bluuebunny/crossref_metadata_2025 huggingface dataset:
Applications of Highly Stretchable and Tough Hydrogels
Authors: Qiao Zhen , Parks Jesse , Choi Phillip , Ji Hai-Feng
Published: October 2019

Stretchable and tough hydrogels have drawn a lot of attention recently. Due to their unique properties, they have great potential in the application in areas such as mechanical sensing, wound healing, and drug delivery. In this review, we will summarize recent developments of stretchable and tough hydrogels in these areas.
Fabrication of Silk Fibroin/Graphene Film with High Electrical Conductivity and Humidity Sensitivity
Authors: Zhang Haoran , Zhao Juntao , Xing Tieling , Lu Shenzhou , Chen Guoqiang
Published: October 2019

Silk fibroin (SF) is a natural material with good biocompatibility and excellent mechanical properties, which are complementary to graphene with ultrahigh electrical conductivity. In this study, to maximally combine graphene and silk fibroin, a well-dispersed silk fibroin/graphene suspension was successfully prepared in a simple and effective way. Then we prepared a flexible conductive SF/graphene film with a minimum resistance of 72.1 ± 4.7 Ω/sq by the casting method. It was found that the electrical conductivity of the SF/graphene film was related to the water content of the film, and the variation was more than 200 times. Therefore, it will play an important role in the field of humidity sensors. It also has excellent mechanical properties in both wet and dry states. These unique features make this material a promising future in the fields of biomedical applications, wearable sensors, and implantable internal sensors.
The Discarded Image and the Debunked Tao: Objective Value in C.S. Lewis’ Medieval Model and His Critique of Modernity
Authors: Su Yuxiao
Published: October 2019

This paper considers C.S. Lewis’ “doctrine of objective value” in two of his major works, The Abolition of Man and The Discarded Image. Lewis uses the Chinese name Tao, albeit with an incomplete understanding of its origins, for the objective worldview. The paper argues that Tao, as an explicit theme of The Abolition of Man, is also a determining undercurrent in The Discarded Image. In the former work, Tao is what Lewis wants to defend and restore against twentieth-century secular ideologies, which Lewis condemns as infected with “the poison of subjectivism”. In the latter work, where Lewis presents one of the best accounts of the European medieval model of the Universe, objective value (the Tao in Lewis’ argument) underlies both how the model has been shaped, and how Lewis, as a medievalist, accounts for and draws upon it as an intellectual and spiritual resource. The purpose of this parallel study is to show that Lewis’ explication of the Tao in The Abolition of Man, which is a “built-in”, implicit belief in The Discarded Image, provides a critique of tendencies towards the subjectivism prevalent in Lewis’ lifetime. These tendencies can be traced into the moral relativism, pluralism and reductionism of the twenty-first century, giving Lewis’ work the status of twentieth-century prophecy.
Negotiating Ambivalent Gender Spaces for Collective and Individual Empowerment: Sikh Women’s Life Writing in the Diaspora
Authors: Singh Jaspal Kaur
Published: October 2019

In order to examine gender and identity within Sikh literature and culture and to understand the construction of gender and the practice of Sikhi within the contemporary Sikh diaspora in the US, I analyze a selection from creative non-fiction pieces, variously termed essays, personal narrative, or life writing, in Meeta Kaur’s edited collection, Her Name is Kaur: Sikh American Women Write About Love, Courage, and Faith. Gender, understood as a social construct (Butler, among others), is almost always inconsistent and is related to religion, which, too, is a construct and is also almost always inconsistent in many ways. Therefore, my reading critically engages with the following questions regarding life writing through a postcolonial feminist and intersectional lens: What are lived religions and how are the practices, narratives, activities and performances of ‘being’ Sikh imagined differently in the diaspora as represent in my chosen essays? What are some of the tenets of Sikhism, viewed predominantly as patriarchal within dominant cultural spaces, and how do women resist or appropriate some of them to reconstruct their own ideas of being a Sikh? In Kaur’s collection of essays, there are elements of traditional autobiography, such as the construction of the individual self, along with the formation of communal identity, in the postcolonial life writing. I will critique four narrative in Kaur’s anthology as testimonies to bear witness and to uncover Sikh women’s hybrid cultural and religious practices as reimagined and practiced by the female Sikh writers.
Validation of the Spiritual Distress Scale in Portuguese Cancer Patients Undergoing Chemotherapy: A Methodological Study
Authors: Martins Helga , Caldeira Sílvia , Dias Domingues Tiago , Vieira Margarida , Ku Ya-Lie
Published: October 2019

Spiritual distress may ascend from unmet spiritual needs. The use of instruments to measure spiritual distress seems to facilitate the approach to spirituality, such as the Spiritual Distress Scale (SDS) that has been used worldwide. No instrument to assess spiritual distress in cancer patients is currently available in Portugal. This study aims to conduct the translation, adaptation and validation of the SDS in Portuguese cancer patients undergoing chemotherapy. Methodological study based on Sousa and Rojjanasrirat (2011), a seven-step approach, started with the linguistic translation to the psychometric tests. The main participants (55.4%) were older than 60 years; about 64.7% were females, married (68.0%), and 86.7% were Catholic. Moderate spiritual distress was experienced by 49.3% of the participants. Linguistic and conceptual equivalences were obtained. The SDS European Portuguese version has an overall Cronbach’s alpha of 0.91, and the subscales were as follows: “relationship with self” (0.92), “relationship with others” (0.63), “relationship with God” (0.64) and “facing death” (0.85). Four factors emerged after Varimax rotation. Overall, these results indicate that the SDS European Portuguese version has good psychometric characteristics and can used in assessing spiritual distress in cancer patients.
Option Implied Stock Buy-Side and Sell-Side Market Depths
Authors: Tsai Feng-Tse
Published: October 2019

This paper investigates option valuation when the underlying market suffers from illiquidity of price impact. Using option data, I infer trading activities and price impacts on the buy side and the sell side in the stock market from option prices across maturities. The finding displays that the stock market is active when the stock prices plummet, but becomes silent after the market crashes. In addition, the difference of option implied price impacts between the buy side and the sell side, which indicates asymmetric liquidity, increases with the time to maturity, especially on the day of the market crisis. Moreover, investors have different perspectives on the future liquidity after liquidity shocks when they are in a bull market or in a bear market according to the option implied price impact (or market depth) curves. I also calibrate three market indices simultaneously and reach the same conclusion that the three markets become erratic on the event date and calm down in the aftermath.
A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17
Authors: Wheeler Kathryn I. , Dietze Michael C.
Published: October 2019

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations.
An Integrated Approach to 3D Web Visualization of Cultural Heritage Heterogeneous Datasets
Authors: Boutsi Argyro-Maria , Ioannidis Charalabos , Soile Sofia
Published: October 2019

The evolution of the high-quality 3D archaeological representations from niche products to integrated online media has not yet been completed. Digital archives of the field often lack multimodal data interoperability, user interaction and intelligibility. A web-based cultural heritage archive that compensates for these issues is presented in this paper. The multi-resolution 3D models constitute the core of the visualization on top of which supportive documentation data and multimedia content are spatial and logical connected. Our holistic approach focuses on the dynamic manipulation of the 3D scene through the development of advanced navigation mechanisms and information retrieval tools. Users parse the multi-modal content in a geo-referenced way through interactive annotation systems over cultural points of interest and automatic narrative tours. Multiple 3D and 2D viewpoints are enabled in real-time to support data inspection. The implementation exploits front-end programming languages, 3D graphic libraries and visualization frameworks to handle efficiently the asynchronous operations and preserve the initial assets’ accuracy. The choice of Greece’s Meteora, UNESCO world site, as a case study accounts for the platform’s applicability to complex geometries and large-scale historical environments.
Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series
Authors: Karasiak Nicolas , Dejoux Jean-François , Fauvel Mathieu , Willm Jérôme , Monteil Claude , Sheeren David
Published: October 2019

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.
A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data
Authors: Ma Xiaolong , Li Chengming , Tong Xiaohua , Liu Sicong
Published: October 2019

Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.
On Sea Ice Measurement by a C-Band Scatterometer at VV Polarization: Methodology Optimization Based on Computer Simulations
Authors: Nekrasov Alexey , Khachaturian Alena , Abramov Evgeny , Markelov Oleg , Bogachev Mikhail
Published: October 2019

We consider sea ice and water microwave backscatter features at the C-band with vertical transmit and receive polarization and present a method for sea ice/water discrimination using a multiple fixed fan-beam satellite scatterometer. The method is based on the criterion of the minimum statistical distance of measured backscatter values to the sea ice and water (CMOD7) geophysical model functions. Implementation of the method is considered both for a typical three fan-beam geometry as well as for a potential five fan-beam geometry of a satellite scatterometer. By using computer simulations, we show explicitly that the number of looks at the same cell from different azimuthal directions needs to be increased to provide better (unambiguous) retrieval of the wind vector and sea ice/water discrimination. The algorithms for sea ice/water discrimination are described, and the results obtained are also discussed along with recommendations for the number of different azimuthal looks (beams) at the same cell from the point of view of sea ice/water discrimination as well as unambiguous wind direction retrieval during the satellite’s single pass.
Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data
Authors: Wu Hua , Ying Wangmin
Published: October 2019

Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m2, and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m2. There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.
Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication
Authors: Kumar Pradeep , Saini Rajkumar , Kaur Barjinder , Roy Partha Pratim , Scheme Erik
Published: October 2019

Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.
An Internet of Things (IoT) Application on Volcano Monitoring
Authors: Awadallah Shadia , Moure David , Torres-González Pedro
Published: October 2019

In the last few years, there has been a huge interest in the Internet of Things (hereinafter IoT) field. Among the large number of IoT technologies, the low-power wide-area network (hereinafter LPWAN) has emerged providing low power, low data-rate communication over long distances, enabling battery-operated devices to operate for long time periods. This paper introduces an application of long-range (hereinafter LoRa) technology, one of the most popular LPWANs, to volcanic surveillance. The first low-power and low-cost wireless network based on LoRa to monitor the soil temperature in thermal anomaly zones in volcanic areas has been developed. A total of eight thermometers (end devices) have been deployed on a Teide volcano in Tenerife (Canary Islands). In addition, a repeater device was developed to extend the network range when the gateway did not have a line of sight connection with the thermometers. Combining LoRa communication capabilities with microchip microcontrollers (end devices and repeater) and a Raspberry Pi board (gateway), three main milestones have been achieved: (i) extreme low-power consumption, (ii) real-time and proper temperature acquisition, and (iii) a reliable network operation. The first results are shown. These results provide enough quality for a proper volcanic surveillance.
High-Sensitivity Pixels with a Quad-WRGB Color Filter and Spatial Deep-Trench Isolation
Authors: Kim Yongnam , Kim Yunkyung
Published: October 2019

The demand for a high-resolution metal-oxide-semiconductor (CMOS) image sensor has increased in recent years, and pixel size has shrunk below 1.0 μm to allow accumulation of numerous pixels in a limited area. However, shrinking the pixel size lowers the sensitivity and increases crosstalk because the aspect ratio is worsened by maintaining the height of the pixel. This work introduces a high-sensitivity pixel with a quad-WRGB (White, Red, Green, Blue) color filter array (CFA), spatial deep-trench isolation (S-DTI), and a spatial tungsten grid (S-WG). The optical performance of the suggested pixel was analyzed by performing 3D optical simulations at 1.0, 0.9, and 0.8 μm pixel pitches as small-sized pixels. The quad-WRGB CFA is compared with the quad-Bayer CFA, and the S-DTI and S-WG are compared with the conventional DTI and WG. We confirmed an improvement in the sensitivity of the suggested pixel using the quad-WRGB CFA with S-DTI and S-WG to a maximum of 58.2%, 67.0%, and 66.3% for 1.0, 0.9, and 0.8 μm pixels, respectively.
Design and Fabrication of a Wideband Cymbal Transducer for Underwater Sensor Networks
Authors: Shim Hayeong , Roh Yongrae
Published: October 2019

Cymbal transducers are characterized by a high mechanical quality factor and low power efficiency. The research conducted so far on cymbal transducers has focused on improving the power efficiency and structural stability, but modern underwater sensor network systems need transducers to have a wide frequency bandwidth as well. In this study, a wideband cymbal transducer was designed to fill that need. First, the effect of various structural parameters on the performance of the cymbal transducer was analyzed with emphasis on the bandwidth using the finite element method. Based on the analysis results, the structure of the cymbal transducer was optimized to have the widest possible bandwidth while maintaining its transmitting voltage response (TVR) level over a typical power requirement as well. The validity of the design was verified by fabricating a prototype of the optimized cymbal transducer and comparing its measured performance with the design.
Meander-Line Slot-Loaded High-Sensitivity Microstrip Patch Sensor Antenna for Relative Permittivity Measurement
Authors: Yeo Junho , Lee Jong-Ig
Published: October 2019

A high-sensitivity microstrip patch sensor antenna (MPSA) loaded with a meander-line slot (MLS) is proposed for the measurement of relative permittivity. The proposed MPSA was designed by etching the MLS along the radiating edge of the patch antenna, and it enhanced the relative permittivity sensitivity with an additional effect of miniaturization in the patch size by increasing the slot length. The sensitivity of the proposed MPSA was compared with that of a conventional rectangular patch antenna and a rectangular slit (RS)-loaded MPSA, by measuring the shift in the resonant frequency of the input reflection coefficient. Three MPSAs were designed and fabricated on a 0.76 mm-thick RF-35 substrate to resonate at 2.5 GHz under unloaded conditions. Sensitivity comparison was performed by using five different standard dielectric samples with dielectric constants ranging from 2.17 to 10.2. The experiment results showed that the sensitivity of the proposed MPSA is 6.84 times higher for a low relative permittivity of 2.17, and 4.57 times higher for a high relative permittivity of 10.2, when compared with the conventional MPSA. In addition, the extracted relative permittivity values of the five materials under tests showed good agreement with the reference data.
Comparative Evaluation of the Autonomic Response to Cognitive and Sensory Stimulations through Wearable Sensors
Authors: Tonacci Alessandro , Billeci Lucia , Burrai Elisa , Sansone Francesco , Conte Raffaele
Published: October 2019

Psychological stress is known to activate the autonomic nervous system (ANS), thus representing a useful target to be monitored to understand the physiological, unconscious effect of stress on the human body. However, little is known about how differently the ANS responds to cognitive and sensory stimulations in healthy subjects. To this extent, we enrolled 23 subjects and administered a stress protocol consisting of the administration of sensory (olfactory) and cognitive (mathematical) stressors. Autonomic parameters were unobtrusively monitored through wearable sensors for capturing electrocardiogram and skin conductance signals. The results obtained demonstrated an increase of the heart rate during both stress protocols, with a similar decrease of the heart rate variability. Cognitive stress test appears to affect the autonomic parameters to a greater extent, confirming its effects on the human body. However, olfactory stimulation could be useful to study stress in specific experimental settings when the administration of complex cognitive testing is not feasible.
Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition
Authors: Sun Yuanxi , Huang Rui , Zheng Jia , Dong Dianbiao , Chen Xiaohong , Bai Long , Ge Wenjie
Published: October 2019

To improve the multi-speed adaptability of the powered prosthetic knee, this paper presented a speed-adaptive neural network control based on a powered geared five-bar (GFB) prosthetic knee. The GFB prosthetic knee is actuated via a cylindrical cam-based nonlinear series elastic actuator that can provide the desired actuation for level-ground walking, and its attitude measurement is realized by two inertial sensors and one load cell on the prosthetic knee. To improve the performance of the control system, the motor control and the attitude measurement of the GFB prosthetic knee are run in parallel. The BP neural network uses input data from only the GFB prosthetic knee, and is trained by natural and artificially modified various gait patterns of different able-bodied subjects. To realize the speed-adaptive control, the prosthetic knee speed and gait cycle percentage are identified by the Gaussian mixture model-based gait classifier. Specific knee motion control instructions are generated by matching the neural network predicted gait percentage with the ideal walking gait. Habitual and variable speed level-ground walking experiments are conducted via an able-bodied subject, and the experimental results show that the neural network control system can handle both self-selected walking and variable speed walking with high adaptability.
Hard Decision-Based Cooperative Localization for Wireless Sensor Networks
Authors: Wang Zhaoyang , Jin Xuebo , Wang Xiaoyi , Xu Jiping , Bai Yuting
Published: October 2019

Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.
Development of a Novel Piezoelectric Sensing System for Pavement Dynamic Load Identification
Authors: Zhao Qian , Wang Linbing , Zhao Kang , Yang Hailu
Published: October 2019

In order to control the adverse effect of vehicles overloading infrastructure and traffic safety, weight-in-motion (WIM)-related research has drawn growing attention. To address the high cost of current piezoelectric sensors in installation and maintenance, a study on developing a low-cost piezoceramic sensing system is presented in this paper. The proposed system features distributed monitoring and integrated packaging, for calculating vehicle’s dynamic load and its wheel position. Results from the laboratory tests show that the total output of the sensing system increases linearly with the increase of the peak load when the loading amplitude is 5–25 kN (equivalent to the half-axis load of 20–100 kN); when the loading frequency is between 15 Hz and 19 Hz (equivalent to a speed of 17.8–23.2 km/h), the total output of the system fluctuates around a value of 1.305 V. Combined with finite-element simulation, the system can locate load lateral position with a resolution of 120 mm. Due to the protection packaging, the peak load transferred to the sensing units is approximately 4.36% of the applied peak load. The study indicates the proposed system can provide a promising low-cost, reliable and practical alternative for current WIM systems.
Double-Diamond Model-Based Orientation Guidance in Wearable Human–Machine Navigation Systems for Blind and Visually Impaired People
Authors: Zhang Xiaochen , Zhang Hui , Zhang Linyue , Zhu Yi , Hu Fei
Published: October 2019

This paper presents the analysis and design of a new, wearable orientation guidance device in modern travel aid systems for blind and visually impaired people. The four-stage double-diamond design model was applied in the design process to achieve human-centric innovation and to ensure technical feasibility and economic viability. Consequently, a sliding tactile feedback wristband was designed and prototyped. Furthermore, a Bezier curve-based adaptive path planner is proposed to guarantee collision-free planned motion. Proof-of-concept experiments on both virtual and real-world scenarios are conducted. The evaluation results confirmed the efficiency and feasibility of the design and imply the design’s remarkable potential in spatial perception rehabilitation.
Highly Sensitive Microstructure-Based Flexible Pressure Sensor for Quantitative Evaluation of Motor Function Recovery after Spinal Cord Injury
Authors: Yang Dan , Yang Wei , Li Lianhui , Zhou Kai , Hao Mingming , Feng Xingyu , Zhang Ting , Liu Yaobo
Published: October 2019

Behavioral assessment, such as systematic scoring or biomechanical measurement, is often used to evaluate the extent of the damage and the degree of recovery after spinal cord injury. However, the use of these methods in standardized evaluation is limited because they are subjective and require complex test systems to implement. Here, we report a novel, flexible, microstructure-based pressure sensor and demonstrate its superior sensitivity (235.12 kPa−1 for 5.5~135 Pa and 2.24 kPa−1 for 0.6~25 kPa), good waterproofness, fast response and recovery times (response time: 8 ms, recovery time: 12 ms), stable response over 8000 loading/unloading cycles, and wide sensing range. These features readily allow the sensor to be comfortably attached to the hindlimbs of mice for full-range, real-time detection of their behavior, such as crawling and swimming, helping to realize quantitative evaluation of animal motor function recovery after spinal cord injury.
Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor
Authors: Yang Feng , Ding Mingyue , Zhang Xuming
Published: October 2019

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.
Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review
Authors: Imtiaz Masudul H. , Ramos-Garcia Raul I. , Wattal Shashank , Tiffany Stephen , Sazonov Edward
Published: October 2019

Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers’ behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.
Chromatic Sensor to Determine Oxygen Presence for Applications in Intelligent Packaging
Authors: López-Carballo Gracia , Muriel-Galet Virginia , Hernández-Muñoz Pilar , Gavara Rafael
Published: October 2019

A chromatic sensor has been designed for the detection of oxygen in package headspace. The sensor is based on the redox change of methylene blue (MB) to its leuco form. Its formulation includes the pigment, glycerol, as a sacrificial electron donor, TiO2, as a photocatalyst and ethylene-vinyl alcohol copolymer (EVOH), as a structural polymer matrix. The final sensor design that allows its manufacture by conventional printing and laminating technologies consists of the sensing polymer matrix (MB-EVOH) sandwiched in a suitable transparent multilayer structure. The outer layers protect the sensor from the external atmosphere and allow visualization of the colour. The inner layer is sufficiently opaque to facilitate sensor reading from the outside, is thick enough to avoid direct contact with food (functional barrier), and is oxygen-permeable to expose the sensing material to the internal package atmosphere. In the absence of oxygen, the sensor becomes white by irradiation with halogen lamps in less than 60 s. All components are substances permitted for food contact except the pigment, but specific migration analysis showed no trace of migration thanks to the functional barrier included in the design.
Two-Layered Graph-Cuts-Based Classification of LiDAR Data in Urban Areas
Authors: Yang Yetao , Wu Ke , Wang Yi , Chen Tao , Wang Xiang
Published: October 2019

Classifying the LiDAR (Light Detection and Ranging) point cloud in the urban environment is a challenging task. Due to the complicated structures of urban objects, it is difficult to find suitable features and classifiers to efficiently category the points. A two-layered graph-cuts-based classification framework is addressed in this study. The hierarchical framework includes a bottom layer that defines the features and classifies point clouds at the point level as well as a top layer that defines the features and classifies the point cloud at the object level. A novel adaptive local modification method is employed to model the interactions between these two layers. The iterative graph cuts algorithm runs around the bottom and top layers to optimize the classification. In this way, the addressed framework benefits from the integration of point features and object features to improve the classification. The experiments demonstrate that the proposed method is capable of producing classification results with high accuracy and efficiency.
Impact of Fabric Properties on Textile Pressure Sensors Performance
Authors: Possanzini Luca , Tessarolo Marta , Mazzocchetti Laura , Campari Enrico Gianfranco , Fraboni Beatrice
Published: October 2019

In recent years, wearable technologies have attracted great attention in physical and chemical sensing applications. Wearable pressure sensors with high sensitivity in low pressure range (<10 kPa) allow touch detection for human-computer interaction and the development of artificial hands for handling objects. Conversely, pressure sensors that perform in a high pressure range (up to 100 kPa), can be used to monitor the foot pressure distribution, the hand stress during movements of heavy weights or to evaluate the cyclist’s pressure pattern on a bicycle saddle. Recently, we developed a fully textile pressure sensor based on a conductive polymer, with simple fabrication and scalable features. In this paper, we intend to provide an extensive description on how the mechanical properties of several fabrics and different piezoresistive ink formulation may have an impact in the sensor’s response during a dynamic operation mode. These results highlight the complexity of the system due to the presence of various parameters such as the fabric used, the conductive polymer solution, the operation mode and the desired pressure range. Furthermore, this work can lead to a protocol for new improvements and optimizations useful for adapting textile pressure sensors to a large variety of applications.
Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network
Authors: Lin Hongze , Li Zejian , Lu Huajin , Sun Shujuan , Chen Fengnong , Wei Kaihua , Ming Dazhou
Published: October 2019

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.
Integrated and Participatory Design of Sustainable Development Strategies on Multiple Governance Levels
Authors: Heitmann Fabian , Halbe Johannes , Pahl-Wostl Claudia
Published: October 2019

An increasing number of sustainable development strategies (SDS) is being developed for cities, municipalities and countries. The design of such strategies is inherently complex. This is a result from intricate relationships between different SDS on different levels, and a large number of requirements that need to be addressed in strategy implementation. A particular challenge is the integration of strategies across different governance levels (e.g., city, federal, and national levels). Methodologies are currently lacking to systematically design SDS which take the full complexity of the dependencies of the strategies into account. In this article, we propose a participatory requirements analyses approach to support strategy building across governance levels. Experience from systems engineering (SE) has shown, that requirements are the basis for designing systems or strategies. We elicit requirements by applying a participatory modeling approach with causal-loop diagrams in an individual interview setting. To illustrate our approach, we test the developed design approach and focus on the interdependencies between SDS at the city level (i.e., the cities of Berlin and Hamburg) and the German national SDS. The design process reveals critical factors which are needed for the overall success of the strategies. The resulting causal models reveal that despite coordination activities of the regional objectives with the national targets, trade-offs exist between the strategies regarding the underlying conditions for their implementation (e.g., national law, federal and state law). In addition, the level of detail of requirements for certain objectives at the national level and across sectors is too general. This hinders the emergence of system-wide co-benefits of possible solution strategies. Requirements analysis can highlight interdependencies, such as trade-offs and synergies, between strategies at multiple governance levels and, based upon this, can support a more coherent strategy design.
Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise
Authors: Tian Yong , Ye Bojia , Wan Lili , Yang Minhao , Xing Dawei
Published: October 2019

This paper first calculates the departure delay and arrival delay of each flight by mining historical flight data. Then, a new method based on density clustering for identification and visualization of restricted airspace units that considers this activity is proposed. The main objective is to identify the restricted airspace units by calculating the average delay time according to the accumulative delay time of airspace units and the accumulative delay flight. Therefore, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is utilized to match the latitude and longitude coordinates of each spatial domain unit with its delay time to construct a feature matrix, and then clustering analysis is conducted according to the time period. The method aims at identifying the most severe restricted units in each period. The reliability and applicability of the proposed method are validated through a real case study with flight information from Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport during a typical day. The investigation shows that the DBSCAN clustering method can identify the restricted spatial units intuitively on the six flight paths between Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport.