bluuebunny/crossref_metadata_2025 huggingface dataset:
Pedestrian Trajectory Prediction for Real-Time Autonomous Systems via Context-Augmented Transformer Networks
Authors: Saleh Khaled
Published: October 2022

Forecasting the trajectory of pedestrians in shared urban traffic environments from non-invasive sensor modalities is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians’ motion trajectories, they were argued to be challenged when dealing with longer sequential data. Additionally, whilst the accommodation for contextual information (such as scene semantics and agents interactions) was shown to be effective for robust trajectory prediction, they can also impact the overall real-time performance of prediction system. Thus, in this work, we are introducing a framework based on the transformer networks that were demonstrated recently to be more efficient and outperformed RNNs in many sequential-based tasks. We relied on a fusion of sensor modalities, namely the past positional information, agent interactions information and scene physical semantics information as an input to our framework in order to not only provide a robust trajectory prediction of pedestrians, but also achieve real-time performance for multi-pedestrians’ trajectory prediction. We have evaluated our framework on three real-life datasets of pedestrians in shared urban traffic environments and it has outperformed the compared baseline approaches in both short-term and long-term prediction horizons. For the short-term prediction horizon, our approach has achieved lower scores according to the average displacement error and the root-mean squared error (ADE/RMSE) of predictions over the state-of-the art (SOTA) approach by more than 11 cm and 23 cm, respectively. While for the long-term prediction horizon, our approach has achieved lower ADE and FDE over the SOTA approach by more than 62 cm and 165 cm, respectively. Additionally, our approach has achieved superior real time performance by scoring only 0.025 s (i.e., it can provide 40 individual trajectory predictions per second).
Review of State-of-the-Art FPGA Applications in IoT Networks
Authors: Magyari Alexander , Chen Yuhua
Published: October 2022

Modern networks used for integrating custom Internet of Things (IoT) systems and devices have restrictions and requirements unique to their individual applications. These application specific demands require custom designed hardware to maximize throughput, security and data integrity whilst minimizing latency, power consumption, and form factor. Within this paper, we describe current, state-of-the-art works that utilize FPGAs for IoT network developments. We analyze two categories of works: those that prioritize reducing power consumption, and those that prioritize networking features. Further, we describe how future works can improve upon these designs and therefore improve the efficiency of resource-constrained IoT networks.
Analysis of Phenolic Compounds in Food by Coulometric Array Detector: A Review
Authors: Razem Mutasem , Ding Yubin , Morozova Ksenia , Mazzetto Fabrizio , Scampicchio Matteo
Published: October 2022

Phenolic compounds are an important group of organic molecules with high radical scavenging, antimicrobial, anti-inflammatory, and antioxidant properties. The emerging interest in phenolic compounds in food products has led to the development of various analytical techniques for their detection and characterization. Among them, the coulometric array detector is a sensitive, selective, and precise method for the analysis of polyphenols. This review discusses the principle of this method and recent advances in its development, as well as trends in its application for the analysis of phenolic compounds in food products, such as fruits, cereals, beverages, herbs, and spices.
Study of the B-Dot Sensor for Aircraft Surface Current Measurement
Authors: Tong Chen , Duan Zemin , Huang Yeyuan , Qiu Shanliang , Si Xiaoliang , Li Zhibao , Yuan Zhijie
Published: October 2022

The B-dot sensor is a type of Rogowski coil widely used in the measurement of current. However, the accuracy of the B-dot for measuring aircraft high-frequency lightning current is greatly affected by factors such as numerical integration drift, high-frequency oscillation, and calibration. In this study, a new design and optimization for improving the B-dot measuring accuracy was carried out. To correct the drift of the numerical integral of the measurement signal in differential mode, the measuring current was reconstructed based on the nonlinear least squares method. The sensor was then optimized by isolating the sampling resistance and matching the impedance with a voltage follower. A low-cost coaxial loop calibration system was also designed to calibrate the high frequency and strong magnetic fields more accurately. Finally, the optimized B-dot sensor accuracy was greatly improved with a measuring range of 30 kA/m, an error of 3.1%, and a high-frequency response of 50 MHz. Our study greatly increases the accuracy of measuring aircraft high-frequency lightning current.
A Communication-Efficient Secure Routing Protocol for IoT Networks
Authors: Shibasaki Yuma , Iwamura Keiichi , Sato Koya
Published: October 2022

This paper proposes a secure routing protocol based on an ad hoc on-demand distance vector to simultaneously achieve communication efficiency and security. Many studies have discussed secure protocols. However, conventional protocols tend to exhibit low communication efficiencies owing to the long packets required by digital signatures, specifically in large-scale networks. Hence, our proposed method aims to allow the intermediate node to initiate a route reply (RREP), which is prohibited in conventional protocols because of digital signature restrictions. Based on an ID-based signature, the proposed protocol allows each intermediate node to hold a packet received from a specific node in the past. Each node then appends it to the route request of another node and generates its own signed RREP. This procedure guarantees that a third party holds the route to the destination. Theoretical evaluations demonstrate that the proposed method outperforms the communication efficiency of conventional secure protocols. We measured the time required for routing (i.e., the sum of communication and cryptographic calculation times) using a Raspberry Pi with C language. We show that the proposed protocol can improve the average routing time by more than 3× compared with conventional methods when 30 relay nodes are randomly distributed in a 300-square meter area.
A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods
Authors: Kotenko Igor , Saenko Igor , Lauta Oleg , Kribel Alexander
Published: October 2022

The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system. The issues of software implementation of the proposed system and the formation of a data set containing network packets of a smart grid system are considered. The experimental results obtained using the generated data set demonstrated and confirmed the high efficiency of the proposed proactive smart grid protection system in detecting cyberattacks in real or near real-time, as well as in predicting the impact of cyberattacks and developing efficient measures to counter them.
Ginkgo Leaf Inspired Fabrication of Micro/Nanostructures and Demonstration of Flexible Enzyme-Free Glucose Sensors
Authors: Jiang Shulan , Chen Yueqi , Peng Yong
Published: October 2022

Flexible enzyme-free glucose sensors have attracted widespread attention due to their importance and potential applications in clinical diagnosis, flexible wearable devices, and implanted devices in vivo. At present, there are still major problems in fabricating flexible enzyme-free glucose sensors with low detection limits, high stability, and high sensitivity at low cost, hindering their practical application. Here, we report a facile strategy for the fabrication of flexible non-enzymatic glucose sensors using ginkgo leaf as a template. NiO film and PEDOT:PSS composite film were deposited on the surface of the ginkgo leaf induced micro-nano hierarchical structure as a sensitive layer and a conductive layer, respectively. The as-prepared, flexible, enzyme-free glucose sensor exhibited excellent electrochemical performance toward glucose oxidation with a sensitivity of 0.7413 mA·mM−1/cm−2, an operating voltage of 0.55 V, a detection limit of 0.329 μM, and good anti-interference. Due to the simple fabrication process and performance reliability, the novel flexible enzyme-free glucose sensor is an attractive candidate for next generation wearable and implantable non-enzymatic glucose diagnostic devices.
Monitoring Argon L-Shell Auger Decay Using 250-eV Attosecond X-ray Pulses
Authors: Han Seunghwoi , Zhao Kun , Chang Zenghu
Published: October 2022

Electron correlation describes the interaction between electrons in a multi-electron system. It plays an important role in determining the speed of relaxation of atoms and molecules excited by XUV/X-ray pulses, such as the argon decay rate. Most research on electron correlation has centered on the role of correlation in stationary states. A time-resolved experimental study of electron correlation is a grand challenge due to the required temporal resolution and photon energy. In this research, we investigated Auger decay in argon using 200-attosecond X-ray pulses reaching the carbon K-edge. At such a high photon energy, ionization occurs not only from the outer most levels (3s and 3p), but also from the 2p core shells. We have measured a lifetime of 4.9 fs of L-shell vacancies of argon in pump–probe experiments with a home-built high-resolution time-of-flight spectrometer.
Hybrid Detection of Breast Abnormalities Based on Contrast Agents: Introducing a Proof of Concept from a Physics Perspective
Authors: Liaparinos Panagiotis
Published: October 2022

This manuscript attempts to present a proof of concept from a physics perspective of a hybrid detective system based on the utilization of contrast agents with the purpose of indicating breast tissue abnormalities. In the present concept, the photon-counting module of the detector is set up to the K-characteristic radiation emitted by the contrast agent. Τwo X-ray spectra were used: 40 kV- W/Al (1.6 mm) and 50 kV- W/Al (1.6 mm) with additional filtration of 0.3 mm Gd. Iodine (I) contrast agent was studied as a ‘‘fingerprint’’ for tissue abnormality indication. A computational Monte Carlo model, based on previously published validated tabulated data and tissue experimental measurements, was developed with the purpose of showing that the present concept has practical potential; however, with a weakness of not being accompanied by experimental validation. The study considered two types of internal tissue layers (fibrous/tumor with thickness values of 0.2–2.5 mm) within an external layer of fat tissue (4 and 8 cm). Quantitative (number of encountered K-photons) and qualitative (tumor–fibrous ratio) advantages of using X-ray spectra of a higher tube voltage (50 kV) and of counting the Κα photons were found. In addition, the quantitative and qualitative benefits were correspondingly more dominant at high (2.5 mm) and low (0.2 mm) tissue thickness values. In conclusion, by utilizing suitable contrast agents as ‘‘fingerprint’’ tissue abnormalities, the acquisition of combined morphological and functional imaging features (through the counting of K-X-rays) could enhance breast imaging in its present form and lead to advanced prognostic capabilities of breast abnormalities.
A Care Robot with Ethical Sensing System for Older Adults at Home
Authors: Kim Jong-Wook , Choi Young-Lim , Jeong Sang-Hyun , Han Jeonghye
Published: October 2022

Many studies have explored emotional and mental services that robots can provide for older adults, such as offering them daily conversation, news, music, or health information. However, the ethical issues raised by using sensors for frail older adults to monitor their daily movements or their medication intake, for instance, are still being discussed. In this study, we develop an older adult-guided, caregiver-monitored robot, Dori, which can detect and recognize movement by sensing human poses in accordance with two factors from the human-centered artificial intelligence (HCAI) framework. To design the care robot’s services based on sensing movement during daily activities, we conducted focus group interviews with two groups—caregivers and medical staff—on the topic of care robot services not for patients but for prefrail and frail elderly individuals living at home. Based on their responses, we derived the focal service areas of cognitive support, emotional support, physical activity support, medication management, and caregiver management. We also found the two groups differed in their ethical judgments in the areas of dignity, autonomy, controllability, and privacy for services utilizing sensing by care robots. Therefore, the pose recognition technology adopted in the present work uses only joint coordinate information extracted from camera images and thus is advantageous for protecting human dignity and personal information.
Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network
Authors: Shi Ruizhe , Du Lijing
Published: October 2022

As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the adjacent road sections. Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic flow data in the past period of time of each adjacent section to jointly predict the traffic flow changes of the target section in a short time. The accurate prediction of future traffic flow changes can be solved based on the model supposed when the traffic flow data of the target road section is partially missing in the past period of time. The accuracy of the prediction results is the same as that of the current mainstream prediction results based on continuous and non-missing target link flow data. Meanwhile, there is a small-scale improvement when the data time interval is short enough. In the case of frequent maintenance of cameras in actual traffic sections, the proposed prediction method is more feasible and can be widely used.
On-Field Biomechanical Assessment of High and Low Dive in Competitive 16-Year-Old Goalkeepers through Wearable Sensors and Principal Component Analysis
Authors: Di Paolo Stefano , Santillozzi Francesco , Zinno Raffaele , Barone Giuseppe , Bragonzoni Laura
Published: October 2022

Diving saves are the main duty of football goalkeepers. Few biomechanical investigations of dive techniques have been conducted, none in a sport-specific environment. The present study investigated the characteristics of goalkeepers’ dive in preferred (PS) and non-preferred (nPS) side through an innovative wearables-plus-principal-component analysis (PCA) approach. Nineteen competitive academy goalkeepers (16.5 ± 3.0 years) performed a series of high and low dives on their PS and nPS. Dives were performed in a regular football goal on the pitch. Full-body kinematics were collected through 17 wearable inertial sensors (MTw Awinda, Xsens). PCA was conducted to reduce data dimensionality (input matrix 310,878 datapoints). PCA scores were extracted for each kinematic variable and compared between PS and nPS if their explained variability was >5%. In high dive, participants exhibited greater hip internal rotation and less trunk lateral tilt (p < 0.047, ES > 0.39) in PS than nPS. In low dives, players exhibited greater ipsilateral hip abduction dominance and lower trunk rotation (p < 0.037, ES > 0.40) in PS than nPS. When diving on their nPS, goalkeepers adopted sub-optimal patterns with less trunk coordination and limited explosiveness. An ecological testing through wearables and PCA might help coaches to inspect relevant diving characteristics and improve training effectiveness.
Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples
Authors: Al-Bakri Amir F. , Martinek Radek , Pelc Mariusz , Zygarlicki Jarosław , Kawala-Sterniuk Aleksandra
Published: October 2022

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.
From Identification to Sensing: RFID Is One of the Key Technologies in the IoT Field
Authors: Duroc Yvan
Published: October 2022

RFID (radio frequency identification) technology appeared nearly 70 years ago. Deployed more widely only from the early 2000s, it is now booming and its development is still accelerating. As its name indicates, its original function was the identification (of objects, animals, people) and its applications were then essentially aimed at traceability, access control and logistics. If this type of use is still relevant today with more and more new application contexts and more and more efficient RFID tags, RFID has also evolved by integrating new capabilities. These new tags, known as augmented tags, include an information capture function. With the explosion of connected objects and the emergence of the Internet of Things (IoT), this old technology that is RFID still has a promising future and will probably be more and more present in our private and professional environments in all fields: logistics, industry, agriculture, building, health and even space.
Instance-Level Contrastive Learning for Weakly Supervised Object Detection
Authors: Zhang Ming , Zeng Bing
Published: October 2022

Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs. Existing methods usually focus on the current individual image to learn object instance representations, while ignoring instance correlations between different images. To address this problem, we propose an instance-level contrastive learning (ICL) framework to mine reliable instance representations from all learned images, and use the contrastive loss to guide instance representation learning for the current image. Due to the diversity of instances, with different appearances, sizes or shapes, we propose an instance-diverse memory updating (IMU) algorithm to mine different instance representations and store them in a memory bank with multiple representation vectors per class, which also considers background information to enhance foreground representations. With the help of memory bank, we further propose a memory-aware instance mining (MIM) algorithm that combines proposal confidence and instance similarity across images to mine more reliable object instances. In addition, we also propose a memory-aware proposal sampling (MPS) algorithm to sample more positive proposals and remove some negative proposals to balance the learning of positive-negative samples. We conduct extensive experiments on the PASCAL VOC2007 and VOC2012 datasets, which are widely used in WSOD, to demonstrate the effectiveness of our method. Compared to our baseline, our method brings 14.2% mAP and 13.4% CorLoc gains on PASCAL VOC2007 dataset, and 12.2% mAP and 8.3% CorLoc gains on PASCAL VOC2012 dataset.
Optimization Method for Wide Beam Sonar Transmit Beamforming
Authors: Rixon Fuchs Louise , Maki Atsuto , Gällström Andreas
Published: October 2022

Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit beampattern should have a wide field of view. In this work, we study the problem of the optimization of wide transmission beampatterns. We consider the conventional phased-array beampattern design problem where all array elements transmit an identical waveform. The complex weight vector is adjusted to create the desired beampattern shape. In our experiments, we consider wide transmission beampatterns (≥20∘) with uniform output power. In this paper, we introduce a new iterative-convex optimization method for narrowband linear phased arrays and compare it to existing approaches for convex and concave–convex optimization. In the iterative-convex method, the phase of the weight parameters is allowed to be complex as in disciplined convex–concave programming (DCCP). Comparing the iterative-convex optimization method and DCCP to the standard convex optimization, we see that the former methods archive optimized beampatterns closer to the desired beampatterns. Furthermore, for the same number of iterations, the proposed iterative-convex method achieves optimized beampatterns, which are closer to the desired beampattern than the beampatterns achieved by optimization with DCCP.
Flexible and Dynamic Scheduling of Mixed-Criticality Systems
Authors: Jiang Xiaowen , Sha Tianyi , Liu Dehong , Chen Junjian , Chen Chen , Huang Kai
Published: October 2022

A mixed-criticality system refers to an integrated embedded system in which tasks with different criticality levels run on a shared computing platform. In the design and development of mixed-criticality systems, how to schedule tasks to ensure that high-criticality tasks are executed in time and low-criticality tasks are served as much as possible is a major problem to be studied. Existing studies tend to consider pessimistic processing strategies to ensure the schedulability of functional tasks with high-criticality requirements. However, excessive pessimistic processing can lead to waste of system resources, thereby reducing the performance of functional tasks with low-criticality requirements. In this paper, we propose an adaptive-service-level adjustment strategy for low-criticality tasks, which solves the problem of waste of resources caused by invalid compensation in the low-criticality task compensation method of flexible mixed-criticality systems. In view of the problem that the existing methods mostly use static budget allocation and static independent mode switching without considering the actual operation of the task, this paper also proposes a flexible and dynamic mixed-criticality system scheduling scheme and designs a system execution framework, scheduling algorithm, and dynamic allocation strategy of maximum execution budget, in order to reduce unnecessary redundant resource expenditures and system switching costs and to improve the performance of low-criticality tasks. Experiments show that the proposed methods are effective compared to the state-of-the-art.
Requirements for Automotive LiDAR Systems
Authors: Dai Zhuoqun , Wolf Alexander , Ley Peer-Phillip , Glück Tobias , Sundermeier Max Caspar , Lachmayer Roland
Published: October 2022

Light detection and ranging (LiDAR) are fundamental sensors that help driving tasks for autonomous driving at various levels. Commercially available systems come in different specialized design schemes and involve plenty of specifications. In the literature, there are insufficient representations of the technical requirements for LiDAR systems in the automotive context, such as range, detection quality, resolving power, field of view, and eye safety. For this reason, the requirements above require to be derived based on ADAS functions. The requirements for various key LiDAR metrics, including detection range, field of view, angular resolution, and laser safety, are analyzed in this paper. LiDAR systems are available with various radiation patterns that significantly impact on detection range. Therefore, the detection range under various radiation patterns is firstly investigated in this paper. Based on ADAS functions, the required detection range and field of view for LiDAR systems are examined, taking into account various travel speeds to avoid collision and the coverage of the entire lane width. Furthermore, the angular resolution limits are obtained utilizing the KITTI dataset and exemplary 3D detection algorithms. Finally, the maximum detection ranges for the different radiation patterns are compared under the consideration of derived requirements and laser safety.
A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery
Authors: Gao Yangde , Piltan Farzin , Kim Jong-Myon
Published: October 2022

Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal data from the rotating machinery is transformed into time–frequency feature 2-D image data by a continuous wavelet transform and used for fault classification with the neural network method. The adaptive deep convolution neural network (ADCNN) is then combined with the generative adversarial networks (GANs) to improve the performance of the feature self-learning ability from input data. Compared with different fault diagnosis methods, the proposed method has better performance for image feature classification in rotating machinery.
Advances in Electrochemical Nano-Biosensors for Biomedical and Environmental Applications: From Current Work to Future Perspectives
Authors: Hassan Rabeay Y. A.
Published: October 2022

Modern life quality is strongly supported by the advances made in biosensors, which has been attributed to their crucial and viable contribution in point-of-care (POC) technology developments. POC devices are exploited for the fast tracing of disease progression, rapid analysis of water, and food quality assessment. Blood glucose meters, home pregnancy strips, and COVID-19 rapid tests all represent common examples of successful biosensors. Biosensors can provide great specificity due to the incorporation of selective bio-recognition elements and portability at significantly reduced costs. Electrochemical biosensor platforms are one of the most advantageous of these platforms because they offer many merits, such as being cheap, selective, specific, rapid, and portable. Furthermore, they can be incorporated into smartphones and various analytical approaches in order to increase their sensitivity and many other properties. As a very broad and interdisciplinary area of research and development, biosensors include all disciplines and backgrounds from materials science, chemistry, physics, medicine, microbiology/biology, and engineering. Accordingly, in this state-of-the-art article, historical background alongside the long journey of biosensing construction and development, starting from the Clark oxygen electrode until reaching highly advanced wearable stretchable biosensing devices, are discussed. Consequently, selected examples among the miscellaneous applications of nanobiosensors (such as microbial detection, cancer diagnosis, toxicity analysis, food quality-control assurance, point of care, and health prognosis) are described. Eventually, future perspectives for intelligent biosensor commercialization and exploitation in real-life that is going to be supported by machine learning and artificial intelligence (AI) are stated.
High-Precision Vital Signs Monitoring Method Using a FMCW Millimeter-Wave Sensor
Authors: Xiang Mingxu , Ren Wu , Li Weiming , Xue Zhenghui , Jiang Xinyue
Published: October 2022

The method of using millimeter-wave radar sensors to detect human vital signs, namely respiration and heart rate, has received widespread attention in non-contact monitoring. These sensors are compact, lightweight, and able to sense and detect various scenarios. However, it still faces serious problems of noisy interference in hardware, which leads to a low signal-to-noise ratio (SNR). We used a frequency-modulated continuous wave (FMCW) radar sensor operating at 77 GHz in an office environment to extract the respiration and heart rate of a person accustomed to sitting in a chair. Indeed, the proposed signal processing includes novel impulse denoising operations and the spectral estimation decision method, which are unique in terms of noise reduction and accuracy improvement. In addition, the proposed method provides high-quality, repeatable respiration and heart rates with relative errors of 1.33% and 1.96% on average compared with the reference values measured by a reliable smart bracelet.
Path Forming of Healthcare Practitioners in an Indoor Space Using Mobile Crowdsensing
Authors: Panlaqui Brixx-John , Fuad Muztaba , Deb Debzani , Mickle Charles
Published: October 2022

While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization.
Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends
Authors: Zhu Hong-Hu , Liu Wei , Wang Tao , Su Jing-Wen , Shi Bin
Published: October 2022

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.
Optical Fiber Sensors and Sensing Networks: Overview of the Main Principles and Applications
Authors: Pendão Cristiano , Silva Ivo
Published: October 2022

Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties of light to obtain measurements of parameters, such as temperature, strain, or angular velocity. In addition, optical fiber sensors can be used to form an Optical Fiber Sensing Network (OFSN) allowing manufacturers to create versatile monitoring solutions with several applications, e.g., periodic monitoring along extensive distances (kilometers), in extreme or hazardous environments, inside structures and engines, in clothes, and for health monitoring and assistance. Most of the literature available on this subject focuses on a specific field of optical sensing applications and details their principles of operation. This paper presents a more broad overview, providing the reader with a literature review that describes the main principles of optical sensing and highlights the versatility, advantages, and different real-world applications of optical sensing. Moreover, it includes an overview and discussion of a less common architecture, where optical sensing and Wireless Sensor Networks (WSNs) are integrated to harness the benefits of both worlds.
Highly Sensitive Strain Sensor by Utilizing a Tunable Air Reflector and the Vernier Effect
Authors: Mumtaz Farhan , Roman Muhammad , Zhang Bohong , Abbas Lashari Ghulam , Ashraf Muhammad Aqueel , Dai Yutang , Huang Jie
Published: October 2022

A highly sensitive strain sensor based on tunable cascaded Fabry–Perot interferometers (FPIs) is proposed and experimentally demonstrated. Cascaded FPIs consist of a sensing FPI and a reference FPI, which effectively generate the Vernier effect (VE). The sensing FPI comprises a hollow core fiber (HCF) segment sandwiched between single-mode fibers (SMFs), and the reference FPI consists of a tunable air reflector, which is constituted by a computer-programable fiber holding block to adjust the desired cavity length. The simulation results predict the dispersion characteristics of modes carried by HCF. The sensor’s parameters are designed to correspond to a narrow bandwidth range, i.e., 1530 nm to 1610 nm. The experimental results demonstrate that the proposed sensor exhibits optimum strain sensitivity of 23.9 pm/με, 17.54 pm/με, and 14.11 pm/με cascaded with the reference FPI of 375 μm, 365 μm, and 355 μm in cavity length, which is 13.73, 10.08, and 8.10 times higher than the single sensing FPI with a strain sensitivity of 1.74 pm/με, respectively. The strain sensitivity of the sensor can be further enhanced by extending the source bandwidth. The proposed sensor exhibits ultra-low temperature sensitivity of 0.49 pm/°C for a temperature range of 25 °C to 135 °C, providing good isolation for eliminating temperature–strain cross-talk. The sensor is robust, cost-effective, easy to manufacture, repeatable, and shows a highly linear and stable response for strain sensing. Based on the sensor’s performance, it may be a good candidate for high-resolution strain sensing.
Ground-Reaction-Force-Based Gait Analysis and Its Application to Gait Disorder Assessment: New Indices for Quantifying Walking Behavior
Authors: Park Ji Su , Kim Choong Hyun
Published: October 2022

Gait assessment is an important tool for determining whether a person has a gait disorder. Existing gait analysis studies have a high error rate due to the heel-contact-event-based technique. Our goals were to overcome the shortcomings of existing gait analysis techniques and to develop more objective indices for assessing gait disorders. This paper proposes a method for assessing gait disorders via the observation of changes in the center of pressure (COP) in the medial–lateral direction, i.e., COPx, during the gait cycle. The data for the COPx were used to design a gait cycle estimation method applicable to patients with gait disorders. A polar gaitogram was drawn using the gait cycle and COPx data. The difference between the areas inside the two closed curves in the polar gaitogram, area ratio index (ARI), and the slope of the tangential line common to the two closed curves were proposed as gait analysis indices. An experimental study was conducted to verify that these two indices can be used to differentiate between stroke patients and healthy adults. The findings indicated the potential of using the proposed polar gaitogram and indices to develop and apply wearable devices to assess gait disorders.
CFD-Based Physical Failure Modeling of Direct-Drive Electro-Hydraulic Servo Valve Spool and Sleeve
Authors: Huang Guoqin , Mi Juncheng , Yang Cheng , Yu Jin
Published: October 2022

Direct-drive electro-hydraulic servo valves are used extensively in aerospace, military and control applications, but little research has been conducted on their service life and physical failure wear. Based on computational fluid dynamics, the main failure forms of direct-drive electro-hydraulic servo valves are explored using their continuous phase flow and discrete phase motion characteristics, and then combined with the theory of erosion for calculation. A mathematical model of the direct-drive electro-hydraulic servo valve is established by using Solidworks software, and then imported into Fluent simulation software to establish its physical failure model and carry out simulation. Finally, the physical failure form of the direct drive electro-hydraulic servo valve is verified by the simulation results, and the performance degradation law is summarized. The results show that temperature, differential pressure, solid particle diameter and concentration, and opening degree all have an impact on the erosion and wear of direct-drive electro-hydraulic servo valves, in which differential pressure and solid particle diameter have a relatively large impact, and the servo valve must avoid working in the range of high differential pressure and solid particle diameter of 20–40 um as far as possible. This also provides further theoretical support and experimental guidance for the industrial application and life prediction of electro-hydraulic servo valves.
Evaluation of Commercial Corrosion Sensors for Real-Time Monitoring of Pipe Wall Thickness under Various Operational Conditions
Authors: Shin Dong-Ho , Hwang Hyun-Kyu , Kim Heon-Hui , Lee Jung-Hyung
Published: October 2022

In this study, we investigated the performance and reliability of commercial corrosion sensors for monitoring the integrity of piping systems in various fluid environments as an alternative to ultrasonic transducers. To this end, we investigated pipes’ wall-thinning using commercial electrical resistance (ER), linear polarization resistance (LPR), and ultrasonic transducer (UT) sensors under various operating environments. A pilot-scale closed-loop test bed was built to simulate a real pipeline flow situation, from which the sensor data were collected and analyzed. Experimental results indicate that, in the case of the LPR sensor, it is challenging to accurately measure the corrosion rate when a specific measure exceeds the threshold in a severe corrosion environment. In contrast, the ER sensor could measure metal loss under all conditions and reflect the corresponding characteristics. The metal loss (about 0.25 mm) of the real pipe after the experiment was confirmed to be equal to the metal loss (0.254 mm) measured by the sensor. Furthermore, the regression analysis revealed a high correlation between the results obtained from the ER and UT sensors. Thus, evaluating the remaining thickness of the piping system using the commercial ER sensor is deemed to be effective and reliable.
Precision Enhancement of Wireless Localization System Using Passive DOA Multiple Sensor Network for Moving Target
Authors: Chen Chien-Bang , Lo Tsu-Yu , Chang Je-Yao , Huang Shih-Ping , Tsai Wei-Ting , Liou Chong-Yi , Mao Shau-Gang
Published: October 2022

Determining the direction-of-arrival (DOA) of any signal of interest has long been of great interest to the wireless localization research community for military and civilian applications. To efficiently facilitate the deployment of DOA systems, the accuracy of wireless localization is critical. Hence, this paper proposes a novel method to improve the prediction result of a wireless DOA localization system. By considering the signal variation existing in the complex environment, the actual location of the target can be determined including the maximum prediction error. Moreover, the scenario of the moving target is further investigated by incorporating the adaptive Kalman Filter algorithm to obtain the prediction route of the flying drone based on the accuracy assessment method. This proposed adaptive Kalman Filter is a high-efficiency algorithm that can filter out the noise in the multipath area and optimize the predicted data in real-time. The simulation result agrees well with the measured data and thus validates the proposed DOA system with the adaptive Kalman Filter algorithm. The measured DOA of the fixed radiation source obtained by a single base station and the moving route of a flying drone from a two-base station localization system are presented and compared with the calculated results. Results show that the prediction error in an outdoor region of 500×500 m2 is about 10–20 m, which demonstrate the usefulness of the proposed wireless DOA system deployment in practical applications.
Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors
Authors: Andreozzi Emilio , Sabbadini Riccardo , Centracchio Jessica , Bifulco Paolo , Irace Andrea , Breglio Giovanni , Riccio Michele
Published: October 2022

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels’ lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical–optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland–Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.
SUDOSCAN, an Innovative, Simple and Non-Invasive Medical Device for Assessing Sudomotor Function
Authors: Gavan Dana Elena , Gavan Alexandru , Bondor Cosmina Ioana , Florea Bogdan , Bowling Frank Lee , Inceu Georgeta Victoria , Colobatiu Liora
Published: October 2022

Diabetic autonomic neuropathy is probably the most undiagnosed but serious complication of diabetes. The main objectives were to assess the prevalence of peripheral and autonomic neuropathy in a population of diabetic patients, analyze it in a real-life outpatient unit scenario and determine the feasibility of performing SUDOSCAN tests together with widely used tests for neuropathy. A total of 33 patients were included in the study. Different scoring systems (the Toronto Clinical Neuropathy Score—TCNS; the Neuropathy Disability Score—NDS; and the Neuropathy Symptom Score—NSS) were applied to record diabetic neuropathy (DN), while the SUDOSCAN medical device was used to assess sudomotor function, detect diabetic autonomic neuropathy and screen for cardiac autonomic neuropathy (CAN). Fifteen (45.5%) patients had sudomotor dysfunction. The SUDOSCAN CAN risk score was positively correlated with the hands’ electrochemical sweat conductance (ESC), diastolic blood pressure (DBP), the level of the glycated hemoglobin, as well as with the TCNS, NDS and NSS. Performing SUDOSCAN tests together with other tests for DN proved to be a feasible approach that could be used in daily clinical practice in order to screen for DN, as well as for the early screening of CAN, before more complex and time-consuming tests.