bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection
Authors: Hsu Chia-Yu , Li Wenwen , Wang Sizhe
Published: May 2021

This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.
A Deep Vector Quantization Clustering Method for Polarimetric SAR Images
Authors: Zuo Yixin , Guo Jiayi , Zhang Yueting , Lei Bin , Hu Yuxin , Wang Mingzhi
Published: May 2021

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.
Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI
Authors: Evans Fiona H. , Shen Jianxiu
Published: May 2021

Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.
Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3
Authors: Wang Chunshan , Wang Qian , Wu Huarui , Zhao Chunjiang , Teng Guifa , Li Jiuxi
Published: May 2021

Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model’s ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.
Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal
Authors: Van Den Hoek Jamon , Smith Alexander , Hurni Kaspar , Saksena Sumeet , Fox Jefferson
Published: May 2021

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016). TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.
Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization
Authors: Benhalouche Fatima Zohra , Deville Yannick , Karoui Moussa Sofiane , Ouamri Abdelaziz
Published: May 2021

Unsupervised hyperspectral unmixing methods aim to extract endmember spectra and infer the proportion of each of these spectra in each observed pixel when considering linear mixtures. However, the interaction between sunlight and the Earth’s surface is often very complex, so that observed spectra are then composed of nonlinear mixing terms. This nonlinearity is generally bilinear or linear quadratic. In this work, unsupervised hyperspectral unmixing methods, designed for the bilinear and linear-quadratic mixing models, are proposed. These methods are based on bilinear or linear-quadratic matrix factorization with non-negativity constraints. Two types of algorithms are considered. The first ones only use the projection of the gradient, and are therefore linked to an optimal manual choice of their learning rates, which remains the limitation of these algorithms. The second developed algorithms, which overcome the above drawback, employ multiplicative projective update rules with automatically chosen learning rates. In addition, the endmember proportions estimation, with three alternative approaches, constitutes another contribution of this work. Besides, the reduction of the number of manipulated variables in the optimization processes is also an originality of the proposed methods. Experiments based on realistic synthetic hyperspectral data, generated according to the two considered nonlinear mixing models, and also on two real hyperspectral images, are carried out to evaluate the performance of the proposed approaches. The obtained results show that the best proposed approaches yield a much better performance than various tested literature methods.
Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot
Authors: Parween Rizuwana , Muthugala M. A. Viraj J. , Heredia Manuel V. , Elangovan Karthikeyan , Elara Mohan Rajesh
Published: May 2021

The inspection and maintenance of drains with varying heights necessitates a drain mapping robot with trained labour to maintain community hygiene and prevent the spread of diseases. For adapting to level changes and navigating in the narrow confined environments of drains, we developed a self-configurable hybrid robot, named Tarantula-II. The platform is a quadruped robot with hybrid locomotion and the ability to reconfigure to achieve variable height and width. It has four legs, and each leg is made of linear actuators and modular rolling wheel mechanisms with bi-directional movement. The platform has a fuzzy logic system for collision avoidance of the side wall in the drain environment. During level shifting, the platform achieves stability by using the pitch angle as the feedback from the inertial measuring unit (IMU) mounted on the platform. This feedback helps to adjust the accurate height of the platform. In this paper, we describe the detailed mechanical design and system architecture, kinematic models, control architecture, and stability of the platform. We deployed the platform both in a lab setting and in a real-time drain environment to demonstrate the wall collision avoidance, stability, and level shifting capabilities of the platform.
Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology
Authors: Capdevila Lluis , Castro-Marrero Jesús , Alegre José , Ramos-Castro Juan , Escorihuela Rosa M
Published: May 2021

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS.
Multi-Criteria Recommendation Systems to Foster Online Grocery
Authors: Hafez Manar Mohamed , Redondo Rebeca P. Díaz , Vilas Ana Fernández , Pazó Héctor Olivera
Published: May 2021

With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system (RS) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. RS also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenge when recommending products is insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package and health) for each document representation method to foster online grocery shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.
Microdisplacement Measurement Based on F-P Etalon: Processing Method and Experiments
Authors: Shen Xiaoyan , Zhou Shinan , Li Dongsheng
Published: May 2021

Herein, a processing method is proposed for accurate microdisplacement measurements from a 2D Fabry–Perot (F-P) fringe pattern. The core of the processing algorithm uses the F-P interference imaging concentric ring pattern to accurately calculate the centre coordinates of the concentric ring. The influencing factors of measurement were analysed, and the basic idea of data processing was provided. In particular, the coordinate rotation by the 45-degree method (CR) was improved; consequently, the virtual pixel interval was reduced by half, and the calculation accuracy of the circle centre coordinate was improved. Experiments were conducted to analyse the influence of the subdivision and circle fitting methods. The results show that the proposed secondary coordinate rotation (SCR) by 45 degrees method can obtain higher accuracy of the centre coordinate than the CR method, and that the multichord averaging method (MCAM) is more suitable for calculation of the centre coordinate than the circular regression method (CRM). Displacement measurement experiments were performed. The results show that the standard experimental deviation of the centre of the circle is approximately 0.009 µm, and the extended uncertainty of the displacement measurement in the range of 5 mm is approximately 0.03 μm. The data processing method studied in this study can be widely used in the field of F-P interferometry.
Optomechanical Design and Application of Solar-Skylight Spectroradiometer
Authors: Qi Zhaoyang , Li Jianyu , Xu Wenqing , Zhu Wenyue , Sun Fengying , Huang Yao , Xu Gang , Dai Congming
Published: May 2021

Using a solar radiometer is an effective approach for improving the remote sensing of solar irradiance distribution and atmospheric composition. Long-term development of a solar scanning radiometer enables frequent and reliable measurement of atmospheric parameters such as the water vapor column and aerosol optical properties. However, the discrete wavelength radiometer has encountered a bottleneck with respect to its insufficient spectral resolution and limited observation waveband, and it has been unable to satisfy the needs of refined and intelligent on-site experiments. This study proposes a solar-skylight spectroradiometer for obtaining visible and near-IR fine spectrum with two types of measurement: direct-sun irradiance and diffuse-sky radiance. The instrument adopts distributed control architecture composed of the ARM-Linux embedded platform and sensor networks. The detailed design of the measuring light-path, two-axis turntable, and master control system will be addressed in this study. To determine all coefficients needed to convert instrument outputs to physical quantities, integrating sphere and Langley extrapolation methods are introduced for diffuse-sky and direct-sun calibration, respectively. Finally, the agreement of experimental results between spectroradiometers and measuring benchmarks (DTF sun-photometer, microwave radiometer, and Combined Atmospheric Radiative Transfer simulation) verifies the feasibility of the spectroradiometer system, and the radiation information of feature wavelengths can be used to retrieve the characteristics of atmospheric optics.
A Scoping Review of Integrated Blockchain-Cloud (BcC) Architecture for Healthcare: Applications, Challenges and Solutions
Authors: Ismail Leila , Materwala Huned , Hennebelle Alain
Published: May 2021

Blockchain is a disruptive technology for shaping the next era of a healthcare system striving for efficient and effective patient care. This is thanks to its peer-to-peer, secure, and transparent characteristics. On the other hand, cloud computing made its way into the healthcare system thanks to its elasticity and cost-efficiency nature. However, cloud-based systems fail to provide a secured and private patient-centric cohesive view to multiple healthcare stakeholders. In this situation, blockchain provides solutions to address security and privacy concerns of the cloud because of its decentralization feature combined with data security and privacy, while cloud provides solutions to the blockchain scalability and efficiency challenges. Therefore a novel paradigm of blockchain-cloud integration (BcC) emerges for the domain of healthcare. In this paper, we provide an in-depth analysis of the BcC integration for the healthcare system to give the readers the motivations behind the emergence of this new paradigm, introduce a classification of existing architectures and their applications for better healthcare. We then review the development platforms and services and highlight the research challenges for the integrated BcC architecture, possible solutions, and future research directions. The results of this paper will be useful for the healthcare industry to design and develop a data management system for better patient care.
Magnetic Lateral Flow Immunoassay for Small Extracellular Vesicles Quantification: Application to Colorectal Cancer Biomarker Detection
Authors: Moyano Amanda , Serrano-Pertierra Esther , Duque José María , Ramos Virginia , Teruel-Barandiarán Estefanía , Fernández-Sánchez María Teresa , Salvador María , Martínez-García José Carlos , Sánchez Luis , García-Flórez Luis , Rivas Montserrat , Blanco-López María del Carmen
Published: May 2021

Colorectal cancer (CRC) is the third leading cause of cancer death and the fourth most common cancer in the world. Colonoscopy is the most sensitive test used for detection of CRC; however, their procedure is invasive and expensive for population mass screening. Currently, the fecal occult blood test has been widely used as a screening tool for CRC but displays low specificity. The lack of rapid and simple methods for mass screening makes the early diagnosis and therapy monitoring difficult. Extracellular vesicles (EVs) have emerged as a novel source of biomarkers due to their contents in proteins and miRNAs. Their detection would not require invasive techniques and could be considered as a liquid biopsy. Specifically, it has been demonstrated that the amount of CD147 expressed in circulating EVs is significant higher for CRC cell lines than for normal colon fibroblast cell lines. Moreover, CD147-containing EVs have been used as a biomarker to monitor response to therapy in patients with CRC. Therefore, this antigen could be used as a non-invasive biomarker for the detection and monitoring of CRC in combination with a Point-of-Care platform as, for example, Lateral Flow Immunoassays (LFIAs). Here, we propose the development of a quantitative lateral flow immunoassay test based on the use of magnetic nanoparticles as labels coupled to inductive sensor for the non-invasive detection of CRC by CD147-positive EVs. The results obtained for quantification of CD147 antigen embedded in EVs isolated from plasma sample have demonstrated that this device could be used as a Point-of-Care tool for CRC screening or therapy monitoring thanks to its rapid response and easy operation.
Real-Time High Resolution THz Imaging with a Fiber-Coupled Photo Conductive Antenna and an Uncooled Microbolometer Camera
Authors: Zolliker Peter , Shalaby Mostafa , Söllinger Elisa , Mavrona Elena , Hack Erwin
Published: May 2021

We present a real-time THz imaging method using a commercial fiber-coupled photo conductive antenna as the THz source and an uncooled microbolometer camera for detection. This new combination of state-of-the-art components is very adaptable due to its compact and uncooled radiation source, whose fiber coupling allows for a flexible placement. Using a camera with high sensitivity renders real-time imaging possible. As a proof-of-concept, the beam shape of a THz Time Domain Spectrometer was measured. We demonstrate real time imaging at nine frames per second and show its potential for practical applications in transmission geometry covering both material science and security tasks. The results suggest that hidden items, complex structures and the moisture content of (biological) materials can be resolved. We discuss the limits of the current setup, possible improvements and potential (industrial) applications, and we outline the feasibility of imaging in reflection geometry or extending it to multi-spectral imaging using band pass filters.
A High-Resolution Reflective Microwave Planar Sensor for Sensing of Vanadium Electrolyte
Authors: Kazemi Nazli , Schofield Kalvin , Musilek Petr
Published: May 2021

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at fres=6 GHz with an extra loss-compensating negative resistance that restores the dissipated power in the sensor that is used in dielectric material characterization. It is shown that the S11 notch of −15 dB can be improved down to −40 dB without loss of sensitivity. An application of this design is shown in discriminating different states of vanadium redox solutions with highly lossy conditions of fully charged V5+ and fully discharged V4+ electrolytes.
Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees
Authors: Alanazi Saad Awadh , Alruwaili Madallah , Ahmad Fahad , Alaerjan Alaa , Alshammari Nasser
Published: May 2021

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).
Remote Monitoring of Heart Failure in Patients with Implantable Cardioverter-Defibrillators: Current Status and Future Needs
Authors: Theuns Dominic A. M. J. , Radhoe Sumant P. , Brugts Jasper J.
Published: May 2021

The management of heart failure remains challenging despite evidence-based medical and pharmacological advances, especially in the ambulatory setting. There is an urgent need to develop strategies to reduce hospitalizations and readmission rates due to heart failure. Frequent monitoring of high-risk patients is imperative, and with the development of wireless and remote technology, frequent monitoring is now possible via remote monitoring. Nowadays, remote management of patients with cardiac implantable electronic devices is being increasingly adopted and integrated into clinical practice. Several clinical trials studied the impact of remote monitoring on clinical outcomes in patients with implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization defibrillators (CRT-Ds). This point of view will focus on the remote monitoring of ICDs and CRT-Ds in patients with heart failure and discusses whether remote monitoring can be used as a potential instrument for the early identification of patients at risk of worsening heart failure.
Development of Prototype Low-Cost QTSS™ Wearable Flexible More Enviro-Friendly Pressure, Shear, and Friction Sensors for Dynamic Prosthetic Fit Monitoring
Authors: Dejke Valter , Eng Mattias P. , Brinkfeldt Klas , Charnley Josephine , Lussey David , Lussey Chris
Published: May 2021

There is a current healthcare need for improved prosthetic socket fit provision for the masses using low-cost and simple to manufacture sensors that can measure pressure, shear, and friction. There is also a need to address society’s increasing concerns regarding the environmental impact of electronics and IoT devices. Prototype thin, low-cost, and low-weight pressure, shear, and loss of friction sensors have been developed and assembled for trans-femoral amputees. These flexible and conformable sensors are simple to manufacture and utilize more enviro-friendly novel magnetite-based QTSS™ (Quantum Technology Supersensor™) quantum materials. They have undergone some initial tests on flat and curved surfaces in a pilot amputee trial, which are presented in this paper. These initial findings indicate that the prototype pressure sensor strip is capable of measuring pressure both on flat and curved socket surfaces in a pilot amputee trial. They have also demonstrated that the prototype shear sensor can indicate increasing shear forces, the resultant direction of the shear forces, and loss of friction/slippage events. Further testing, amputee trials, and ongoing optimization is continuing as part of the SocketSense project to assist prosthetic comfort and fit.
Dynamic Optimization Method for Broadband ADCP Waveform with Environment Constraints
Authors: Yang Yongshou , Fang Shiliang
Published: May 2021

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.
LoPATraN: Low Power Asset Tracking by Means of Narrow Band IoT (NB-IoT) Technology
Authors: Parrino Stefano , Peruzzi Giacomo , Pozzebon Alessandro
Published: May 2021

The narrowband Internet-of-Things (NB-IoT) communication standard is gaining momentum within the big picture of the Internet-of-Things (IoT) owing to its capabilities of ensuring pervasive and wide coverage while limiting power consumption. Therefore, it turns out to be a valuable enabling technology within a considerable number of applications. Apart from traditional remote monitoring and data acquisition purposes where comparable Low Power Wide Area Network (LPWAN) facilities have ruled for years, NB-IoT can potentially carve out space within specific alcoves in which low latency, low power, high data-rates and ubiquitous coverage are fundamentals requirements. Long term asset tracking definitely falls within such niches, and in particular NB-IoT can become a valuable alternative to be exploited by both replacing the conventional Global Position System (GPS) system, or supporting it. To this end, this paper proposes an innovative tracking system prototype for asset shipping which relies on two enabling technologies: GPS and NB-IoT. While position transmission is always put into effect via NB-IoT, it can be fetched by resorting to both GPS (like a standard tracker) or NB-IoT (thus establishing a GPS-less method). As a result, two localization techniques are arranged: the former one is preciser but energy hungrier, while the latter one is coarser but more low power. Such working principles were successfully tested on the field by means of two road tests in as much itineraries. Tests results are in agreement with the expectations underlying the two working principles since the finer one provides a more accurate tracking. In addition, a consumption analysis was also performed aiming at assessing the prototype lifetime. Finally, tests pursuing the assessment of the tracking error were carried out underling the fact that it strongly depends on the geographic deployment of NB-IoT towers.
A Low-Cost Multi-Parameter Water Quality Monitoring System
Authors: Alam Arif Ul , Clyne Dennis , Deen M. Jamal
Published: May 2021

Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors’ signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.
Plasmonic Sensing Studies of a Gas-Phase Cystic Fibrosis Marker in Moisture Laden Air
Authors: Sun Libin , Conrad Douglas , Hall Drew A. , Benkstein Kurt D. , Semancik Steve , Zaghloul Mona E.
Published: May 2021

A plasmonic sensing platform was developed as a noninvasive method to monitor gas-phase biomarkers related to cystic fibrosis (CF). The nanohole array (NHA) sensing platform is based on localized surface plasmon resonance (LSPR) and offers a rapid data acquisition capability. Among the numerous gas-phase biomarkers that can be used to assess the lung health of CF patients, acetaldehyde was selected for this investigation. Previous research with diverse types of sensing platforms, with materials ranging from metal oxides to 2-D materials, detected gas-phase acetaldehyde with the lowest detection limit at the µmol/mol (parts-per-million (ppm)) level. In contrast, this work presents a plasmonic sensing platform that can approach the nmol/mol (parts-per-billion (ppb)) level, which covers the required concentration range needed to monitor the status of lung infection and find pulmonary exacerbations. During the experimental measurements made by a spectrometer and by a smartphone, the sensing examination was initially performed in a dry air background and then with high relative humidity (RH) as an interferent, which is relevant to exhaled breath. At a room temperature of 23.1 °C, the lowest detection limit for the investigated plasmonic sensing platform under dry air and 72% RH conditions are 250 nmol/mol (ppb) and 1000 nmol/mol (ppb), respectively.
Connected Skiing: Motion Quality Quantification in Alpine Skiing
Authors: Snyder Cory , Martínez Aaron , Jahnel Rüdiger , Roe Jason , Stöggl Thomas
Published: May 2021

Recent developments in sensing technology have made wearable computing smaller and cheaper. While many wearable technologies aim to quantify motion, there are few which aim to qualify motion. (2) To develop a wearable system to quantify motion quality during alpine skiing, IMUs were affixed to the ski boots of nineteen expert alpine skiers while they completed a set protocol of skiing styles, included carving and drifting in long, medium, and short radii. The IMU data were processed according to the previously published skiing activity recognition chain algorithms for turn segmentation, enrichment, and turn style classification Principal component models were learned on the time series variables edge angle, symmetry, radial force, and speed to identify the sources of variability in a subset of reference skiers. The remaining data were scored by comparing the PC score distributions of variables to the reference dataset. (3) The algorithm was able to differentiate between an expert and beginner skier, but not between an expert and a ski instructor, or a ski instructor and a beginner. (4) The scoring algorithm is a novel concept to quantify motion quality but is limited by the accuracy and relevance of the input data.
A Highly Sensitive Refractive Index Sensor Based on a V-Shaped Photonic Crystal Fiber with a High Refractive Index Range
Authors: Yan Xin , Fu Rao , Cheng Tonglei , Li Shuguang
Published: May 2021

This paper proposes a highly sensitive surface plasmon resonance (SPR) refractive index sensor based on the photonic crystal fiber (PCF). The optical properties of the PCF are investigated by modulating the refractive index of a liquid analyte. The finite element method (FEM) is used to calculate and analyze the PCF structure. After optimization, the fiber can achieve high linearity of 0.9931 and an average refractive index sensitivity of up to 14,771.4 nm/RIU over a refractive index range from 1.47 to 1.52, with the maximum wavelength sensitivity of 18,000.5 nm/RIU. The proposed structure can be used in various sensing applications, including biological monitoring, environmental monitoring, and chemical production with the modification and analysis of the proposed structure.
An IoT-Based Smart Home Automation System
Authors: Stolojescu-Crisan Cristina , Crisan Calin , Butunoi Bogdan-Petru
Published: May 2021

Home automation has achieved a lot of popularity in recent years, as day-to-day life is getting simpler due to the rapid growth of technology. Almost everything has become digitalized and automatic. In this paper, a system for interconnecting sensors, actuators, and other data sources with the purpose of multiple home automations is proposed. The system is called qToggle and works by leveraging the power of a flexible and powerful Application Programming Interface (API), which represents the foundation of a simple and common communication scheme. The devices used by qToggle are usually sensors or actuators with an upstream network connection implementing the qToggle API. Most devices used by qToggle are based on ESP8266/ESP8285 chips and/or on Raspberry Pi boards. A smartphone application has been developed that allows users to control a series of home appliances and sensors. The qToggle system is user friendly, flexible, and can be further developed by using different devices and add-ons.
Performance Study of a Zirconia-Doped Fiber for Distributed Temperature Sensing by OFDR at 800 °C
Authors: Bulot Patrick , Bernard Rémy , Cieslikiewicz-Bouet Monika , Laffont Guillaume , Douay Marc
Published: May 2021

Optical Frequency Domain Reflectometry (OFDR) is used to make temperature distributed sensing measurements along a fiber by exploiting Rayleigh backscattering. This technique presents high spatial and high temperature resolutions on temperature ranges of several hundred of degrees Celsius. With standard telecommunications fibers, measurement errors coming from the correlation between a high temperature Rayleigh trace and the one taken as a reference at room temperature could be present at extremely high temperatures. These correlation errors, due to low backscattering signal amplitude and unstable backscattering signal, induce temperature measurement errors. Thus, for high temperature measurement ranges and at extremely high temperatures (e.g., at 800 °C), a known solution is to use fibers with femtosecond laser inscribed nanograting. These fs-laser-insolated fibers have a high amplitude and thermally stable scattering signal, and they exhibit lower correlation errors. In this article, temperature sensing at 800 °C is reported by using an annealed zirconia-doped optical fiber with an initial 40.5-dB enhanced scattering signal. The zirconia-doped fiber presents initially OFDR losses of 2.8 dB/m and low OFDR signal drift at 800 °C. The ZrO2-doped fiber is an alternative to nanograting-inscribed fiber to make OFDR distributed fiber sensing on several meters with gauge lengths of 1 cm at high temperatures.
QMMAC: Quorum-Based Multichannel MAC Protocol for Wireless Sensor Networks
Authors: Alzahrani Eman , Bouabdallah Fatma
Published: May 2021

In wireless sensor networks, energy conservation is a critical task. Thus, it is crucial to design an effective MAC protocol that minimizes energy consumption while guaranteeing high network throughput and low delay. In this article, we propose a quorum-based multichannel MAC protocol (QMMAC) for corona-based WSNs. QMMAC utilizes the multichannel communication feature and the quorum concept to greatly increase the throughput while conserving energy. The aim of this protocol is to allow each node and all its forwarders to wake up at the same time while avoiding collision and overhearing by separating their simultaneous communications using the multichannel feature. More precisely, the main idea of QMMAC is twofold. First, QMMAC wakeup scheduling is designed to minimize the end-to-end delay by allowing nodes to wake up at exactly the same time as their potential forwarders, whereas nodes that are not acting as forwarders for each other wake up at a completely different time, and thus overhearing, idle listening and collisions are avoided. Second, channel assignment, which also uses the concept of quorums, is used to share data channels so that there is no conflict or additional packet exchange required to negotiate the availability of channels. Thus, the end-to-end delay is further minimized as well as collisions between conflicting neighbors are avoided. Simulation results indicate that the network performance is improved by QMMAC in terms of energy efficiency, throughput and end-to-end delay.
Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique
Authors: Choffin Zachary , Jeong Nathan , Callihan Michael , Olmstead Savannah , Sazonov Edward , Thakral Sarah , Getchell Camilee , Lombardi Vito
Published: May 2021

Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.
Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey
Authors: Alzate-Mejía Néstor , Santos-Boada Germán , de Almeida-Amazonas José Roberto
Published: May 2021

Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.
Zero-Shot Action Recognition with Three-Stream Graph Convolutional Networks
Authors: Wu Nan , Kawamoto Kazuhiko
Published: May 2021

Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive. This has encouraged research in zero-shot action recognition (ZSAR). Presently, most ZSAR methods recognize actions according to each video frame. These methods are affected by light, camera angle, and background, and most methods are unable to process time series data. The accuracy of the model is reduced owing to these reasons. In this paper, in order to solve these problems, we propose a three-stream graph convolutional network that processes both types of data. Our model has two parts. One part can process RGB data, which contains extensive useful information. The other part can process skeleton data, which is not affected by light and background. By combining these two outputs with a weighted sum, our model predicts the final results for ZSAR. Experiments conducted on three datasets demonstrate that our model has greater accuracy than a baseline model. Moreover, we also prove that our model can learn from human experience, which can make the model more accurate.