bluuebunny/crossref_metadata_2025 huggingface dataset:
A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
Authors: Mizeraczyk Jerzy , Studanski Ryszard , Zak Andrzej , Czapiewska Agnieszka
Published: December 2021

Wireless data transmission in the hydroacoustic channel under non-line-of-sight (NLOS) propagation conditions, for example, during a wreck penetration, is difficult to implement reliably. This is mostly due to the multipath propagation, which causes a reduction in the quality of data reception. Therefore, in this work an attempt has been made to develop a reliable method of wireless underwater communication test it under the NLOS conditions. In our method, we used multiple frequency-shift keying (MFSK) modulation, sending a single bit on two carriers, and diversity combining. The method was tested in laboratory conditions which simulated underwater signal propagation during the penetration of the wreck. The propagation conditions were investigated by determining the impulse responses at selected measurement points using the correlation method. Additionally, for comparison, the data transmission quality was determined by the bit error rate (BER) under the same conditions using direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation. The obtained results confirmed the usefulness of the application of the developed method for wireless data transmission in a hydroacoustic channel under NLOS conditions.
A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study
Authors: Stączek Paweł , Pizoń Jakub , Danilczuk Wojciech , Gola Arkadiusz
Published: December 2021

The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company’s production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs.
Enhanced Network Intrusion Detection System
Authors: Kotecha Ketan , Verma Raghav , Rao Prahalad V. , Prasad Priyanshu , Mishra Vipul Kumar , Badal Tapas , Jain Divyansh , Garg Deepak , Sharma Shakti
Published: December 2021

A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.
Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison
Authors: Yu Haoxuan , Zhao Chenxi , Li Shuai , Wang Zijian , Zhang Yulin
Published: December 2021

With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation.
An Adaptive Routing Algorithm Based on Relation Tree in DTN
Authors: Chen Diyue , Cui Hongyan , Welsch Roy E.
Published: December 2021

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.
Review of Underwater Sensing Technologies and Applications
Authors: Sun Kai , Cui Weicheng , Chen Chi
Published: December 2021

As the ocean development process speeds up, the technical means of ocean exploration are being upgraded. Due to the characteristics of seawater and the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply in the underwater environment directly. Especially for the seabed topography, it is impossible to carry out long-distance and accurate detection via electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have come into use. Equipped by submersibles, those underwater sensors can sense underwater wide-range and accurately. Moreover, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. This paper has made a summary of the ocean sensing technologies applied in some critical underwater scenarios, including geological surveys, navigation and communication, marine environmental parameters, and underwater inspections. In order to contain as many submersible-based sensors as possible, we have to make a trade-off on breadth and depth. In the end, the authors predict the development trend of underwater sensor technology based on the future ocean exploration requirements.
LMI-Based H∞ Controller of Vehicle Roll Stability Control Systems with Input and Output Delays
Authors: Redondo Jonatan Pajares , Boada Beatriz L. , Díaz Vicente
Published: December 2021

Many of the current research works are focused on the development of different control systems for commercial vehicles in order to reduce the incidence of risky driving situations, while also improving stability and comfort. Some works are focused on developing low-cost embedded systems with enough accuracy, reliability, and processing time. Previous research works have analyzed the integration of low-cost sensors in vehicles. These works demonstrated the feasibility of using these systems, although they indicate that this type of low-cost kit could present relevant delays and noise that must be compensated to improve the performance of the device. For this purpose, it is necessary design controllers for systems with input and output delays. The novelty of this work is the development of an LMI-Based H∞ output-feedback controller that takes into account the effect of delays in the network, both on the sensor side and the actuator side, on RSC (Roll Stability Control) systems. The controller is based on an active suspension with input and output delays, where the anti-roll moment is used as a control input and the roll rate as measured data, both with delays. This controller was compared with a controller system with a no-delay consideration that was experiencing similar delays. The comparison was made through simulation tests with a validated vehicle on the TruckSim® software.
A Domain-Independent Generative Adversarial Network for Activity Recognition Using WiFi CSI Data
Authors: Zinys Augustinas , van Berlo Bram , Meratnia Nirvana
Published: December 2021

Over the past years, device-free sensing has received considerable attention due to its unobtrusiveness. In this regard, context recognition using WiFi Channel State Information (CSI) data has gained popularity, and various techniques have been proposed that combine unobtrusive sensing and deep learning to accurately detect various contexts ranging from human activities to gestures. However, research has shown that the performance of these techniques significantly degrades due to change in various factors including sensing environment, data collection configuration, diversity of target subjects, and target learning task (e.g., activities, gestures, emotions, vital signs). This problem, generally known as the domain change problem, is typically addressed by collecting more data and learning the data distribution that covers multiple factors impacting the performance. However, activity recognition data collection is a very labor-intensive and time consuming task, and there are too many known and unknown factors impacting WiFi CSI signals. In this paper, we propose a domain-independent generative adversarial network for WiFi CSI based activity recognition in combination with a simplified data pre-processing module. Our evaluation results show superiority of our proposed approach compared to the state of the art in terms of increased robustness against domain change, higher accuracy of activity recognition, and reduced model complexity.
Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach
Authors: Amrallah Amr , Mohamed Ehab Mahmoud , Tran Gia Khanh , Sakaguchi Kei
Published: December 2021

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.
Multipath Lightweight Deep Network Using Randomly Selected Dilated Convolution
Authors: Park Sangun , Chang Dong Eui
Published: December 2021

Robot vision is an essential research field that enables machines to perform various tasks by classifying/detecting/segmenting objects as humans do. The classification accuracy of machine learning algorithms already exceeds that of a well-trained human, and the results are rather saturated. Hence, in recent years, many studies have been conducted in the direction of reducing the weight of the model and applying it to mobile devices. For this purpose, we propose a multipath lightweight deep network using randomly selected dilated convolutions. The proposed network consists of two sets of multipath networks (minimum 2, maximum 8), where the output feature maps of one path are concatenated with the input feature maps of the other path so that the features are reusable and abundant. We also replace the 3×3 standard convolution of each path with a randomly selected dilated convolution, which has the effect of increasing the receptive field. The proposed network lowers the number of floating point operations (FLOPs) and parameters by more than 50% and the classification error by 0.8% as compared to the state-of-the-art. We show that the proposed network is efficient.
Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System
Authors: Méndez Juana Isabel , Meza-Sánchez Ana Victoria , Ponce Pedro , McDaniel Troy , Peffer Therese , Meier Alan , Molina Arturo
Published: December 2021

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.
Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions
Authors: Miklusis Donatas , Markevicius Vytautas , Navikas Dangirutis , Cepenas Mindaugas , Balamutas Juozas , Valinevicius Algimantas , Zilys Mindaugas , Cuinas Inigo , Klimenta Dardan , Andriukaitis Darius
Published: December 2021

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.
Magnetic Carbon Nanofiber Mats for Prospective Single Photon Avalanche Diode (SPAD) Sensing Applications
Authors: Trabelsi Marah , Mamun Al , Klöcker Michaela , Moulefera Imane , Pljonkin Anton , Elleuch Khaled , Sabantina Lilia
Published: December 2021

Electrospinning enables simple and cost-effective production of magnetic nanofibers by adding nanoparticles to a polymer solution. In order to increase the electrical conductivity of such nanofibers, the carbonization process is crucial. In this study, the chemical and morphological properties of magnetic nanofiber mats prepared from polyacrylonitrile (PAN)/magnetite were investigated. In our previous studies, PAN/magnetite nanofiber mats were carbonized at 500 °C, 600 °C, and 800 °C. Here, PAN/magnetite nanofiber mats were carbonized at 1000 °C. The surface morphology of these PAN/magnetite nanofiber mats is not significantly different from nanofiber mats thermally treated at 800 °C and have remained relatively flexible at 1000 °C, which can be advantageous for various application fields. The addition of nanoparticles increased the average fiber diameter compared to pure PAN nanofiber mats and improved the dimensional stability during thermal processes. The high conductivity, the high magnetization properties, as well as shielding against electromagnetic interference of such carbonized nanofibers can be proposed for use in single photon avalanche diode (SPAD), where these properties are advantageous.
Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications
Authors: Lo Li-Yu , Yiu Chi Hao , Tang Yu , Yang An-Shik , Li Boyang , Wen Chih-Yung
Published: December 2021

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.
An Effective Conversion of Visemes to Words for High-Performance Automatic Lipreading
Authors: Fenghour Souheil , Chen Daqing , Guo Kun , Li Bo , Xiao Perry
Published: December 2021

As an alternative approach, viseme-based lipreading systems have demonstrated promising performance results in decoding videos of people uttering entire sentences. However, the overall performance of such systems has been significantly affected by the efficiency of the conversion of visemes to words during the lipreading process. As shown in the literature, the issue has become a bottleneck of such systems where the system’s performance can decrease dramatically from a high classification accuracy of visemes (e.g., over 90%) to a comparatively very low classification accuracy of words (e.g., only just over 60%). The underlying cause of this phenomenon is that roughly half of the words in the English language are homophemes, i.e., a set of visemes can map to multiple words, e.g., “time” and “some”. In this paper, aiming to tackle this issue, a deep learning network model with an Attention based Gated Recurrent Unit is proposed for efficient viseme-to-word conversion and compared against three other approaches. The proposed approach features strong robustness, high efficiency, and short execution time. The approach has been verified with analysis and practical experiments of predicting sentences from benchmark LRS2 and LRS3 datasets. The main contributions of the paper are as follows: (1) A model is developed, which is effective in converting visemes to words, discriminating between homopheme words, and is robust to incorrectly classified visemes; (2) the model proposed uses a few parameters and, therefore, little overhead and time are required to train and execute; and (3) an improved performance in predicting spoken sentences from the LRS2 dataset with an attained word accuracy rate of 79.6%—an improvement of 15.0% compared with the state-of-the-art approaches.
Design of a Novel Wearable System for Foot Clearance Estimation
Authors: Jacob Shilpa , Fernie Geoff , Roshan Fekr Atena
Published: December 2021

Trip-related falls are one of the major causes of injury among seniors in Canada and can be attributable to an inadequate Minimum Toe Clearance (MTC). Currently, motion capture systems are the gold standard for measuring MTC; however, they are expensive and have a restricted operating area. In this paper, a novel wearable system is proposed that can estimate different foot clearance parameters accurately using only two Time-of-Flight (ToF) sensors located at the toe and heel of the shoe. A small-scale preliminary study was conducted to investigate the feasibility of foot clearance estimation using the proposed wearable system. We recruited ten young, healthy females to walk at three self-selected speeds (normal, slow, and fast) while wearing the system. Our data analysis showed an average correlation coefficient of 0.94, 0.94, 0.92 for the normal, slow, and fast speed, respectively, when comparing the ToF signals with motion capture. The ANOVA analysis confirmed these results further by revealing no statistically significant differences between the ToF signals and motion capture data for most of the gait parameters after applying the newly proposed foot angle and offset compensation. In addition, the proposed system can measure the MTC with an average Mean Error (ME) of −0.08 ± 3.69 mm, −0.12 ± 4.25 mm, and −0.10 ± 6.57 mm for normal, slow, and fast walking speeds, respectively. The proposed affordable wearable system has the potential to perform real-time MTC estimation and contribute to future work focused on minimizing tripping risks.
Change of Ruminoreticular Temperature and Body Activity before and after Parturition in Hanwoo (Bos taurus coreanae) Cows
Authors: Kim Daehyun , Ha Jaejung , Kwon Woo-Sung , Moon Joonho , Gim Gyeong-Min , Yi Junkoo
Published: December 2021

How do body temperature and activity change before and after parturition in pregnant cows? Changes in body temperature such as ruminal, rectal, and vaginal temperature during the parturition have been reported, but there are no results of the simultaneous observation of body temperature and activity. The aim of this study was to simultaneously confirm changes in the ruminoreticular temperature and body activity before and after parturition using the ruminoreticular bio-capsule sensor every 1 h. The 55 pregnant cows were used for the experiment, the ruminoreticular bio-capsule sensor was inserted and stabilized, and the ruminoreticular temperature and body activity were measured. The ruminoreticular temperature was lower by 0.5° from −24 h to −3 h in parturition compared to 48 h before parturition and then recovered again after parturition. Body activity increased temporarily at the time of parturition and 12 h after parturition. Therefore, the ruminoreticular temperature and body activity before and after parturition was simultaneously confirmed in pregnant cows.
Design of a Millimeter-Wave Radar Remote Monitoring System for the Elderly Living Alone Using WIFI Communication
Authors: Guo Kai , Liu Chang , Zhao Shasha , Lu Jingxin , Zhang Senhao , Yang Hongbo
Published: December 2021

In response to the current demand for the remote monitoring of older people living alone, a non-contact human vital signs monitoring system based on millimeter wave radar has gradually become the object of research. This paper mainly carried out research regarding the detection method to obtain human breathing and heartbeat signals using a frequency modulated continuous wave system. We completed a portable millimeter-wave radar module for wireless communication. The radar module was a small size and had a WIFI communication interface, so we only needed to provide a power cord for the radar module. The breathing and heartbeat signals were detected and separated by FIR digital filter and the wavelet transform method. By building a cloud computing framework, we realized remote and senseless monitoring of the vital signs for older people living alone. Experiments were also carried out to compare the performance difference between the system and the common contact detection system. The experimental results showed that the life parameter detection system based on the millimeter wave sensor has strong real-time performance and accuracy.
Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction
Authors: Yang Zhengni , Yang Rui , Huang Mengjie
Published: December 2021

Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues.
Influence of Trajectory and Dynamics of Vehicle Motion on Signal Patterns in the WIM System
Authors: Ryguła Artur , Maczyński Andrzej , Brzozowski Krzysztof , Grygierek Marcin , Konior Aleksander
Published: December 2021

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent.
Plasmonic Field-Effect Transistors (TeraFETs) for 6G Communications
Authors: Shur Michael , Aizin Gregory , Otsuji Taiichi , Ryzhii Victor
Published: December 2021

Ever increasing demands of data traffic makes the transition to 6G communications in the 300 GHz band inevitable. Short-channel field-effect transistors (FETs) have demonstrated excellent potential for detection and generation of terahertz (THz) and sub-THz radiation. Such transistors (often referred to as TeraFETs) include short-channel silicon complementary metal oxide (CMOS). The ballistic and quasi-ballistic electron transport in the TeraFET channels determine the TeraFET response at the sub-THz and THz frequencies. TeraFET arrays could form plasmonic crystals with nanoscale unit cells smaller or comparable to the electron mean free path but with the overall dimensions comparable with the radiation wavelength. Such plasmonic crystals have a potential of supporting the transition to 6G communications. The oscillations of the electron density (plasma waves) in the FET channels determine the phase relations between the unit cells of a FET plasmonic crystal. Excited by the impinging radiation and rectified by the device nonlinearities, the plasma waves could detect both the radiation intensity and the phase enabling the line-of-sight terahertz (THz) detection, spectrometry, amplification, and generation for 6G communication.
Design and Performance Analysis of a Compact Planar MIMO Antenna for IoT Applications
Authors: Thiruvenkadam Saminathan , Parthasarathy Eswaran , Palaniswamy Sandeep Kumar , Kumar Sachin , Wang Lulu
Published: December 2021

This article presents a quad-band multiple-input-multiple-output (MIMO) antenna for the Internet of Things (IoT) applications. The proposed antenna consists of four quarter-wavelength asymmetrical meandered radiators, microstrip feed lines, and modified ground planes. The antenna elements are arranged in a chiral pattern to improve isolation between them, with two radiators and two ground planes placed on the front side of the substrate and the other two on the back side. The MIMO antenna has an operating bandwidth (S11 ≤ −10 dB) of 1.76–1.84 GHz, 2.37–2.56 GHz, 3.23–3.68 GHz, and 5.34–5.84 GHz, covering GSM, WLAN, WiMAX, and 5G frequency bands. The isolation between the radiating elements is greater than 18 dB in the operating bands. The peak gain of the antenna is 3.6 dBi, and the envelope correlation coefficient (ECC) is less than 0.04. Furthermore, the proposed antenna is validated for IoT-based smart home (SH) applications. The prototype MIMO antenna is integrated with a commercially available ZigBee device, and the measured values are found to be consistent with the expected results. The proposed MIMO antenna could be a good candidate for IoT systems/modules due to its low profile, compact size, lightweight, and easy integration with wireless communication devices.
Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving
Authors: Mishra Ashutosh , Kim Jinhyuk , Cha Jaekwang , Kim Dohyun , Kim Shiho
Published: December 2021

An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, such as failing to recognize other humans on the road, identifying a variety of ATCs, and gloves in the hands of ATCs. We propose a situation-aware traffic control hand-gesture recognition system, which includes ATC detection and gesture recognition. Three-dimensional (3D) hand model-based gesture recognition is used to mitigate the problem associated with gloves. Our database contains separate training and test videos of approximately 60 min length, captured at a frame rate of 24 frames per second. It has 35,291 different frames that belong to traffic control hand gestures. Our approach correctly recognized traffic control hand gestures; therefore, the proposed system can be considered as an extension of the operational domain of the AV.
Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage
Authors: Pham Quang-Quang , Dang Ngoc-Loi , Kim Jeong-Tae
Published: December 2021

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar–aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar–aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar–aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar–aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.
Airport Spatial Usability in Measuring the Spherical Antenna Properties on Small Aircraft
Authors: Labun Ján , Kurdel Pavol , Češkovič Marek , Nekrasov Alexey , Gamcová Mária , Gecejová Natália
Published: December 2021

The strict safety requirements of air transport for nonstandard placement of electronic onboard systems require an innovative approach to the experimental verification of the placement of these devices. Particular attention is required to the location of these electronic devices’ antenna systems on the fuselage. A prerequisite for determining the location of the antenna and verifying its radiation is a thorough knowledge of the radio communication transmission of onboard electronic systems in cooperation with terrestrial or satellite systems. From this point of view, this article focuses on an innovative method of verifying the spherical radiation characteristics of the antenna of an onboard rescue system emergency locator transmitter (ELT) to assess its communication link with the Cospas-Sarsat satellite system. The measurement is performed on a small sports two-seater aircraft with an antenna placed in an unusual place in the aircraft’s cabin, between the seats. It was impossible to use a suitable nonreflective attenuation chamber for the measurement, so we present a method and procedure for this type of measurement in the open space of an airport. The achieved results prove the plausibility and reproducibility of the measurement. Furthermore, combining several polar radiation characteristics makes it possible to obtain an idea, even if only a part, of the spatial (spherical) radiation characteristic. This article presents a simple method of measuring the characteristics of aircraft antennas when it is not possible to use a suitable professional nonreflective attenuation chamber for measurements for various reasons. This method can also be used on other larger means of transport or other objects that experience the same problem.
Residual Spatial and Channel Attention Networks for Single Image Dehazing
Authors: Jiang Xin , Zhao Chunlei , Zhu Ming , Hao Zhicheng , Gao Wen
Published: December 2021

Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.
Multi-Objective Optimization of Energy Saving and Throughput in Heterogeneous Networks Using Deep Reinforcement Learning
Authors: Ryu Kyungho , Kim Wooseong
Published: December 2021

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO2 emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX.
Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification
Authors: Hao Shuang , Cui Yuhuan , Wang Jie
Published: December 2021

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.
Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization
Authors: Lawrence Tom , Zhang Li , Rogage Kay , Lim Chee Peng
Published: December 2021

Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.
Optical Coherence Tomography for Patients with Developmental Disabilities: A Preliminary Study
Authors: Espinoza Kimberly , Hayashi Juri , Shimada Yasushi , Tagami Junji , Sadr Alireza
Published: December 2021

Dental radiographs are essential for diagnosis and treatment planning, but are sometimes difficult to acquire for patients with developmental disabilities (PDD). Optical Coherence Tomography (OCT) is a non-ionizing imaging modality that has the potential application as an alternative to dental radiographs for PDD. This study aimed to determine the feasibility of intraoral OCT imaging for PDD. Ten participants were recruited in the Dental Education in the Care of Persons with Disabilities (DECOD) Clinic to explore the utility of dental OCT. The prototype system (Yoshida Dental) creates in-depth and three-dimensional images of teeth. The participants indicated their degree of pain during imaging on the Wong-Baker FACES Pain Rating Scale, and the degree of discomfort after imaging on a visual analog scale. OCT can be used for patients with developmental disabilities with minimal levels of pain and discomfort, without ionizing radiation.