids to Study the Electrochemical Activity of Nitro Compounds
Authors: Banga Ivneet , Paul Anirban , Muthukumar Sriram , Prasad Shalini
Published: February 2020

Over the past few years, room-temperature ionic liquid (RTIL) has evolved as an important solvent-cum-electrolyte because of its high thermal stability and excellent electrochemical activity. Due to these unique properties, RTILs have been used as a solvent/electrolyte/mediator in many applications. There are many RTILs, which possess good conductivity as well as an optimal electrochemical window, thus enabling their application as a transducer for electrochemical sensors. Nitroaromatics are a class of organic compounds with significant industrial applications; however, due to their excess use, detection is a major concern. The electrochemical performance of a glassy carbon electrode modified with three different RTILs, [EMIM][BF4], [BMIM][BF4] and [EMIM][TF2N], has been evaluated for the sensing of two different nitroaromatic analytes: 2,6-dinitrotoluene (2,6 DNT) and ethylnitrobenzene (ENB). Three RTILs have been chosen such that they have either a common anion or cation amongst them. The sensory response has been measured using square wave voltammetry (SQWV). We found the transducing ability of [EMIM][BF4] to be superior compared to the other two RTILs. A low limit of detection (LOD) of 1 ppm has been achieved with a 95% confidence interval for both the analytes. The efficacy of varying the cationic and anionic species of RTIL to obtain a perfect combination has been thoroughly investigated in this work, which shows a novel selection process of RTILs for specific applications. Moreover, the results obtained from testing with a glassy carbon electrode (GCE) have been replicated using a miniaturized sensor platform that can be deployed easily for on-site sensing applications.
Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm
Authors: Ningombam Devarani Devi , Shin Seokjoo
Published: February 2020

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity.
Genetic Improvement of Sawn-Board Stiffness and Strength in Scots Pine (Pinus sylvestris L.)
Authors: Fundova Irena , Hallingbäck Henrik R. , Jansson Gunnar , Wu Harry X.
Published: February 2020

Given an overall aim of improving Scots pine structural wood quality by selective tree breeding, we investigated the potential of non-destructive acoustic sensing tools to accurately predict wood stiffness (modulus of elasticity, MOE) and strength (modulus of rupture, MOR) of sawn boards. Non-destructive measurements of wood density (DEN), acoustic velocity (VEL) and MOE were carried out at different stages of wood processing chain (standing trees, felled logs and sawn boards), whilst destructively measured stiffness and strength served as benchmark traits. All acoustic based MOE and VEL estimates proved to be good proxies (rA > 0.65) for sawn-board stiffness while MOETREE, VELHIT and resistograph wood density (DENRES) measured on standing trees and MOELOG and VELFAK measured on felled logs well reflected board strength. Individual-tree narrow-sense heritability ( h i 2 ) for VEL, MOE and MOR were weak (0.05–0.26) but were substantially stronger for wood density (0.34–0.40). Moreover, additive genetic coefficients of variation for MOE and MOR were in the range from 5.4% to 9.1%, offering potential targets for exploitation by selective breeding. Consequently, selective breeding based on MOETREE, DENRES or stem straightness (STR) could improve several structural wood traits simultaneously.
Optimal Design and Analysis on High Overload Buffer Structure of Passive Semi-Strapdown Inertial Navigation System
Authors: Li Jinqiang , Li Jie , Qin Li , Liu Wei , Wei Xiaokai , Gao Ning , Liu Yang
Published: February 2020

The isolation rolling platform inside a passive semi-strapdown inertial navigation system can isolate the high-speed rotation of a projectile via bearing to provide a low rotating speed environment for the angular rate sensors inside the platform in order to further improve the accuracy by reducing its measurement range. Aiming at the problem that the internal bearing cannot withstand high overload, an optimal design method for a high overload buffer structure based on point contact spherical cap structure is proposed. Changing the materials of the spherical caps can reduce the deformation of the two spherical caps during impact and reduce the pivoting friction; at the same time, the upper and lower spherical caps are both forced to separate by the spring force after the impact and to eliminate the influence of the pivoting friction torque that is generated by the contact of two spherical caps on the stability of the isolated rolling platform. By virtue of finite element analysis and ground semi-physical simulation experiments, the feasibility of the design is verified. The experiment results show that the design can play an effectively protective role in anti-high overload, and the maximum deformation radius of the optimized point contact spherical cap structure can be reduced by 40.8%; after the upper and lower spherical caps are separated, the isolation rolling platform’ capability of anti-high-speed rotation can be improved by 52% under the rotation speed of the main shaft at 10 r/s. In this way, the stability of the platform is improved, thus proving the value of the design method in engineering applications.
Motion-To-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users
Authors: Yao Yumin , Song Ling , Ye Jin
Published: February 2020

Obesity has become a widespread health problem worldwide. The body mass index (BMI) is a simple and reliable index based on weight and height that is commonly used to identify and classify adults as underweight, normal, overweight (pre-obesity), or obese. In this paper, we propose a hybrid deep neural network for predicting the BMI of smartphone users, based only on the characteristics of body movement captured by the smartphone’s built-in motion sensors without any other sensitive data. The proposed deep learning model consists of four major modules: a transformation module for data preprocessing, a convolution module for extracting spatial features, a long short-term memory (LSTM) module for exploring temporal dependency, and a fully connected module for regression. We define motion entropy (MEn), which is a measure of the regularity and complexity of the motion sensor, and propose a novel MEn-based filtering strategy to select parts of sensor data that met certain thresholds for training the model. We evaluate this model using two public datasets in comparison with baseline conventional feature-based methods using leave-one-subject-out (LOSO) cross-validation. Experimental results show that the proposed model with the MEn-based filtering strategy outperforms the baseline approaches significantly. The results also show that jogging may be a more suitable activity of daily living (ADL) for BMI prediction than walking and walking upstairs. We believe that the conclusions of this study will help to develop a long-term remote health monitoring system.
An UWB/Vision Fusion Scheme for Determining Pedestrians’ Indoor Location
Authors: Liu Fei , Zhang Jixian , Wang Jian , Han Houzeng , Yang Deng
Published: February 2020

This paper proposes a method for determining a pedestrian’s indoor location based on an UWB (ultra-wideband) and vison fusion algorithm. Firstly, an UWB localization algorithm based on EKF (extended Kalman filter) is proposed, which can achieve indoor positioning accuracy of 0.3 m. Secondly, a method to solve scale ambiguity and repositioning of the monocular ORB-SLAM (oriented fast and rotated brief-simultaneous localization and mapping) algorithm based on EKF is proposed, which can calculate the ambiguity in real time and can quickly reposition when the vision track fails. Lastly, two experiments were carried out, one in a corridor with sparse texture and the other with the light brightness changing frequently. The results show that the proposed scheme can reliably achieve positioning accuracy on the order of 0.2 m; with the combination of algorithms, the scale ambiguity of monocular ORB-Slam can be solved, with the failed vision trace repositioned by UWB, and the positioning accuracy of UWB can be improved, making it suitable for pedestrian location in indoor environments with sparse texture and frequent light brightness changes.
Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network
Authors: Wang Xinying , Wu Yingdan , Ming Yang , Lv Hui
Published: February 2020

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).
Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings
Authors: Ye Xijun , Wu Yingfeng , Zhang Liwen , Mei Liu , Zhou Yunlai
Published: February 2020

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.
Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations
Authors: Bandopadhyay Subhajit , Rastogi Anshu , Juszczak Radosław
Published: February 2020

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.
Sustainable, Alginate-Based Sensor for Detection of Escherichia coli in Human Breast Milk
Authors: Kikuchi Nicholas , May Margaret , Zweber Matthew , Madamba Jerard , Stephens Craig , Kim Unyoung , Mobed-Miremadi Maryam
Published: February 2020

There are no existing affordable diagnostics for sensitive, rapid, and on-site detection of pathogens in milk. To this end, an on-site colorimetric-based sustainable assay has been developed and optimized using an L16 (54) Taguchi design to obtain results in hours without PCR amplification. To determine the level of Escherichia coli (E. coli) contamination, after induction with 150 µL of breast milk, the B-Per bacterial protein extraction kit was added to a solution containing an alginate-based microcapsule assay. Within this 3 mm spherical novel sensor design, X-Gal (5-Bromo-4-Chloro-3-Indolyl β-d-Galactopyranoside) was entrapped at a concentration of 2 mg/mL. The outward diffusing X-Gal was cleaved by β-galactosidase from E. coli and dimerized in the solution to yield a blue color after incubation at 40 °C. Color intensity was correlated with the level of E. coli contamination using a categorical scale. After an 8 h incubation period, a continuous imaging scale based on intensity normalization was used to determine a binary lower limit of detection (LOD), which corresponded to 102 colony forming unit per mL (CFU/mL) and above. The cost of the overall assay was estimated to be $0.81 per sample, well under the $3 benchmark for state-of-the-art immune-based test kits for pathogen detection in biofluids. Considering the reported binary LOD cutoff of 102 CFU/mL and above, this proposed hydrogel-based assay is suited to meet global requirements for screening breast milk or milk for pathogenic organisms of 104 CFU/mL, with a percentage of false positives to be determined in future efforts.
Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method
Authors: Sheng Jing , Liu Rui , Liu Guoman
Published: February 2020

To solve the problem of the weak knock characteristic extraction for a port-injected two-stoke spark ignition (SI) unmanned aerial vehicle (UAV) engine burning aviation kerosene fuel, which is also known as the Rocket Propellant 3 (RP-3), the Intrinsic modal Functions Energy (IMFE) method is proposed according to the orthogonality of the intrinsic modal functions (IMFs). In this method, engine block vibration signals of the two-stroke SI UAV engine are decomposed into a finite number of intrinsic modal function (IMF) components. Then, the energy weight value of each IMF component is calculated, and the IMF component with the largest energy weight value is selected as the dominant characteristic component. The knock characteristic frequency of the two-stroke SI UAV engine is obtained by analyzing the frequency spectrum of the dominant characteristic component. A simulation experiment is designed and the feasibility of the algorithm is verified. The engine block vibration signals of the two-stroke SI UAV engine at 5100 rpm and 5200 rpm were extracted by this method. The results showed that the knock characteristic frequencies of engine block vibration signals at 5100 rpm and 5200 rpm were 3.320 kHz and 3.125 kHz, respectively. The Wavelet Packet Energy method was used to extract the characteristics of the same engine block vibration signal at 5200 rpm, and the same result as the IMFE method is obtained, which verifies the effectiveness of the IMFE method.
Reply to Comments: Comparison of Methods Study between a Photonic Crystal Biosensor and Certified ELISA to Measure Biomarkers of Iron Deficiency in Chronic Kidney Disease Patients
Authors: Peterson Ross D. , Wilund Kenneth R. , Cunningham Brian T. , Andrade Juan E.
Published: February 2020

In this brief note, we respond to the comments made by Dr [...]