bluuebunny/crossref_metadata_2025 huggingface dataset:
An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation
Authors: Liu Lutao , Rao Zejing
Published: February 2022

In this paper, we propose a new direction of arrival (DOA) estimation algorithm, in which DOA estimation is achieved by finding the sparsest support set of multiple measurement vectors (MMV) in an over-complete dictionary. The proposed algorithm is based on ℓp norm minimization, which belongs to non-convex optimization. Therefore, the quasi-Newton method is used to converge the iterative process. There are two advantages of this algorithm: one is the higher possibility and resolution of distinguishing closely spaced sources, and the other is the adaptive regularization parameter adjustment. Moreover, an accelerating strategy is applied in the computation, and a weighted method of the proposed algorithm is also introduced to improve the accuracy. We conducted experiments to validate the effectiveness of the proposed algorithm. The performance was compared with several popular DOA estimation algorithms and the Cramer–Rao bound (CRB).
Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection
Authors: Yang Nachuan , Zhao Yongjun , Chen Jinyang
Published: February 2022

Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy.
Opto-Microfluidic Integration of the Bradford Protein Assay in Lithium Niobate Lab-on-a-Chip
Authors: Zanini Leonardo , Zaltron Annamaria , Turato Enrico , Zamboni Riccardo , Sada Cinzia
Published: February 2022

This paper deals with the quantification of proteins by implementing the Bradford protein assay method in a portable opto-microfluidic platform for protein concentrations lower than 1.4 mg/mL. Absorbance is measured by way of optical waveguides integrated to a cross-junction microfluidic circuit on a single lithium niobate substrate. A new protocol is proposed to perform the protein quantification based on the high correlation of the light absorbance at 595 nm, as commonly used in the Bradford method, with the one achieved at 633 nm with a cheap commercially available diode laser. This protocol demonstrates the possibility to quantify proteins by using nL volumes, 1000 times less than the standard technique such as paper-analytical devices. Moreover, it shows a limit of quantification of at least 0.12 mg/mL, which is four times lower than the last literature, as well as a better accuracy (98%). The protein quantification is obtained either by using one single microfluidic droplet as well by performing statistical analysis over ensembles of several thousands of droplets in less than 1 min. The proposed methodology presents the further advantage that the protein solutions can be reused for other investigations and the same pertains to the opto-microfluidic platform.
In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks
Authors: Bąk Beata , Szkoła Jarosław , Wilk Jakub , Artiemjew Piotr , Wilde Jerzy
Published: February 2022

American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether an electronic nose can distinguish between colonies affected by American foulbrood and healthy ones. The experiment was conducted in an apiary with 18 bee families, 9 of which showed symptoms of the disease confirmed by laboratory diagnostics. Three units of the Beesensor V.2 device based on an array of six semiconductor TGS gas sensors, manufactured by Figaro, were tested. Each copy of the device was tested in all bee colonies: sick and healthy. The measurement session per bee colony lasted 40 min and yielded results from four 10 min measurements. One 10-min measurement consisted of a 5 min regeneration phase and a 5 min object-measurement phase. For the experiments, we used both classical classification methods such as k-nearest neighbour, Naive Bayes, Support Vector Machine, discretized logistic regression, random forests, and committee of classifiers, that is, methods based on extracted representative data fragments. We also used methods based on the entire 600 s series, in this study of sequential neural networks. We considered, in this study, six options for data preparation as part of the transformation of data series into representative results. Among others, we used single stabilised sensor readings as well as average values from stable areas. For verifying the quality of the classical classifiers, we used the 25-fold train-and-test method. The effectiveness of the tested methods reached a threshold of 75 per cent, with results stable between 65 and 70 per cent. As an element to confirm the possibility of class separation using an artificial nose, we used applied visualisations of classes. It is clear from the experiments conducted that the artificial nose tested has practical potential. Our experiments show that the approach to the problem under study by sequential network learning on a sequence of data is comparable to the best classical methods based on discrete data samples. The results of the experiment showed that the Beesensor V.2 along with properly selected classification techniques can become a tool to facilitate rapid diagnosis of American foulbrood under field conditions.
A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios
Authors: Nadeem Adnan , Ashraf Muhammad , Rizwan Kashif , Qadeer Nauman , AlZahrani Ali , Mehmood Amir , Abbasi Qammer H.
Published: February 2022

The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person’s profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person’s identification in an extremely challenging large-gathering scenario.
Patterns in Dried Droplets to Detect Unfolded BSA
Authors: Carreón Yojana J. P. , Gómez-López Mary Luz , Díaz-Hernández Orlando , Vazquez-Vergara Pamela , Moctezuma Rosario E. , Saniger José M. , González-Gutiérrez Jorge
Published: February 2022

The morphological analysis of patterns in dried droplets has allowed the generation of efficient techniques for the detection of molecules of medical interest. However, the effectiveness of this method to reveal the coexistence of macromolecules of the same species, but different conformational states, is still unknown. To address this problem, we present an experimental study on pattern formation in dried droplets of bovine serum albumin (BSA), in folded and unfolded conformational states, in saline solution (NaCl). Folded proteins produce a well-defined coffee ring and crystal patterns all over the dry droplet. Depending on the NaCl concentration, the crystals can be small, large, elongated, entangled, or dense. Optical microscopy reveals that the relative concentration of unfolded proteins determines the morphological characteristics of deposits. At a low relative concentration of unfolded proteins (above 2%), small amorphous aggregates emerge in the deposits, while at high concentrations (above 16%), the “eye-like pattern”, a large aggregate surrounded by a uniform coating, is produced. The radial intensity profile, the mean pixel intensity, and the entropy make it possible to characterize the patterns in dried droplets. We prove that it is possible to achieve 100% accuracy in identifying 4% of unfolded BSA contained in a protein solution.
Modeling and Verification of Asynchronous Systems Using Timed Integrated Model of Distributed Systems
Authors: Daszczuk Wiktor B.
Published: February 2022

In modern computer systems, distributed systems play an increasingly important role, and modeling and verification are crucial in their development. The specificity of many systems requires taking this into account in real time, as time dependencies significantly affect the system’s behavior, when achieving the goals of its processes or with adverse phenomena such as deadlocks. The natural features of distributed systems include the asynchrony of actions and communication, the autonomy of nodes, and the locality of behavior, i.e., independence from any global or non-local features. Most modeling formalisms are derived from parallel centralized systems, in which the behavior of components depends on the global state or the simultaneous achievement of certain states by components. This approach is unrealistic for distributed systems. This article presents the formalism of a timed integrated model of distributed systems that supports all of the mentioned features. The formalism is based on the relation between the states of the distributed nodes and the messages of distributed computations, called agents. This relation creates system actions. A specification in this formalism can be translated into timed automata, the most popular formalism for specifying and verifying timed parallel systems. The translation rules ensure that the semantics of T-IMDS and timed automata are consistent, allowing use of the Uppaal validator for system verification. The development of general formulas for checking the deadlock freedom and termination efficiency allows for automated verification, without learning temporal logics and time-dependent formulas. An important and rare feature is the finding of partial deadlocks, because in a distributed system a common situation occurs in which some nodes/processes are deadlocked, while others work. Examples of checking timed distributed systems are included.
Mixed Reality-Based Interaction between Human and Virtual Cat for Mental Stress Management
Authors: Na Heewon , Park Soyeon , Dong Suh-Yeon
Published: February 2022

Human–animal interaction (HAI) has been observed to effectively reduce stress and induce positive emotions owing to the process of directly petting and interacting with animals. Interaction with virtual animals has recently emerged as an alternative due to the limitations in general physical interactions, both due to the COVID-19 pandemic and, more generally, due to the difficulties involved in providing adequate care for animals. This study proposes mixed reality (MR)-based human–animal interaction content along with presenting the experimental verification of its effect on the reduction of mental stress. A mental arithmetic task was employed to induce acute mental stress, which was followed by either MR content, in which a participant interacted with virtual animals via gestures and voice commands, or a slide show of animal images. During the experiment, an electrocardiogram (ECG) was continuously recorded with a patch-type, wireless ECG sensor on the chest of the subject, and their psychological state was evaluated with the help of questionnaires after each task. The findings of the study demonstrate that the MR-based interaction with virtual animals significantly reduces mental stress and induces positive emotions. We expect that this study could provide a basis for the widespread use of MR-based content in the field of mental health.
Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm
Authors: Kovács Szilárd , Bolemányi Balázs , Botzheim János
Published: February 2022

This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.
Low-Complexity Filter for Software-Defined Radio by Modulated Interpolated Coefficient Decimated Filter in a Hybrid Farrow
Authors: Otunniyi Temidayo O. , Myburgh Hermanus C.
Published: February 2022

Realising a low-complexity Farrow channelisation algorithm for multi-standard receivers in software-defined radio is a challenging task. A Farrow filter operates best at low frequencies while its performance degrades towards the Nyquist region. This makes wideband channelisation in software-defined radio a challenging task with high computational complexity. In this paper, a hybrid Farrow algorithm that combines a modulated Farrow filter with a frequency response interpolated coefficient decimated masking filter is proposed for the design of a novel filter with low computational complexity. A design example shows that the HFarrow filter bank achieved multiplier reduction of 50%, 70% and 64%, respectively, in comparison with non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms. The HFarrow filter bank is able to provide the same number of sub-band channels as other algorithms such as non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms, but with less computational complexity.
Direct Measurements of Turbulence in the Upper Western Pacific North Equatorial Current over a 25-h Period
Authors: Yang Wenlong , Zhou Hui , Wang Yonggang , Liu Juan , Liu Hengchang , Liu Chenglong , Dewar William
Published: February 2022

Measurements of the turbulent kinetic energy dissipation rate (ε) were conducted by a free-fall microstructure profiler in the western Pacific North Equatorial Current (WPNEC) during a continuous period of 25 h, from the sea surface to about 160 m depth. In the mixed layer (ML), ε values were typically on the order of 10−8∼10−7 W kg−1, and an obvious diurnal cycle existed in the upper 40 m of the surface mixing layer. Below the ML, ε was reduced to 10−9∼10−8 W kg−1 with some patches of high ε reaching 10−7.5 W kg−1. The barrier layer was identified in the nighttime with a maximum thickness of 20 m, and it was eroded by the advection of freshwater within the lower part of the isothermal layers associated with an anticyclonic eddy in the afternoon. A simple scaling relevant to shear (S2) instability and stratification (N2) that can predict turbulent dissipation rates in the transition layer, between the well-mixed layer and the thermocline below, was obtained through the scaling ε∼S−0.40N0.20. Besides turbulence, double-diffusive processes also contributed to the vertical mixing levels in the upper WPNEC.
Flexible Inkjet-Printed Heaters Utilizing Graphene-Based Inks
Authors: Barmpakos Dimitris , Belessi Vassiliki , Xanthopoulos Nikolaos , Krontiras Christoforos A. , Kaltsas Grigoris
Published: February 2022

Thermal sensors are mainly based on the selective heating of specific areas, which in most cases is a critical feature for both the operation and the performance of the thermal device. In this work, we evaluate the thermoelectrical response of two graphitic materials, namely (a) a commercial 2.4%wt graphene–ethyl cellulose dispersion in cycloxehanone and terpineol (G) and (b) a custom functionalized reduced graphene oxide (f-rGO) ink in the range of −40 to 100 °C. Both inks were printed on a flexible polyimide substrate and the Thermal Coefficients of Resistance (TCR) were extracted as TCRG = −1.05 × 10−3 °C−1 (R2 = 0.9938) and TCRf-rGO = −3.86 × 10−3 °C−1 (R2 = 0.9967). Afterward, the inkjet-printed devices were evaluated as microheaters, in order to exploit their advantage for cost-effective production with minimal material waste. f-rGO and G printed heaters reached a maximum temperature of 97.5 °C at 242 mW and 89.9 °C at 314 mW, respectively, applied by a constant current source and monitored by an infrared camera. Repeatability experiments were conducted, highlighting the high robustness in long-term use. The power–temperature behavior was extracted by self-heating experiments to demonstrate the ability of the devices to serve as heaters. Both static and dynamic evaluation were performed in order to study the device behaviors and extract the corresponding parameters. After all the experimental processes, the resistance of the samples was again evaluated and found to differ less than 13% from the initial value. In this work, fabrication via inkjet printing and demonstration of efficient and stable microheaters utilizing a custom ink (f-rGO) and a commercial graphene ink are presented. This approach is suitable for fabricating selectively heated geometries on non-planar substrate with high repeatability and endurance in heat cycles.
Feasibility Study of Glucose Concentration Measurement of Aqueous Solution Using Time Domain Reflected Signals
Authors: Saeedi Samira , Chammani Somayyeh , Fischer Georg
Published: February 2022

Recently, wideband microwave spectroscopy (WBMS) has been applied for material characterization. Blood glucose sensing through microwave spectroscopy is usually done with resonant frequency-domain methods. Time-domain (TD) WBMS is a low-cost and convenient technique that can be used for glucose sensing of the aqueous solution. In this paper, early research for the implementation of a TD dielectric spectroscopy setup for glucose concentration measurement is presented. TD reflected signals from water with different glucose content are calculated using inverse Laplace transform. The proposed setup is a quasi-monostatic setup in which measurements are done with two different devices in the frequency range of 0.1 to 6 GHz to make a comparison between frequency domain (FD) and TD methods. Frequency domain (FD) measurement is performed with VNA and two Vivaldi antennas. Then, TD data is obtained using the transforming option of VNA. Direct TD measurement is operated with a maximum length sequence (m-sequence) transceiver. Measurement and numerical results follow the same trend and show good agreement with each other. A monotonic relation between peaks of TD signals and the corresponding glucose concentration is achieved. The variation of the height of the reflected signal’s peak is 0.00002 and 0.0005 for each 50 mg/dL glucose concentration with FD measurements and direct TD measurements, respectively. The glucose concentration range of 25 mg/dL to 400 mg/dL is investigated, and the worst repeatability of this method is 3.65% for 300 mg/dL.
A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD
Authors: Liu Siyun , Qi Qingjie , Cheng Huifeng , Sun Lifeng , Zhao Youxin , Chai Jiamei
Published: February 2022

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.
Developing Affordable, Portable and Simplistic Diagnostic Sensors to Improve Access to Care
Authors: Hafezi Nikki L. , Hafezi Farhad
Published: February 2022

Ophthalmology is a highly technical specialty, especially in the area of diagnostic equipment. While the field is innovative, the access to cutting-edge technology is limited with reference to the global population. A significant way to improve overall healthcare is to understand the needs and possibilities of all possible consumers when developing sophisticated and accurate medical devices. The Smartphone-based Keratograph (SBK), is an example of a new project that uses real world feedback, addresses an unmet medical need, and implements commercially available components to create a device that is affordable, portable and simplistic to operate. The long-term goal of the SBK is to collect data from users for supervised machine-learning. This machine-learning aspect will ultimately aid in the development of an artificial intelligence device to enable even earlier detection of keratoconus, especially in children and adolescents. Again, the ultimate goal of any medical device should be to improve patient care, and to make a significant improvement on vision healthcare for the global population, providing access to this technology is essential.
Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction
Authors: El-Hasnony Ibrahim M. , Elzeki Omar M. , Alshehri Ali , Salem Hanaa
Published: February 2022

The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.
Research on Variable Parameter Drilling Method of Ti-CFRP-Ti Laminated Stacks Based on Real-Time Sensing of Drilling Axial Force
Authors: Zhang Zhengzhu , Zhang Ning , Wu Fenghe , Teng Weixiang , Sun Yingbing , Guo Baosu
Published: February 2022

Ti-CFRP-Ti laminated stacks have been widely used in aviation, aerospace, shipbuilding and other industries, owing to its excellent physical and electrochemical properties. However, chip blockages occur easily when drilling into Ti-CFRP-Ti laminated stacks, resulting in a rapid rise of drilling temperature and an increase of axial drilling force, which may lead to the intensification of tool wear and a decline of drilling quality. Cutting force signals can effectively reflect the drilling process and tool condition, however, the traditional plate dynamometer is typically difficult in realizing the follow-up online measurement. Therefore, an intelligent tool holder system for real-time sensing of the cutting force is developed and constructed in this paper, and the variable parameter drilling method of Ti-CFRP-Ti laminated stacks is studied on this basis. Firstly, an intelligent tool holder system with high flexibility and adaptability is designed; Secondly, a cutting force signal processing method based on compressed sensing (CS) theory is proposed to solve the problem of high-frequency signal transmission; Lastly, the drilling experiment of Ti-CFRP-Ti laminated stacks is carried out based on the intelligent tool holder system, and the drilling parameters are optimized using a compromise programming approach and analytic hierarchy process (AHP). The comparison of results show that the optimized drilling parameters can effectively reduce the hole wall surface roughness and improve the drilling efficiency while ensuring a small axial force.
A Resource-Efficient CNN-Based Method for Moving Vehicle Detection
Authors: Charouh Zakaria , Ezzouhri Amal , Ghogho Mounir , Guennoun Zouhair
Published: February 2022

There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems. Although these methods provide high accuracy, they are computationally expensive. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information for tasks such as monitoring driving behavior and detecting traffic rules violations. In this paper, we propose a framework to reduce the complexity of CNN-based AVS methods, where a BS-based module is introduced as a preprocessing step to optimize the number of convolution operations executed by the CNN module. The BS-based module generates image-candidates containing only moving objects. A CNN-based detector with the appropriate number of convolutions is then applied to each image-candidate to handle the overlapping problem and improve detection performance. Four state-of-the-art CNN-based detection architectures were benchmarked as base models of the detection cores to evaluate the proposed framework. The experiments were conducted using a large-scale dataset. The computational complexity reduction of the proposed framework increases with the complexity of the considered CNN model’s architecture (e.g., 30.6% for YOLOv5s with 7.3M parameters; 52.2% for YOLOv5x with 87.7M parameters), without undermining accuracy.
Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features
Authors: Wang Zheng , Ding Jianli , Zhang Zipeng
Published: February 2022

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.
Envelope Extraction for Vibration Locating in Coherent Φ-OTDR
Authors: Zan Wei , Wang Yu , Liu Xin , Wang Pengfei , Jin Baoquan
Published: February 2022

In a coherent phase-sensitive optical time-domain reflectometry (Φ-OTDR) sensing system, a frequency shift of hundreds of MHz generated by the pulse modulation of an acoustic optic modulator results in a high central frequency of a beating signal spectrum. In order to reduce the high-performance hardware requirement of signal acquisition, the coherent Φ-OTDR based on envelope extraction is proposed in this paper. Firstly, a theoretical model of a quasi-sinusoidal amplitude-modulated signal is built for the beating signal between local oscillator light and Rayleigh backward scattering light. An envelope detector is then utilized to realize the envelope extraction of beating signals with advantages of a simple structure and quick response. The extracted envelope can be directly used for vibration locating without the conventional orthogonal demodulation. Experiment results present that the sampling rate can be reduced to 10 MHz under the spatial resolution of 10 m and the sensing distance of 31 km. This scheme proves that envelope extraction is a reliable technical route for vibration locating, which can effectively reduce the sampling rate and simplify the data demodulation.
Concept of Fluorescent Transport Activity Biosensor for the Characterization of the Arabidopsis NPF1.3 Activity of Nitrate
Authors: Chen Yen-Ning , Ho Cheng-Hsun
Published: February 2022

The NRT1/PTR FAMILY (NPF) in Arabidopsis (Arabidopsis thaliana) plays a major role as a nitrate transporter. The first nitrate transporter activity biosensor NiTrac1 converted the dual-affinity nitrate transceptor NPF6.3 into fluorescence activity sensors. To test whether this approach is transferable to other members of this family, screening for genetically encoded fluorescence transport activity sensor was performed with the member of the NPF family in Arabidopsis. In this study, NPF1.3, an uncharacterized member of NPF in Arabidopsis, was converted into a transporter activity biosensor NiTrac-NPF1.3 that responds specifically to nitrate. The emission ratio change of NiTrac-NPF1.3 triggered by the addition of nitrate reveals the important function of NPF1.3 in nitrate transport in Arabidopsis. A functional analysis of Xenopus laevis oocytes confirmed that NPF1.3 plays a role as a nitrate transporter. This new technology is applicable in plant and medical research.
Image Dehazing Using LiDAR Generated Grayscale Depth Prior
Authors: Chung Won Young , Kim Sun Young , Kang Chang Ho
Published: February 2022

In this paper, the dehazing algorithm is proposed using a one-channel grayscale depth image generated from a LiDAR point cloud 2D projection image. In depth image-based dehazing, the estimation of the scattering coefficient is the most important. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal coefficients for effective dehazing must be obtained depending on the level of haze generation. Thus, we estimated the optimal scattering coefficient for 100 synthetic haze images and represented the distribution between the optimal scattering coefficient and dark channels. Moreover, through linear regression of the aforementioned distribution, the equation between scattering coefficients and dark channels was estimated, enabling the estimation of appropriate scattering coefficient. Transmission image for dehazing is defined with a scattering coefficient and a grayscale depth image, obtained from LiDAR 2D projection. Finally, dehazing is performed based on the atmospheric scattering model through the defined atmospheric light and transmission image. The proposed method was quantitatively and qualitatively analyzed through simulation and image quality parameters. Qualitative analysis was conducted through YOLO v3 and quantitative analysis was conducted through MSE, PSNR, SSIM, etc. In quantitative analysis, SSIM showed an average performance improvement of 24%.
The Association between Tensiomyography and Elastography Stiffness Measurements in Lower Limb Skeletal Muscles
Authors: Alfuraih Abdulrahman M. , Alhowimel Ahmed , Alghanim Sara , Khayat Yaaqoub , Aljamaan Abdulaziz , Alsobayel Hana I.
Published: February 2022

The objective was to test the measurements association between tensiomyography (TMG) and shear wave elastography (SWE) when evaluating the skeletal muscle stiffness of healthy subjects. The secondary objective was to evaluate the effect of superficial non-muscular tissues thickness on the measurements. A cross-sectional study was conducted with adults who are asymptomatic and had no previous history of musculoskeletal conditions. The vastus lateralis (VL) and biceps femoris (BF) muscle contraction was tested using TMG and SWE. The TMG parameters included time of contraction (Tc), sustain time (Ts), relaxation time (Tr), delay time (Td), and maximal displacement (Dm). The skin, subcutaneous fat, and fascia thicknesses were investigated using ultrasound imaging. A total of 25 participants were enrolled in the study. Six participants were females (24%). The mean age (SD) was 26.5 years (4.7). There was a statistically significant difference (p < 0.001) in SWE between VL (8.1 kPa) compared with the BF (10.8 kPa). As for Dm, which reflects stiffness in TMG, no difference was detected (p = 0.90), as both muscles had a maximum displacement of 3.7 mm. The correlation coefficients failed to detect any significant correlation (r ≤ 0.300, p ≥ 0.1) between SWE and TMG variables. There was no significant difference between male and female participants across all TMG and SWE variables (p > 0.10). Overall, there was no association between TMG parameters and SWE measurements, indicating that each technique might be evaluating a different biomechanical property of skeletal muscle.
Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
Authors: Xu Ming , Liu Wei , Xu Jinwei , Xia Yu , Mao Jing , Xu Cheng , Hu Shunren , Huang Daqing
Published: February 2022

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.
Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN
Authors: Xu Xiangyang , Zhao Mian , Shi Peixin , Ren Ruiqi , He Xuhui , Wei Xiaojun , Yang Hao
Published: February 2022

The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.
New Label-Free Biosensing for the Evaluation of the AX-024 Inhibitor: Case Study for the Development of New Drugs in Autoimmune Diseases
Authors: Ramírez Yolanda , Laguna María Fe , Holgado Miguel
Published: February 2022

We developed a new label-free assay to evaluate the inhibition capacity of AX-024 by means of a new Point-of-Care (PoC) device for application in the development of new drugs in autoimmune diseases. The technology of PoC is based on interferometric optical detection method (IODM). For this purpose, we have optimized and developed an assay protocol whereby a Glutathione S-Transferase modified protein (GST-SH3.1), which contains a functional domain of a protein involved in T-cell activation, together with the AX-024 inhibitor has been studied. The chips used are a sensing surface based on nitrocellulose. We used streptavidin and a biotinylated peptide as links for the immobilization process on the sensing surface. The biotinylated peptide and AX-024 inhibitor compete for the same functional group of the GST-SH3.1 modified protein. When the inhibitor binds its binding site on GST-SH3.1, the biotinylated peptide cannot bind to its pocket on the protein. This competition reduces the total molecular mass of protein fixed onto the biosensor. In order to quantify the inhibition capacity of AX-024, several Ax-024:GST-SH3.1 ratios have been studied. We have compared the read-out signal for GST-SH3.1 protein not interfered by the drug, which served as a positive blank, and the response of the GST-SH3.1 modified protein blocked by the inhibitor. The technology has been correlated with confocal fluorescence microscopy.
Indoor Location Data for Tracking Human Behaviours: A Scoping Review
Authors: Shum Leia C. , Faieghi Reza , Borsook Terry , Faruk Tamim , Kassam Souraiya , Nabavi Hoda , Spasojevic Sofija , Tung James , Khan Shehroz S. , Iaboni Andrea
Published: February 2022

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.
Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing
Authors: Fé Iure , Matos Rubens , Dantas Jamilson , Melo Carlos , Nguyen Tuan Anh , Min Dugki , Choi Eunmi , Silva Francisco Airton , Maciel Paulo Romero Martins
Published: February 2022

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.
Development of a Robot Arm Link System Embedded with a Three-Axis Sensor with a Simple Structure Capable of Excellent External Collision Detection
Authors: Yun Alchan , Lee Woosub , Kim Soonkyum , Kim Jong-Ho , Yoon Hyungseok
Published: February 2022

In order to effectively detect the contact state between the operator and the collaborative robot, a sensor with excellent external force detection performance is needed. The existing force/torque sensor and joint torque sensor, which are the two main external force sensors methods in cooperative robots, have limitations; only the force exerted at the end effector is detected, and it induces a low stiffness in the overall structure which affects the control performance. In the case of sensorless collision detection methods that utilize the current sensor that is used for motor control, the estimation of the performance of external force is sensitive to the sensor noise and dynamic model accuracy only to the extent that it can be used for collision detection. In this paper, we propose a strain gauge-based three-axis sensor of a cylindrical shape, which is often used as a link in a robot. By integrating sensors with robot links, the external force can be precisely measured without compromising the stiffness and is decoupled with joint disturbances, such as motor friction. Sensor calibration is conducted using static load evaluation equipment, and the reliability of collision detection is confirmed by comparing the theoretical/structural analysis results. Through the weight test and sensor characteristic evaluation, the performance and output stability are validated.