bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring and Detecting Archaeological Features with Multi-Frequency Polarimetric Analysis
Authors: Patruno Jolanda , Fitrzyk Magdalena , Delgado Blasco Jose Manuel
Published: December 2019

In remote sensing for archaeology, an unequivocal method capable of automatic detection of archaeological features still does not exists. Applications of Synthetic Aperture Radar (SAR) remote sensing for archaeology mainly focus on high spatial resolution SAR sensors, which allow the recognition of structures of small dimension and give information of the surface topography of sites. In this study we investigated the potential of combined dual and fully polarized SAR data and performed polarimetric multi-frequency and multi-incidence angle analysis of C-band Sentinel-1, L-band Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and of C-band Radar Satellite-2 (RADARSAT-2) datasets for the detection of surface and subsurface archaeological structures over the United Nations Educational, Scientific and Cultural Organization (UNESCO) site of Gebel Barkal (Sudan). While PALSAR offers a good historical reference, Sentinel-1 time series provide recent and systematic monitoring opportunities. RADARSAT-2 polarimetric data have been specifically acquired in 2012/2013, and have been scheduled to achieve a multi-temporal observation of the archaeological area under study. This work demonstrated how to exploit a complex but significant dataset composed of SAR full polarimetric and dual polarimetric acquisitions, with the purpose of identifying the most suitable earth observation technique for the preservation and identification of archaeological features. The scientific potential of the illustrated analysis fits perfectly with the current delicate needs of cultural heritage; such analysis demonstrates how multi-temporal and multi-data cultural heritage monitoring can be applied not only for documentation purposes, but can be addressed especially to those areas exposed to threats of different nature that require a constant and prompt intervention plans.
Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index
Authors: Yin Tiangang , Qi Jianbo , Cook Bruce D. , Morton Douglas C. , Wei Shanshan , Gastellu-Etchegorry Jean-Philippe
Published: December 2019

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.
A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts
Authors: Wierzbicki Damian , Kedzierski Michal , Sekrecka Aleksandra
Published: December 2019

Unmanned aerial vehicles (UAVs) equipped with compact digital cameras and multi-spectral sensors are used in remote sensing applications and environmental studies. Recently, due to the reduction of costs of these types of system, the increase in their reliability, and the possibility of image acquisition with very high spatial resolution, low altitudes imaging is used in many qualitative and quantitative analyses in remote sensing. Also, there has been an enormous development in the processing of images obtained with UAV platforms. Until now, research on UAV imaging has focused mainly on aspects of geometric and partially radiometric correction. And consideration of the effects of low atmosphere and haze on images has so far been neglected due to the low operating altitudes of UAVs. However, it proved to be the case that the path of sunlight passing through various layers of the low atmosphere causes refraction and causes incorrect registration of reflection by the imaging sensor. Images obtained from low altitudes may be degraded due to the scattering process caused by fog and weather conditions. These negative atmospheric factors cause a reduction in contrast and colour reproduction in the image, thereby reducing its radiometric quality. This paper presents a method of dehazing images acquired with UAV platforms. As part of the research, a methodology for imagery acquisition from a low altitude was introduced, and methods of atmospheric calibration based on the atmosphere scattering model were presented. Moreover, a modified dehazing model using Wiener’s adaptive filter was presented. The accuracy assessment of the proposed dehazing method was made using qualitative indices such as structural similarity (SSIM), peak signal to noise ratio (PSNR), root mean square error (RMSE), Correlation Coefficient, Universal Image Quality Index (Q index) and Entropy. The experimental results showed that using the proposed dehazing method allowed the removal of the negative impact of haze and improved image quality, based on the PSNR index, even by an average of 34% compared to other similar methods. The obtained results show that our approach allows processing of the images to remove the negative impact of the low atmosphere. Thanks to this technique, it is possible to obtain a dehazing effect on images acquired at high humidity and radiation fog. The results from this study can provide better quality images for remote sensing analysis.
Combining Multiband Remote Sensing and Hierarchical Distance Sampling to Establish Drivers of Bird Abundance
Authors: Richter Ronny , Heim Arend , Heim Wieland , Kamp Johannes , Vohland Michael
Published: December 2019

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures.
Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data
Authors: Cairo Carolline , Barbosa Claudio , Lobo Felipe , Novo Evlyn , Carlos Felipe , Maciel Daniel , Flores Júnior Rogério , Silva Edson , Curtarelli Victor
Published: December 2019

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.
Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles
Authors: Brooke Christopher , Clutterbuck Ben
Published: December 2019

There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.
External Groundwater Alleviates the Degradation of Closed Lakes in Semi-Arid Regions of China
Authors: Chen Jiaqi , Lv Jiming , Li Ning , Wang Qingwei , Wang Jian
Published: December 2019

There are a large number of lakes with beaded distribution in the semi-arid areas of the Inner Mongolian Plateau, and some of them have degraded or even disappeared during the past three decades. We studied the reasons of the disappearance of these lakes by determining the way of replenishment of these lakes and the impact of the natural-social environment of the basin, with the aim of saving these gradually disappearing lakes. Based on remote sensing image and hydrological analysis, this paper studied the recharge of Daihai Lake and Huangqihai Lake. The deep learning method was used to establish the time-series of lake evolution. The same method was combined with the innovative woodland and farmland extraction method to set up the time-series of ground classification composition in the basins. Using relevant survey data, combined with soil water infiltration test, water chemical, and isotopic signature analysis of various water bodies, we found that the Daihai Lake area is the largest in dry season and the smallest in rainy season and the other lake is not satisfied with this phenomenon. In addition, we calculated the specific recharge and consumption of the study basin. These experiments indicated that the exogenous groundwater is recharged directly through the faults at the bottom of Daihai Lake, while the exogenous groundwater is recharged in Huangqihai Lake through rivers indirectly. Large-scale exploitation of groundwater for agricultural irrigation and industrial production is the main cause of lake degradation. Reducing the extraction of groundwater for agricultural irrigation is an important measure to restore lake ecology.
Maintaining Transparency of a Heated MEMS Membrane for Enabling Long-Term Optical Measurements on Soot-Containing Exhaust Gas
Authors: Middelburg Luke M. , Ghaderi Mohammadamir , Bilby David , Visser Jaco H. , Zhang Guo Qi , Lundgren Per , Enoksson Peter , Wolffenbuttel Reinoud F.
Published: December 2019

Ensuring optical transparency over a wide spectral range of a window with a view into the tailpipe of the combustion engine, while it is exposed to the harsh environment of soot-containing exhaust gas, is an essential pre-requisite for introducing optical techniques for long-term monitoring of automotive emissions. Therefore, a regenerable window composed of an optically transparent polysilicon-carbide membrane with a diameter ranging from 100 µm up to 2000 µm has been fabricated in microelectromechanical systems (MEMS) technology. In the first operating mode, window transparency is periodically restored by pulsed heating of the membrane using an integrated resistor for heating to temperatures that result in oxidation of deposited soot (600–700 °C). In the second mode, the membrane is kept transparent by repelling soot particles using thermophoresis. The same integrated resistor is used to yield a temperature gradient by continuous moderate-temperature heating. Realized devices have been subjected to laboratory soot exposure experiments. Membrane temperatures exceeding 500 °C have been achieved without damage to the membrane. Moreover, heating of membranes to ΔT = 40 °C above gas temperature provides sufficient thermophoretic repulsion to prevent particle deposition and maintain transparency at high soot exposure, while non-heated identical membranes on the same die and at the same exposure are heavily contaminated.
Analysis of Hot Bar Soldering, Insulation Displacement Connections (IDC), and Anisotropic Conductive Adhesives (ACA), for the Automated Production of Smart Textiles
Authors: Micus Sebastian , Kirsten Ivan , Haupt Michael , Gresser Götz T.
Published: December 2019

Despite all the growth forecasts of the smart textiles market, there is no stable automated manufacturing process for attaching classic electronics to textiles. The great amount of manual production steps causes high prices, which slow down market growth. During the production process, the contacting step offers the greatest potential to reduce manual manufacturing steps. For this reason, we have analyzed various contacting methods for electronic parts on conductive yarns that have a high potential for automation. The chosen methods were thermode soldering, insulation–displacement connectors and anisotropic conductive adhesives. In order to ensure reliable mechanical contacting, the samples were tested in a peeling experiment. The examination of the contact resistances took place in the context of a resistance test using four-wire measuring technology.
A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection
Authors: Vargas-Lopez Olivia , Amezquita-Sanchez Juan P. , De-Santiago-Perez J. Jesus , Rivera-Guillen Jesus R. , Valtierra-Rodriguez Martin , Toledano-Ayala Manuel , Perez-Ramirez Carlos A.
Published: December 2019

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.
A 3D Wrist Pulse Signal Acquisition System for Width Information of Pulse Wave
Authors: Chen Chuanglu , Li Zhiqiang , Zhang Yitao , Zhang Shaolong , Hou Jiena , Zhang Haiying
Published: December 2019

During pulse signal collection, width information of pulse waves is essential for the diagnosis of disease. However, currently used measuring instruments can only detect the amplitude while can’t acquire the width information. This paper proposed a novel wrist pulse signal acquisition system, which could realize simultaneous measurements of the width and amplitude of dynamic pulse waves under different static forces. A tailor-packaged micro-electro-mechanical system (MEMS) sensor array was employed to collect pulse signals, a conditioning circuit was designed to process the signals, and a customized algorithm was developed to compute the width. Experiments were carried out to validate the accuracy of the sensor array and system effectiveness. The results showed the system could acquire not only the amplitude of pulse wave but also the width of it. The system provided more information about pulse waves, which could help doctors make the diagnosis.
Highly Sensitive Detection of NO2 by Au and TiO2 Nanoparticles Decorated SWCNTs Sensors
Authors: Fort Ada , Panzardi Enza , Al-Hamry Ammar , Vignoli Valerio , Mugnaini Marco , Addabbo Tommaso , Kanoun Olfa
Published: December 2019

The aim of this work is to investigate the gas sensing performance of single wall carbon nanotubes (SWCNTs)-based conductive sensors operating at low–medium temperatures (<250 °C). The investigated sensing films consists of an SWCNT network obtained by drop-casting a SWCNT suspension. Starting from this base preparation, different sensing devices were obtained by decorating the SWCNT network with materials suitable for enhancing the sensitivity toward the target gas. In particular, in this paper, nano-particles of gold and of TiO2 were used. In the paper, the performance of the different sensing devices, in terms of response time, sensitivity toward NO2 and cross-sensitivity to O2, CO and water vapor, were assessed and discussed. Sensors based on decorated SWCNT films showed high performance; in particular, the decoration with Au nano-particles allows for a large enhancement of sensitivity (reaching 10%/1 ppm at 240 °C) and a large reduction of response time. On the other hand, the addition of TiO2 nanoparticles leads to a satisfactory improvement of the sensitivity as well as a significant reduction of the response time at moderate temperatures (down to 200 °C). Finally, the suitability of using Au decorated SWCNTs-based sensors for room temperature sensing is demonstrated.
An Optical Filter-Less CMOS Image Sensor with Differential Spectral Response Pixels for Simultaneous UV-Selective and Visible Imaging
Authors: Sipauba Carvalho da Silva Yhang Ricardo , Kuroda Rihito , Sugawa Shigetoshi
Published: December 2019

This paper presents a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) capable of capturing UV-selective and visible light images simultaneously by a single exposure and without employing optical filters, suitable for applications that require simultaneous UV and visible light imaging, or UV imaging in variable light environment. The developed CIS is composed by high and low UV sensitivity pixel types, arranged alternately in a checker pattern. Both pixel types were designed to have matching sensitivities for non-UV light. The UV-selective image is captured by extracting the differential spectral response between adjacent pixels, while the visible light image is captured simultaneously by the low UV sensitivity pixels. Also, to achieve high conversion gain and wide dynamic range simultaneously, the lateral overflow integration capacitor (LOFIC) technology was introduced in both pixel types. The developed CIS has a pixel pitch of 5.6 µm and exhibits 172 µV/e− conversion gain, 131 ke− full well capacity (FWC), and 92.3 dB dynamic range. The spectral sensitivity ranges of the high and low UV sensitivity pixels are of 200–750 nm and 390–750 nm, respectively. The resulting sensitivity range after the differential spectral response extraction is of 200–480 nm. This paper presents details regarding the CIS pixels structures, doping profiles, device simulations, and the measurement results for photoelectric response and spectral sensitivity for both pixel types. Also, sample images of UV-selective and visible spectral imaging using the developed CIS are presented.
Designing Silver Nanoparticles for Detecting Levodopa (3,4-Dihydroxyphenylalanine, L-Dopa) Using Surface-Enhanced Raman Scattering (SERS)
Authors: Rubira Rafael Jesus Gonçalves , Camacho Sabrina Alessio , Martin Cibely Silva , Mejía-Salazar Jorge Ricardo , Reyes Gómez Faustino , da Silva Robson Rosa , Oliveira Junior Osvaldo Novais de , Alessio Priscila , Constantino Carlos José Leopoldo
Published: December 2019

Detection of the drug Levodopa (3,4-dihydroxyphenylalanine, L-Dopa) is essential for the medical treatment of several neural disorders, including Parkinson’s disease. In this paper, we employed surface-enhanced Raman scattering (SERS) with three shapes of silver nanoparticles (nanostars, AgNS; nanospheres, AgNP; and nanoplates, AgNPL) to detect L-Dopa in the nanoparticle dispersions. The sensitivity of the L-Dopa SERS signal depended on both nanoparticle shape and L-Dopa concentration. The adsorption mechanisms of L-Dopa on the nanoparticles inferred from a detailed analysis of the Raman spectra allowed us to determine the chemical groups involved. For instance, at concentrations below/equivalent to the limit found in human plasma (between 10−7–10−8 mol/L), L-Dopa adsorbs on AgNP through its ring, while at 10−5–10−6 mol/L adsorption is driven by the amino group. At even higher concentrations, above 10−4 mol/L, L-Dopa polymerization predominates. Therefore, our results show that adsorption depends on both the type of Ag nanoparticles (shape and chemical groups surrounding the Ag surface) and the L-Dopa concentration. The overall strategy based on SERS is a step forward to the design of nanostructures to detect analytes of clinical interest with high specificity and at varied concentration ranges.
Turbidity and RI Dependency of a Polymer Optical Fiber-Based Chromatic Sensor
Authors: Duarte Daniel P. , Nogueira Rogério N. , Bilro Lucia
Published: December 2019

An in-line and real time chromatic sensor for liquids based on plastic optical fiber was developed. It uses an air gap, fiber to fiber, transmission principle. Its dependency to turbidity and refractive index is studied and characterized. This information will provide the necessary knowledge for future implementation of more complex auto-compensations routines. Due to the predictable behavior of the sensor to variations of turbidity and refractive index, it is shown that a posterior compensation could be applied for the discrimination of color. The real-time color sensor can be used in different turbid liquids and contain different refractive indices.
Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis
Authors: Ma Huiqin , Huang Wenjiang , Jing Yuanshu , Pignatti Stefano , Laneve Giovanni , Dong Yingying , Ye Huichun , Liu Linyi , Guo Anting , Jiang Jing
Published: December 2019

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.
Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service
Authors: Zgank Andrej
Published: December 2019

Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.
Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
Authors: Millán Pere , Aliagas Carles , Molina Carlos , Meseguer Roc , Ochoa Sergio F. , Santos Rodrigo M.
Published: December 2019

The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.
An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network
Authors: Su Mu-Chun , U Tat-Meng , Hsieh Yi-Zeng , Yeh Zhe-Fu , Lee Shu-Fang , Lin Shih-Syun
Published: December 2019

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.
Evaluation of Home-Based Rehabilitation Sensing Systems with Respect to Standardised Clinical Tests
Authors: Vourganas Ioannis , Stankovic Vladimir , Stankovic Lina , Michala Anna Lito
Published: December 2019

With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83 % of the respondents supporting the system’s motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant ( ϱ c > 0.99 , R 2 > 0.94 , I C C > 0.96 ) and unbiased correlation to the golden standard.
Non-Fragile Robust H∞ Filtering of Takagi-Sugeno Fuzzy Networked Control Systems with Sensor Failures
Authors: Wang Hao , Xie Shousheng , Zhou Bin , Wang Weixuan
Published: December 2019

The fault-tolerant robust non-fragile H∞ filtering problem for networked control systems with sensor failures is studied in this paper. The Takagi-Sugeno fuzzy model which can appropriate any nonlinear systems is employed. Based on the model, a filter which can maintain stability and H∞ performance level under the influence of gain perturbation of the filter and sensor failures is designed. Moreover, the gain matrix of sensor failures is converted into a dynamic interval to expand the range of allowed failures. And the sufficient condition for the existence of the desired filter is derived in terms of linear matrix inequalities (LMIs) solutions. Finally a simulation example is given to illustrate the effectiveness of the proposed method.
Modeling and Research on Power System of Distributed Sensor Networks for Long Streamers in Marine Seismic Exploration
Authors: Yu Hongwei , Song Kezhu , Yang Junfeng , Wu Chuan , Zhong Ke , Lv Wengui
Published: December 2019

The electric power system plays an important role in sensor networks. In the marine seismic exploration streamer system (MSESS), an underwater power system transmits high-voltage direct current to all nodes in the streamer through a daisy chain structure. As offshore oil exploration develops toward deep water, it is necessary to study long streamers with large-scale sensor networks for deep water exploration. When the length of a streamer is increased to a certain value, the output current of the power supply increases sharply. This results in the activation of the overcurrent protection and the power supply shuts down. This paper puts forward an accurate model for an underwater power system applied to MSESS. Using the Newton iteration algorithm and a reverse algorithm, equations established by the model are solved and laboratory test results are used to verify the accuracy of the model. Based on simulation and analysis of the model, we explain why the power system crashes when the streamer is too long. Software that can quickly calculate the maximum number of nodes (the maximum length with which the system works normally) is developed and it is significant for the design of MSESS. The method of research could also be applied to relevant work such as large-scale sensor networks with daisy-chaining power supply in land seismic exploration.
CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems
Authors: Park Jisoo , Chen Jingdao , Cho Yong K. , Kang Dae Y. , Son Byung J.
Published: December 2019

Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.
Toward an End-to-End Calibration for Mobile C-Arm in Combination with a Depth Sensor for Surgical Augmented Reality Applications
Authors: Hosseinian Sahar , Arefi Hossein , Navab Nassir
Published: December 2019

C-arm X-ray imaging is commonly applied in operating rooms for guiding orthopedic surgeries. Augmented Reality (AR) with C-arm X-ray images during surgery is an efficient way to facilitate procedures for surgeons. However, the accurate calibration process for surgical AR based on C-arm is essential and still challenging due to the limitations of C-arm imaging systems, such as instability of C-arm calibration parameters and the narrow field of view. We extend existing methods using a depth camera and propose a new calibration procedure consisting of calibration of the C-arm imaging system, and 3D/2D calibration of an RGB-D camera and C-arm system with a new method to achieve reliable data and promising accuracy and, at the same time, consistent with standard surgical protocols. For the calibration procedure, we apply bundle adjustment equations with a 3D designed Lego multi-modal phantom, in contrast to the previous methods in which planar calibration phantoms were applied. By using our method, the visualization of the X-ray image upon the 3D data was done, and the achieved mean overlay error was 1.03 mm. The evaluations showed that the proposed calibration procedure provided promising accuracy for AR surgeries and it improved the flexibility and robustness of existing C-arm calibration methods for surgical augmented reality (using C-arm and RGB-D sensor). Moreover, the results showed the efficiency of our method to compensate for the effects of the C-arm movement on calibration parameters. It was shown that the obtained overlay error was improved for the non-zero rotation movement of C-arm by using a virtual detector.
A Novel Real-Valued DOA Algorithm Based on Eigenvalue
Authors: Yang De-Sen , Chen Feng , Mo Shi-Qi
Published: December 2019

To solve the high complexity of the subspace-based direction-of-arrival (DOA) estimation algorithm, a super-resolution DOA algorithm is built in this paper. However, in this method, matrix decomposition is required for each search angle. Therefore, in this paper, real-valued processing is used to reduce the scanning range by half, which is less effective in algorithm complexity. The super-resolution algorithm mainly uses the conservation of energy. By exploring the relationship between the covariance matrix and its complex conjugate, we constructed the real-valued matrix and introduced a real-valued searching source to make the operation of the matrix real-valued. Finally, the simulation experiments show that the proposed algorithm not only reduces the spectral search range by half but also has a higher angular resolution than the traditional algorithm.
Comparison between Resonance and Non-Resonance Type Piezoelectric Acoustic Absorbers
Authors: Pyun Joo Young , Kim Young Hun , Kwon Soo Won , Choi Won Young , Park Kwan Kyu
Published: December 2019

In this study, piezoelectric acoustic absorbers employing two receivers and one transmitter with a feedback controller were evaluated. Based on the target and resonance frequencies of the system, resonance and non-resonance models were designed and fabricated. With a lateral size less than half the wavelength, the model had stacked structures of lossy acoustic windows, polyvinylidene difluoride, and lead zirconate titanate-5A. The structures of both models were identical, except that the resonance model had steel backing material to adjust the center frequency. Both models were analyzed in the frequency and time domains, and the effectiveness of the absorbers was compared at the target and off-target frequencies. Both models were fabricated and acoustically and electrically characterized. Their reflection reduction ratios were evaluated in the quasi-continuous-wave and time-transient modes.
Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles
Authors: Chen Bin , Pei Xiaofei , Chen Zhenfu
Published: December 2019

Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors’ local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.
Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation
Authors: Wang Junting , Xu Tianhe , Wang Zhenjie
Published: December 2019

Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber’s equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.
Ultra-Low Power Wearable Infant Sleep Position Sensor
Authors: Yun Inyeol , Jeung Jinpyeo , Kim Mijung , Kim Young-Seok , Chung Yoonyoung
Published: December 2019

Numerous wearable sensors have been developed for a variety of needs in medical/healthcare/wellness/sports applications, but there are still doubts about their usefulness due to uncomfortable fit or frequent battery charging. Because the size or capacity of battery is the major factor affecting the convenience of wearable sensors, power consumption must be reduced. We developed a method that can significantly reduce the power consumption by introducing a signal repeater and a special switch that provides power only when needed. Antenna radiation characteristics are an important factor in wireless wearable sensors, but soft material encapsulation for comfortable fit results in poor wireless performance. We improved the antenna radiation characteristics by a local encapsulation patterning. In particular, ultra-low power operation enables the use of paper battery to achieve a very thin and flexible form factor. Also, we verified the human body safety through specific absorption rate simulations. With these methods, we demonstrated a wearable infant sleep position sensor. Infants are unable to call for help in unsafe situations, and it is not easy for caregivers to observe them all the time. Our wearable sensor detects infants’ sleep positions in real time and automatically alerts the caregivers when needed.
Reactive Postural Responses to Continuous Yaw Perturbations in Healthy Humans: The Effect of Aging
Authors: Mileti Ilaria , Taborri Juri , Rossi Stefano , Del Prete Zaccaria , Paoloni Marco , Suppa Antonio , Palermo Eduardo
Published: December 2019

Maintaining balance stability while turning in a quasi-static stance and/or in dynamic motion requires proper recovery mechanisms to manage sudden center-of-mass displacement. Furthermore, falls during turning are among the main concerns of community-dwelling elderly population. This study investigates the effect of aging on reactive postural responses to continuous yaw perturbations on a cohort of 10 young adults (mean age 28 ± 3 years old) and 10 older adults (mean age 61 ± 4 years old). Subjects underwent external continuous yaw perturbations provided by the RotoBit1D platform. Different conditions of visual feedback (eyes opened and eyes closed) and perturbation intensity, i.e., sinusoidal rotations on the horizontal plane at different frequencies (0.2 Hz and 0.3 Hz), were applied. Kinematics of axial body segments was gathered using three inertial measurement units. In order to measure reactive postural responses, we measured body-absolute and joint absolute rotations, center-of-mass displacement, body sway, and inter-joint coordination. Older adults showed significant reduction in horizontal rotations of body segments and joints, as well as in center-of-mass displacement. Furthermore, older adults manifested a greater variability in reactive postural responses than younger adults. The abnormal reactive postural responses observed in older adults might contribute to the well-known age-related difficulty in dealing with balance control during turning.