bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions
Authors: Fecarotta Oreste , Carravetta Armando , Morani Maria Cristina , Padulano Roberta
Published: November 2018

The paper is focused on the optimal scheduling of a drainage pumping station, complying with variations in the pump rotational speed and a recurrent pattern for the inflow discharge. The paper is structured in several consecutive steps. In the first step, the experimental set-up is described and results of calibration tests on different pumping machines are presented to obtain equations linking significant variables (discharge, head, power, efficiency). Then, those equations are utilized to build a mixed-integer optimization model able to find the scheduling solution that minimizes required pumping energy. The model is solved with respect to a case study referred to a urban drainage system in Naples (Italy) and optimization results are analysed to provide insights on the algorithm computational performance and on the influence of pumping machine characteristics on the overall efficiency savings. With reference to the simulated scenarios, an average value of 32% energy can be saved with an optimized control. Its actual value depends on the hydraulic characteristics of the system.
Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record
Authors: Brodzik Mary J. , Long David G. , Hardman Molly A.
Published: November 2018

Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record.
Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia
Authors: Rybakov Georgy , Peuhkurinen Jussi , Latva-Käyrä Petri , Villikka Maria , Sirparanta Sanna , Kolesnikov Alexander , Junttila Virpi , Kauranne Tuomo
Published: November 2018

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making.
Wind-Induced Fatigue and Asymmetric Damage in a Timber Bridge
Authors: Thalla Olga , Stiros Stathis C.
Published: November 2018

The transformation of a 30 m long timber pedestrian bridge into a wobbly (laterally swaying) bridge with a dramatically reduced first lateral modal frequency has been monitored by seven annual, multi-sensor surveys. This evidence, in combination with analysis of the wind record, observations of local damage and evidence of wind-induced excitations from other bridges, is used to present a multi-stage scenario of the extraordinary structural weakening of our study bridge in only a few years. Our analysis is constrained by observations of asymmetric damage (longitudinal splitting cracks around metallic connections along the south side of the deck, not explained by ordinary, essentially symmetric lateral oscillations) and over-threshold analysis of strong northerly wind events, including gusts. The proposed scenario is that an unexpected for the area icing event took advantage of construction vicissitudes and produced damage that reduced the lateral stiffness of the bridge, especially of the arch superstructure. In addition, strong winds sharing common direction with gusts produced a combination of semi-static lateral bending and of dynamic oscillations, leading to numerous cycles of asymmetric high amplitude lateral deflections producing tensile stress normal to grain, cracks localized in connections, and fatigue. The vertical stiffness of the bridge was only slightly affected.
Specification and Performance Indicators of AeroRing—A Multiple-Ring Ethernet Network for Avionics Embedded Systems
Authors: Amari Ahmed , Mifdaoui Ahlem
Published: November 2018

The complexity and costs of the avionics communication architecture are increasing exponentially with the increasing number of embedded computers over the last few decades. To limit the cabling complexity and the deployment costs of such a communication architecture, we specify a new Gigabit multiple-ring Ethernet network, called AeroRing, while meeting the avionics requirements. First, we describe the current Aircraft Data Communication Network (ADCN) to highlight the main characteristics and requirements that have to be fulfilled by our solution. Then, we give an overview of the most relevant solutions to improve ADCN performance and relate them to AeroRing. Afterwards, we detail the specifications and the main Performance Indicators (PIs) of AeroRing. Finally, sensitivity and validation analyses of AeroRing are conducted through a realistic avionics application, regarding the various PIs, in comparison to the backbone network of the ADCN, the Avionics Full DupleX Switched Ethernet (AFDX). The computed AeroRing performance metrics show its ability to guarantee the avionics requirements.
Development of a Protein Microarray Chip with Enhanced Fluorescence for Identification of Semen and Vaginal Fluid
Authors: Abbas Naseem , Lu Xun , Badshah Mohsin Ali , In Jung Bin , Heo Won Il , Park Kui Young , Lee Mi-Kyung , Kim Cho Hee , Kang Pilwon , Chang Woo-Jin , Kim Seok-Min , Seo Seong Jun
Published: November 2018

The detection of body fluids has been used to identify a suspect and build a criminal case. As the amount of evidence collected at a crime site is limited, a multiplex identification system for body fluids using a small amount of sample is required. In this study, we proposed a multiplex detection platform using an Ag vertical nanorod metal enhanced fluorescence (MEF) substrate for semen and vaginal fluid (VF), which are important evidence in cases of sexual crime. The Ag nanorod MEF substrate with a length of 500 nm was fabricated by glancing angle deposition, and amino functionalization was conducted to improve binding ability. The effect of incubation time was analyzed, and an incubation time of 60 min was selected, at which the fluorescence signal was saturated. To assess the performance of the developed identification chip, the identification of semen and VF was carried out. The developed sensor could selectively identify semen and VF without any cross-reactivity. The limit of detection of the fabricated microarray chip was 10 times better than the commercially available rapid stain identification (RSID) Semen kit.
Design of a Measurement System for Simultaneously Measuring Six-Degree-Of-Freedom Geometric Errors of a Long Linear Stage
Authors: Liu Chien-Sheng , Pu Yu-Fan , Chen Yu-Ta , Luo Yong-Tai
Published: November 2018

This study designs and characterizes a novel precise measurement system for simultaneously measuring six-degree-of-freedom geometric motion errors of a long linear stage of a machine tool. The proposed measurement system is based on a method combined with the geometrical optics method and laser interferometer method. In contrast to conventional laser interferometers using only the interferometer method, the proposed measurement system can simultaneously measure six-degree-of-freedom geometric motion errors of a long linear stage with lower cost and faster operational time. The proposed measurement system is characterized numerically using commercial software ZEMAX and mathematical modeling established by using a skew-ray tracing method, a homogeneous transformation matrix, and a first-order Taylor series expansion. The proposed measurement system is then verified experimentally using a laboratory-built prototype. The experimental results show that, compared to conventional laser interferometers, the proposed measurement system better achieves the ability to simultaneously measure six-degree-of-freedom geometric errors of a long linear stage (a traveling range of 250 mm).
Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images
Authors: Xie Tao , Zhang Weike , Yang Linna , Wang Qingping , Huang Jingjian , Yuan Naichang
Published: November 2018

Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.
Image Segmentation Based on Dynamic Particle Swarm Optimization for Crystal Growth
Authors: Li Yu , Wang Shouyu , Xiao Jialin
Published: November 2018

In order to realize the intelligent production of sapphire crystal, it is important to obtain the growth status from the furnace by charge coupled device (CCD). However, a significant challenge is that traditional approaches are often not valid to separate the images of the melting interface well due to the low contrast and uneven brightness from the heater. In this paper, an improved Otsu algorithm based on dynamic particle swarm optimization (DPSO) is proposed to find the exact threshold band as contour of the crystal. In this method, the Otsu method is constructed firstly, then DPSO is used to find the optimal threshold band. Experimental results show that the proposed algorithm can separate the texture of crystal growth images well and has high robustness.
Photoacoustic Energy Sensor for Nanosecond Optical Pulse Measurement
Authors: Sang Pil , Heo Junseok , Park Hui , Baac Hyoung
Published: November 2018

We demonstrate a photoacoustic sensor capable of measuring high-energy nanosecond optical pulses in terms of temporal width and energy fluence per pulse. This was achieved by using a hybrid combination of a carbon nanotube-polydimethylsiloxane (CNT-PDMS)-based photoacoustic transmitter (i.e., light-to-sound converter) and a piezoelectric receiver (i.e., sound detector). In this photoacoustic energy sensor (PES), input pulsed optical energy is heavily absorbed by the CNT-PDMS composite film and then efficiently converted into an ultrasonic output. The output ultrasonic pulse is then measured and analyzed to retrieve the input optical characteristics. We quantitatively compared the PES performance with that of a commercial thermal energy meter. Due to the efficient energy transduction and sensing mechanism of the hybrid structure, the minimum-measurable pulsed optical energy was significantly lowered, ~157 nJ/cm2, corresponding to 1/760 of the reference pyroelectric detector. Moreover, despite the limited acoustic frequency bandwidth of the piezoelectric receiver, laser pulse widths over a range of 6–130 ns could be measured with a linear relationship to the ultrasound pulse width of 22–153 ns. As CNT has a wide electromagnetic absorption spectrum, the proposed pulsed sensor system can be extensively applied to high-energy pulse measurement over visible through terahertz spectral ranges.
Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry
Authors: González-Quiñones Juan J. , Reinoso-Gordo Juan F. , León-Robles Carlos A. , García-Balboa José L. , Ariza-López Francisco J.
Published: November 2018

Point cloud (PC) generation from photogrammetry–remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications. For several years, photogrammetry–RPAS has been used to recover civil engineering works such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, orthophotographs, etc. This study analyzes the influence of variables involved in the accuracy of PC generation over asphalt shapes and determines the most influential variable based on the development of an artificial neural network (ANN) with patterns identified in the test flights. The input variables were those involved, and output was the three-dimension root mean square error (3D-RMSE) of the PC in each ground control point (GCP). The result of the study shows that the most influential variable over PC accuracy is the modulation transfer function 50 (MTF50). In addition, the study obtained an average 3D-RMSE of 1 cm. The results can be used by the scientific and civil engineering communities to consider MTF50 variables in obtaining images from RPAS cameras and to predict the accuracy of a PC over asphalt based on the ANN developed. Also, this ANN could be the beginning of a large database containing patterns from several cameras and lenses in the world market.
Microscopic Three-Dimensional Measurement Based on Telecentric Stereo and Speckle Projection Methods
Authors: Chen Kepeng , Shi Tielin , Liu Qiang , Tang Zirong , Liao Guanglan
Published: November 2018

Three-dimensional (3D) measurement of microstructures has become increasingly important, and many microscopic measurement methods have been developed. For the dimension in several millimeters together with the accuracy at sub-pixel or sub-micron level, there is almost no effective measurement method now. Here we present a method combining the microscopic stereo measurement with the digital speckle projection. A microscopy experimental setup mainly composed of two telecentric cameras and an industrial projection module is established and a telecentric binocular stereo reconstruction procedure is carried out. The measurement accuracy has firstly been verified by performing 3D measurements of grid arrays at different locations and cylinder arrays with different height differences. Then two Mitutoyo step masters have been used for further verification. The experimental results show that the proposed method can obtain 3D information of the microstructure with a sub-pixel and even sub-micron measuring accuracy in millimeter scale.
Design, Modeling, and Evaluation of the Eddy Current Sensor Deeply Implanted in the Human Body
Authors: Khokle Rajas Prakash , Esselle Karu P. , Bokor Desmond J.
Published: November 2018

Joint replacement surgeries have enabled motion for millions of people suffering from arthritis or grave injuries. However, over 10% of these surgeries are revision surgeries. We have first analyzed the data from the worldwide orthopedic registers and concluded that the micromotion of orthopedic implants is the major reason for revisions. Then, we propose the use of inductive eddy current sensors for in vivo micromotion detection of the order of tens of μ m. To design and evaluate its characteristics, we have developed efficient strategies for the accurate numerical simulation of eddy current sensors implanted in the human body. We present the response of the eddy current sensor as a function of its frequency and position based on the robust curve fit analysis. Sensitivity and Sensitivity Range parameters are defined for the present context and are evaluated. The proposed sensors are fabricated and tested in the bovine leg.
Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers
Authors: Dutta Arindam , Ma Owen , Toledo Meynard , Pregonero Alberto Florez , Ainsworth Barbara E. , Buman Matthew P. , Bliss Daniel W.
Published: November 2018

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18–64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.
Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks ‡
Authors: Dong Yuan , Pu Lina , Luo Yu , Peng Zheng , Mo Haining , Meng Yun , Zhao Yi , Zhang Yuzhi
Published: November 2018

In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications.
A Comparative Study of Interdigitated Electrode and Quartz Crystal Microbalance Transduction Techniques for Metal–Organic Framework-Based Acetone Sensors
Authors: Chappanda Karumbaiah N. , Tchalala Mohamed R. , Shekhah Osama , Surya Sandeep G. , Eddaoudi Mohamed , Salama Khaled N.
Published: November 2018

We present a comparative study of two types of sensor with different transduction techniques but coated with the same sensing material to determine the effect of the transduction mechanism on the sensing performance of sensing a target analyte. For this purpose, interdigitated electrode (IDE)-based capacitors and quartz crystal microbalance (QCM)-based resonators were coated with a zeolitic–imidazolate framework (ZIF-8) metal–organic framework thin films as the sensing material and applied to the sensing of the volatile organic compound acetone. Cyclic immersion in methanolic precursor solutions technique was used for depositing the ZIF-8 thin films. The sensors were exposed to various acetone concentrations ranging from 5.3 to 26.5 vol % in N2 and characterized/compared for their sensitivity, hysteresis, long-term and short-term stability, selectivity, detection limit, and effect of temperature. Furthermore, the IDE substrates were used for resistive transduction and compared using capacitive transduction.
A Classification-Lock Tracking Strategy Allowing a Person-Following Robot to Operate in a Complicated Indoor Environment †
Authors: Jiang Shenlu , Yao Wei , Hong Zhonghua , Li Ling , Su Cheng , Kuc Tae-Yong
Published: November 2018

Person-following technology is an important robot service. The major trend of person-following is to utilize computer vision technology to localize the target person, due to the wide view and rich information that is obtained from the real world through a camera. However, most existing approaches employ the detecting-by-tracking strategy, which suffers from low speed, accompanied with more complicated detecting models and unstable region of interest (ROI) outputs in unexpressed situations. In this paper, we propose a novel classification-lock strategy to localize the target person, which incorporates the visual tracking technology with object detection technology, to adapt the localization model to different environments online, and to keep a high frame-per-second (FPS) on the mobile platform. This person-following approach consists of three key parts. In the first step, a pairwise cluster tracker is employed to localize the person. A positive and negative classifier is then utilized to verify the tracker’s result and to update the tracking model. In addition, a detector pre-trained by a CPU-optimized convolutional neural network is used to further improve the result of tracking. In the experiment, our approach is compared with other state-of-art approaches by a Vojir tracking dataset, with three sequences in the items of human to prove the quality of person localization. Moreover, the common challenges during the following task are evaluated by several image sequences in a static scene, and a dynamic scene is used to evaluate the improvement from the classification-lock strategy. Finally, our approach is deployed on a mobile robot to test its performance on the function of the person-following. Compared with other state-of-art methods, our approach achieves the highest score (0.91 recall rate). In the static and dynamic scene, the output of the ROI based on the classification-lock strategy is significantly better than that without it. Our approach also succeeds in a long-term following task in an indoor multi-floor scenario.
A Novel Sensorized Heart Valve Prosthesis: Preliminary In Vitro Evaluation
Authors: Marcelli Emanuela , Bortolani Barbara , Corazza Ivan , Cercenelli Laura
Published: November 2018

Background: Recent studies have shown that subclinical valve thrombosis in heart valve prosthesis (HVP) can be responsible for reduced leaflet motion detectable only by advanced imaging diagnostics. We conceived a novel sensorized HVP able to detect earlier any thrombus formation that may alter the leaflets motion using an electric impedance measurement, IntraValvular Impedance (IVI). Methods: For IVI measurement, dedicated electrodes are embedded in the structure of the HVP to generate a local electric field that is altered by the moving valve leaflets during their cyclic opening/closing. We present preliminary in vitro results using a first prototype of sensorized mechanical heart valve connected to an external impedance measurement system. The prototype was tested on a circulatory mock loop system and the IVI signals were recorded during both normal dynamics and experimentally induced altered working of the leaflets. Results: Recordings showed a very repetitive and stable IVI signal during the normal cyclic opening/closing of the HVP. The induced alterations in leaflet motion were reflected in the IVI signal. Conclusions: The novel sensorized HVP has great potential to give early warning of possible subclinical valve thrombosis altering the valve leaflet motion, and to help in tailoring the anticoagulation therapy.
Yield Visualization Based on Farm Work Information Measured by Smart Devices
Authors: Hashimoto Yoshiki , Arita Daisaku , Shimada Atsushi , Yoshinaga Takashi , Okayasu Takashi , Uchiyama Hideaki , Taniguchi Rin-Ichiro
Published: November 2018

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse.
Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
Authors: Yang Guang , Dai Lie , Wei Zhiqiang
Published: November 2018

With the advances in technology, there has been an increasing interest from researchers and industrial institutions in the use of underwater wireless sensor networks (UWSNs). Constrained by the open acoustic channel, harsh underwater environment, and their own particularities, UWSNs are vulnerable to a wide class of security threats and malicious attacks. However, most existing research into UWSNs has not taken security into consideration. Moreover, the existing relatively mature security mechanisms for WSNs cannot be directly utilized in UWSNs. For these reasons, this article aims to present a comprehensive overview of the particularities, constraints, attacks, challenges and current security mechanisms of UWSNs. In addition, challenging, open and hot research topics are outlined.
A FAST-BRISK Feature Detector with Depth Information
Authors: Liu Yanli , Zhang Heng , Guo Hanlei , Xiong Neal N.
Published: November 2018

RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance.
Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses
Authors: Yi Sang-ri , Song Junho
Published: November 2018

Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.
Matching SDN and Legacy Networking Hardware for Energy Efficiency and Bounded Delay
Authors: Fondo-Ferreiro Pablo  , Rodríguez-Pérez Miguel , Fernández-Veiga Manuel  , Herrería-Alonso Sergio 
Published: November 2018

Both economic and environmental costs are driving much research in the area of the energy efficiency of networking equipment. This research has produced a great amount of proposals. However, the majority of them remain unimplemented due to the lack of flexibility of current hardware devices and a certain lack of enthusiasm from commercial vendors. At the same time, Software-Defined Networking (SDN) has allowed customers to control switching decisions with a flexibility and precision previously unheard of. This paper explores the potential convergence between the two aforementioned trends and presents a promising power saving algorithm that can be implemented using standard SDN capabilities of current switches, reducing operation costs on both data centers and wired access networks. In particular, we focus on minimizing the energy consumption in bundles of energy-efficient Ethernet links leveraging SDN. For this, we build on an existing theoretical algorithm and adapt it for implementing with an SDN solution. We study several approaches and compare the resulting algorithms not only according to their energy efficiency, but also taking into account additional QoS metrics. The results show that the resulting algorithm is able to closely match the theoretical results, even when taking into account the requirements of delay-sensitive traffic.
Monitoring Migraine Cycle Dynamics with an Easy-to-Use Electrophysiological Marker—A Pilot Study
Authors: Shahaf Goded , Kuperman Pora , Bloch Yuval , Yariv Shahak , Granovsky Yelena
Published: November 2018

Migraine attacks can cause significant discomfort and reduced functioning for days at a time, including the pre-ictal and post-ictal periods. During the inter-ictsal period, however, migraineurs seem to function normally. It is puzzling, therefore, that event-related potentials of migraine patients often differ in the asymptomatic and inter-ictal period. Part of the electrophysiological dynamics demonstrated in the migraine cycle are attention related. In this pilot study we evaluated an easy-to-use new marker, the Brain Engagement Index (BEI), for attention monitoring during the migraine cycle. We sampled 12 migraine patients for 20 days within one calendar month. Each session consisted of subjects’ reports of stress level and migraine-related symptoms, and a 5 min EEG recording, with a 2-electrode EEG device, during an auditory oddball task. The first minute of the EEG sample was analyzed. Repetitive samples were also obtained from 10 healthy controls. The brain engagement index increased significantly during the pre-ictal (p ≈ 0.001) and the ictal (p ≈ 0.020) periods compared with the inter-ictal period. No difference was observed between the pre-ictal and ictal periods. Control subjects demonstrated intermediate Brain Engagement Index values, that is, higher than inter-ictal, yet lower than pre-ictal. Our preliminary results demonstrate the potential advantage of the use of a simple EEG system for improved prediction of migraine attacks. Further study is required to evaluate the efficacy of the Brain Engagement Index in monitoring the migraine cycle and the possible effects of interventions.
Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
Authors: Wang Lei , Chen Ruizhi , Li Deren , Zhang Guo , Shen Xin , Yu Baoguo , Wu Cailun , Xie Song , Zhang Peng , Li Ming , Pan Yuanjin
Published: November 2018

A low Earth orbiter (LEO)-based navigation signal augmentation system is considered as a complementary of current global navigation satellite systems (GNSS), which can accelerate precise positioning convergence, strengthen the signal power, and improve signal quality. Wuhan University is dedicated to LEO-based navigation signal augmentation research and launched one scientific experimental satellite named Luojia-1A. The satellite is capable of broadcasting dual-frequency band ranging signals over China. The initial performance of the Luojia-1A satellite navigation augmentation system is assessed in this study. The ground tests indicate that the phase noise of the oscillator is sufficiently low to support the intended applications. The field ranging tests achieve 2.6 m and 0.013 m ranging precision for the pseudorange and carrier phase measurements, respectively. The in-orbit test shows that the internal precision of the ephemeris is approximate 0.1 m and the clock stability is 3 × 10−10. The pseudorange and carrier phase measurement noise evaluated from the geometry-free combination is about 3.3 m and 1.8 cm. Overall, the Luojia-1A navigation augmentation system is capable of providing useable LEO navigation augmentation signals with the empirical user equivalent ranging error (UERE) no worse than 3.6 m, which can be integrated with existing GNSS to improve the real-time navigation performance.
Hyperspectral Imaging for Evaluating Impact Damage to Mango According to Changes in Quality Attributes
Authors: Xu Duohua , Wang Huaiwen , Ji Hongwei , Zhang Xiaochuan , Wang Yanan , Zhang Zhe , Zheng Hongfei
Published: November 2018

Evaluation of impact damage to mango (Mangifera indica Linn) as a result of dropping from three different heights, namely, 0.5, 1.0 and 1.5 m, was conducted by hyperspectral imaging (HSI). Reflectance spectra in the 900–1700 nm region were used to develop prediction models for pulp firmness (PF), total soluble solids (TSS), titratable acidity (TA) and chroma (∆b*) by a partial least squares (PLS) regression algorithm. The results showed that the changes in the mangoes’ quality attributes, which were also reflected in the spectra, had a strong relationship with dropping height. The best predictive performance measured by coefficient of determination (R2) and root mean square errors of prediction (RMSEP) values were: 0.84 and 31.6 g for PF, 0.9 and 0.49 oBrix for TSS, 0.65 and 0.1% for TA, 0.94 and 0.96 for chroma, respectively. Classification of the degree of impact damage to mango achieved an accuracy of more than 77.8% according to ripening index (RPI). The results show the potential of HSI to evaluate impact damage to mango by combining with changes in quality attributes.
Sensor-Based Detection of the Severity of Hyperkeratosis in the Teats of Dairy Cows
Authors: Demba Susanne , Hoffmann Gundula , Ammon Christian , Rose-Meierhöfer Sandra
Published: November 2018

The aim of this study was to evaluate whether the severity of hyperkeratosis (HK) in the teats of dairy cows can be assessed by a dielectric measurement. The study focused on surveying the occurrence of hyperkeratosis in a total of 241 teats of lactating dairy cows. A scoring system consisting of four categories was used to macroscopically assess the severity of HK. Additionally, the dielectric constant (DC) of all teats with milkability was measured in a double iteration with the MoistureMeterD (Delfin Technologies, Kuopio, Finland) on four different days. The Spearman rank correlation coefficient revealed a negative correlation between the DC and HK score (rs = −0.55 to −0.36). The results of the regression analysis showed that the DC values differed significantly between healthy teat ends (≤2) and teat ends with HK (≥3). Thus, the non-invasive measurement of DC provides a promising method of objectively assessing the occurrence and severity of HK.
Novel Competitive Fluorescence Sensing Platform for L-carnitine Based on Cationic Pillar[5]Arene Modified Gold Nanoparticles
Authors: Tan Xiaoping , Yang Yang , Luo Shasha , Zhang Zhong , Zeng Wenjie , Zhang Tingying , Su Fawu , Zhou Linzong
Published: November 2018

Supramolecular host-guest interaction and sensing between cationic pillar[5]arenes (CP5) and L-carnitine were developed by the competitive host-guest recognition for the first time. The fluorescence sensing platform was constructed by CP5 functionalized Au nanoparticles (CP5@Au-NPs) as receptor and probe (rhodamine 123, R123), which shown high sensitivity and selectivity for L-carnitine detection. Due to the negative charge and molecular size properties of L-carnitine, it can be highly captured by the CP5 via electrostatic interactions and hydrophobic interactions. The host-guest mechanism between PP5 and L-carnitine was studied by 1H NMR and molecular docking, indicating that more affinity binding force of CP5 with L-carnitine. Therefore, a selective and sensitive fluorescent method was developed. It has a linear response of 0.1–2.0 and 2.0–25.0 μM and a detection limit of 0.067 μM (S/N = 3). The fluorescent sensing platform was also used to detect L-carnitine in human serum and milk samples, which provided potential applications for the detection of drugs abuse and had path for guarding a serious food safety issues.
Sensors for Deformation Monitoring of Large Civil Infrastructures
Authors: Marsella Maria , Scaioni Marco
Published: November 2018

In the maintenance of large infrastructures such as dams, bridges, railways, underground structures (tunnels, mines) and others, monitoring of deformations plays a key role in maintaining the safety serviceability conditions and for mitigating any consequences due to ageing factors and possible structural failures. [...]
Detection and Tracking of Moving Targets for Thermal Infrared Video Sequences
Authors: Li Chenming , Wang Wenguang
Published: November 2018

The joint detection and tracking of multiple targets from raw thermal infrared (TIR) image observations plays a significant role in the video surveillance field, and it has extensive applied foreground and practical value. In this paper, a novel multiple-target track-before-detect (TBD) method, which is based on background subtraction within the framework of labeled random finite sets (RFS) is presented. First, a background subtraction method based on a random selection strategy is exploited to obtain the foreground probability map from a TIR sequence. Second, in the foreground probability map, the probability of each pixel belonging to a target is calculated by non-overlapping multi-target likelihood. Finally, a δ generalized labeled multi-Bernoulli ( δ -GLMB) filter is employed to produce the states of multi-target along with their labels. Unlike other RFS-based filters, the proposed approach describes the target state by a pixel set instead of a single point. To meet the requirement of factual application, some extra procedures, including pixel sampling and update, target merging and splitting, and new birth target initialization, are incorporated into the algorithm. The experimental results show that the proposed method performs better in multi-target detection than six compared methods. Also, the method is effective for the continuous tracking of multi-targets.
THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion
Authors: Ge Hongyi , Jiang Yuying , Zhang Yuan
Published: November 2018

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.
Outage-Based Resource Allocation for DF Two-Way Relay Networks with Energy Harvesting
Authors: Peng Chunling , Li Fangwei , Liu Huaping , Wang Guozhong
Published: November 2018

A joint resource allocation algorithm to minimize the system outage probability is proposed for a decode-and-forward (DF) two-way relay network with simultaneous wireless information and power transfer (SWIPT) under a total power constraint. In this network, the two sources nodes exchange information with the help of a passive relay, which is assumed to help the two source nodes’ communication without consuming its own energy by exploiting an energy-harvesting protocol, the power splitting (PS) protocol. An optimization framework to jointly optimize power allocation (PA) at the source nodes and PS at the relay is developed. Since the formulated joint optimization problem is non-convex, the solution is developed in two steps. First, the conditionally optimal PS ratio at the relay node for a given PA ratio is explored; then, the closed-form of the optimal PA in the sense of minimizing the system outage probability with instantaneous channel state information (CSI) is derived. Analysis shows that the optimal design depends on the channel condition and the rate threshold. Simulation results are obtained to validate the analytical results. Comparison with three existing schemes shows that the proposed optimized scheme has the minimum system outage probability.
A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data
Authors: Saif Shahela , Tehseen Samabia , Kausar Sumaira
Published: November 2018

Recognition of human actions form videos has been an active area of research because it has applications in various domains. The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, among others. Any advancements in this field are tied to advances in the interrelated fields of object recognition, spatio- temporal video analysis and semantic segmentation. Activity recognition is a challenging task since it faces many problems such as occlusion, view point variation, background differences and clutter and illumination variations. Scientific achievements in the field have been numerous and rapid as the applications are far reaching. In this survey, we cover the growth of the field from the earliest solutions, where handcrafted features were used, to later deep learning approaches that use millions of images and videos to learn features automatically. By this discussion, we intend to highlight the major breakthroughs and the directions the future research might take while benefiting from the state-of-the-art methods.