bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Rotational Angle Measurement for the Smart Wheel Force Sensor
Authors: Wang Dong , Chen Siwei , Li Xuanpeng , Zhang Weigong , Jin Haolong
Published: February 2020

The measurement of the rotational angle of the wheel is critical for the smart wheel force sensor (SWFS) to obtain the wheel forces defined in the vehicle coordinates. To simplify the structure of the SWFS and overcome the shortcomings of the traditional angular transducer, a new method to evaluate the rotational speed of the wheel and then calculate the rotational angle is proposed in this paper. In this method, the centripetal acceleration caused by the rotation is recorded by three accelerometers and used carefully. What’s more, the possible sources of error are classified and analyzed. Simulations and stand experiment are carried out to demonstrate the effectiveness of the proposed method.
Recent Advances in Electrochemical and Optical Sensing of Dopamine
Authors: Kamal Eddin Faten Bashar , Wing Fen Yap
Published: February 2020

Nowadays, several neurological disorders and neurocrine tumours are associated with dopamine (DA) concentrations in various biological fluids. Highly accurate and ultrasensitive detection of DA levels in different biological samples in real-time can change and improve the quality of a patient’s life in addition to reducing the treatment cost. Therefore, the design and development of diagnostic tool for in vivo and in vitro monitoring of DA is of considerable clinical and pharmacological importance. In recent decades, a large number of techniques have been established for DA detection, including chromatography coupled to mass spectrometry, spectroscopic approaches, and electrochemical (EC) methods. These methods are effective, but most of them still have some drawbacks such as consuming time, effort, and money. Added to that, sometimes they need complex procedures to obtain good sensitivity and suffer from low selectivity due to interference from other biological species such as uric acid (UA) and ascorbic acid (AA). Advanced materials can offer remarkable opportunities to overcome drawbacks in conventional DA sensors. This review aims to explain challenges related to DA detection using different techniques, and to summarize and highlight recent advancements in materials used and approaches applied for several sensor surface modification for the monitoring of DA. Also, it focuses on the analytical features of the EC and optical-based sensing techniques available.
Spectral Phase Shift Interferometry for Refractive Index Monitoring in Micro-Capillaries
Authors: Bello Valentina , Simoni Alberto , Merlo Sabina
Published: February 2020

In this work, we demonstrate spectral phase-shift interferometry operating in the near-infrared wavelength range for refractive index (RI) monitoring of fluidic samples in micro-capillaries. A detailed theoretical model was developed to calculate the phase-sensitive spectral reflectivity when low-cost rectangular glass micro-capillaries, filled with samples with different refractive indices, are placed at the end of the measurment arm of a Michelson interferometer. From the phase-sensitive spectral reflectivity, we recovered the cosine-shaped interferometric signal as a function of the wavelength, as well as its dependence on the sample RI. Using the readout radiation provided by a 40-nm wideband light source with a flat emission spectrum centered at 1.55 µm and a 2 × 1 fiberoptic coupler on the common input-output optical path, experimental results were found to be in good agreement with the expected theoretical behavior. The shift of the micro-capillary optical resonances, induced by RI variations in the filling fluids (comparing saline solution with respect to distilled water, and isopropanol with respect to ethanol) were clearly detected by monitoring the positions of steep phase jumps in the cosine-shaped interferometric signal recorded as a function of the wavelength. By adding a few optical components to the instrumental configuration previously demonstrated for the spectral amplitude detection of resonances, we achieved phase-sensitive detection of the wavelength positions of the resonances as a function of the filling fluid RI. The main advantage consists of recovering RI variations by detecting the wavelength shift of “sharp peaks”, with any amplitude above a threshold in the interferometric signal derivative, instead of “wide minima” in the reflected power spectra, which are more easily affected by uncertainties due to amplitude fluctuations.
IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings
Authors: Ahajjam Mohamed Aymane , Bonilla Licea Daniel , Ghogho Mounir , Kobbane Abdellatif
Published: February 2020

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.
Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments
Authors: Abdelgawwad Ahmed , Borhani Alireza , Pätzold Matthias
Published: February 2020

This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving object is modelled by a point scatterer which travels along a trajectory. The trajectory is described by the object’s TV speed, TV horizontal angle of motion, and TV vertical angle of motion. An expression of the TV Doppler frequency caused by the moving scatterer is derived. Furthermore, an expression of the TV complex channel transfer function (CTF) of the received signal is provided, which accounts for the influence of a moving object and fixed objects, such as walls, ceiling, and furniture. An approximate analytical solution of the spectrogram of the CTF is derived. The proposed channel model is confirmed by measurements obtained from a pendulum experiment. In the pendulum experiment, the trajectory of the pendulum has been measured by using an inertial-measurement unit (IMU) and simultaneously collecting CSI data. For validation, we have compared the spectrogram of the proposed channel model fed with IMU data with the spectrogram characteristics of the measured CSI data. The proposed channel model paves the way towards designing simulation-based activity recognition systems.
Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization
Authors: Deng Zhen , Jonetzko Yannick , Zhang Liwei , Zhang Jianwei
Published: February 2020

Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.
On Field Infrared Thermography Sensing for PV System Efficiency Assessment: Results and Comparison with Electrical Models
Authors: Muttillo Mirco , Nardi Iole , Stornelli Vincenzo , de Rubeis Tullio , Pasqualoni Giovanni , Ambrosini Dario
Published: February 2020

The evaluation of photovoltaic (PV) system’s efficiency loss, due to the onset of faults that reduce the output power, is crucial. The challenge is to speed up the evaluation of electric efficiency by coupling the electric characterization of panels with information gathered from module diagnosis, amongst which the most commonly employed technique is thermographic inspection. The aim of this work is to correlate panels’ thermal images with their efficiency: a “thermal signature” of panels can be of help in identifying the fault typology and, moreover, for assessing efficiency loss. This allows to identify electrical power output losses without interrupting the PV system operation thanks to an advanced PV thermography characterization. In this paper, 12 faulted working panels were investigated. Their electrical models were implemented in MATLAB environment and developed to retrieve the ideal I-V characteristic (from ratings), the actual (operative) I-V characteristics and electric efficiency. Given the curves shape and relative difference, based on three reference points (namely, open circuit, short circuit, and maximum power points), faults’ typology has been evidenced. Information gathered from infrared thermography imaging, simultaneously carried out on panels during operation, were matched with those from electrical characterization. Panels’ “thermal signature” has been coupled with the “electrical signature”, to obtain an overall depiction of panels’ health status.
Employing Shadows for Multi-Person Tracking Based on a Single RGB-D Camera
Authors: Gai Wei , Qi Meng , Ma Mingcong , Wang Lu , Yang Chenglei , Liu Juan , Bian Yulong , de Melo Gerard , Liu Shijun , Meng Xiangxu
Published: February 2020

Although there are many algorithms to track people that are walking, existing methods mostly fail to cope with occluded bodies in the setting of multi-person tracking with one camera. In this paper, we propose a method to use people’s shadows as a clue to track them instead of treating shadows as mere noise. We introduce a novel method to track multiple people by fusing shadow data from the RGB image with skeleton data, both of which are captured by a single RGB Depth (RGB-D) camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multi-person tracking, even with other kinds of cameras.
Development of a Resonant Microwave Sensor for Sediment Density Characterization
Authors: Mansour R. , Rioual S. , Lescop B. , Talbot P. , Abboud M. , Farah W. , Tanné G.
Published: February 2020

In this study, a sensor based on the development of a planar antenna immersed in sediments dedicated to water content monitoring in this type of material is proposed and experimentally validated. It is produced by a conventional Printed Circuit Board (PCB) manufacturing process on a double-sided metalized FR4 substrate. The sensitivity of the sensor is ensured by the variation of the real part of the complex dielectric permittivity of sediments with water content at around 1 GHz. As shown, in this frequency range, electrode polarization and Maxwell–Wagner polarization effects become negligible, leading to only a bulk water polarization sensitivity. The sensor operates in the reflection mode by monitoring the variation of the resonant frequency as a function of the sediment density through the S11 reflection measurements. An experimental sensitivity of 820   MHz . g − 1 . cm 3 was achieved. Despite the simplification of data interpretation at the considered frequency, the influence of ionic species such as NaCl in sediments on the real part of the relative complex dielectric permittivity is highlighted. This demonstrates the importance of considering a second parameter such as the S11 level at low frequency or the electrical conductivity to extract the density from the frequency measurements.
Location-Aware Wi-Fi Authentication Scheme Using Smart Contract
Authors: Chen Yongle , Wang Xiaojian , Yang Yuli , Li Hong
Published: February 2020

Advanced wireless technology in Internet of Things (IoT) devices is increasing and facing various security threats. The authentication of IoT devices is the first line of defense for the wireless network. Especially in a Wi-Fi network, the existing authentication methods mainly use a password or digital certificate, these methods are inconvenient to manage due to certificate issuance or prone to be attacked because passwords are easily cracked. In this paper, we propose a location-aware authentication scheme using smart contracts to ensure that IoT devices can securely perform Wi-Fi network authentication. The scheme adopts the concept of secondary authentication and consists of two phases: the registration phase, which is mainly designed to complete the generation of the public and private keys, and to link the device information with its related device information; the authentication phase, which is mainly designed to determine whether the requesting device is within a legal location range. We use the smart contract to ensure the credibility and irreparability of the authentication process. Analysis of the attack model and the attacks at different stages proves that this certification scheme is assured, and the simulation results show that the overhead introduced by this scheme is acceptable, this scheme can provide greater security for the Wi-Fi authentication of IoT devices.
Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets
Authors: Zhang Huihui , Shao Wenqing , Qiu Shanshan , Wang Jun , Wei Zhenbo
Published: February 2020

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).
Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network
Authors: Manie Yibeltal Chanie , Li Jyun-Wei , Peng Peng-Chun , Shiu Run-Kai , Chen Ya-Yu , Hsu Yuan-Ta
Published: February 2020

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.
Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification
Authors: Sadeghian Broujeny Roozbeh , Madani Kurosh , Chebira Abdennasser , Amarger Veronique , Hurtard Laurent
Published: February 2020

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces’ heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space’s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building’s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron’s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.
Applications of Graphene Quantum Dots in Biomedical Sensors
Authors: Mansuriya Bhargav , Altintas Zeynep
Published: February 2020

Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in biological matrices. Considering these pathophysiologies, various research works have become necessary to fabricate biosensors for their early diagnosis and treatment, using nanomaterials like quantum dots (QDs). These nanomaterials effectively ameliorate the sensor performance with respect to their reproducibility, selectivity as well as sensitivity. In particular, graphene quantum dots (GQDs), which are ideally graphene fragments of nanometer size, constitute discrete features such as acting as attractive fluorophores and excellent electro-catalysts owing to their photo-stability, water-solubility, biocompatibility, non-toxicity and lucrativeness that make them favorable candidates for a wide range of novel biomedical applications. Herein, we reviewed about 300 biomedical studies reported over the last five years which entail the state of art as well as some pioneering ideas with respect to the prominent role of GQDs, especially in the development of optical, electrochemical and photoelectrochemical biosensors. Additionally, we outline the ideal properties of GQDs, their eclectic methods of synthesis, and the general principle behind several biosensing techniques.
Optimal Sensor and Relay Nodes Power Scheduling for Remote State Estimation with Energy Constraint
Authors: Han Yufei , Cui Mengqi , Liu Shaojun
Published: February 2020

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.
Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning
Authors: Tiitta Markku , Tiitta Valtteri , Heikkinen Jorma , Lappalainen Reijo , Tomppo Laura
Published: February 2020

Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.
Design for a Highly Stable Laser Source Based on the Error Model of High-Speed High-Resolution Heterodyne Interferometers
Authors: Yang Hongxing , Yin Ziqi , Yang Ruitao , Hu Pengcheng , Li Jing , Tan Jiubin
Published: February 2020

Heterodyne interferometers with two opposite Doppler shift interference signals have been proposed for high-resolution measurement with high measurement speed, which can be used in the background of high-speed high-resolution measurement. However, a measurement error model for high-speed high-resolution heterodyne interferometers (HSHR-HIs) has not yet been proposed. We established a HSHR-HI measurement error model, analyzed the influence of beat frequency stability with a simplified optical structure, and then designed an offset-locked dual-frequency laser source with a digital control system to reduce the impact of beat frequency drift. Experiments were used to verify the correction of the measurement error model and the validity of the laser source. The results show that the new laser source has a maximum beat frequency range of 45 MHz, which shows the improvements in the measuring speed and resolution.
Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks
Authors: Zhang Kaifeng , Li Dan , Huang Jiayun , Chen Yifei
Published: February 2020

The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.
Lightweight Fine-Grained Access Control for Wireless Body Area Networks
Authors: Ali Mohammad , Sadeghi Mohammad-Reza , Liu Ximeng
Published: February 2020

Wireless Body Area Network (WBAN) is a highly promising technology enabling health providers to remotely monitor vital parameters of patients via tiny wearable and implantable sensors. In a WBAN, medical data is collected by several tiny sensors and usually transmitted to a server-side (e.g., a cloud service provider) for long-term storage and online/offline processing. However, as the health data includes several sensitive information, providing confidentiality and fine-grained access control is necessary to preserve the privacy of patients. In this paper, we design an attribute-based encryption (ABE) scheme with lightweight encryption and decryption mechanisms. Our scheme enables tiny sensors to encrypt the collected data under an access control policy by performing very few computational operations. Also, the computational overhead on the users in the decryption phase is lightweight, and most of the operations are performed by the cloud server. In comparison with some excellent ABE schemes, our encryption mechanism is more than 100 times faster, and the communication overhead in our scheme decreases significantly. We provide the security definition for the new primitive and prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.
Single-Shot Structured Light Sensor for 3D Dense and Dynamic Reconstruction
Authors: Gu Feifei , Song Zhan , Zhao Zilong
Published: February 2020

Structured light (SL) has a trade-off between acquisition time and spatial resolution. Temporally coded SL can produce a 3D reconstruction with high density, yet it is not applicable to dynamic reconstruction. On the contrary, spatially coded SL works with a single shot, but it can only achieve sparse reconstruction. This paper aims to achieve accurate 3D dense and dynamic reconstruction at the same time. A speckle-based SL sensor is presented, which consists of two cameras and a diffractive optical element (DOE) projector. The two cameras record images synchronously. First, a speckle pattern was elaborately designed and projected. Second, a high-accuracy calibration method was proposed to calibrate the system; meanwhile, the stereo images were accurately aligned by developing an optimized epipolar rectification algorithm. Then, an improved semi-global matching (SGM) algorithm was proposed to improve the correctness of the stereo matching, through which a high-quality depth map was achieved. Finally, dense point clouds could be recovered from the depth map. The DOE projector was designed with a size of 8 mm × 8 mm. The baseline between stereo cameras was controlled to be below 50 mm. Experimental results validated the effectiveness of the proposed algorithm. Compared with some other single-shot 3D systems, our system displayed a better performance. At close range, such as 0.4 m, our system could achieve submillimeter accuracy.
A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling
Authors: Hu Qiong , Cai Miao , Mohabbati-Kalejahi Nasrin , Mehdizadeh Amir , Alamdar Yazdi Mohammad Ali , Vinel Alexander , Rigdon Steven E. , Davis Karen C. , Megahed Fadel M.
Published: February 2020

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.
A Low-Cost Paper Glucose Sensor with Molecularly Imprinted Polyaniline Electrode
Authors: Chen Zheyuan , Wright Christopher , Dincel Onder , Chi Ting-Yen , Kameoka Jun
Published: February 2020

For the hundreds of millions of worldwide diabetic patients, glucose test strips are the most important and commonly used tool for monitoring blood glucose levels. Commercial test strips use glucose oxidases as recognition agents, which increases the cost and reduces the durability of test strips. To lower the cost of glucose sensors, we developed a paper-based electrical sensor with molecularly imprinted glucose recognition sites and demonstrated the determination of various glucose concentrations in bovine blood solutions. The sensing electrode is integrated with molecular recognition sites in the conductive polymer. A calibration graph as a function of glucose concentration in aqueous solution was acquired and matched with a correlation coefficient of 0.989. We also demonstrated the determination of the added glucose concentrations ranging from 2.2 to 11.1 mM in bovine blood samples with a linear correlation coefficient of 0.984. This non-enzymatic glucose sensor has the potential to reduce the health care cost of test strips as well as make glucose sensor test strips more accessible to underserved communities.
On the Use of Weighted Least-Squares Approaches for Differential Interferometric SAR Analyses: The Weighted Adaptive Variable-lEngth (WAVE) Technique
Authors: Falabella Francesco , Serio Carmine , Zeni Giovanni , Pepe Antonio
Published: February 2020

This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the “good” interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided.
Star-Tracker Algorithm for Smartphones and Commercial Micro-Drones
Authors: Marbel Revital , Ben-Moshe Boaz , Yozevitch Roi
Published: February 2020

This paper presents a star-tracking algorithm to determine the accurate global orientation of autonomous platforms such as nano satellites, U A V s, and micro-drones using commercial-off-the-shelf ( C O T S ) mobile devices such as smartphones. Such star-tracking is especially challenging because it is based on existing cameras which capture a partial view of the sky and should work continuously and autonomously. The novelty of the proposed framework lies both in the computational efficiency and the ability of the star-tracker algorithm to cope with noisy measurements and outliers using affordable C O T S mobile platforms. The presented algorithm was implemented and tested on several popular platforms including: Android mobile devices, commercial-micro drones, and Raspberry Pi. The expected accuracy of the reported orientation is [0.1°,0.5°].
A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling
Authors: Mehdizadeh Amir , Cai Miao , Hu Qiong , Alamdar Yazdi Mohammad Ali , Mohabbati-Kalejahi Nasrin , Vinel Alexander , Rigdon Steven E. , Davis Karen C. , Megahed Fadel M.
Published: February 2020

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.
Bridge Displacement Estimation Using a Co-Located Acceleration and Strain
Authors: Sarwar Muhammad Zohaib , Park Jong-Woong
Published: February 2020

Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure that limits the field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimating the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.
FW-PSO Algorithm to Enhance the Invulnerability of Industrial Wireless Sensor Networks Topology
Authors: Zhang Ying , Yang Guangyuan , Zhang Bin
Published: February 2020

When an industrial wireless sensor network (WSN) is seriously disturbed and intentionally attacked, sometimes it fails easily, even leading to the paralysis of the entire industrial wireless network. In order to improve the invulnerability of networks, in this paper, the scale-free network in complex networks is taken as the research object, and the industrial WSN with scale-free characteristics is modeled. Based on the advantages of the fireworks algorithm, such as strong searching ability and diversity of population, a so-called fireworks and particle swarm optimization (FW-PSO) algorithm is proposed, which can improve the global search ability and convergence speed effectively. The proposed FW-PSO algorithm is used to optimize the network topology and form a network with the largest natural connectivity, which can effectively promote the ability of network to resist the cascade failure problem. The dynamic invulnerability of the optimized network under highest-degree (HD) attack and lowest-degree (LD) attack strategies, as well as the static invulnerability under random attack, were evaluated respectively. Simulation experiments show that the industrial WSN optimized by FW-PSO can significantly improve the performance of the dynamic and static invulnerabilities compared with the initial network and the networks optimized by the other two existing algorithms.
Stronger Association between High Intensity Physical Activity and Cardiometabolic Health with Improved Assessment of the Full Intensity Range Using Accelerometry
Authors: Fridolfsson Jonatan , Börjesson Mats , Ekblom-Bak Elin , Ekblom Örjan , Arvidsson Daniel
Published: February 2020

An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method.
A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
Authors: Niu Jianwei , Wang Xiai , Wang Dan , Ran Linghua
Published: February 2020

Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.
Embedded Fiber Sensors to Monitor Temperature and Strain of Polymeric Parts Fabricated by Additive Manufacturing and Reinforced with NiTi Wires
Authors: Nascimento Micael , Inácio Patrick , Paixão Tiago , Camacho Edgar , Novais Susana , Santos Telmo , Fernandes Francisco , Pinto João
Published: February 2020

This paper focuses on three main issues regarding Material Extrusion (MEX) Additive Manufacturing (AM) of thermoplastic composites reinforced by pre-functionalized continuous Nickel–Titanium (NiTi) wires: (i) Evaluation of the effect of the MEX process on the properties of the pre-functionalized NiTi, (ii) evaluation of the mechanical and thermal behavior of the composite material during usage, (iii) the inspection of the parts by Non-Destructive Testing (NDT). For this purpose, an optical fiber sensing network, based on fiber Bragg grating and a cascaded optical fiber sensor, was successfully embedded during the 3D printing of a polylactic acid (PLA) matrix reinforced by NiTi wires. Thermal and mechanical perturbations were successfully registered as a consequence of thermal and mechanical stimuli. During a heating/cooling cycle, a maximum contraction of ≈100 µm was detected by the cascaded sensor in the PLA material at the end of the heating step (induced by Joule effect) of NiTi wires and a thermal perturbation associated with the structural transformation of austenite to R-phase was observed during the natural cooling step, near 33.0 °C. Regarding tensile cycling tests, higher increases in temperature arose when the applied force ranged between 0.7 and 1.1 kN, reaching a maximum temperature variation of 9.5 ± 0.1 °C. During the unload step, a slope change in the temperature behavior was detected, which is associated with the material transformation of the NiTi wire (martensite to austenite). The embedded optical sensing methodology presented here proved to be an effective and precise tool to identify structural transformations regarding the specific application as a Non-Destructive Testing for AM.