bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process and Frequency Ratio Techniques in the Northwest Himalayas, Pakistan
Authors: Rehman Adnanul , Song Jinxi , Haq Fazlul , Mahmood Shakeel , Ahamad Muhammad Irfan , Basharat Muhammad , Sajid Muhammad , Mehmood Muhammad Sajid
Published: January 2022

Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) in the Geographic Information System (GIS). The results reveal that the most suitable areas are distributed in the southern and northwestern parts, which can be used for future sustainable development and other economic activities. In contrast, the eastern and western regions, including Muzaffarabad City, are within high and very susceptibility zones. Finally, more than 50% of the land area is located in very low and low susceptibility zones. The validation of the proposed model was checked by using three different techniques: the Receiver Operative Characteristic (ROC) curve, Seed Cell Area Index (SCAI), and Frequency Ratio (FR). Both ROCs, the Success Rate Curve (SRC) and the Predictive Rate Curve (PRC), showed the goodness of fit for both the selected geo-hazards: landslides (81.3%) and floods (93.2%), at 80.1% and 91.7%, respectively. All the validation techniques showed good fitness for both the individual and multi-hazard maps. The proposed model sets a baseline for policy implementation for all the stakeholders to minimize the risk and sustainable future development in areas of high frequent geo-hazards.
Polarization Lidar Measurements of Dust Optical Properties at the Junction of the Taklimakan Desert–Tibetan Plateau
Authors: Dong Qingqing , Huang Zhongwei , Li Wuren , Li Ze , Song Xiaodong , Liu Wentao , Wang Tianhe , Bi Jianrong , Shi Jinsen
Published: January 2022

Previous studies have shown that dust aerosols may accelerate the melting of snow and glaciers over the Tibetan Plateau. To investigate the vertical structure of dust aerosols, we conducted a ground-based observation by using multi-wavelength polarization lidar which is designed for continuous network measurements. In this study, we used the lidar observation from September to October 2020 at the Ruoqiang site (39.0°N, 88.2°E; 894 m ASL), located at the junction of the Taklimakan Desert–Tibetan Plateau. Our results showed that dust aerosols can be lifted up to 5 km from the ground, which is comparable with the elevation of the Tibetan Plateau in autumn with a mass concentration of 400–900 μg m−3. Moreover, the particle depolarization ratio (PDR) of the lifted dust aerosols at 532 nm and 355 nm are 0.34 ± 0.03 and 0.25 ± 0.04, respectively, indicating the high degree of non-sphericity in shape. In addition, extinction-related Ångström exponents are very small (0.11 ± 0.24), implying the large values in size. Based on ground-based lidar observation, this study proved that coarse non-spherical Taklimakan dust with high concentration can be transported to the Tibetan Plateau, suggesting its possible impacts on the regional climate and ecosystem.
Reflective Noise Filtering of Large-Scale Point Cloud Using Transformer
Authors: Gao Rui , Li Mengyu , Yang Seung-Jun , Cho Kyungeun
Published: January 2022

Point clouds acquired with LiDAR are widely adopted in various fields, such as three-dimensional (3D) reconstruction, autonomous driving, and robotics. However, the high-density point cloud of large scenes captured with Lidar usually contains a large number of virtual points generated by the specular reflections of reflective materials, such as glass. When applying such large-scale high-density point clouds, reflection noise may have a significant impact on 3D reconstruction and other related techniques. In this study, we propose a method that uses deep learning and multi-position sensor comparison method to remove noise due to reflections from high-density point clouds in large scenes. The proposed method converts large-scale high-density point clouds into a range image and subsequently uses a deep learning method and multi-position sensor comparison method for noise detection. This alleviates the limitation of the deep learning networks, specifically their inability to handle large-scale high-density point clouds. The experimental results show that the proposed algorithm can effectively detect and remove noise due to reflection.
Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
Authors: Chen Zhuo , Liu Yang , Guo Kai , Wang Jinling
Published: January 2022

The ionosphere has important influences on trans-ionosphere radio propagation. When signals pass through ionospheric irregularities, their amplitude and phase are often attenuated and distorted. In this work, the statistical features of scintillation observed by the Global Navigation Satellite System (GNSS) and low earth orbit (LEO) satellites are investigated with Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) data in solar cycle 24. The amplitude scintillation propagation channel is fitted by the Nakagami-m, α-μ and κ-μ models. The performance is evaluated in terms of root mean square error (RMSE), kurtosis and information entropy. The results reveal that the α-μ model achieves the best performance in all considered scintillation intensities, while the Nakagami-m model achieves better performance under severe scintillation in the GNSS-LEO propagation channels.
Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions
Authors: Leo Marco , Bernava Giuseppe Massimo , Carcagnì Pierluigi , Distante Cosimo
Published: January 2022

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.
An 11.8-fJ/Conversion-Step Noise Shaping SAR ADC with Embedded Passive Gain for Energy-Efficient IoT Sensors
Authors: Choi Changhyung , Lee Jong-Wook
Published: January 2022

Herein, we present a noise shaping successive-approximation-register (SAR) analog-to-digital converter (ADC) with an embedded passive gain multiplication technique. The noise shaping moves the in-band quantization noise from the signal band to out-of-band for improved signal-to-noise ratio (SNR). The proposed approach tackles the drawback of the previous active noise shaping (increased power and extra noise) and passive noise shaping (limited noise suppression and signal loss). Both noise shaping and gain multiplication are realized on-chip in an energy-efficient manner without an opamp. This approach uses only capacitors and switches in the finite impulse response (FIR) and infinite impulse response (IIR) filters. A comparator suppressing kickback noise is presented to handle the tradeoff between noise suppression and the filter capacitor size. The energy-efficient merged-capacitor switching (MCS) technique is effectively combined with rail-to-rail swing comparator and thermometer-coded capacitor array, which reduces the settling error in the digital to analog converter (DAC). The process-induced mismatch effect in the capacitive DAC is investigated using a behavioral model of the ADC. Additionally, we propose dynamic element matching (DEM) for the thermometer-coded capacitor array. The ADC is fabricated using a 0.18 μm CMOS process in an area of 0.26 mm2. Consuming 4.1 μW, the ADC achieves a signal-to-noise and distortion ratio (SNDR) of 66.5 dB and a spurious-free dynamic range (SFDR) of 79.1 dB. The figure-of-merit (FoM) of the ADC is 11.8 fJ/conversion-step.
Optoelectronic Angular Displacement Measurement Technology for 2-Dimensional Mirror Galvanometer
Authors: Hung Shao-Kang , Chung Yu-Hsin , Chen Cheng-Lung , Chang Kai-Hung
Published: January 2022

The mirror galvanometer is a crucial component of laser cutters/engravers. Novel two-dimensional mirror galvanometers demonstrate less trajectory distortion than traditional one-dimensional ones. This article proposes an optoelectronic sensor that measures a mirror’s inclinations in two dimensions simultaneously. The measuring range, resolution, and sampling rate are ±10°, 0.0265°, and 2 kHz, respectively. With the proposed sensor, a closed-loop control can be further implemented to achieve precision laser machining. Its compact size and low cost meet the requirements of miniature laser engravers, which have become popular in recent years.
Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario
Authors: Antonić Martina , Antonić Aleksandar , Podnar Žarko Ivana
Published: January 2022

Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach.
Non-Intrusive Contact Respiratory Sensor for Vehicles
Authors: Meteier Quentin , Kindt Michiel , Angelini Leonardo , Abou Khaled Omar , Mugellini Elena
Published: January 2022

In this work, we propose a low-cost solution capable of collecting the driver’s respiratory signal in a robust and non-intrusive way by contact with the chest and abdomen. It consists of a microcontroller and two piezoelectric sensors with their respective 3D printed plastic housings attached to the seat belt. An iterative process was conducted to find the optimal shape of the sensor housing. The location of the sensors can be easily adapted by sliding them along the seat belt. A few participants took part in three test sessions in a driving simulator. They had to perform various activities: resting, deep breathing, manual driving, and a non-driving-related task during automated driving. The subjects’ breathing rates were calculated from raw data collected with a reference chest belt, each sensor alone, and the fusion of the two. Results indicate that respiratory rate could be assessed from a single sensor located on the chest with an average absolute error of 0.92 min−1 across all periods, dropping to 0.13 min−1 during deep breathing. Sensor fusion did not improve system performance. A 4-pole filter with a cutoff frequency of 1 Hz emerged as the best option to minimize the error during the different periods. The results suggest that such a system could be used to assess the driver’s breathing rate while performing various activities in a vehicle.
Economic Optimal Allocation of Mine Water Based on Two-Stage Adaptive Genetic Algorithm and Particle Swarm Optimization
Authors: Zhang Zihang , Liu Yang , Bo Lei , Yue Yuangan , Wang Yiying
Published: January 2022

The waste mine water is produced in the process of coal mining, which is the main cause of mine flood and environmental pollution. Therefore, economic treatment and efficient reuse of mine water is one of the main research directions in the mining area at present. It is an urgent problem to use an intelligent algorithm to realize optimal allocation and economic reuse of mine water. In order to solve this problem, this paper first designs a reuse mathematical model according to the mine water treatment system, which includes the mine water reuse rate, the reuse cost at different stages and the operational efficiency of the whole mine water treatment system. Then, a hybrid optimization algorithm, GAPSO, was proposed by combining genetic algorithm (GA) and particle swarm optimization (PSO), and adaptive improvement (TSA-GAPSO) was carried out for the two optimization stages. Finally, simulation analysis and actual data detection of the mine water reuse model are carried out by using four algorithms, respectively. The results show that the hybrid improved algorithm has better convergence speed and precision in solving the mine water scheduling problem. TSA-GAPSO algorithm has the best effect and is superior to the other three algorithms. The cost of mine water reuse is reduced by 9.09%, and the treatment efficiency of the whole system is improved by 5.81%, which proves the practicability and superiority of the algorithm.
MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images
Authors: Ma Jinming , Shi Gang , Li Yanxiang , Zhao Ziyu
Published: January 2022

One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the Earth’s surface. A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks. First, a Feature Enhancement Module (FEM) is proposed. The FEM introduces Coordinate Attention (CA). The CA block embeds the position information into the channel attention to obtain the accurate position information and channel relationships of the remote sensing images. An updated feature map is obtained by using an element-wise summation of the input of the FEM and the output of the CA. The FEM enhances the feature representation in the network. Then, an attention-based Feature Fusion Module (FFM) is designed. It changes the previous idea of layer-by-layer fusion and chooses cross-layer aggregation. The FFM is to compensate for some semantic information missing as the number of layers increases. FFM plays an important role in the communication of feature maps at different scales. To further refine the feature representation, a Refinement Residual Block (RRB) is proposed. The RRB changes the number of channels of the aggregated features and uses convolutional blocks to further refine the feature representation. Compared with all compared methods, MAFF-Net improves the F1-Score scores by 4.9%, 3.2%, and 1.7% on three publicly available benchmark datasets, the CDD, LEVIR-CD, and WHU-CD datasets, respectively. The experimental results show that MAFF-Net achieves state-of-the-art (SOTA) CD performance on these three challenging datasets.
A Design of FPGA-Based Neural Network PID Controller for Motion Control System
Authors: Wang Jun , Li Moudao , Jiang Weibin , Huang Yanwei , Lin Ruiquan
Published: January 2022

In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.
Free-Space Transmission and Detection of Variously Polarized Near-IR Beams Using Standard Communication Systems with Embedded Singular Phase Structures
Authors: Karpeev Sergey V. , Podlipnov Vladimir V. , Khonina Svetlana N. , Ivliev Nikolay A. , Ganchevskay Sofia V.
Published: January 2022

We propose to achieve multichannel information transmission in free space by means of variously polarized beams. The interaction of vortex beams of various orders with the main polarization states is theoretically analyzed. The passage of beams with different polarization states through multi-order diffractive optical elements (DOEs) is simulated numerically. Using the simulation results, tables of code correspondence of diffraction order numbers to the presence of phase vortices in the analyzed beams are constructed, which allow one to determine diffraction orders that carry information about various polarization states. The performed experiment made it possible to study the recognition of the first order cylindrical polarization state formed by a Q-plate converter using a phase DOE. In the experiment, these elements were built into a commercial fiber-optic communication system operating at the near-IR frequencies. After detecting the beam polarization state, beams of the required diffraction orders are efficiently coupled into optical fiber using an additional phase element. The developed optical detection system also provides channel suppression of homogeneously polarized components, which are supposed to be used for transmission of other channels.
Optimizing Radiation Patterns of Thinned Arrays with Deep Nulls Fixed through Their Representation in the Schelkunoff Unit Circle and a Simulated Annealing Algorithm
Authors: Raíndo-Vázquez Mateo , Salas-Sánchez Aarón Ángel , Rodríguez-González Juan Antonio , López-Martín María Elena , Ares-Pena Francisco José
Published: January 2022

The present work develops an innovative methodology for fixing deep nulls in radiation patterns of symmetrical thinned arrays while maintaining a low side lobe level (SLL) and a high directivity, implementing an optimization strategy based on the simulated annealing algorithm (SA). This procedure optimizes a cost function that has a term for each characteristic of the desired radiation pattern and can distinguish between the deep nulls and the filled ones depending on whether they are on the Schelkunoff unit circle or not. Then, a direct extension of the methodology for planar arrays based on the separable distribution procedure is addressed. Consequently, some examples with half-wavelength spacing are presented, where the fixing of one, two, or three deep nulls in arrays of 40, 60, and 80 elements are illustrated as well as an extension to a 40 × 40-element planar array with rectangular grid and rectangular boundary, with two deep nulls fixed on each one of its main axes. Additionally, a comparison of the obtained results with a genetic algorithm (GA) alternative is performed. The main advantage of the proposed method is its ability to fix deep nulls in the radiation patterns, while maintaining an easy feeding network implementation.
Recent Advances in Phthalocyanine and Porphyrin-Based Materials as Active Layers for Nitric Oxide Chemical Sensors
Authors: Klyamer Darya , Shutilov Roman , Basova Tamara
Published: January 2022

Nitric oxide (NO) is a highly reactive toxic gas that forms as an intermediate compound during the oxidation of ammonia and is used for the manufacture of hydroxylamine in the chemical industry. Moreover, NO is a signaling molecule in many physiological and pathological processes in mammals, as well as a biomarker indicating the course of inflammatory processes in the respiratory tract. For this reason, the detection of NO both in the gas phase and in the aqueous media is an important task. This review analyzes the state of research over the past ten years in the field of applications of phthalocyanines, porphyrins and their hybrid materials as active layers of chemical sensors for the detection of NO, with a primary focus on chemiresistive and electrochemical ones. The first part of the review is devoted to the study of phthalocyanines and porphyrins, as well as their hybrids for the NO detection in aqueous solutions and biological media. The second part presents an analysis of works describing the latest achievements in the field of studied materials as active layers of sensors for the determination of gaseous NO. It is expected that this review will further increase the interest of researchers who are engaged in the current level of evaluation and selection of modern materials for use in the chemical sensing of nitric oxide.
Non-Invasive Blood Flow Speed Measurement Using Optics
Authors: Zhang Alex Ce , Lo Yu-Hwa
Published: January 2022

Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We demonstrated a simple technique to directly measure the blood flow speed in main arteries based on the diffused light model. The concept is demonstrated with a phantom that uses intralipid hydrogel to model the biological tissue and an embedded glass tube with flowing human blood to model the blood vessel. The correlation function of the measured photocurrent was used to find the electrical field correlation function via the Siegert relation. We have shown that the characteristic decorrelation rate (i.e., the inverse of the decoherent time) is linearly proportional to the blood speed and independent of the tube diameter. This striking property can be explained by an approximate analytic solution for the diffused light equation in the regime where the convective flow is the dominating factor for decorrelation. As a result, we have demonstrated a non-invasive method of measuring arterial blood speed without any prior knowledge or assumption about the geometric or mechanic properties of the blood vessels.
Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
Authors: Pincheira Miguel , Antonini Mattia , Vecchio Massimo
Published: January 2022

Inspection of mining assets is a crucial part of the maintenance process and is of interest to several stakeholders (e.g., OEMs, owners, users, and inspectors). Inspections require an inspector to verify several characteristics of the assets onsite, typically using legacy and poorly digitized procedures. Thus, many research opportunities arise from the adoption of digital technologies to make these procedures more efficient, reliable, and straightforward. In addition to cloud computing, the ubiquitous presence of modern mobile devices, new measurement tools with embedded connectivity capabilities, and blockchain technologies could greatly improve trust and transparency between the stakeholders interested in the inspection. However, there has been little discussion on integrating these technologies into the mining domain. This paper presents and evaluates an end-to-end system to conduct inspections using mobile devices that directly interact with constrained IoT sensor devices. Furthermore, our proposal provides a method to integrate constrained IoT devices as smart measuring tools that directly interact with a blockchain system, guaranteeing data integrity and increasing the trustworthiness of the data. Finally, we highlight the benefits of our proposed architecture by evaluating a real case study in a mining inspection scenario.
Influence of Turn Cycle Structure on Performance of Elite Alpine Skiers Assessed through an IMU in Different Slalom Course Settings
Authors: Pérez-Chirinos Buxadé Carla , Padullés Riu Josep Maria , Gavaldà Castet Dani , Trabucchi Michela , Fernández-Valdés Bruno , Tuyà Viñas Sílvia , Moras Feliu Gerard
Published: January 2022

Small differences in turn cycle structure, invisible to the naked eye, could be decisive in improving descent performance. The aim of this study was to assess the influence of turn cycle structure on the performance of elite alpine skiers using an inertial measurement unit (IMU) in different slalom (SL) course settings. Four SL courses were set: a flat-turned (FT), a steep-turned (ST), a flat-straighter (FS) and a steep-straighter (SS). Five elite alpine skiers (21.2 ± 3.3 years, 180.2 ± 5.6 cm, 72.8 ± 6.6 kg) completed several runs at maximum speed for each SL course. A total of 77 runs were obtained. Fast total times correlate with a longer initiation (INI) time in FT, a shorter steering time out of the turn (STEOUT) in the FT and FS and a shorter total steering time (STEIN+OUT) in the FT and SS courses. The linear mixed model used for the analysis revealed that in the FT-course for each second increase in the INI time, the total time is reduced by 0.45 s, and for every one-second increase in the STEOUT and STEIN+OUT times, the total time increases by 0.48 s and 0.31 s, respectively. Thus, to enhance descent performance, the skier should lengthen the INI time and shorten the STEOUT and STEIN+OUT time. Future studies could use an IMU to detect turn phases and analyze them using the other built-in sensors.
Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset
Authors: Pałczyński Krzysztof , Śmigiel Sandra , Ledziński Damian , Bujnowski Sławomir
Published: January 2022

The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I–XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5–89.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2–77.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.
Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor
Authors: Hadas Zdenek , Rubes Ondrej , Ksica Filip , Chalupa Jan
Published: January 2022

This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance solutions of a rail trackside typically consist of a large number of integrated sensing systems, which greatly benefit from autonomous source of energy. Although the amount of energy provided by conventional energy harvesting devices is usually only around several milliwatts, it is sufficient as a source of electrical power for low power sensing devices. The main aim of this paper is to design and test a kinetic electromagnetic energy harvesting system that could use energy from a passing train to deliver sufficient electrical power for sensing nodes. Measured mechanical vibrations of regional and express trains were used in laboratory testing of the developed energy harvesting device with an integrated resistive load and wireless transmission system, and based on these tests the proposed technology shows a high potential for railway applications.
An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography
Authors: Cao Weiguo , Pomeroy Marc J. , Zhang Shu , Tan Jiaxing , Liang Zhengrong , Gao Yongfeng , Abbasi Almas F. , Pickhardt Perry J.
Published: January 2022

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration.
The RapidIO Routing Strategy Based on the Double-Antibody Group Multi-Objective Artificial Immunity Algorithm
Authors: Fu Yanming , Jia Youquan , Huang Baohua , Zhou Xing , Qin Xiaoqiong
Published: January 2022

The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.
Empirical Performance Analysis of Hyperledger LTS for Small and Medium Enterprises
Authors: Khan Dodo , Jung Low Tang , Hashmani Manzoor Ahmed , Cheong Moke Kwai
Published: January 2022

The massive success of blockchain technology in Bitcoin is taking the world by storm by attracting vast acceptance from both the public and private sectors. Blockchain allows digital transactions between two parties without a third party as a broker. Blockchain is now applicable beyond fintech to various other industries. Among these, Hyperledger fabric has emerged as the most popular blockchain-based open-source permissioned platform targeting business applications. It has been used in over 400 proofs-of-concept blockchain and is well proven in applications, such as supply chain, healthcare, and so on. Despite the many obvious benefits observed in blockchain-enhanced platforms, there still exist technical challenges in scalability, causing performance deficiency, which includes latency and throughput. There is an exigent need to improve the current blockchain-based applications to have the blockchain nodes be scalable without compromising the blockchain performance. In this study, we present the impact of workload variance of up to 1000 transactions with the setup of 20 blockchain nodes in the Hyperledger LTS platform. The evaluation metrics are transaction success and failure rate, throughput, and latency in the blockchain. The transaction throughput was found to be consistent with the increasing workload on a constant number of nodes. However, it showed a declining trend with an increasing number of nodes. As far as the latency, it was in tandem with the increased workload and the number of nodes. We, therefore, conclude that the LTS version is suitable for small and medium enterprises that do not scale up.
LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index
Authors: Michańków Jakub , Sakowski Paweł , Ślepaczuk Robert
Published: January 2022

We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of the forecasting ability of the LSTM model in algorithmic investment strategies. Based on the forecasts from the LSTM model we generate buy and sell investment signals, employ them in algorithmic investment strategies and create equity lines for our investment. For this purpose we use various combinations of LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We pay special attention to data preprocessing in the input layer, to avoid overfitting in the estimation and optimization process, and assure correct selection of hyperparameters at the beginning of our tests. The next stage is devoted to the conjunction of signals from various frequencies into one ensemble model, and the selection of best combinations for the out-of-sample period, through optimization of the given criterion in a similar way as in the portfolio analysis. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.
AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing
Authors: Nabi Said , Ahmad Masroor , Ibrahim Muhammad , Hamam Habib
Published: January 2022

Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.
A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation
Authors: Cheng Sheng-Wei , Lin Yi-Ting , Peng Yan-Tsung
Published: January 2022

Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges in denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that exploits an edge-preserving noise-reduced guidance image to compute the range kernel, removing isolated noisy pixels for better denoising results. Furthermore, we approximate the spatial kernel using mean filtering based on column histogram construction to achieve constant-time filtering regardless of the kernel radius’ size and achieve better smoothing. Experimental results on multiple benchmark datasets for denoising show that the proposed DHBF outperforms other state-of-the-art BF methods.
SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals
Authors: Maddirala Ajay Kumar , Veluvolu Kalyana C.
Published: January 2022

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.
High-Responsivity, Low-Leakage Current, Ultra-Fast Terahertz Detectors Based on a GaN High-Electron-Mobility Transistor with Integrated Bowtie Antennas
Authors: Huang Zhen , Yan Wei , Li Zhaofeng , Dong Hui , Yang Fuhua , Wang Xiaodong
Published: January 2022

In this study, we fabricated three kinds of terahertz detectors with different leakage currents to analyze the plateau-like effect. The results indicate that the platform becomes increasingly apparent with the decrease in the leakage current. The fabricated device with the lowest leakage current shows a responsivity of 4.9 kV/W and noise equivalent power (NEP) of 72 pW/Hz. Further, it can be used for broadband detection between 215 GHz and 232 GHz with a voltage responsivity of more than 3.4 kV/W, and the response time can be up to 8 ns. Overall, the proposed device exhibits high sensitivity, large modulation frequency, and fast response, which indicates its excellent potential for detection and imaging applications in the THz range, including the detection of the 220 GHz atmospheric window.
The Long-Term Care with Focus on an Integrative Care Model in the Slovak Republic: A Pilot Study
Authors: Vallušová Anna , Seberíni Andrea , Kaščáková Alena , Horehájová Mária , Tokovska Miroslava
Published: January 2022

An integrative care model is a challenge and the future of social and healthcare systems globally in establishing interdisciplinary cooperation. The integrative care model responds to the needs of patients suffering from various diseases as well as their families. Planning and policy making should involve professionals in both the medical and social care disciplines; additionally, the management and administrators of social and healthcare services, at both the national and community levels, are crucial in integrating health services. This article aims to present and describe the selection methods of functional groupings of municipalities as appropriate locations for implementing care practices for elderly, in this case an integrative social and health services model. The study employed an exploratory mixed methods design. The proposed methodology of the selection of the functional groupings of municipalities has a mixed-methodological character, consisting of quantitative as well as qualitative methods. Our research focused on quantitative data processing using two approaches: the multicriteria evaluation method to create a composite index and the ArcGIS system to express the geographical distribution of the value of the composite index. The qualitative document study was applied to analyses of community-based plans for health and social services. The methodology also includes an evaluation of municipalities, which was not the subject of our paper. Furthermore, the article suggests the need to consider other factors in connection with the methodology of the selection of functional groupings and explains some of its limitations in the discussion.
The Great Change: Impact of Social Media on the Relationship between Journalism and Politics—Introduction to the Special Issue
Authors: Casero-Ripollés Andreu
Published: January 2022

Digital media have become an indispensable element of a growing number of human practices that depend on these platforms to a great extent [...]