bluuebunny/crossref_metadata_2025 huggingface dataset:
Stride Lengths during Maximal Linear Sprint Acceleration Obtained with Foot-Mounted Inertial Measurement Units
Authors: de Ruiter Cornelis J. , Wilmes Erik , van Ardenne Pepijn S. , Houtkamp Niels , Prince Reinder A. , Wooldrik Maarten , van Dieën Jaap H.
Published: January 2022

Inertial measurement units (IMUs) fixed to the lower limbs have been reported to provide accurate estimates of stride lengths (SLs) during walking. Due to technical challenges, validation of such estimates in running is generally limited to speeds (well) below 5 m·s−1. However, athletes sprinting at (sub)maximal effort already surpass 5 m·s−1 after a few strides. The present study aimed to develop and validate IMU-derived SLs during maximal linear overground sprints. Recreational athletes (n = 21) completed two sets of three 35 m sprints executed at 60, 80, and 100% of subjective effort, with an IMU on the instep of each shoe. Reference SLs from start to ~30 m were obtained with a series of video cameras. SLs from IMUs were obtained by double integration of horizontal acceleration with a zero-velocity update, corrected for acceleration artefacts at touch-down of the feet. Peak sprint speeds (mean ± SD) reached at the three levels of effort were 7.02 ± 0.80, 7.65 ± 0.77, and 8.42 ± 0.85 m·s−1, respectively. Biases (±Limits of Agreement) of SLs obtained from all participants during sprints at 60, 80, and 100% effort were 0.01% (±6.33%), −0.75% (±6.39%), and −2.51% (±8.54%), respectively. In conclusion, in recreational athletes wearing IMUs tightly fixed to their shoes, stride length can be estimated with reasonable accuracy during maximal linear sprint acceleration.
Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving
Authors: Yoon Ha-Yeong , Kim Jung-Hwa , Jeong Jin-Woo
Published: January 2022

The demand for wheelchairs has increased recently as the population of the elderly and patients with disorders increases. However, society still pays less attention to infrastructure that can threaten the wheelchair user, such as sidewalks with cracks/potholes. Although various studies have been proposed to recognize such challenges, they mainly depend on RGB images or IMU sensors, which are sensitive to outdoor conditions such as low illumination, bad weather, and unavoidable vibrations, resulting in unsatisfactory and unstable performance. In this paper, we introduce a novel system based on various convolutional neural networks (CNNs) to automatically classify the condition of sidewalks using images captured with depth and infrared modalities. Moreover, we compare the performance of training CNNs from scratch and the transfer learning approach, where the weights learned from the natural image domain (e.g., ImageNet) are fine-tuned to the depth and infrared image domain. In particular, we propose applying the ResNet-152 model pre-trained with self-supervised learning during transfer learning to leverage better image representations. Performance evaluation on the classification of the sidewalk condition was conducted with 100% and 10% of training data. The experimental results validate the effectiveness and feasibility of the proposed approach and bring future research directions.
On Theoretical and Numerical Aspects of Bifurcations and Hysteresis Effects in Kinetic Energy Harvesters
Authors: Litak Grzegorz , Margielewicz Jerzy , Gąska Damian , Rysak Andrzej , Trigona Carlo
Published: January 2022

The piezoelectric energy-harvesting system with double-well characteristics and hysteresis in the restoring force is studied. The proposed system consists of a bistable oscillator based on a cantilever beam structure. The elastic force potential is modified by magnets. The hysteresis is an additional effect of the composite beam considered in this system, and it effects the modal solution with specific mass distribution. Consequently, the modal response is a compromise between two overlapping, competing shapes. The simulation results show evolution in the single potential well solution, and bifurcations into double-well solutions with the hysteretic effect. The maximal Lyapunov exponent indicated the appearance of chaotic solutions. Inclusion of the shape branch overlap parameter reduces the distance between the external potential barriers and leads to a large-amplitude solution and simultaneously higher voltage output with smaller excitation force. The overlap parameter works in the other direction: the larger the overlap value, the smaller the voltage output. Presumably, the successful jump though the potential barrier is accompanied by an additional switch between the corresponding shapes.
Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection
Authors: Ogawa Naoki , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: January 2022

This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps for estimation by using an attention map. The attention mechanism using an effective attention map can improve feature maps. However, the conventional attention mechanisms have a problem as they fail to highlight important regions for estimation when an ineffective attention map is mistakenly used. To solve the above problem, this paper introduces the confidence-aware attention mechanism that reduces the effect of ineffective attention maps by considering the confidence corresponding to the attention map. The confidence is calculated from the entropy of the estimated class probabilities when generating the attention map. Because the proposed method can effectively utilize the attention map by considering the confidence, it can focus more on the important regions in the final estimation. This is the most significant contribution of this paper. The experimental results using images from actual infrastructure inspections confirm the performance improvement of the proposed method in estimating the deterioration level.
On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
Authors: Mukase Sandrine , Xia Kewen , Umar Abubakar , Owoola Eunice Oluwabunmi
Published: January 2022

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.
Kinematic Analysis of 360° Turning in Stroke Survivors Using Wearable Motion Sensors
Authors: Abdollahi Masoud , Kuber Pranav Madhav , Shiraishi Michael , Soangra Rahul , Rashedi Ehsan
Published: January 2022

Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. Using inertial sensors to record the digital biomarkers during turning could reveal the relevant turning alterations. Objectives: In this study, movement alterations in stroke survivors (SS) were studied and compared to healthy individuals (HI) in the entire turning task due to its requirement of synergistic application of multiple bodily systems. Methods: The motion of 28 participants (14 SS, 14 HI) during turning was captured using a set of four Inertial Measurement Units, placed on their sternum, sacrum, and both shanks. The motion signals were segmented using the temporal and spatial segmentation of the data from the leading and trailing shanks. Several kinematic parameters, including the range of motion and angular velocity of the four body segments, turning time, the number of cycles involved in the turning task, and portion of the stance phase while turning, were extracted for each participant. Results: The results of temporal processing of the data and comparison between the SS and HI showed that SS had more cycles involved in turning, turn duration, stance phase, range of motion in flexion–extension, and lateral bending for sternum and sacrum (p-value < 0.035). However, HI exhibited larger angular velocity in flexion–extension for all four segments. The results of the spatial processing, in agreement with the prior method, showed no difference between the range of motion in flexion–extension of both shanks (p-value > 0.08). However, it revealed that the angular velocity of the shanks of leading and trailing legs in the direction of turn was more extensive in the HI (p-value < 0.01). Conclusions: The changes in upper/lower body segments of SS could be adequately identified and quantified by IMU sensors. The identified kinematic changes in SS, such as the lower flexion–extension angular velocity of the four body segments and larger lateral bending range of motion in sternum and sacrum compared to HI in turning, could be due to the lack of proper core stability and effect of turning on vestibular system of the participants. This research could facilitate the development of a targeted and efficient rehabilitation program focusing on the affected aspects of turning movement for the stroke community.
Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review
Authors: Bouillod Anthony , Soto-Romero Georges , Grappe Frederic , Bertucci William , Brunet Emmanuel , Cassirame Johan
Published: January 2022

A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).
Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking
Authors: Moraffah Bahman , Papandreou-Suppappola Antonia
Published: January 2022

The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman–Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter.
Negative Effects of Annealed Seed Layer on the Performance of ZnO-Nanorods Based Nitric Oxide Gas Sensor
Authors: Singh Pragya , Simanjuntak Firman Mangasa , Hu Li-Lun , Tseng Tseung-Yuen , Zan Hsiao-Wen , Chu Jinn P.
Published: January 2022

Nitric oxide (NO) is a toxic gas, which is dangerous for human health and causes many respiratory infections, poisoning, and lung damage. In this work, we have successfully grown ZnO nanorod film on annealed ZnO seed layer in different ambient temperatures, and the morphology of the nanorods sensing layer that affects the gas sensing response to nitric oxide (NO) gas were investigated. To acknowledge the effect of annealing treatment, the devices were fabricated with annealed seed layers in air and argon ambient at 300 °C and 500 °C for 1 h. To simulate a vertical device structure, a silver nanowire electrode covered in ZnO nanorod film was placed onto the hydrothermal grown ZnO nanorod film. We found that annealing treatment changes the seed layer’s grain size and defect concentration and is responsible for this phenomenon. The I–V and gas sensing characteristics were dependent on the oxygen defects concentration and porosity of nanorods to react with the target gas. The resulting as-deposited ZnO seed layer shows better sensing response than that annealed in an air and argon environment due to the nanorod morphology and variation in oxygen defect concentration. At room temperature, the devices show good sensing response to NO concentration of 10 ppb and up to 100 ppb. Shortly, these results can be beneficial in the NO breath detection for patients with chronic inflammatory airway disease, such as asthma.
Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi
Authors: Li Zhonghan , Zhang Yongbo
Published: January 2022

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.
Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
Authors: Bogatskaya Anna V. , Schegolev Andrey E. , Klenov Nikolay V. , Lobov Evgeniy M. , Tereshonok Maxim V. , Popov Alexander M.
Published: January 2022

We consider two of the most relevant problems that arise when modeling the properties of a tunnel radio communication channel through a plasma layer. First, we studied the case of the oblique incidence of electromagnetic waves on a layer of ionized gas for two wave polarizations. The resonator parameters that provide signal reception at a wide solid angle were found. We also took into account the unavoidable presence of a protective layer between the plasma and the resonator, as well as the conducting elements of the antenna system in the dielectric itself. This provides the first complete simulation for a tunnel communication channel. Noise immunity and communication range studies were conducted for a prospective spacecraft radio line.
QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
Authors: Syed Sidra Abid , Rashid Munaf , Hussain Samreen , Azim Fahad , Zahid Hira , Umer Asif , Waheed Abdul , Zareei Mahdi , Vargas-Rosales Cesar
Published: January 2022

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.
WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals
Authors: Hao Zhanjun , Niu Juan , Dang Xiaochao , Qiao Zhiqiang
Published: January 2022

Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance.
Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network
Authors: Bhatia Yajurv , Bari ASM Hossain , Hsu Gee-Sern Jison , Gavrilova Marina
Published: January 2022

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance.
Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach
Authors: Chang Ching-Wei , Lo Li-Yu , Cheung Hiu Ching , Feng Yurong , Yang An-Shik , Wen Chih-Yung , Zhou Weifeng
Published: January 2022

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.
SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies
Authors: Chromik Jonas , Kirsten Kristina , Herdick Arne , Kappattanavar Arpita Mallikarjuna , Arnrich Bert
Published: January 2022

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks
Authors: Awan Saba , Javaid Nadeem , Ullah Sameeh , Khan Asad Ullah , Qamar Ali Mustafa , Choi Jin-Ghoo
Published: January 2022

In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network’s resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs’ energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest–Shamir–Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.
Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor
Authors: Borzì Luigi , Mazzetta Ivan , Zampogna Alessandro , Suppa Antonio , Irrera Fernanda , Olmo Gabriella
Published: January 2022

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks
Authors: Subramani Neelakandan , Mohan Prakash , Alotaibi Youseef , Alghamdi Saleh , Khalaf Osamah Ibrahim
Published: January 2022

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.
Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors
Authors: Li Jinlong , Chen Bingren , Yuan Meng , Zhao Qian , Luo Lin , Gao Xiaorong
Published: January 2022

Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.
A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings
Authors: Tomita Ko , Chew Michael Yit Lin
Published: January 2022

This paper provides a comprehensive review on the use of infrared thermography to detect delamination on infrastructures and buildings. Approximately 200 pieces of relevant literature were evaluated, and their findings were summarized. The factors affecting the accuracy and detectability of infrared thermography were consolidated and discussed. Necessary measures to effectively capture latent defects at the early stage of delamination before crack formation were investigated. The results of this study could be used as the benchmarks for setting standardized testing criteria as well as for comparison of results for future works on the use of infrared thermography for detection of delamination on infrastructures and buildings.
Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective
Authors: Kakkavas Grigorios , Diamanti Maria , Stamou Adamantia , Karyotis Vasileios , Bouali Faouzi , Pinola Jarno , Apilo Olli , Papavassiliou Symeon , Moessner Klaus
Published: January 2022

The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis.
Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds
Authors: Cao Yang , Zhang Yuchen , Lin Menghua , Wu Di , Chen Kunsong
Published: January 2022

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography–mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.
A Constrained Kalman Filter for Wi-Fi-Based Indoor Localization with Flexible Space Organization
Authors: Sircoulomb Vincent , Chafouk Houcine
Published: January 2022

This paper presents a constrained Kalman filter for Wi-Fi-based indoor localization. The contribution of this work is to introduce constraints on the object speed and to provide a numerically optimized form for fast computation. The proposed approach is suitable to flexible space organization, as in warehouses, and when objects can be spun around, for example barcode readers in a hand. We experimented with the proposed technique using a robot and three devices, on five different journeys, in a 6000 m2 warehouse equipped with six Wi-Fi access points. The results highlight that the proposed approach provides a 19% improvement in localization accuracy.
Vehicle Interaction Behavior Prediction with Self-Attention
Authors: Li Linhui , Sui Xin , Lian Jing , Yu Fengning , Zhou Yafu
Published: January 2022

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.
Research on the Dual Modulation of All-Fiber Optic Current Sensor
Authors: Wu Jianhua , Zhang Xiaofeng , Chen Liang
Published: January 2022

Acousto-optic modulator (AOM) and electro-optical modulator (EOM) are applied to realize the all-fiber current sensor with a pulsed light source. The pulsed light is realized by amplitude modulation with AOM. The reflected interferometer current sensor is constructed by the mirror and phase modulation with EOM to improve the anti-interference ability. A correlation demodulation algorithm is applied for data processing. The influence of the modulation frequency and duty cycle of AOM on the optical system is determined by modeling and experiment. The duty cycle is the main factor affecting the normalized scale factor of the system. The modulation frequency mainly affects the output amplitude of the correlation demodulation and the system signal-to-noise ratio. The frequency multiplication factor links AOM and EOM, primarily affecting the ratio error. When the frequency multiplication factor is equal to the duty cycle of AOM and it is an integer multiple of 0.1, the ratio error of the system is less than 1.8% and the sensitivity and the resolution of AFOCS are 0.01063 mV/mA and 3 mA, respectively. The measurement range of AFOCS is from 11 mA to 196.62 A, which is excellent enough to meet the practical requirements for microcurrent measurement.
Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario
Authors: Troscia Martina , Sgambelluri Andrea , Paolucci Francesco , Castoldi Piero , Pagano Paolo , Cugini Filippo
Published: January 2022

Software Defined Networking represents a mature technology for the control of optical networks, though all open controller implementations present in the literature still lack the adequate level of maturity and completeness to be considered for (pre)-production network deployments. This work aims at experimenting on, assessing and discussing the use of the OneM2M open-source platform in the context of optical networks. Network elements and devices are implemented as IoT devices, and the control application is built on top of an OneM2M-compliant server. The work concretely addresses the scalability and flexibility performances of the proposed solution, accounting for the expected growth of optical networks. The two experiment scenarios show promising results and confirm that the OneM2M platform can be adopted in such a context, paving the way to other researches and studies.
Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways
Authors: Nguyen Xuan-Ha , Nguyen Xuan-Duong , Huynh Hoang-Hai , Le Kim-Hung
Published: January 2022

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.
Validation of an Embedded Motion-Capture and EMG Setup for the Analysis of Musculoskeletal Disorder Risks during Manhole Cover Handling
Authors: Hubaut Rémy , Guichard Romain , Greenfield Julia , Blandeau Mathias
Published: January 2022

Musculoskeletal disorders in the workplace are a growing problem in Europe. The measurement of these disorders in a working environment presents multiple limitations concerning equipment and measurement reliability. The aim of this study was to evaluate the use of inertial measurement units against a reference system for their use in the workplace. Ten healthy volunteers conducted three lifting methods (snatching, pushing, and pulling) for manhole cover using a custom-made tool weighting 20 and 30 kg. Participants’ back and dominant arm were equipped with IMU, EMG, and reflective markers for VICON analysis and perception of effort was estimated at each trial using a Visual Analog Scale (VAS). The Bland–Altman method was used and results showed good agreement between IMU and VICON systems for Yaw, Pitch and Roll angles (bias values < 1, −4.4 < LOA < 3.6°). EMG results were compared to VAS results and results showed that both are a valuable means to assess efforts during tasks. This study therefore validates the use of inertial measurement units (IMU) for motion capture and its combination with electromyography (EMG) and a Visual Analogic Scale (VAS) to assess effort for use in real work situations.
Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges
Authors: Subramaniam Sophini , Majumder Sumit , Faisal Abu Ilius , Deen M. Jamal
Published: January 2022

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.