bluuebunny/crossref_metadata_2025 huggingface dataset:
A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR
Authors: Wang Wensheng , Gade Martin , Stelzer Kerstin , Kohlus Jörn , Zhao Xinyu , Fu Kun
Published: January 2021

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).
Multi-Block Mixed Sample Semi-Supervised Learning for SAR Target Recognition
Authors: Tian Ye , Sun Jianguo , Qi Pengyuan , Yin Guisheng , Zhang Liguo
Published: January 2021

In recent years, synthetic aperture radar (SAR) automatic target recognition has played a crucial role in multiple fields and has received widespread attention. Compared with optical image recognition with massive annotation data, lacking sufficient labeled images limits the performance of the SAR automatic target recognition (ATR) method based on deep learning. It is expensive and time-consuming to annotate the targets for SAR images, while it is difficult for unsupervised SAR target recognition to meet the actual needs. In this situation, we propose a semi-supervised sample mixing method for SAR target recognition, named multi-block mixed (MBM), which can effectively utilize the unlabeled samples. During the data preprocessing stage, a multi-block mixed method is used to interpolate a small part of the training image to generate new samples. Then, the new samples are used to improve the recognition accuracy of the model. To verify the effectiveness of the proposed method, experiments are carried out on the moving and stationary target acquisition and recognition (MSTAR) data set. The experimental results fully demonstrate that the proposed MBM semi-supervised learning method can effectively address the problem of annotation insufficiency in SAR data sets and can learn valuable information from unlabeled samples, thereby improving the recognition performance.
Identification of the Characteristic Scale of Fine Ground Objects: A Case Study of the Core Observation Area in the Middle Reaches of the Heihe River Basin
Authors: Wu Xiuyi , Yu Wenping , Shi Jinan , Ma Mingguo , Li Xiaolu , Wu Wenjian
Published: January 2021

Capturing the spatial heterogeneity and characteristic scale is the key to determining the spatial patterns of land surfaces. In this research, the core observation area of the middle reaches of the Heihe River Basin was selected as the study area, and the scale identification of several typical objects was carried out by implementing experiments on moderate- and high-resolution remotely sensed ASTER and CASI NDVI images. The aim was to evaluate the potential of the local variance and semivariance analysis to characterize the spatial heterogeneity of objects, track their changes with scale, and obtain their scales. Our results show that natural objects have multiscale structures. For a single object with a recognizable size, the results of the two methods are relatively consistent. For continuously distributed samples of indistinctive size, the scale obtained by the local variance is smaller than that obtained by the semivariance. As the image resolution becomes coarser and the research scopes expand, the scales of objects are also increasing. This article also indicates that with a small research area of uniform objects, the local variance and semivariance are easy to facilitate researchers to quickly select the appropriate spatial resolution of remote sensing data according to the research area.
Climatology of Cloud Phase, Cloud Radiative Effects and Precipitation Properties over the Tibetan Plateau
Authors: Wang Jing , Jian Bida , Wang Guoyin , Zhao Yuxin , Li Yarong , Letu Husi , Zhang Min , Li Jiming
Published: January 2021

Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season.
A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia
Authors: Wang Ying , Gu Xingfa , Li Jian , Mi Xiaofei
Published: January 2021

A NOAA/AVHRR dual-channel method over land is proposed to simultaneously retrieve aerosol optical depth (AOD) at 0.55 μm, and surface reflectance at 0.63 and 0.85 μm. Compared with previous well-established one-channel retrieval algorithms, this algorithm takes advantage of the surface reflectance ratio between 0.63 and 0.85 μm in an attempt to account for the effect induced by the surface bidirectional reflectance distribution function (BRDF). This effect cannot be negligible due to the orbit drift and phasing running of NOAA satellites, both of which potentially cause large solar angular variation. Meanwhile, the observation posture change of AVHRR would cause large sensor angular variation in time series measurements. The used surface reflectance ratio based on dual channels at 0.63 and 0.85 μm is found more reasonable to be assumed as unchanged during a certain period of time, compared to the traditional ratio when addressing the BRDF issue. AOD retrievals have been carried out over Eastern Asia. Validation against aerosol robotic network (AERONET) measurements shows that up to 83% of AOD validation collocations are within error lines (±0.15 ± 0.15τ, τ is AOD) with an R of 0.88 and an root mean square error (RMSE) of 0.15. The dual-channel algorithm taking into account the surface BRDF effect is proved outperforming the conventional 0.63 μm-channel method. It indicates that our algorithm has the potential to be applied to the retrieval of long series of AOD, especially to the AOD retrieval of the sensors which lack a shortwave infrared channel required in the MODerate resolution Imaging Spectroradiometer (MODIS) dark target AOD algorithm.
Remote Sensing Applications for Landslide Monitoring and Investigation in Western Canada
Authors: Macciotta Renato , Hendry Michael T.
Published: January 2021

Transportation infrastructure in mountainous terrain and through river valleys is exposed to a variety of landslide phenomena. This is particularly the case for highway and railway corridors in Western Canada that connect towns and industries through prairie valleys and the Canadian cordillera. The fluidity of these corridors is important for the economy of the country and the safety of workers, and users of this infrastructure is paramount. Stabilization of all active slopes is financially challenging given the extensive area where landslides are a possibility, and monitoring and minimization of slope failure consequences becomes an attractive risk management strategy. In this regard, remote sensing techniques provide a means for enhancing the monitoring toolbox of the geotechnical engineer. This includes an improved identification of active landslides in large areas, robust complement to in-place instrumentation for enhanced landslide investigation, and an improved definition of landslide extents and deformation mechanisms. This paper builds upon the extensive literature on the application of remote sensing techniques and discusses practical insights gained from a suite of case studies from the authors’ experience in Western Canada. The review of the case studies presents a variety of landslide mechanisms and remote sensing technologies. The aim of the paper is to transfer some of the insights gained through these case studies to the reader.
A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds
Authors: Ji Mengyu , Peng Gaoliang , He Jun , Liu Shaohui , Chen Zhao , Li Sijue
Published: January 2021

When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.
IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks
Authors: Zgank Andrej
Published: January 2021

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.
A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision
Authors: Escalona José L. , Urda Pedro , Muñoz Sergio
Published: January 2021

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.
Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning
Authors: Jeon Byoungil , Kim Junha , Lee Eunjoong , Moon Myungkook , Cho Gyuseong
Published: January 2021

Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties.
Numerical Study of the Coupling of Sub-Terahertz Radiation to n-Channel Strained-Silicon MODFETs
Authors: Calvo-Gallego Jaime , Delgado-Notario Juan A. , Velázquez-Pérez Jesús E. , Ferrando-Bataller Miguel , Fobelets Kristel , Moussaouy Abdelaziz El , Meziani Yahya M.
Published: January 2021

This paper reports on a study of the response of a T-gate strained-Si MODFETs (modulation-doped field-effect transistor) under continuous-wave sub-THz excitation. The sub-THz response was measured using a two-tones solid-state source at 0.15 and 0.30 THz. The device response in the photovoltaic mode was non-resonant, in agreement with the Dyakonov and Shur model for plasma waves detectors. The maximum of the photoresponse was clearly higher under THz illumination at 0.15 THz than at 0.3 THz. A numerical study was conducted using three-dimensional (3D) electromagnetic simulations to delve into the coupling of THz radiation to the channel of the transistor. 3D simulations solving the Maxwell equations using a time-domain solver were performed. Simulations considering the full transistor structure, but without taking into account the bonding wires used to contact the transistor pads in experiments, showed an irrelevant role of the gate length in the coupling of the radiation to the device channel. Simulations, in contradiction with measurements, pointed to a better response at 0.3 THz than under 0.15 THz excitation in terms of the normalized electric field inside the channel. When including four 0.25 mm long bonding wires connected to the contact pads on the transistor, the normalized internal electric field induced along the transistor channel by the 0.15 THz beam was increased in 25 dB, revealing, therefore, the important role played by the bonding wires at this frequency. As a result, the more intense response of the transistor at 0.15 THz than at 0.3 THz experimentally found, must be attributed to the bonding wires.
A CNN-Based Length-Aware Cascade Road Damage Detection Approach
Authors: Xu Huiqing , Chen Bin , Qin Jian
Published: January 2021

Accurate and robust detection of road damage is essential for public transportation safety. Currently, deep convolutional neural networks (CNNs)-based road damage detection algorithms to localize and classify damage with a bounding box have achieved remarkable progress. However, research in this field fails to take into account two key characteristics of road damage: weak semantic information and abnormal geometric properties, resulting in inappropriate feature representation and suboptimal detection results. To boost the performance, we propose a CNN-based cascaded damage detection network, called CrdNet. The proposed model has three parts: (1) We introduce a novel backbone network, named LrNet, that reuses low-level features and mixes suitable range dependency features to learn high-to-low level feature fusions for road damage weak semantic information representation. (2) We apply multi-scale and multiple aspect ratios anchor mechanism to generate high-quality positive samples regarding the damage with abnormal geometric properties for network training. (3) We designed an adaptive proposal assignment strategy and performed cascade predictions on corresponding branches that can establish different range dependencies. The experiments show that the proposed method achieves mean average precision (mAP) of 90.92% on a collected road damage dataset, demonstrating the good performance and robustness of the model.
Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks
Authors: Isolani Pedro H. , Kulenkamp Daniel J. , Marquez-Barja Johann M. , Granville Lisandro Z. , Latré Steven , Syrotiuk Violet R.
Published: January 2021

With the emergence of 5G networks and the stringent Quality of Service (QoS) requirements of Mission-Critical Applications (MCAs), co-existing networks are expected to deliver higher-speed connections, enhanced reliability, and lower latency. IEEE 802.11 networks, which co-exist with 5G, continue to be the access choice for indoor networks. However, traditional IEEE 802.11 networks lack sufficient reliability and they have non-deterministic latency. To dynamically control resources in IEEE 802.11 networks, in this paper we propose a delay-aware approach for Medium Access Control (MAC) management via airtime-based network slicing and traffic shaping, as well as user association while using Multi-Criteria Decision Analysis (MCDA). To fulfill the QoS requirements, we use Software-Defined Networking (SDN) for airtime-based network slicing and seamless handovers at the Software-Defined Radio Access Network (SD-RAN), while traffic shaping is done at the Stations (STAs). In addition to throughput, channel utilization, and signal strength, our approach monitors the queueing delay at the Access Points (APs) and uses it for centralized network management. We evaluate our approach in a testbed composed of APs controlled by SD-RAN and SDN controllers, with STAs under different workload combinations. Our results show that, in addition to load balancing flows across APs, our approach avoids the ping-pong effect while enhancing the QoS delivery at runtime. Under varying traffic demands, our approach maintains the queueing delay requirements of 5 ms for most of the experiment run, hence drawing closer to MCA requirements.
Industry 4.0 towards Forestry 4.0: Fire Detection Use Case
Authors: Sahal Radhya , Alsamhi Saeed H. , Breslin John G. , Ali Muhammad Intizar
Published: January 2021

Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution. It is ushering in a new era for efficient and sustainable forest management. Environmental sustainability and climate change are related challenges to promote sustainable forest management of natural resources. Internet of Forest Things (IoFT) is an emerging technology that helps manage forest sustainability and protect forest from hazards via distributing smart devices for gathering data stream during monitoring and detecting fire. Stream processing is a well-known research area, and recently, it has gained a further significance due to the emergence of IoFT devices. Distributed stream processing platforms have emerged, e.g., Apache Flink, Storm, and Spark, etc. Querying windowing is the heart of any stream-processing platform which splits infinite data stream into chunks of finite data to execute a query. Dynamic query window-based processing can reduce the reporting time in case of missing and delayed events caused by data drift.In this paper, we present a novel dynamic mechanism to recommend the optimal window size and type based on the dynamic context of IoFT application. In particular, we designed a dynamic window selector for stream queries considering input stream data characteristics, application workload and resource constraints to recommend the optimal stream query window configuration. A research gap on the likelihood of adopting smart IoFT devices in environmental sustainability indicates a lack of empirical studies to pursue forest sustainability, i.e., sustainable forestry applications. So, we focus on forest fire management and detection as a use case of Forestry 4.0, one of the dynamic environmental management challenges, i.e., climate change, to deliver sustainable forestry goals. According to the dynamic window selector’s experimental results, end-to-end latency time for the reported fire alerts has been reduced by dynamical adaptation of window size with IoFT stream rate changes.
Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement
Authors: Chen Haipeng , Xie Zeyu , Huang Yongping , Gai Di
Published: January 2021

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.
Parameter Determination and Ion Current Improvement of the Ion Current Sensor Used for Flame Monitoring
Authors: Xu Hanqing , Fan Weijun , Feng Jianwei , Yan Peiliang , Qi Shuchan , Zhang Rongchun
Published: January 2021

Flame monitoring of industrial combustors with high-reliability sensors is essential to operation security and performance. An ion current flame sensor with a simple structure has great potential to be widely used, but a weak ion current is the critical defect to its reliability. In this study, parameters of the ion current sensor used for monitoring flames on a Bunsen burner are suggested, and a method of further improving the ion current is proposed. Effects of the parameters, including the excitation voltage, electrode area, and electrode radial and vertical positions on the ion current, were investigated. The ion current grew linearly with the excitation voltage. Given that the electrodes were in contact with the flame fronts, the ion current increased with the contact area of the cathode but independent of the contact area of the anode. The smaller electrode radial position resulted in a higher ion current. The ion current was insensitive to the anode vertical position but largely sensitive to the cathode vertical position. Based on the above ion current regularities, the sensor parameters were suggested as follows: The burner served as a cathode and the platinum wire acted as an anode. The excitation voltage, anode radial and vertical positions were 120 V, 0 mm, and 6 mm, respectively. The method of further improving the ion current by adding multiple sheet cathodes near the burner exit was proposed and verified. The results show that the ion current sensor with the suggested parameters could correctly identify the flame state, including the ignition, combustion, and extinction, and the proposed method could significantly improve the magnitude of the ion current.
Development of an Automated Optical Inspection System for Rapidly and Precisely Measuring Dimensions of Embedded Microchannel Structures in Transparent Bonded Chips
Authors: Chen Pin-Chuan , Lin Ya-Ting , Truong Chi-Minh , Chen Pai-Shan , Chiang Huihua-Kenny
Published: January 2021

This study aimed to develop an automated optical inspection (AOI) system that can rapidly and precisely measure the dimensions of microchannels embedded inside a transparent polymeric substrate, and can eventually be used on the production line of a factory. The AOI system is constructed based on Snell’s law. The concept holds that, when light travels through two transparent media (air and the microfluidic chip transparent material), by capturing the parallel refracted light from a light source that went through the microchannel using a camera with a telecentric lens, the image can be analyzed using formulas derived from Snell’s law to measure the dimensions of the microchannel cross-section. Through the NI LabVIEW 2018 SP1 programming interface, we programmed this system to automatically analyze the captured image and acquire all the needed data. The system then processes these data using custom-developed formulas to calculate the height and width measurements of the microchannel cross-sections and presents the results on the human–machine interface (HMI). In this study, a single and straight microchannel with a cross-sectional area of 300 μm × 300 μm and length of 44 mm was micromachined and sealed with another polymeric substrate by a solvent bonding method for experimentations. With this system, 45 cross-sectional areas along the straight microchannel were measured within 20 s, and experiment results showed that the average measured error was less than 2%.
How to Quantitatively Balance a Total Knee? A Surgical Algorithm to Assure Balance and Control Alignment
Authors: Moore Ryan E. , Conditt Michael A. , Roche Martin W. , Verstraete Matthias A.
Published: January 2021

To achieve a balanced total knee, various surgical corrections can be performed, while intra-operative sensors and surgical navigation provide quantitative, patient-specific feedback. To understand the impact of these corrections, this paper evaluates the quantitative impact of both soft tissue releases and bone recuts on knee balance and overall limb alignment. This was achieved by statistically analyzing the alignment and load readings before and after each surgical correction performed on 479 consecutive primary total knees. An average of three surgical corrections were required following the initial bone cuts to achieve a well aligned, balanced total knee. Various surgical corrections, such as an arcuate release or increasing the tibial polyethylene insert thickness, significantly affected the maximum terminal extension. The coronal alignment was significantly impacted by pie-crusting the MCL, adding varus to the tibia, or releasing the arcuate ligament or popliteus tendon. Each surgical correction also had a specific impact on the intra-articular loads in flexion and/or extension. A surgical algorithm is presented that helps achieve a well-balanced knee while maintaining the sagittal and coronal alignment within the desired boundaries. This analysis additionally indicated the significant effect that soft tissue adjustments can have on the limb alignment in both anatomical planes.
Joint Image Encryption and Screen-Cam Robust Two Watermarking Scheme
Authors: Chen Weitong , Ren Na , Zhu Changqing , Keskinarkaus Anja , Seppänen Tapio , Zhou Qifei
Published: January 2021

This paper proposes a joint encryption and screen-cam robust watermarking scheme. This method combines the advantages of smartphone, encryption and watermarking technologies, thereby achieving watermark extraction with a smartphone, partial decryption and tracking leakage from sneak shots. We design a dual watermarking algorithm to achieve watermark detection from both encrypted and decrypted images. First, a watermark is embedded in the discrete Fourier transform (DFT) domain to enable leakage tracking. Then, a second watermark is generated based on QR (Quick response) code encoding and inverse DFT to achieve high watermark capacity and error correction ability, where the secret key for decryption is included in the watermark message. By hiding this message carrying the watermark for the encrypted image in the changes caused by embedding the first watermark, we can improve imperceptibility and will not affect the effectiveness of the proposed scheme. Finally, to enhance the robustness of watermark after encryption, a chaotic mapping-based segment encryption algorithm is proposed. In the process of watermark detection, to cope with perspective correction, a frame locating based algorithm is employed to achieve watermark synchronization from a recaptured picture of the encrypted image. Considering the severe quality degradation, we use a noise component and local statistic feature-based method to extract the message bits. The experimental results show that the proposed scheme is secure, and highly robust, to screen-cam the process for both before and after decryption. Additionally, after decryption, the proposed scheme also has high robustness against common image processing attacks.
Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks
Authors: Ali Khurshed , Li Cheng-Te , Chen Yi-Shin
Published: January 2021

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.
An Electrochemical Amperometric Ethylene Sensor with Solid Polymer Electrolyte Based on Ionic Liquid
Authors: Kuberský Petr , Navrátil Jiří , Syrový Tomáš , Sedlák Petr , Nešpůrek Stanislav , Hamáček Aleš
Published: January 2021

An electrochemical amperometric ethylene sensor with solid polymer electrolyte (SPE) and semi-planar three electrode topology involving a working, pseudoreference, and counter electrode is presented. The polymer electrolyte is based on the ionic liquid 1-butyl 3-methylimidazolium bis(trifluoromethylsulfonyl)imide [BMIM][NTf2] immobilized in a poly(vinylidene fluoride) matrix. An innovative aerosol-jet printing technique was used to deposit the gold working electrode (WE) on the solid polymer electrolyte layer to make a unique electrochemical active SPE/WE interface. The analyte, gaseous ethylene, was detected by oxidation at 800 mV vs. the platinum pseudoreference electrode. The sensor parameters such as sensitivity, response/recovery time, repeatability, hysteresis, and limits of detection and quantification were determined and their relation to the morphology and microstructure of the SPE/WE interface examined. The use of additive printing techniques for sensor preparation demonstrates the potential of polymer electrolytes with respect to the mass production of printed electrochemical gas sensors.
Security Privacy and Policy for Cryptographic Based Electronic Medical Information System
Authors: Chen Hsuan-Yu , Wu Zhen-Yu , Chen Tzer-Long , Huang Yao-Min , Liu Chia-Hui
Published: January 2021

With the development of the internet, applications have become complicated, and the relevant technology has diversified. Compared with medical applications, the significance of information technology has been expanding to include clinical auxiliary functions of medical information. This includes electronic medical records, electronic prescriptions, medical information systems, etc. Although research on the data processing structure and format of various related systems is becoming mature, the integration is insufficient. An integrated medical information system with security policy and privacy protection, which combines e-patient records, e-prescriptions, modified smart cards, and fingerprint identification systems, and applies proxy signature and group signature, is proposed in this study. This system effectively applies and saves medical resources—satisfying the mobility of medical records, presenting the function, and security of medicine collection, and avoiding medical conflicts and profiteering to further acquire the maximum effectiveness with the least resources. In this way, this medical information system may be developed into a comprehensive function that eliminates the transmission of manual documents and maintains the safety of patient medical information. It can improve the quality of medical care and indispensable infrastructure for medical management.
Fundamental Limits of Non-Orthogonal Multiple Access (NOMA) for the Massive Gaussian Broadcast Channel in Finite Block-Length
Authors: Gorce Jean-Marie , Mary Philippe , Anade Dadja , Kélif Jean-Marc
Published: January 2021

Superposition coding (SC) has been known to be capacity-achieving for the Gaussian memoryless broadcast channel for more than 30 years. However, SC regained interest in the context of non-orthogonal multiple access (NOMA) in 5G. From an information theory point of view, SC is capacity-achieving in the broadcast Gaussian channel, even when the number of users tends to infinity. However, using SC has two drawbacks: the decoder complexity increases drastically with the number of simultaneous receivers, and the latency is unbounded since SC is optimal only in the asymptotic regime. To evaluate these effects quantitatively in terms of fundamental limits, we introduce a finite time transmission constraint imposed at the base station, and we evaluate fundamental trade-offs between the maximal number of superposed users, the coding block-length and the block error probability. The energy efficiency loss due to these constraints is evaluated analytically and by simulation. Orthogonal sharing appears to outperform SC for hard delay constraints (equivalent to short block-length) and in low spectral efficiency regime (below one bit per channel use). These results are obtained by the association of stochastic geometry and finite block-length information theory.
Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs
Authors: Shan Axida , Fan Xiumei , Wu Celimuge , Zhang Xinghui , Fan Shujia
Published: January 2021

Cooperative communication and resource limitation are two main characteristics of mobile ad hoc networks (MANETs). On one hand, communication among the nodes in MANETs highly depends on the cooperation among nodes because of the limited transmission range of the nodes, and multi-hop communications are needed in most cases. On the other hand, every node in MANETs has stringent resource constraints on computations, communications, memory, and energy. These two characteristics lead to the existence of selfish nodes in MANETs, which affects the network performance in various aspects. In this paper, we quantitatively investigate the impacts of node selfishness caused by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. We conducted extensive measurements on a proper simulation platform incorporating an OMNeT++ and INET Framework. Our experimental results quantitatively indicate the impact of node selfishness on the network performance in MANETs. The results also imply that it is important to evaluate the impact of node selfishness by jointly considering selfish nodes’ mobility models, densities, proportions, and combinations.
Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †
Authors: Shamsfakhr Farhad , Motroni Andrea , Palopoli Luigi , Buffi Alice , Nepa Paolo , Fontanelli Daniele
Published: January 2021

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.
Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole
Authors: Ayena Johannes C. , Chioukh Lydia , Otis Martin J.-D. , Deslandes Dominic
Published: January 2021

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.
Combined SPRi Sensor for Simultaneous Detection of Nitrate and Ammonium in Wastewater
Authors: Vráblová Martina , Koutník Ivan , Smutná Kateřina , Marková Dominika , Veverková Nikola
Published: January 2021

Water pollution is a serious problem in modern society. Agriculture, being responsible for the discharge of agrochemicals, organic matter, or drug residues, produces a huge amount of wastewater. Aquaponics has the potential to reduce both water consumption and the impact of water pollution on fish farming and plant production. In the aquatic environment, inorganic nitrogen is mostly present in the form of nitrate and ammonium ions. Nitrate, as a final product of ammonia mineralization, is the most common chemical contaminant in aquifers around the world. For continuous monitoring of nitrogen compounds in wastewater, we propose a sensor for the simultaneous detection of nitrate and ammonium. A surface plasmon resonance imaging method with enzyme-mediated detection was used. Active layers of nitrate reductase and glutamine synthetase were created on the gold surface of a biochip and tested for the sensing of nitrate and ammonium in water from an aquaponic system. The proposed sensor was applied in water samples with a concentration of NO3− and NH4+ in a range between 24–780 mg·L−1 and 0.26–120 mg·L−1, respectively, with minimal pretreatment of a sample by its dilution with a buffer prior to contact on a biochip surface.
New Omnidirectional Sensor Based on Open-Source Software and Hardware for Tracking and Backtracking of Dual-Axis Solar Trackers in Photovoltaic Plants
Authors: Gómez-Uceda Francisco J. , Ramirez-Faz José , Varo-Martinez Marta , Fernández-Ahumada Luis Manuel
Published: January 2021

In this work, an omnidirectional sensor that enables identification of the direction of the celestial sphere with maximum solar irradiance is presented. The sensor, based on instantaneous measurements, functions as a position server for dual-axis solar trackers in photovoltaic plants. The proposed device has been developed with free software and hardware, which makes it a pioneering solution because it is open and accessible as well as capable of being improved by the scientific community, thereby contributing to the rapid advancement of technology. In addition, the device includes an algorithm developed ex professo that makes it possible to predetermine the regions of the celestial sphere for which, according to the geometric characteristics of the PV plant, there would be shading between the panels. In this way, solar trackers do not have to locate the Sun’s position at all times according to astronomical models, while taking into account factors such as shadows or cloudiness that also affect levels of incident irradiance on solar collectors. Therefore, with this device, it is possible to provide photovoltaic plants with dual-axis solar tracking with a low-cost device that helps to optimise the trajectory of the trackers and, consequently, their radiative capture and energy production.
Comparative Analyses of Parameters Influencing Children Pedestrian Behavior in Conflict Zones of Urban Intersections
Authors: Deluka-Tibljaš Aleksandra , Ištoka Otković Irena , Campisi Tiziana , Šurdonja Sanja
Published: January 2021

Children pedestrians make up 30% of the total number of children injured in road traffic in the EU. Research shows that children are injured more often in the urban areas, in residential areas near schools and parks, often at intersections and pedestrian crossings. In this study, children’s traffic behavior was analyzed by observation of signalized pedestrian crosswalks. According to the same methodology, the research was conducted in three cities in two countries (Enna, Italy, Osijek and Rijeka, Croatia) with different urban and traffic characteristics. A total of 900 measurements were analyzed, 300 in each of the cities at 18 pedestrian crosswalks located in an urban setting in the vicinity of primary schools. A detailed statistical analysis of the influence parameters shows that, as general influence parameters, pedestrian crosswalk length, movement in a group and the age of children can be distinguished. Factors that have proven to have a significant influence on the movement of children in two of the three cities observed are gender, supervision by adults, running and cellphone use. The result can serve as a valuable input for interventions in traffic education as well as a basis for the improvement of traffic conditions at intersections where children are regularly present.
Porophyllum Genus Compounds and Pharmacological Activities: A Review
Authors: Vázquez-Atanacio María José , Bautista-Ávila Mirandeli , Velázquez-González Claudia , Castañeda-Ovando Araceli , González-Cortazar Manasés , Sosa-Gutiérrez Carolina Guadalupe , Ojeda-Ramírez Deyanira
Published: January 2021

The genus Porophyllum (family Asteraceae) is native to the western hemisphere, growing in tropical and subtropical North and South America. Mexico is an important center of diversification of the genus. Plants belong of genus Porophyllum have been used in Mexican traditional medicine to treat kidney and intestinal diseases, parasitic, bacterial, and fungal infections and anti-inflammatory and anti-nociceptive activities. In this sense, several trials have been made on its chemical and in vitro and in vivo pharmacological activities. These studies were carried on the extracts and isolated compounds and support most of their reported uses in folk medicine as antifungal, antileishmanial, anti-inflammatory, anti-nociceptive and burn repair activities, and as a potential source of new class of insecticides. Bio guided phytochemical studies showed the isolation of thiophenes, terpenes and phenolics compounds, which could be responsible for the pharmacological activities. However, more pre-clinical assays that highlight the mechanisms of action of the compounds involved in pharmacological function are lacking. This review discusses the current knowledge of their chemistry, in vitro and in vivo pharmacological activities carried out on the plants belonging to the Porophyllum genus.