bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Influence of the Breaking Radiation Magnetic Field of a 10 kV Intelligent Circuit Breaker on an Electronic Transformer
Authors: Lu Wenchao , Duan Jiandong , Cheng Lin , Lu Jiangping , Du Xiaotong
Published: December 2021

The development of the smart grid requires the distribution switch to not be limited to the original breaking function. More functional requirements lead to more complex switch structures, especially the intelligent processing unit on the secondary side. A technology called primary and secondary integration optimizes the structure of the switch, which greatly increases the intelligence level of the switch, but also has disadvantages. The secondary intelligent unit is arranged close to the primary high-voltage electromagnetic environment, and the distribution switch is prone to failure due to electromagnetic interference. In order to explore the influence of electromagnetic interference on it, a transient electromagnetic interference simulation test platform was built for a 10 kV intelligent distribution switch based on the principle of spherical gap arc discharge, and the interference signal of the intelligent distribution switch was measured; the law of the spatial magnetic field near the electronic transformer is mainly studied in this paper. The shielding effectiveness of the distribution terminal of the switch was analyzed, and the interference of the power line of the sensor merging unit circuit board was calculated. The results show that the electronic transformer may have serious faults under continuous strong transient electromagnetic interference. The electromagnetic transient simulation test system studied in this paper can evaluate the anti strong electromagnetic interference ability of the electronic transformer.
A Multi-Center Disclusion Time Reduction (DTR) Randomized Controlled Occlusal Adjustment Study Using Occlusal Force and Timing Sensors Synchronized with Muscle Physiology Sensors
Authors: Thumati Prafulla , Thumati Roshan P , Poovani Shwetha , Sattur Atul P , Srinivas Srividya , Kerstein Robert B , Radke John
Published: December 2021

Objective—To perform a Randomized Controlled Trial (RCT) Disclusion Time Reduction (DTR) study at five Dental Colleges, using intraoral sensors and muscular electrodes. Methods and Materials—One hundred students were randomly assigned to a treatment group to receive the ICAGD coronoplasty, or a control group that received tooth polishing. All subjects answered symptom questionnaires: Beck Depression Inventory-II, Functional Restrictions, and Chronic Pain Symptom and Frequency. Subjects self-reported after ICAGD or placebo at 1 week, 1 month, 3 months, and 6 months. The Student’s t-Test analyzed the measured data. The Mann–Whitney U Test analyzed the subjective data (Alpha = 0.05). Results—The Disclusion Times, BDI-II scores, and Symptom Scales were similar between groups prior to treatment (p > 0.05). At 1 week, all three measures reduced in the treatment group, continuing to decline over 6 months (p < 0.05), but not for the controls (p > 0.05). Symptom Frequency, Functional Restrictions, and Pain Frequencies were higher in the treated group (p < 0.05), but declined after ICAGD compared to the control group (p < 0.05). Conclusions—ICAGD reduced Pain, Functional Restrictions, Symptom Frequency, and Emotional Depression within 1 week, which continued for 6 months. The tooth polishing did not initiate a placebo response.
Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution
Authors: Huang Chi-En , Li Yung-Hui , Aslam Muhammad Saqlain , Chang Ching-Chun
Published: December 2021

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.
3D Electrochemical Sensor and Microstructuration Using Aerosol Jet Printing
Authors: Fapanni Tiziano , Sardini Emilio , Serpelloni Mauro , Tonello Sarah
Published: December 2021

Electrochemical sensors are attracting great interest for their different applications. To improve their performances, basic research focuses on two main issues: improve their metrological characteristics (e.g., repeatability, reusability and sensitivity) and investigate innovative fabrication processes. In this work, we demonstrate an innovative microstructuration technique aimed at increasing electrochemical sensor sensitivity to improve electrode active area by an innovative fabrication technique. The process is empowered by aerosol jet printing (AJP), an additive-manufacturing and non-contact printing technique that allows depositing functional inks in precise patterns such as parallel lines and grids. The 3D printed microstructures increased the active surface area by up to 130% without changing the substrate occupancy. Further, electrochemical detection of ferro/ferri-cyanide was used to evaluate the sensitivity of the electrodes. This evaluation points out a sensitivity increase of 2.3-fold on average between bare and fully microstructured devices. The increase of surface area and sensitivity are well linearly correlated as expected, verifying the fitness of our production process. The proposed microstructuration is a viable solution for many applications that requires high sensitivity, and the proposed technique, since it does not require masks or complex procedures, turns out to be flexible and applicable to infinite construction geometries.
Validation of GPS-Based Monitoring and Remote Sensing of Ice-Shelf and Ice-Sheet Movement Changes
Authors: Li Xiuhong , Hao Xuejie , Yin Lizeyan , Liu Lu , Ma Yushuang , Yang Rongjin , Song Qiao
Published: December 2021

The north and south poles of the earth (hereinafter referred to as the polar regions) are important components of the earth system. Changes in the material balance and movement of the polar ice shelf reflect the influence of the polar regions on global climate change and are also a response to global climate change. Through a comprehensive investigation of ice-shelf kinematics, with sufficient accuracy, it is possible to obtain ice-shelf elevation, movement-state data, ice-shelf material balance state, and the ice-shelf movement dynamics mechanism. Due to the extremely harsh environment in polar regions, remote sensing is currently widely used. Manual and equipment monitoring methods show insufficient accuracy or discontinuous time series. There is an urgent need to obtain continuous real-time ice-shelf kinematics-related parameters on the ground to verify the reliability of the parameters obtained by satellite remote sensing. These parameters should be combined with remote sensing monitoring to provide data support. In this paper, a monitoring system for the movement of polar ice and shelf ice cover is developed, and it is proposed that various data can be acquired by integrating high-precision GPS (global positioning system) and other sensors. Solutions to the problem of low-temperature power supply in the polar regions, data acquisition and storage strategies, and remote communication methods are proposed. Testing and remote sensing validation verified that the developed acquisition system can fulfill the requirements for monitoring the movement of the polar unmanned ice shelves and ice sheets.
GPS Week Number Rollover Timestamp Complement
Authors: Qabalin Majdi K. , Naser Muawya , Hawajreh Wafa M. , Abu-Zaideh Saja
Published: December 2021

Global Positioning System (GPS) is a global navigation satellite system and the most common satellite system used in navigation and tracking devices. The phenomenon of week number rollover happened recently—a year ago—due to a design limitation in the week number variable that counting weeks which causes vast losses. As many fleet management systems depend on GPS raw data, such systems stopped working due to inaccurate data provided by GPS receivers. In this paper, we propose a technical and mathematical analysis for the GPS week number rollover phenomenon and suggest a solution to avoid the resulting damage to other subsystems that depend on the GPS device’s raw data. In addition, this paper seeks to provide precautionary measures to deal with the problem proactively. The Open Systems Interconnection model (OSI) and transport layer level solution that has been suggested depends on a TCP packet reforming tool that re-formats the value of the week number according to a mathematical model based on a timestamp complement. At the level of the database, a solution is also suggested which uses triggers. A hardware-level solution is suggested by applying a timestamp complement over the GPS internal controller. Complete testing is applied for all suggested solutions using actual data provided by Traklink—a leading company in navigation and fleet management solutions. After testing, it is evident that the transport layer level solution was the most effective in terms of speed, efficiency, accuracy, cost, and complexity. Applying a transport layer level complement mathematical model can fix the consequences of GPS week number rollover and provide stability to all subsystems that used GPS data from infected devices.
A Coherent on Receive X-Band Marine Radar for Ocean Observations
Authors: Horstmann Jochen , Bödewadt Jan , Carrasco Ruben , Cysewski Marius , Seemann Jörg , Streβer Michael
Published: December 2021

Marine radars are increasingly popular for monitoring meteorological and oceanographic parameters such as ocean surface wind, waves and currents as well as bathymetry and shorelines. Within this paper a coherent on receive marine radar is introduced, which is based on an incoherent off the shelf pulsed X-band radar. The main concept of the coherentization is based on the coherent on receive principle, where the coherence is achieved by measuring the phase of the transmitted pulse from a leak in the radar circulator, which then serves as a reference phase for the transmitted pulse. The Doppler shift frequency can be computed from two consecutive pulse-pairs in the time domain or from the first moment of the Doppler spectrum inferred by means of a short time Fast Fourier Transform. From the Doppler shift frequencies, radial speed maps of the backscatter of the ocean surface are retrieved. The resulting backscatter intensity and Doppler speed maps are presented for horizontal as well as vertical polarization, and discussed with respect to meteorological and oceanographic applications.
Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control
Authors: Pina Rafael , Tibebu Haileleol , Hook Joosep , De Silva Varuna , Kondoz Ahmet
Published: December 2021

Reinforcement learning (RL) is a booming area in artificial intelligence. The applications of RL are endless nowadays, ranging from fields such as medicine or finance to manufacturing or the gaming industry. Although multiple works argue that RL can be key to a great part of intelligent vehicle control related problems, there are many practical problems that need to be addressed, such as safety related problems that can result from non-optimal training in RL. For instance, for an RL agent to be effective it should first cover all the situations during training that it may face later. This is often difficult when applied to the real-world. In this work we investigate the impact of RL applied to the context of intelligent vehicle control. We analyse the implications of RL in path planning tasks and we discuss two possible approaches to overcome the gap between the theorical developments of RL and its practical applications. Specifically, firstly this paper discusses the role of Curriculum Learning (CL) to structure the learning process of intelligent vehicle control in a gradual way. The results show how CL can play an important role in training agents in such context. Secondly, we discuss a method of transferring RL policies from simulation to reality in order to make the agent experience situations in simulation, so it knows how to react to them in reality. For that, we use Arduino Yún controlled robots as our platforms. The results enhance the effectiveness of the presented approach and show how RL policies can be transferred from simulation to reality even when the platforms are resource limited.
Low-Complexity Transmit Power Control for Secure Communications in Wireless-Powered Cognitive Radio Networks
Authors: Lee Kisong
Published: December 2021

In this study, wireless-powered cognitive radio networks (WPCRNs) are considered, in which N sets of transmitters, receivers and energy-harvesting (EH) nodes in secondary networks share the same spectrum with primary users (PUs) and none of the EH nodes is allowed to decode information but can harvest energy from the signals. Given that the EH nodes are untrusted nodes from the point of view of information transfer, the eavesdropping of secret information can occur if they decide to eavesdrop on information instead of harvesting energy from the signals transmitted by secondary users (SUs). For secure communications in WPCRNs, we aim to find the optimal transmit powers of SUs that maximize the average secrecy rate of SUs while maintaining the interference to PUs below an allowable level, while guaranteeing the minimum EH requirement for each EH node. First, we derive an analytical expression for the transmit power via dual decomposition and propose a suboptimal transmit power control algorithm, which is implemented in an iterative manner with low complexity. The simulation results confirm that the proposed scheme outperforms the conventional distributed schemes by more than 10% in terms of the average secrecy rate and outage probability and can also considerably reduce the computation time compared with the optimal scheme.
ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter
Authors: Colonnier Fabien , Della Vedova Luca , Orchard Garrick
Published: December 2021

Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution.
Stainless-Steel Antenna on Conductive Substrate for an SHM Sensor System with High Power Demand
Authors: Bornemann Sarah , Haus Jan Niklas , Sinapius Michael , Lüssem Björn , Dietzel Andreas , Lang Walter
Published: December 2021

This paper presents the novel concept of structuring a planar coil antenna structured into the outermost stainless-steel layer of a fiber metal laminate (FML) and investigating its performance. Furthermore, the antenna is modified to sufficiently work on inhomogeneous conductive substrates such as carbon-fiber-reinforced polymers (CFRP) independent from their application-dependent layer configuration, since the influence on antenna performance was expected to be configuration-dependent. The effects of different stack-ups on antenna characteristics and strategies to cope with these influences are investigated. The purpose was to create a wireless self-sustained sensor node for an embedded structural health monitoring (SHM) system inside the monitored material itself. The requirements of such a system are investigated, and measurements on the amount of wireless power that can be harvested are conducted. Mechanical investigations are performed to identify the antenna shape that produces the least wound to the material, and electrical investigations are executed to prove the on-conductor optimization concept. Furthermore, a suitable process to fabricate such antennas is introduced. First measurements fulfilled the expectations: the measured antenna structure prototype could provide up to 11 mW to a sensor node inside the FML component.
Influence of Two-Plane Position and Stress on Intensity-Variation-Based Sensors: Towards Shape Sensing in Polymer Optical Fibers
Authors: Biazi Vitorino , Avellar Letícia , Frizera Anselmo , Leal-Junior Arnaldo
Published: December 2021

Shape reconstruction is growing as an important real-time monitoring strategy for applications that require rigorous control. Polymer optical fiber sensors (POF) have mechanical properties that allow the measurement of large curvatures, making them appropriate for shape sensing. They are also lightweight, compact and chemically stable, meaning they are easy to install and safer in risky environments. This paper presents a sensor system to detect angles in multiple planes using a POF-intensity-variation-based sensor and a procedure to detect the angular position in different planes. Simulations are performed to demonstrate the correlation between the sensor’s mechanical bending response and their optical response. Cyclic flexion experiments are performed at three test frequencies to obtain the sensitivities and the calibration curves of the sensor at different angular positions of the lateral section. A Fast Fourier Transform (FFT) analysis is tested as a method to estimate angular velocities using POF sensors. The experimental results show that the prototype had high repeatability since its sensitivity was similar using different test frequencies at the same lateral section position. The proposed approach proved itself feasible considering that all linear calibration curves presented a coefficient of determination (R2) higher than 0.9.
Linguistic Patterns for Code Word Resilient Hate Speech Identification
Authors: Calderón Fernando H. , Balani Namrita , Taylor Jherez , Peignon Melvyn , Huang Yen-Hao , Chen Yi-Shin
Published: December 2021

The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, “skypes, googles, bing, yahoos” are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.
One-Dimensional Systemic Modeling of Thermal Sensors Based on Miniature Bead-Type Thermistors
Authors: Heyd Rodolphe
Published: December 2021

Accurate measurements of thermal properties is a major concern, for both scientists and the industry. The complexity and diversity of current and future demands (biomedical applications, HVAC, smart buildings, climate change adapted cities, etc.) require making the thermal characterization methods used in laboratory more accessible and portable, by miniaturizing, automating, and connecting them. Designing new materials with innovative thermal properties or studying the thermal properties of biological tissues often require the use of miniaturized and non-invasive sensors, capable of accurately measuring the thermal properties of small quantities of materials. In this context, miniature electro-thermal resistive sensors are particularly well suited, in both material science and biomedical instrumentation, both in vitro and in vivo. This paper presents a one-dimensional (1D) electro-thermal systemic modeling of miniature thermistor bead-type sensors. A Godunov-SPICE discretization scheme is introduced, which allows for very efficient modeling of the entire system (control and signal processing circuits, sensors, and materials to be characterized) in a single workspace. The present modeling is applied to the thermal characterization of different biocompatible liquids (glycerol, water, and glycerol–water mixtures) using a miniature bead-type thermistor. The numerical results are in very good agreement with the experimental ones, demonstrating the relevance of the present modeling. A new quasi-absolute thermal characterization method is then reported and discussed. The multi-physics modeling described in this paper could in the future greatly contribute to the development of new portable instrumental approaches.
Field-Effect Sensors Using Biomaterials for Chemical Sensing
Authors: Wu Chunsheng , Zhu Ping , Liu Yage , Du Liping , Wang Ping
Published: December 2021

After millions of years of evolution, biological chemical sensing systems (i.e., olfactory and taste systems) have become very powerful natural systems which show extreme high performances in detecting and discriminating various chemical substances. Creating field-effect sensors using biomaterials that are able to detect specific target chemical substances with high sensitivity would have broad applications in many areas, ranging from biomedicine and environments to the food industry, but this has proved extremely challenging. Over decades of intense research, field-effect sensors using biomaterials for chemical sensing have achieved significant progress and have shown promising prospects and potential applications. This review will summarize the most recent advances in the development of field-effect sensors using biomaterials for chemical sensing with an emphasis on those using functional biomaterials as sensing elements such as olfactory and taste cells and receptors. Firstly, unique principles and approaches for the development of these field-effect sensors using biomaterials will be introduced. Then, the major types of field-effect sensors using biomaterials will be presented, which includes field-effect transistor (FET), light-addressable potentiometric sensor (LAPS), and capacitive electrolyte–insulator–semiconductor (EIS) sensors. Finally, the current limitations, main challenges and future trends of field-effect sensors using biomaterials for chemical sensing will be proposed and discussed.
NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation
Authors: Hyun Jieum , Myung Hyun
Published: December 2021

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.
Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing
Authors: Yun Deok-Won , Lee Won-Cheol
Published: December 2021

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.
Hybrid Sol-Gel Surface-Enhanced Raman Sensor for Xylene Detection in Solution
Authors: Weber Verena , Brigo Laura , Brusatin Giovanna , Mattei Giovanni , Pedron Danilo , Pilot Roberto , Signorini Raffaella
Published: December 2021

This paper reports on the fabrication and characterization of a plasmonic/sol-gel sensor for the detection of aromatic molecules. The sol-gel film was engineered using polysilsesquioxanes groups to capture the analyte, through π-π interaction, and to concentrate it close to the plasmonic surface, where Raman amplification occurs. Xylene was chosen as an analyte to test the sensor. It belongs to the general class of volatile organic compounds and can be found in water or in the atmosphere as pollutants released from a variety of processes; its detection with SERS is typically challenging, due to its low affinity toward metallic surfaces. The identification of xylene was verified in comparison with that of other aromatic molecules, such as benzene and toluene. Investigations were carried out on solutions of xylene in cyclohexane, using concentrations in the range from 0 to 800 mM, to evaluate the limit of detection (LOD) of about 40 mM.
Fuzzy Logic in Aircraft Onboard Systems Reliability Evaluation—A New Approach
Authors: Żyluk Andrzej , Kuźma Konrad , Grzesik Norbert , Zieja Mariusz , Tomaszewska Justyna
Published: December 2021

This paper is a continuation of research into the possibility of using fuzzy logic to assess the reliability of a selected airborne system. The research objectives include an analysis of statistical data, a reliability analysis in the classical approach, a reliability analysis in the fuzzy set theory approach, and a comparison of the obtained results. The system selected for the investigation was the aircraft gun system. In the first step, after analysing the statistical (operational) data, reliability was assessed using a classical probabilistic model in which, on the basis of the Weibull distribution fitted to the operational data, the basic reliability characteristics were determined, including the reliability function for the selected aircraft system. The second reliability analysis, in a fuzzy set theory approach, was conducted using a Mamdani Type Fuzzy Logic Controller developed in the Matlab software with the Fuzzy Logic Toolbox package. The controller was designed on the basis of expert knowledge obtained by a survey. Based on the input signals in the form of equipment operation time (number of flying hours), number of shots performed (shots), and the state of equipment corrosion (corrosion), the controller determines the reliability of air armament. The final step was to compare the results obtained from two methods: classical probabilistic model and fuzzy logic. The authors have proved that the reliability model using fuzzy logic can be used to assess the reliability of aircraft airborne systems.
Hybrid FSK-PSK Waveform Optimization for Radar Based on Alternating Direction Method of Multiplier (ADMM)
Authors: Fei Zhiting , Zhao Jiachen , Geng Zhe , Zhu Xiaohua , Zhang Jindong
Published: December 2021

In this paper, a new radar signal modulated with a hybrid of the frequency shift keying (FSK) and the phase shift keying (PSK) signal—i.e., the FSK-PSK signal—is studied. Different phase encoding sequences are used to modulate the sub-pulses to obtain lower sidelobe levels and ensure signal orthogonality. In addition, to counter intra-pulse slice repeater jamming of specific length generated by the enemy jammer, an orthogonal waveform made of sub-pulses of equal length based on the FSK-PSK modulation scheme is designed. The simulation results show that the optimized discrete phase encoding sequence can significantly enhance the orthogonality of the sub-pulse in the FSK-PSK signal and effectively suppress the slice repeater jamming. Two algorithms are proposed: (1) the low sidelobe waveform optimization algorithm based on ADMM (LSW-ADMM); and (2) the anti-slice-repeater-jamming algorithm based on ADMM (ASRJ-ADMM). Both algorithms exhibit fast convergence speed and low computational complexity.
Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals
Authors: Kang Mingu , Shin Siho , Zhang Gengjia , Jung Jaehyo , Kim Youn Tae
Published: December 2021

Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.
Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations
Authors: Ratingen Sjoerd van , Vonk Jan , Blokhuis Christa , Wesseling Joost , Tielemans Erik , Weijers Ernie
Published: December 2021

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.
An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management
Authors: Ntakolia Charis , Kokkotis Christos , Karlsson Patrik , Moustakidis Serafeim
Published: December 2021

Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding cost, ordering cost, and backorder cost. Backorder occurs when a product is temporarily unavailable or out of stock and the customer places an order for future production and shipment. Therefore, stock unavailability and prolonged delays in product delivery will lead to additional production costs and unsatisfied customers, respectively. Thus, it is of high importance to develop models that will effectively predict the backorder rate in an inventory system with the aim of improving the effectiveness of the supply chain and, consequentially, the performance of the company. However, traditional approaches in the literature are based on stochastic approximation, without incorporating information from historical data. To this end, machine learning models should be employed for extracting knowledge of large historical data to develop predictive models. Therefore, to cover this need, in this study, the backorder prediction problem was addressed. Specifically, various machine learning models were compared for solving the binary classification problem of backorder prediction, followed by model calibration and a post-hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to material backorder. The results showed that the RF, XGB, LGBM, and BB models reached an AUC score of 0.95, while the best-performing model was the LGBM model after calibration with the Isotonic Regression method. The explainability analysis showed that the inventory stock of a product, the volume of products that can be delivered, the imminent demand (sales), and the accurate prediction of the future demand can significantly contribute to the correct prediction of backorders.
Multi-Target Tracking Using Windowed Fourier Single-Pixel Imaging
Authors: Zhang Jinyu , Hu Taiyang , Shao Xiaolang , Xiao Mengxuan , Rong Yingjiao , Xiao Zelong
Published: December 2021

The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing 6K measurements and calculating 2K windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI.
Compact Dual-Band Antenna with Paired L-Shape Slots for On- and Off-Body Wireless Communication
Authors: Ahmad Sarosh , Ghaffar Adnan , Hussain Niamat , Kim Nam
Published: December 2021

A simple dual-band patch antenna with paired L-shap slots for on- and off-body communications has been presented in this article. The proposed antenna resonates in the industrial, scientific, and medical (ISM) band at two different frequencies, at 2.45 GHz and 5.8 GHz. At the lower frequency band, the antenna’s radiation pattern is broadsided directional, whereas it is omni-directional at the higher frequency band. The efficiency and performance of the proposed antenna under the influence of the physical body are improved, and the specific absorption rate (SAR) value is significantly reduced by creating a full ground plane behind the substrate. The substrate’s material is FR-4, the thickness of which is 1.6 mm and it has a loss tangent of tanδ = 0.02. The overall size of the proposed design is 40 mm × 30 mm × 1.6 mm. Physical phantoms, such as skin, fat and muscle, are used to evaluate the impact of physical layers at 2.45 GHz and 5.8 GHz. The SAR values are assessed and found to be 0.19 W/kg and 1.18 W/kg at 2.45 GHz and 5.8 GHz, respectively, over 1 gram of mass tissue. The acquired results indicate that this antenna can be used for future on- and off-body communications and wireless services.
Fabrication and Performance Evolution of AgNP Interdigitated Electrode Touch Sensor for Automotive Infotainment
Authors: Srinivasan K. P. , Muthuramalingam T.
Published: December 2021

In the present scenario, a considerable assiduity is provided to develop novel human-machine interface technologies that rapidly outpace the capabilities of display technology in automotive industries. It is necessary to use a new cockpit design in conjunction with a fully automated driving environment in order to enhance the driving experience. It can create a seamless and futuristic dashboard for automotive infotainment application. In the present study, an endeavor was made to equip the In-vehicle bezels with printed capacitive sensors for providing superior sensing capabilities. Silver Nanoparticles based interdigitated pattern electrodes were formed over polycarbonate substrates to make printed capacitive sensors using screen printing process. The developed sensor was investigated to evaluate the qualitative and quantitative measures using direct and in-direct contact of touch. The proposed approach for sensors pattern and fabrication can highly impact on sensor performance in automotive infotainment application due to the excellent spatial interpolation with lower cost, light weight, and mechanical flexibility.
Application of Factorisation Methods to Analysis of Elemental Distribution Maps Acquired with a Full-Field XRF Imaging Spectrometer
Authors: Łach Bartłomiej , Fiutowski Tomasz , Koperny Stefan , Krupska-Wolas Paulina , Lankosz Marek , Mendys-Frodyma Agata , Mindur Bartosz , Świentek Krzysztof , Wiącek Piotr , Wróbel Paweł M. , Dąbrowski Władysław
Published: December 2021

The goal of the work was to investigate the possible application of factor analysis methods for processing X-ray Fluorescence (XRF) data acquired with a full-field XRF spectrometer employing a position-sensitive and energy-dispersive Gas Electron Multiplier (GEM) detector, which provides only limited energy resolution at a level of 18% Full Width at Half Maximum (FWHM) at 5.9 keV. In this article, we present the design and performance of the full-field imaging spectrometer and the results of case studies performed using the developed instrument. The XRF imaging data collected for two historical paintings are presented along with the procedures applied to data calibration and analysis. The maps of elemental distributions were built using three different analysis methods: Region of Interest (ROI), Non-Negative Matrix Factorisation (NMF), and Principal Component Analysis (PCA). The results obtained for these paintings show that the factor analysis methods NMF and PCA provide significant enhancement of selectivity of the elemental analysis in case of limited energy resolution of the spectrometer.
Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove
Authors: Lazazzera Remo , Laguna Pablo , Gil Eduardo , Carrault Guy
Published: December 2021

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.
Characterization of the Relationship between the Loess Moisture and Image Grayscale Value
Authors: Liu Qingbing , Wang Jinge , Zheng Hongwei , Hu Tie , Zheng Jie
Published: December 2021

This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.
A Quick Start Method for MEMS Disk Resonant Gyroscope
Authors: Xu Xiaodong , Liu Xiaowei , Zhang Yufeng
Published: December 2021

High-precision disk resonator gyroscope has a high quality factor in order to improve the performance of the gyroscope, as the high quality factor can lead to a long starting time. In this paper, a control system of the driving loop of the disk MEMS resonant gyroscope with the quick start is designed. The control system has functions of quick frequency locking and fast step response. Coarse-precision mode transition system is designed for quick frequency locking. A large-small mode transition system is designed for fast step response. The correctness of the design is verified by circuit test. The test results show that the start-up time is reduced by over 80% compared with the traditional control loop.
A Positioning Method Based on Place Cells and Head-Direction Cells for Inertial/Visual Brain-Inspired Navigation System
Authors: Chen Yudi , Xiong Zhi , Liu Jianye , Yang Chuang , Chao Lijun , Peng Yang
Published: December 2021

Mammals rely on vision and self-motion information in nature to distinguish directions and navigate accurately and stably. Inspired by the mammalian brain neurons to represent the spatial environment, the brain-inspired positioning method based on multi-sensors’ input is proposed to solve the problem of accurate navigation in the absence of satellite signals. In the research related to the application of brain-inspired engineering, it is not common to fuse various sensor information to improve positioning accuracy and decode navigation parameters from the encoded information of the brain-inspired model. Therefore, this paper establishes the head-direction cell model and the place cell model with application potential based on continuous attractor neural networks (CANNs) to encode visual and inertial input information, and then decodes the direction and position according to the population neuron firing response. The experimental results confirm that the brain-inspired navigation model integrates a variety of information, outputs more accurate and stable navigation parameters, and generates motion paths. The proposed model promotes the effective development of brain-inspired navigation research.