bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of User Feedback-Based Optimized Encoding System for 3D-Printed Tactile Maps
Authors: Kaplan Howard , Pyayt Anna
Published: June 2022

The wide availability of 3D-printers makes possible the simple creation of tactile maps for individuals with blindness. However, to be useful, these maps have to be designed using feedback from the future users. In this study, we worked with fifteen individuals with blindness to develop an optimized encoding system for 3D-printed tactile maps for building interiors. Based on their requests, we focused on the development of maps with information about safety routes and emergency exits. Additionally, users identified a set of important physical objects to be encoded on the map. After that, multiple encodings were evaluated individually and when integrated in maps. This resulted in a set of optimal encodings that was most positively perceived by all participants.
A Design Approach for Simultaneous Cooperative Interception Based on Area Coverage Optimization
Authors: Wang Long , Liu Kai , Yao Yu , He Fenghua
Published: June 2022

In this paper, a design approach for simultaneous cooperative interception is presented for a scenario where the successful handover cannot be guaranteed by a single interceptor due to the target maneuver and movement information errors at the handover moment. Firstly, the concepts of the reachable interception area and predicted interception area are introduced, a performance index function is constructed, and the probability of a successful handover is described by considering the coverage of the predicted interception area. Taking the probability of successful handover as a constraint, the simultaneous cooperative interception design problem is formulated based on area coverage. Then, an area coverage optimization algorithm is presented to design the spatial distributions of the interceptors. In order to enhance the handover probability, a simultaneous cooperative interception design approach is proposed to obtain the number of interceptors and the corresponding spatial distributions. Finally, simulation experiments are carried out to validate the effectiveness of the proposed approach.
Quantum Simulation of Pseudo-Hermitian-φ-Symmetric Two-Level Systems
Authors: Zheng Chao
Published: June 2022

Non-Hermitian (NH) quantum theory has been attracting increased research interest due to its featured properties, novel phenomena, and links to open and dissipative systems. Typical NH systems include PT-symmetric systems, pseudo-Hermitian systems, and their anti-symmetric counterparts. In this work, we generalize the pseudo-Hermitian systems to their complex counterparts, which we call pseudo-Hermitian-φ-symmetric systems. This complex extension adds an extra degree of freedom to the original symmetry. On the one hand, it enlarges the non-Hermitian class relevant to pseudo-Hermiticity. On the other hand, the conventional pseudo-Hermitian systems can be understood better as a subgroup of this wider class. The well-defined inner product and pseudo-inner product are still valid. Since quantum simulation provides a strong method to investigate NH systems, we mainly investigate how to simulate this novel system in a Hermitian system using the linear combination of unitaries in the scheme of duality quantum computing. We illustrate in detail how to simulate a general P-pseudo-Hermitian-φ-symmetric two-level system. Duality quantum algorithms have been recently successfully applied to similar types of simulations, so we look forward to the implementation of available quantum devices.
Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes
Authors: Li Hongyang , Yang Guanci
Published: June 2022

In order to automatically perceive the user’s dietary nutritional information in the smart home environment, this paper proposes a dietary nutritional information autonomous perception method based on machine vision in smart homes. Firstly, we proposed a food-recognition algorithm based on YOLOv5 to monitor the user’s dietary intake using the social robot. Secondly, in order to obtain the nutritional composition of the user’s dietary intake, we calibrated the weight of food ingredients and designed the method for the calculation of food nutritional composition; then, we proposed a dietary nutritional information autonomous perception method based on machine vision (DNPM) that supports the quantitative analysis of nutritional composition. Finally, the proposed algorithm was tested on the self-expanded dataset CFNet-34 based on the Chinese food dataset ChineseFoodNet. The test results show that the average recognition accuracy of the food-recognition algorithm based on YOLOv5 is 89.7%, showing good accuracy and robustness. According to the performance test results of the dietary nutritional information autonomous perception system in smart homes, the average nutritional composition perception accuracy of the system was 90.1%, the response time was less than 6 ms, and the speed was higher than 18 fps, showing excellent robustness and nutritional composition perception performance.
Bendlet Transform Based Adaptive Denoising Method for Microsection Images
Authors: Mei Shuli , Liu Meng , Kudreyko Aleksey , Cattani Piercarlo , Baikov Denis , Villecco Francesco
Published: June 2022

Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.
Quantum Thermodynamic Uncertainties in Nonequilibrium Systems from Robertson-Schrödinger Relations
Authors: Dong Hang , Reiche Daniel , Hsiang Jen-Tsung , Hu Bei-Lok
Published: June 2022

Thermodynamic uncertainty principles make up one of the few rare anchors in the largely uncharted waters of nonequilibrium systems, the fluctuation theorems being the more familiar. In this work we aim to trace the uncertainties of thermodynamic quantities in nonequilibrium systems to their quantum origins, namely, to the quantum uncertainty principles. Our results enable us to make this categorical statement: For Gaussian systems, thermodynamic functions are functionals of the Robertson-Schrödinger uncertainty function, which is always non-negative for quantum systems, but not necessarily so for classical systems. Here, quantum refers to noncommutativity of the canonical operator pairs. From the nonequilibrium free energy, we succeeded in deriving several inequalities between certain thermodynamic quantities. They assume the same forms as those in conventional thermodynamics, but these are nonequilibrium in nature and they hold for all times and at strong coupling. In addition we show that a fluctuation-dissipation inequality exists at all times in the nonequilibrium dynamics of the system. For nonequilibrium systems which relax to an equilibrium state at late times, this fluctuation-dissipation inequality leads to the Robertson-Schrödinger uncertainty principle with the help of the Cauchy-Schwarz inequality. This work provides the microscopic quantum basis to certain important thermodynamic properties of macroscopic nonequilibrium systems.
Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets
Authors: Perafan-Lopez Juan Carlos , Ferrer-Gregory Valeria Lucía , Nieto-Londoño César , Sierra-Pérez Julián
Published: June 2022

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.
Densely Connected Neural Networks for Nonlinear Regression
Authors: Jiang Chao , Jiang Canchen , Chen Dongwei , Hu Fei
Published: June 2022

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science.
NUTS III as Decision-Making Vehicles for Diffusion and Implementation of Education for Entrepreneurship Programmes in the European Union: Some Lessons from the Portuguese Case
Authors: Banha Francisco , Flores Adão , Coelho Luís Serra
Published: June 2022

The questions of how Entrepreneurship Education (EE) initiatives can be successfully spread and what political structures can help with their implementation during compulsory school remain open. We will add to the literature by proposing that the scale of the NUTS III and its governance model/characteristics are ideal for carrying out initiatives related to EE in the EU and, thus, developing local entrepreneurship ecosystems. We will do so based on the evidence gathered from the case study of the Intermunicipal Community of Viseu Dão-Lafões. To underpin our main argument, we will explain the successful case of how the Intermunicipal Community of Viseu Dão-Lafões (a NUTS III) managed to become an exception in the Portuguese scenario when it comes to the funding, administrative articulation, and implementation of EE programmes for youths in the region.
The Implementation of Cognitively Challenging Tasks: The Role of Science Teachers’ Professional Development and Teaching Experience
Authors: Pečiuliauskienė Palmira , Kaminskienė Lina
Published: June 2022

This study deals with the implementation of cognitively challenging tasks for students in science education following professional development interventions for teachers based on the theory of change. These tasks require non-routine behaviors and encourage students to work beyond instructions and accept a high level of responsibility. This study explores how the content of professional development, the duration of professional development, and the extent of the teaching experience of science teachers are associated with the implementation of cognitively challenging science tasks in science education. A secondary data analysis of Trends in International Mathematics and Science Study (TIMSS) 2019 data from three countries (Singapore, Lithuania, and South Africa) was conducted. Ordinal logistic regression (OLR) was used to predict an ordinal dependent variable (the implementation of cognitively challenging science tasks) according to the independent variables (the content of professional development, the duration of professional development, and the extent of the teaching experience of science teachers). The results of the OLR confirmed that the implementation of cognitively challenging tasks is associated with professional development content (improving students’ critical thinking or inquiry skills and science content) and the extent of the teaching experience of science teachers from the selected countries.
Fostering Pre-Service Physics Teachers’ Pedagogical Content Knowledge Regarding Digital Media
Authors: Große-Heilmann Rike , Riese Josef , Burde Jan-Philipp , Schubatzky Thomas , Weiler David
Published: June 2022

Since the use of digital media opens new possibilities in physics education, pre-service teachers should develop pedagogical content knowledge (PCK) regarding digital media during teacher education. In the joint project DiKoLeP (German for: digital competencies of pre-service teachers in physics) of RWTH Aachen University, the University of Graz and the University of Tübingen, we therefore developed and implemented a university teaching concept with certain core elements to foster this digital-media PCK of pre-service physics teachers. The teaching concept was implemented as a university seminar at the three universities, considering the common core elements as well as individual curricular requirements. We evaluated the teaching concept in a pre-post-design with a recently developed knowledge test to measure digital-media PCK. Our findings indicated that the developed knowledge test is suitable to validly measure the digital-media PCK of pre-service physics teachers. Furthermore, first results of the empirical evaluation at the three participating universities showed a significant increase in students’ digital-media PCK with a small-to-medium effect for students who attended the theoretical as well as the practical sessions of the seminar.
Combination of Optimized Variational Mode Decomposition and Deep Transfer Learning: A Better Fault Diagnosis Approach for Diesel Engines
Authors: Bai Huajun , Zhan Xianbiao , Yan Hao , Wen Liang , Jia Xisheng
Published: June 2022

Extracting features manually and employing preeminent knowledge is overly utilized in methods to conduct fault diagnosis. A diagnosis approach utilizing intelligent methods of the optimized variational mode decomposition and deep transfer learning is proposed in this manuscript to deal with fault diagnosis. Firstly, the variational mode decomposition is optimized by K values of the dispersion entropy to realize an adaptive decomposition and reduce the noise of the signal. Secondly, an image with two dimensions is generated by a vibration signal with one dimension utilizing a short-time Fourier transform, after conducting noise reduction. Then, the ResNet18 network model is used to pre-train the model. Finally, the model transfer method is used to detect faults of a diesel engine. The results show that the proposed method outperforms the deep learning methods available in the literature. Besides, better noise reduction ability and higher diagnostic accuracy are attained.
Cutting Compensation in the Time-Frequency Domain for Smeared Spectrum Jamming Suppression
Authors: Zeng Li , Chen Hui , Zhang Zhaojian , Liu Weijian , Wang Yongliang , Ni Liuliu
Published: June 2022

Smeared spectrum (SMSP) jamming is a new type of distance false-target jamming. It consists of multiple sub-pulses, which results in dense false targets at the radar receiver and affects the detection of target signal. Aiming at the suppression of SMSP jamming, in this paper we propose a fast jamming suppression method based on the time-frequency domain according to the time-frequency distribution characteristic of SMSP jamming. This method completely suppresses SMSP jamming in the time-frequency domain, retains the time-frequency points of the remaining target signal, uses the compensation method to obtain the lost target signal, and then restores the time-frequency distribution characteristic of the target signal. It will not produce jamming sidelobe after the recovered signal matched filtering in the time domain. Moreover, we can obtain the Doppler frequency in the time-frequency domain, which can be adopted in practical engineering applications. The simulation results illustrate the effectiveness of the proposed method.
A Wearable Prefrontal Cortex Oxygen Saturation Measurement System Based on Near Infrared Spectroscopy
Authors: Han Yi , Zhai Qian , Yu Yinkai , Wang Shuoyu , Liu Tao
Published: June 2022

The measurement of blood oxygen saturation in the prefrontal cortex (PFC), especially during sleep, is of great significance for clinical research. Herein, we present a wearable PFC oxygen saturation measurement system using dual-wavelength functional near-infrared spectroscopy. The system is well designed for user-friendly donning and has the advantages of comfort, convenience, portability, and affordability. The performance of the proposed system is investigated by the calibration and experimental results. The wearable system has demonstrated great potential to conduct the physiological monitoring of PFC, and it can be widely deployed in daily life.
Design Space of GaN Vertical Trench Junction Barrier Schottky Diodes: Comprehensive Study and Analytical Modeling
Authors: Yin Jian , Chen Sihao , Chen Hang , Li Shuti , Fu Houqiang , Liu Chao
Published: June 2022

We report gallium nitride (GaN) vertical trench junction barrier Schottky (TJBS) diodes and systematically analyzed the effects of the key design parameters on the reverse and forward characteristics of the devices. By taking advantage of the shielding effects from both the trenches and pn junctions in the TJBS structure, the high electric field at the Schottky contact region can be effectively suppressed. We found that the doping concentration, thickness, and spacing of p-GaN, as well as the depth and angle of the trench sidewalls are closely associated with the electric field distribution and the reverse characteristics of the TJBS diodes. With an optimal set of design parameters, the local electric field crowding at either the corner of the trench or the edge of the p-GaN can also be alleviated, resulting in a boosted breakdown voltage of up to 1250 V in the TJBS diodes. In addition, an analytical model was developed to explore the physical mechanism behind the forward conduction behaviors. We believe that the results can provide a systematical design strategy for the development of low-loss, high-voltage, and high-power GaN power diodes towards an efficient power system.
A Dual-Buck-Boost DC–DC/AC Universal Converter
Authors: Ong Yao Rong , Cao Shuyu , Lee Sze Sing , Lim Chee Shen , Chen Max M. , Kurdkandi Naser Vosoughi , Barzegarkhoo Reza , Siwakoti Yam P.
Published: June 2022

This paper proposes a universal converter that is capable of operating in three modes for generating positive dc voltage, negative dc voltage, and sinusoidal ac voltage. By controlling the duty-cycle of two half-bridges, an inductor is operated at a high frequency to control the voltage across two film capacitors that constitute a dual-buck-boost converter. Two additional half-bridges operating at a fixed state or line frequency are used to select the mode of operation. Compared to the latest universal converter in the recent literature, the proposed topology has the same switch count while reducing the number of conducting switches for inductor current and reducing the number of switches operating at high frequency. The operation of the proposed dual-buck-boost dc–dc/ac universal converter is analyzed. Experimental results are presented for validation. The power conversion efficiency of the 100 W experimental prototype modeled in PLECS is approximately 98%.
A Practical Guide to Estimating Coil Inductance for Magnetic Resonance Applications
Authors: Giovannetti Giulio , Frijia Francesca , Flori Alessandra , Galante Angelo , Rizza Carlo , Alecci Marcello
Published: June 2022

Radiofrequency (RF) coils are employed to transmit and/or receive signals in Magnetic Resonance (MR) systems. The design of home-made, organ-specific RF coils with optimized homogeneity and/or Signal-to-Noise Ratio (SNR) can be a plus in many research projects. The first step requires accurate inductance calculation, this depending on the conductor’s geometry, to later define the tuning capacitor necessary to obtain the desired resonance frequency. To fulfil such a need it is very useful to perform a priori inductance estimation rather than relying on the time-consuming trial-and-error approach. This paper describes and compares two different procedures for coil inductance estimation to allow for a fast coil-prototyping process. The first method, based on calculations in the quasi-static approximation, permits an investigation on how the cross-sectional geometry of the RF coil conductors affects the total inductance and can be easily computed for a wide variety of coil geometries. The second approach uses a numerical full-wave method based on the Finite-Difference Time-Domain (FDTD) algorithm, and permits the simulation of RF coils with any complex geometry, including the case of multi-element phased array. Comparison with workbench measurements validates both the analytical and numerical results for RF coils operating within a wide field range (0.18–7 T).
A High-Performance and Accurate FPGA-Based Flow Monitor for 100 Gbps Networks
Authors: Sha Meng , Guo Zhichuan , Wang Ke , Zeng Xuewen
Published: June 2022

Network monitoring is important to improve network performance and security. The separation of the data plane and the control plane of SDN gives the network flexibility. This flexibility facilitates network management and gives rise to the need for accurate and fine-grained management of networks. However, the traditional software-based flow monitoring cannot easily keep up with today’s high-speed networks such as 100 GbE, even with the help of high-performance frameworks such as DPDK. To meet the need for fine-grained management of high-speed networks, an accurate hardware-based flow monitor design is presented in this paper. In our design, the FPGA-based pipelined cuckoo hashing is used to achieve efficient storage of flow entries. The flow information is accurately recorded without any sampling. The proposed design can achieve a higher performance than the non-sketch CPU-based methods and higher accuracy than sketch methods. Compared with other state-of-the-art flow monitors, the proposed design can achieve a performance of 100 Gbps while maintaining high accuracy.
Detection of Adversarial DDoS Attacks Using Symmetric Defense Generative Adversarial Networks
Authors: Shieh Chin-Shiuh , Nguyen Thanh-Tuan , Lin Wan-Wei , Lai Wei Kuang , Horng Mong-Fong , Miu Denis
Published: June 2022

DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a critical component of the creation of an integrated defensive system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers have applied machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have continuously increased and evolved since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric generative adversarial network (GAN) architecture called CycleGAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized attack can easily penetrate ML-based detection systems, including RF (random forest), KNN (k-nearest neighbor), SVM (support vector machine), and naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversarial DDoS attacks. We present a novel DDoS detection framework that incorporates GAN with a symmetrically built generator and discriminator defense system (SDGAN) to deal with these problems. Both symmetric discriminators are intended to simultaneously identify adversarial DDoS traffic. As demonstrated by the experimental results, the suggested SDGAN can be an effective solution against adversarial DDoS attacks. We train SDGAN on adversarial DDoS data generated by CycleGAN and compare it to four previous machine learning-based detection systems. SDGAN outperformed the other machine learning models, with a TPR (true positive rate) of 87.2%, proving its protection ability. Additionally, a more comprehensive test was undertaken to evaluate SDGAN’s capacity to defend against unseen adversarial threats. SDGAN was evaluated using non-training data-generated adversarial traffic. SDGAN remained effective, with a TPR of around 70.9%, compared to RF’s 9.4%.
Performance Analysis on Low-Power Energy Harvesting Wireless Sensors Eco-Friendly Networks with a Novel Relay Selection Scheme
Authors: Nguyen Hoang-Sy , Sevcik Lukas , Van Hoang-Phuong
Published: June 2022

Simultaneous wireless information and power transfer (SWIPT) has been utilized widely in wireless sensor networks (WSNs) to design systems that can be sustained by harvesting energy from the surrounding areas. In this study, we investigated the performance of the low-power energy harvesting (LPEH) WSN. We equipped each relay with a battery that consisted of an on/off (1/0) decision scheme according to the Markov property. In this context, an optimal loop interference relay selection was proposed and investigated. Moreover, the crucial role of the log-normal distribution method in characterizing the LPEH WSN’s constraints was proven and emphasized. System performance was evaluated in terms of the overall ergodic outage probability (OP) both analytically and numerically with Monte Carlo simulation. The system had the lowest overall ergodic OP, thus, performed the best with an energy harvesting time switch of 0.175. Following the increase in the signal-to-noise ratio (SNR), the system without a direct link performed the worst. Furthermore, as more relays were deployed, the better the system performed. Finally, results showed that more than 80% of the data rates can be obtained under the household condition, without the need for extra bandwidth and power supply.
Model-Based Design and Experimental Validation of Control System for a Three-Level Inverter
Authors: Li Wen-Juan , Li Ding-Sheng , Zhang Jing-Wei
Published: June 2022

Considering the disadvantages of the traditional development pattern in an embedded control system for an inverter, that is, the single-process thinking and separation of software and hardware, a novel method, which is a model-based design, for developing a double closed-loop control system for the diode-clamped three-level inverter was proposed. System control models, including the PWM control algorithm model, the voltage control model, the neutral-point potential balancing model, and the frequency control model, were built with the MATLAB platform. The code-generation capacity and the operation effect were verified through a series of tests. The inverter with diode clamp and neutral-point potential control was developed. Codes were generated automatically and downloaded to the eZdsp28335 control chip. Experimental waveforms of the phase voltage, line voltage and current were analyzed under regulating the voltage and frequency. The experimental results demonstrate that the models and the generated codes are correct. Further studies have proven the feasibility of the system development model.
Feature Activation through First Power Linear Unit with Sign
Authors: Duan Boxi , Yang Yufei , Dai Xianhua
Published: June 2022

The activation function represents a crucial component in the design of a convolutional neural network (CNN). It enables the efficient extraction of multiple features from visual patterns, and introduces systemic non-linearity to data processing. This paper proposes a novel and insightful activation method termed FPLUS, which exploits mathematical power function with polar signs in form. It is enlightened by common inverse operations while endowed with an intuitive meaning of bionics. The formulation is derived theoretically under conditions of some prior knowledge and anticipative properties. Subsequently, its feasibility is verified through a series of experiments using typical benchmark datasets. The results indicate that our approach bears superior competitiveness among numerous activation functions, as well as compatible stability across many CNN architectures. Furthermore, we extend the function presented to a more generalized type called PFPLUS with two parameters that can be fixed or learnable, so as to augment its expressive capacity. The outcomes of identical tests serve to validate this improvement. Therefore, we believe the work in this paper holds a certain value in enriching the family of activation units.
The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm
Authors: Guo Feng , Zou Fumin , Luo Sijie , Liao Lyuchao , Wu Jinshan , Yu Xiang , Zhang Cheng
Published: June 2022

As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country.
Using Zone Impedance Matching Technique to Improve the Power Transfer Capability of an Inductive Charging System over a Long Distance
Authors: Hu Jintao , He Liangxi , Liu Heng , Ng Raymond Wai Man , Lee Chi-Kwan
Published: June 2022

In many outdoor inductive charging applications, the objects can move quickly, and the environment can change unpredictably. It is therefore difficult to design a fast controller that tracks the change in mutual coupling between the transmitter and receiver. The diminished power delivery to the objects either leads to incomplete charging or prolongs the charging time. In this paper, the concept of a zone impedance matching technique is proposed. This technique pre-matches the transmitter coil to the desired coupling coefficients. Thus, the power transfer capability of the inductive charger can be maintained at a reasonably high level over a long transmission distance and under a wide receiver misalignment. Based on a three-coil inductive power transfer (IPT) system, the proposed concept is practically implemented with a simple control scheme. The proposed IPT system equips a multi-tap transmitter coil to provide flexibility in selecting different numbers of turns in the transmitter coil. The controller senses the currents of the transmitter coil and the repeater coil to determine the zone of impedance to adapt to the change in mutual coupling of the coils. A mathematical analysis is conducted to formulate the design procedures for the proposed system. Under a wide range of distance and misalignment conditions, practical measurement results verified that the proposed system achieves higher power delivery than an ordinary design.
Antenna Systems in Medical Masks: Applications for 5G FR1 and Wi-Fi 7 Wireless Systems
Authors: Chung Ming-An , Lee Ming-Chang , Hsiao Cheng-Wei
Published: June 2022

This paper proposes a small antenna system (47 mm × 8 mm × 0.2 mm) to be used in a medical mask. The medical mask is composed of a frame and shield. The frame is made of polycarbonate (PC), and the shield is made of polyethylene terephthalate (PET). The author sets two groups of antennas on the upper side of the frame and sets two other groups of antennas on the sides facing away from the face of the shield. The substrates of the four antennas are all FR4 (εr = 4.4, tanδ = 0.02), so the first antenna type is a combination of PC and FR4, and the second antenna type is a combination of PET and FR4. The antenna system has three working frequency bands, in which the reflection coefficient is lower than −10 dB after actual measurement, and its working frequency bandwidth is 2.38–2.62 GHz, 3.38–3.74 GHz, and 5.14–8 GHz, respectively. It can be effectively used in 5G FR1 and Wi-Fi 7 frequency bands and can easily be combined with medical masks of different materials. This antenna system can use Wi-Fi 7 for wireless transmission indoors and use the 5G FR1 frequency band for wireless transmission outdoors, achieving seamless transmission capabilities.
Design and Analysis of Polarization-Independent, Wide-Angle, Broadband Metasurface Absorber Using Resistor-Loaded Split-Ring Resonators
Authors: Amer Abdulrahman Ahmed Ghaleb , Sapuan Syarfa Zahirah , Alzahrani Abdullah , Nasimuddin Nasimuddin , Salem Ali Ahmed , Ghoneim Sherif S. M.
Published: June 2022

Metasurface (MS) absorbers with polarization-insensitivity and wide-angle reception features have attracted much attention due to their unique absorption property. A polarization-insensitive broadband MS absorber structure, having wide-angle reception based on square split-ring resonators (SSRRs) and loaded with lumped resistors, is proposed in this paper. The proposed MS unit cell consists of a fixed-thickness FR4 dielectric substrate and a variable air-thickness substrate. The simulation results show that the proposed MS absorber is stable across a wide angular range for both normal and oblique incidences. Furthermore, the simulated results show that some parameters, such as unit-cell geometry and lumped resistors, can be varied to improve the performance of the MS absorber. The experimental results indicate that the proposed MS absorber can be achieved an absorption higher than 90% across the frequency range from 1.89 GHz to 6.85 GHz with a relative bandwidth of 113%, which is in agreement with simulation results. Thus, the proposed MS absorber can be more suitable in RF energy harvesting or wireless power transfer applications.
Comparison of Efficiency-Based Optimal Load Distribution for Modular SSTs with Biologically Inspired Optimization Algorithms
Authors: Mughees Mariam , Sadaf Munazza , Erteza Gelani Hasan , Bilal Abdullah , Saeed Faisal , Chowdhury Md. Shahariar , Techato Kuaanan , Channumsin Sittiporn , Ullah Nasim
Published: June 2022

The battle of currents between AC and DC reignited as a result of the development in the field of power electronics. The efficiency of DC distribution systems is highly dependent on the efficiency of distribution converter, which calls for optimized schemes for the efficiency enhancement of distribution converters. Modular solid-state transformers (SSTs) play a vital role in DC distribution networks and renewable energy systems (RES). This paper deals with efficiency-based load distribution for solid-state transformers (SSTs) in DC distribution networks. The aim is to achieve a set of minimum inputs that are consistent with the output while considering the constraints and efficiency. As the main feature of modularity is associated with a three-stage structure of SSTs, this modular structure is optimized using ant lion optimizer (ALO) and validated by applying it to the EIA (Energy Information Agency) DC distribution network which contains SSTs. In the DC distribution grid, modular SSTs provide the promising conversion of DC power from medium voltage to lower DC range (400 V). The proposed algorithm is simulated in MATLAB and also compared with two other metaheuristic algorithms. The obtained results prove that the proposed method can significantly reduce the input requirements for producing the same output while satisfying the specified constraints.
A Novel Bandstop Filter Based on Two-Port Coaxial Cavities for the Installation of Metallic Mode Stirrers in Microwave Ovens
Authors: Monzo-Cabrera Juan , Díaz-Morcillo Alejandro , Martínez-Gonzalez Antonio , Lozano-Guerrero Antonio , Fayos-Fernández José , Pérez-Campos Rafael , Pedreño-Molina Juan
Published: June 2022

Metallic stirrers are commonly used in mode-stirred reverberation chambers but are less common in microwave ovens, because the use of a metallic axis creates a coaxial port in the cavity wall where the axis is introduced, which may result in significant microwave leakage levels. However, due to electromagnetic, mechanical, temperature, or chemical requirements, metallic axes must be used in some cases. In this paper, we present a high power coaxial filter that enables the use of metallic stirrers in microwave applications. The filter employs two-port coaxial cavities that are analyzed to determine their design parameters. These cavities are coupled via coaxial lines to achieve proper attenuation levels. The cavities were designed using commercial electromagnetic (EM) software, and a filter prototype was validated using vector network analyzer (VNA) measurements. The simulated and measured results show that a three-stage configuration can achieve attenuations greater than 70 dB at the 2.45 GHz ISM band, allowing the use and external handling of the metallic axis inserted into microwave ovens with negligible microwave radiation leakage.
Editable and Verifiable Anonymous Authentication Incorporating Blockchain in the Internet of Energy
Authors: Zhang Qiaolian , Bai Fenhua , Yu Zhuo , Liu Yingli , Shen Tao , Xie Anke , Huang Lin
Published: June 2022

With the continuous development of the Internet of Energy, the access of a large number of distributed renewable energy has caused difficulties in energy management. The traditional energy management mode cannot meet the needs of existing energy management and trading. Therefore, this paper proposes an integrated energy management platform based on blockchain, which allows multiple decentralized energy management systems to conduct unified identity authentication. At the same time, an editable and verifiable anonymous authentication (EVAA) scheme integrating blockchain in the Internet of Energy is designed, which combines the idea of chameleon hash (CH) and the algorithm of elliptic curve encryption (ECC) to realize the dual identity authentication of user information. Finally, the generated certificate is stored in the chain through blockchain consensus. The scheme is mainly constructed using multiple secure cryptographic algorithms, which combine the advantages of blockchain for information authentication. Thus, it can accomplish user cross-system authentication and transaction, and achieve more secure editable anonymous identity authentication through the algorithm performance test, compared with the existing similar schemes under the unified test standard. This paper not only ensures the operation efficiency, but also reflects the obvious advantage of realizing higher intensity security performance.
Model of Hybrid Electric Vehicle with Two Energy Sources
Authors: Brtka Eleonora , Jotanovic Gordana , Stjepanovic Aleksandar , Jausevac Goran , Kosovac Amel , Cvitić Ivan , Kostadinovic Miroslav
Published: June 2022

The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal.
A Microscopic Platoon Stability Model Using Vehicle-to-Vehicle Communication
Authors: Ahmad Mushtaq , Khan Zahid , Koubaa Anis , Boulila Wadii
Published: June 2022

With Vehicle-to-Vehicle (V2V) communication capability, vehicle platoon on the highway helps to reduce traffic congestion. However, the dynamic nature of vehicles imposes challenges on the V2V-based platoon management. In this paper, by considering the characteristics of a Vehicular Ad-hoc Network (VANET), a microscopic platoon management scheme is proposed to deal with three basic dynamic platoon maneuvers, namely merging, splitting, and speed-change. The congestion detection feature of VANET is used as a scale for platoon merging, splitting, and speed selection. Real-time congestion is detected if the number of vehicles in a given road segment exceeds the occupancy rate or the time headway is less than the thresholds. In the proposed platoon management scheme, platoon maintenance is triggered in congestion detection. Finally, a VANET-based platoon platform is built by using Network Simulator Version 2 (NS2) network simulation to assess the performance over some real road traces generated by Simulation of Urban MObility (SUMO). It is shown that V2V-based dynamic vehicle platoon management provides an inexpensive technique to cope with the dynamic platoon management requirement.
Optimum Extrapolation Techniques for Two-Dimensional Antenna Array Tapered Beamforming
Authors: Albagory Yasser , Alraddady Fahad
Published: June 2022

Optimizing antenna arrays is essential for achieving efficient beamforming with very low sidelobe level (SLL) where adopting tapered window functions is one of the straightforward efficient techniques for achieving this goal. Recently, two-dimensional (2D) beamforming has been extensively required for many applications; therefore, this paper proposes two extrapolation techniques applied to one-dimensional (1D) tapered functions to efficiently feed 2D antenna arrays using cross-linear and adaptive radial tapering techniques. The first proposed 2D cross-linear tapering technique determines the 2D tapering coefficients by Hadamard multiplication of two right-angled grids of repeated 1D functions, while the second proposed adaptive radial tapering technique locates the antenna element in the 2D array in terms of its radial distance with respect to the array center, then converts this distance to an element index in a virtual 1D tapering window to determine the element weighting value. The adaptive radial tapering technique is optimized for achieving the minimum SLLs. The two proposed techniques are analyzed and discussed, where it is found that the adaptive radial tapering provides deeper SLLs compared to the cross-linear tapering technique. The two extrapolation techniques are examined for four window functions including triangular (Bartlett), Hamming, cosine-square, and Blackman windows, and the simulation results show that for extrapolating the Blackman window using adaptive radial tapering, a −50 dB SLL can be achieved which is independent on the array size, while cross-linear tapering provides −35 dB and −41 dB SLLs for 16×16 and 32×32 antenna arrays, respectively.
Power-Efficient Deep Neural Network Accelerator Minimizing Global Buffer Access without Data Transfer between Neighboring Multiplier—Accumulator Units
Authors: Lee Jeonghyeok , Han Sangwook , Choi Seungwon , Choi Jungwook
Published: June 2022

This paper presents a novel method for minimizing the power consumption of weight data movements required by a convolutional operation performed on a two-dimensional multiplier–accumulator (MAC) array of a deep neural-network accelerator. The proposed technique employs a local register file (LRF) at each MAC unit in a manner such that once weight pixels are read from the global buffer into the LRF, they are reused from the LRF as many times as desired instead of being repeatedly fetched from the global buffer in each convolutional operation. One of the most evident merits of the proposed method is that the procedure is completely free from the burden of data transfer between neighboring MAC units. It was found from our simulations that the proposed method provides a power saving of approximately 83.33% and 97.62% compared with the power savings recorded by the conventional methods, respectively, when the dimensions of the input data matrix and weight matrix are 128 × 128 and 5 × 5, respectively. The power savings increase as the dimensions of the input data matrix or weight matrix increase.
A Visualized Experimental Study on the Influence of Reflux Hole on the Double Blades Self-Priming Pump Performance
Authors: Qian Heng , Wu Denghao , Xiang Chun , Jiang Junwei , Zhu Zhibing , Zhou Peijian , Mou Jiegang
Published: June 2022

The self-priming pump is a kind of centrifugal pump product with self-priming function, and the structural parameters of its reflux hole determine the performance. In order to reveal the mechanism of the self-priming process, we summarized the influence of structure parameters of the reflux hole on the performance of the self-priming pump. In this study, the transparent experimental pump was designed and manufactured, and a visual test bench was built. The gas–liquid two-phase flow pattern during the self-priming process with different reflux hole structure parameters was captured by high-speed camera. Results showed that: (1) the reflux hole of the self-priming pump affected the self-priming performance of the pump by affecting the backflow rate of the gas and liquid phases during the self-priming process. (2) Due to the uneven distribution of liquid velocity in the pump, the position of reflux hole had an obvious impact on the duration of self-priming middle stage, and the shortest duration was 13 s when φ = +15° and the longest duration was 45 s when φ = −30°. (3) The diameter of reflux hole had a very significant impact on the duration of the self-priming middle stage, and the shortest duration was 17 s when d = 10 mm and the longest duration was 94 s when d = 0 mm.