bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping of the Acoustic Environment at an Urban Park in the City Area of Milan, Italy, Using Very Low-Cost Sensors
Authors: Benocci Roberto , Potenza Andrea , Bisceglie Alessandro , Roman Hector Eduardo , Zambon Giovanni
Published: May 2022

The-growing influence of urbanisation on green areas can greatly benefit from passive acoustic monitoring (PAM) across spatiotemporal continua to provide biodiversity estimation and useful information for conservation planning and development decisions. The capability of eco-acoustic indices to capture different sound features has been harnessed to identify areas within the Parco Nord of Milan, Italy, characterised by different degrees of anthropic disturbance and biophonic activity. For this purpose, we used a network of very low-cost sensors distributed over an area of approximately 20 hectares to highlight areas with different acoustic properties. The audio files analysed in this study were recorded at 16 sites on four sessions during the period 25–29 May (2015), from 06:30 a.m. to 10:00 a.m. Seven eco-acoustic indices, namely Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-Acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI), and Dynamic Spectral Centroid (DSC) were computed at 1 s integration time and the resulting time series were described by seven statistical descriptors. A dimensionality reduction of the indices carrying similar sound information was obtained by performing principal component analysis (PCA). Over the retained dimensions, describing a large (∼80%) variance of the original variables, a cluster analysis allowed discriminating among sites characterized by different combination of eco-acoustic indices (dimensions). The results show that the obtained groups are well correlated with the results of an aural survey aimed at determining the sound components at the sixteen sites (biophonies, technophonies, and geophonies). This outcome highlights the capability of this analysis of discriminating sites with different environmental sounds, thus allowing to create a map of the acoustic environment over an extended area.
Theoretical and Experimental Studies of Micro-Surface Crack Detections Based on BOTDA
Authors: Yuan Baolong , Ying Yu , Morgese Maurizio , Ansari Farhad
Published: May 2022

Micro-surface crack detection is important for the health monitoring of civil structures. The present literature review shows that micro-surface cracks can be detected by the Brillouin scattering process in optical fibers. However, the existing reports focus on experiment research. The comparison between theory and experiment for Brillouin-scattering-based optical sensors is rarely reported. In this paper, a distributed optical fiber sensor for monitoring micro-surface cracks is presented and demonstrated. In the simulation, by using finite element methods, an assemblage of a three-dimensional beam model for Brillouin optical time domain analysis (BOTDA) was built. The change in Brillouin frequency (distributed strain) as a function of different cracks was numerically investigated. Simulation results indicate that the amplitudes of the Brillouin peak increase from 27 με to 140 με when the crack opening displacement (COD) is enlarged from 0.002 mm to 0.009 mm. The experiment program was designed to evaluate the cracks in a beam with the length of 15 m. Experimental results indicate that it is possible to detect the COD in the length of 0.002~0.009 mm, which is consistent with the simulation data. The limitations of the proposed sensing method are discussed, and the future research direction is prospected.
Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
Authors: Bogue-Jimenez Brian , Huang Xiaolei , Powell Douglas , Doblas Ana
Published: May 2022

Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to measure the blood glucose based on chemical reactions with the blood. Unlike traditional glucometer devices, noninvasive continuous glucose monitoring (NICGM) devices aim to solve these issues by consistently monitoring users’ blood glucose levels (BGLs) without invasively acquiring a sample. In this work, we investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable sensors and learning-based models (i.e., machine learning) to predict blood glucose. Two datasets were used for this study: (1) the OhioT1DM dataset, provided by the Ohio University; and (2) the UofM dataset, created by our research team. The UofM dataset consists of fourteen features provided by six sensors for studying possible relationships between glucose and noninvasive biometric measurements. Both datasets are passed through a machine learning (ML) pipeline that tests linear and nonlinear models to predict BGLs from the set of noninvasive features. The results of this pilot study show that the combination of fourteen noninvasive biometric measurements with ML algorithms could lead to accurate BGL predictions within the clinical range; however, a larger dataset is required to make conclusions about the feasibility of this approach.
Can the Standard Configuration of a Cardiac Monitor Lead to Medical Errors under a Stress Induction?
Authors: Dzisko Maja , Lewandowska Anna , Wudarska Beata
Published: May 2022

The essential factor that enables medical patient monitoring is the vital signs monitor, whereas the key in communication with the monitor is the user interface. The way the data display on the monitors is standard, and it is often not changed; however, vital signs monitors are now configurable. Not all the data have to be displayed concurrently; not all data are necessary at a given moment. There arises a question: is the standard monitor configuration sufficient, or can it lead to mistakes related to delays in perceiving parameter changes? Some researchers argue that mistakes in life-saving activities is not mainly due to medical mistakes but due to poorly designed patient life monitor interfaces, among other reasons. In addition, it should be emphasized that the activity that saves the patient’s life is accompanied by stress, which is often caused by the chaos occurring in the hospital emergency department. This raises the following question: is the standard user interface, which they are used to, still effective under stress conditions? Therefore, our primary consideration is the measure of reaction speed of medical staff, which means the perception of the changes of vital signs on the patient’s monitor, for stress and stressless situations. The paper attempts to test the thesis of the importance of the medical interface and its relation to medical mistakes, extending it with knowledge about the difference in speed of making decisions by the medical staff with regard to the stress stimulus.
Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection
Authors: Du Fu-Jun , Jiao Shuang-Jian
Published: May 2022

To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.
An Adaptive LoRaWAN MAC Protocol for Event Detection Applications
Authors: Tsakmakis Athanasios , Valkanis Anastasios , Beletsioti Georgia , Kantelis Konstantinos , Nicopolitidis Petros , Papadimitriou Georgios
Published: May 2022

In recent years, the Internet of Things (IoT) is growing rapidly and gaining ground in a variety of fields. Such fields are environmental disasters, such as forest fires, that are becoming more common because of the environmental crisis and there is a need to properly manage them. Therefore, utilizing IoT for event detection and monitoring is an effective solution. A technique for monitoring such events over a large area is proposed in this research. This work makes use of the Long-Range Wide Area Network (LoRaWAN) protocol, which is capable to connect low-power devices distributed on large geographical areas. A learning-automata-based hybrid MAC model is suggested to reduce the transmission delay, when a small part of the network produces event packets stemming from an event occurrence that is related to environmental monitoring applications, such as events related to forest fires. The proposed hybrid MAC is evaluated via simulation, which indicates that it achieves significantly higher performance in terms of packet delay, when compared to traditional LoRaWAN schemes.
End-to-End Residual Network for Light Field Reconstruction on Raw Images and View Image Stacks
Authors: Salem Ahmed , Ibrahem Hatem , Yagoub Bilel , Kang Hyun-Soo
Published: May 2022

Light field (LF) technology has become a focus of great interest (due to its use in many applications), especially since the introduction of the consumer LF camera, which facilitated the acquisition of dense LF images. Obtaining densely sampled LF images is costly due to the trade-off between spatial and angular resolutions. Accordingly, in this research, we suggest a learning-based solution to this challenging problem, reconstructing dense, high-quality LF images. Instead of training our model with several images of the same scene, we used raw LF images (lenslet images). The raw LF format enables the encoding of several images of the same scene into one image. Consequently, it helps the network to understand and simulate the relationship between different images, resulting in higher quality images. We divided our model into two successive modules: LFR and LF augmentation (LFA). Each module is represented using a convolutional neural network-based residual network (CNN). We trained our network to lessen the absolute error between the novel and reference views. Experimental findings on real-world datasets show that our suggested method has excellent performance and superiority over state-of-the-art approaches.
Quantum LFSR Structure for Random Number Generation Using QCA Multilayered Shift Register for Cryptographic Purposes
Authors: Kim Hyun-Il , Jeon Jun-Cheol
Published: May 2022

A random number generator (RNG), a cryptographic technology that plays an important role in security and sensor networks, can be designed using a linear feedback shift register (LFSR). This cryptographic transformation is currently done through CMOS. It has been developed by reducing the size of the gate and increasing the degree of integration, but it has reached the limit of integration due to the quantum tunneling phenomenon. Quantum-dot cellular automata (QCA), one of the quantum circuit design technologies to replace this, has superior performance compared to CMOS in most performance areas, such as space, speed, and power. Most of the LFSRs in QCA are designed as shift registers (SR), and most of the SR circuits proposed based on the existing QCA have a planar structure, so the cell area is large and the signal is unstable when a plane intersection is implemented. Therefore, in this paper, we propose a multilayered 2-to-1 QCA multiplexer and a D-latch, and we make blocks based on D-latch and connect these blocks to make SR. In addition, the LFSR structure is designed by adding an XOR operation to it, and we additionally propose an LFSR capable of dual-edge triggering. The proposed structures were completed with a very meticulous design technique to minimize area and latency using cell interaction, and they achieve high performance compared to many existing circuits. For the proposed structures, the cost and energy dissipation are calculated through simulation using QCADesigner and QCADesigner-E, and their efficiency is verified.
Evaluation of Driver’s Reaction Time Measured in Driving Simulator
Authors: Čulík Kristián , Kalašová Alica , Štefancová Vladimíra
Published: May 2022

This article evaluates the driver’s reaction times in a driving simulator environment. The research focused mainly on young drivers under the age of 26, who cause many accidents. Each participating driver provided basic information later used for mathematical-statistical analysis. The main advantage of driving simulators is limitless usage. It is possible to simulate situations that would be unacceptable in real road traffic. Therefore, this study is also able to examine drunk driving. The main goal of the article is to evaluate if gender, practice, or alcohol significantly affected the reaction time of 30 drivers. We also focused on drinking before driving for a smaller number of the drivers; ten of them performed driving under the influence of alcohol. For these mathematical-statistical purposes, we used a one-sample t-test, a paired-samples t-test, an independent-sample t-test, and a correlation analysis together with the assessment of its statistical significance.
Visual Servoed Autonomous Landing of an UAV on a Catamaran in a Marine Environment
Authors: Delbene Andrea , Baglietto Marco , Simetti Enrico
Published: May 2022

This paper introduces a procedure for autonomous landing of a quadrotor on an unmanned surface vehicle in a marine environment. The relative pose and velocity of the vehicle with respect to the quadrotor are estimated using a combination of data coming from a vision system, which recognizes a set of AprilTags located on the vehicle itself, and an ultrasonic sensor, to achieve further robustness during the final landing phase. The considered software and hardware architecture is provided, and the details about the landing procedure are presented. Software-in-the-loop tests were performed as a validation step for the proposed algorithms; to recreate realistic conditions, the movements of the landing platform have been replicated from data of a test in a real marine environment. In order to provide further proof of the reliability of the vision system, a video sequence from a manual landing of a quadrotor on the surface vehicle in a real marine environment has been processed, and the results are presented.
LiDAR- and Radar-Based Robust Vehicle Localization with Confidence Estimation of Matching Results
Authors: Yanase Ryo , Hirano Daichi , Aldibaja Mohammad , Yoneda Keisuke , Suganuma Naoki
Published: May 2022

Localization is an important technology for autonomous driving. Map-matching using road surface pattern features gives accurate position estimation and has been used in autonomous driving tests on public roads. To provide highly safe autonomous driving, localization technology that is not affected by the environment is required. In particular, in snowy environments, the features of the road surface pattern may not be used for matching because the road surface is hidden. In such cases, it is necessary to construct a robust system by rejecting the matching results or making up for them with other sensors. On the other hand, millimeter-wave radar-based localization methods are not as accurate as LiDAR-based methods due to their ranging accuracy, but it has successfully achieved autonomous driving in snowy environments. Therefore, this paper proposes a localization method that combines LiDAR and millimeter-wave radar. We constructed a system that emphasizes LiDAR-based matching results during normal conditions when the road surface pattern is visible and emphasizes radar matching results when the road surface is not visible due to snow cover or other factors. This method achieves an accuracy that allows autonomous driving to continue regardless of normal or snowy conditions and more robust position estimation.
Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities
Authors: Ionescu Liviu , Rusu-Casandra Alexandru , Bira Calin , Tatomirescu Alexandru , Tramandan Ionut , Scagnoli Roberto , Istriteanu Dan , Popa Andrei-Edward
Published: May 2022

The constant increase in the number of space objects and debris orbiting the Earth poses risks to satellites and other spacecraft, both in orbit and during the launching process. Therefore, the monitoring of space hazards to assess risk and prevent collisions has become part of the European Space Policy and requires the establishment of a dedicated Framework for Space Surveillance and Tracking (EU SST) Support. This article presents the CHEIA SST Radar, a new space tracking radar sensor developed and installed in Romania with the purpose of being included in the EU SST sensor network and of contributing to the joint database of space objects orbiting the Earth. The paper describes the processes of design, simulation, and implementation of the hardware and software building blocks that make up the radar system. It emphasizes the particular case of using an already existing system of two large parabolic antennas requiring an innovative retrofitting design to include them as the basis for a new quasi-monostatic radar using LFMCW probing signals. The preliminary design was validated by extensive simulations, and the initial operational testing carried out in December 2021 demonstrated the good performance of the radar in the measuring range and radial speed of LEO space objects.
Development of an IoT Electrostimulator with Closed-Loop Control
Authors: De Almeida Túlio Fernandes , Borges Luiz Henrique Bertucci , Dantas André Felipe Oliveira de Azevedo
Published: May 2022

The most used approach in the motor rehabilitation of spinal cord injury is functional electrical stimulation. However, current devices do not provide real-time feedback, work in the closed-loop, and became remotely operable. In this scenario, this paper presents the development of an open access 4-channel IoT electrostimulator device with an inertial sensor. The electrostimulator circuit was designed with four modules: Boost Converter, H-bridge, Inertial Measurement Unit, and Processing Module. The firmware was implemented in the processing module to manage the modules to perform closed-loop stimulation (using PID controller). To perform the proof of concept of the device, a closed loop test was performed to control the ankle joint, performing the movements of dorsiflexion, plantar flexion, inversion, and eversion. The designed hardware allowed one to freely change the boost converter voltage and modulate the signal with 200 μs of pulse duration and 50 Hz of period in a safe and stable way. Furthermore, the controller was able to move the ankle joint in all desired directions following the reference values and respecting the imposed constraints. In general, the developed hardware was able to safely control a closed-loop joint.
Front-End Design for SiPM-Based Monolithic Neutron Double Scatter Imagers
Authors: Cates Joshua W. , Steele John , Balajthy Jon , Negut Victor , Hausladen Paul , Ziock Klaus , Brubaker Erik
Published: May 2022

Neutron double scatter imaging exploits the kinematics of neutron elastic scattering to enable emission imaging of neutron sources. Due to the relatively low coincidence detection efficiency of fast neutrons in organic scintillator arrays, imaging efficiency for double scatter cameras can also be low. One method to realize significant gains in neutron coincidence detection efficiency is to develop neutron double scatter detectors which employ monolithic blocks of organic scintillator, instrumented with photosensor arrays on multiple faces to enable 3D position and multi-interaction time pickoff. Silicon photomultipliers (SiPMs) have several advantageous characteristics for this approach, including high photon detection efficiency (PDE), good single photon time resolution (SPTR), high gain that translates to single photon counting capabilities, and ability to be tiled into large arrays with high packing fraction and photosensitive area fill factor. However, they also have a tradeoff in high uncorrelated and correlated noise rates (dark counts from thermionic emissions and optical photon crosstalk generated during avalanche) which may complicate event positioning algorithms. We have evaluated the noise characteristics and SPTR of Hamamatsu S13360-6075 SiPMs with low noise, fast electronic readout for integration into a monolithic neutron scatter camera prototype. The sensors and electronic readout were implemented in a small-scale prototype detector in order to estimate expected noise performance for a monolithic neutron scatter camera and perform proof-of-concept measurements for scintillation photon counting and three-dimensional event positioning.
A Novel Walking Activity Recognition Model for Rotation Time Series Collected by a Wearable Sensor in a Free-Living Environment
Authors: Brard Raphaël , Bellanger Lise , Chevreuil Laurent , Doistau Fanny , Drouin Pierre , Stamm  Aymeric
Published: May 2022

Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable sensors are powerful enough to integrate complex walking activity recognition models, non-invasive lightweight sensors do not always have the computing or memory capacity to run them. In this paper, we propose a walking activity recognition model that offers a viable solution to this problem for any wearable sensors that measure rotational motion of body parts. Specifically, the model was trained and tuned using data collected by a motion sensor in the form of a unit quaternion time series recording the hip rotation over time. This time series was then transformed into a real-valued time series of geodesic distances between consecutive quaternions. Moving average and moving standard deviation versions of this time series were fed to standard machine learning classification algorithms. To compare the different models, we used metrics to assess classification performance (precision and accuracy) while maintaining the detection prevalence at the level of the prevalence of walking activities in the data, as well as metrics to assess change point detection capability and computation time. Our results suggest that the walking activity recognition model with a decision tree classifier yields the best compromise in terms of precision and computation time. The sensor that was used had purposely low computing and memory capacity so that reported performances can be thought of as the lower bounds of what can be achieved. Walking activity recognition is performed online, i.e., on-the-fly, which further extends the range of applicability of our model to sensors with very low memory capacity.
Portable Sequentially Shifted Excitation Raman Spectroscopy to Examine Historic Powders Enclosed in Glass Vials
Authors: Innocenti Silvia , Quintero Balbas Diego , Pezzati Luca , Fontana Raffaella , Striova Jana
Published: May 2022

Raman spectroscopy (RS) is a powerful non-invasive tool for the characterization of materials. However, the fluorescence effect often hampers the detectability of the relatively weak vibrational Raman signal. Several approaches were exploited to overcome this limit. This work, in particular, evaluates the performance of an in situ portable sequentially shifted excitation (SSE™) Raman spectrometer applied to the examination of artistic historical pigment powders enclosed in glass vials. The explored handheld spectrometer employs a dual, temperature-shifted, 785 nm and 852 nm laser excitation to optimize both spectral coverage and fluorescence subtraction. The study demonstrates the feasibility of the SSE RS approach for non-invasive identification of art materials, and its applicability in complex situations where the examined material cannot be removed from its container. Laboratory measurements using benchtop dispersive micro-Raman spectroscopy at 785 nm are reported for comparison.
Compact Interrogation System of Fiber Bragg Grating Sensors Based on Multiheterodyne Dispersion Interferometry for Dynamic Strain Measurements
Authors: Poiana Dragos A. , Posada-Roman Julio E. , Garcia-Souto Jose A.
Published: May 2022

Dual-comb multiheterodyne spectroscopy is a well-established technology for the highly sensitive real-time detection and measurement of the optical spectra of samples, including gases and fiber sensors. However, a common drawback of dual-comb spectroscopy is the need for a broadband amplitude-resolved absorption or reflection measurement, which increases the complexity of the dual comb and requires the precise calibration of the optical detection. In the present study, we present an alternative dispersion-based approach applied to fiber Bragg grating sensors in which the dual comb is compacted by a single dual-drive-unit optical modulator, and the fiber sensor is part of a dispersion interferometer. The incident dual comb samples a few points in the spectrum that are sensitive to Bragg wavelength changes through the optical phase. The spectra reading is improved due to the external interferometer and is desensitized to changes in the amplitude of the comb tones. The narrow-band detection of the fiber sensor dispersion changes that we demonstrate enables the compact, cost-effective, high-resolution multiheterodyne interrogation of high-throughput interferometric fiber sensors. These characteristics open its application both to the detection of fast phenomena, such as ultrasound, and to the precise measurement at high speed of chemical-/biological-sensing samples. The results with a low-reflectivity fiber Bragg grating show the detection of dynamic strain in the range of 215 nε with a 30 dB signal to noise ratio and up to 130 kHz (ultrasonic range).
C-ITS Relevant Critical Vehicle-to-Vehicle Accident Scenarios for Accident Analysis
Authors: Bauder Maximilian , Böhm Klaus , Kubjatko Tibor , Wech Lothar , Schweiger Hans-Georg
Published: May 2022

The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.
A Low-Profile SIW-Based CTS Array with Reconfigurable Four Beams and Dual Polarizations for K-Band Sensing
Authors: Jin Yitong , Chen Yuanqing , Ding Yafei , Zou Ziwen , Qian Feng , Luo Yong , Yang Guangli
Published: May 2022

A dual-polarized continuous transverse stub (CTS) K-band antenna with reconfigurable four beams and low profile is proposed based on substrate-integrated-waveguide (SIW) design. It consists of a line source generator (LSG) on the bottom surface, a spherical-wave to plane-wave transforming part on the middle layer, and CTS radiators on the top surface. Particularly, the LSG has four SIW-based H-plane horns, and a chip is integrated to switch among the two pairs of horns, so as to transfer the quasi-TEM waves on the bottom surface by a ±10° deflection angle to the middle layer for the CTS radiators on the top surface, resulting in four reconfigurable scanning beams with 10° for two polarizations. The measurements show that it realizes four reconfigurable beams with a 25.8 dBi gain at 24 GHz, verifying the design. The proposed antenna takes into account the advantages of reconfigurable multi-beam, dual polarization, low side lobes, low profile, and high gain, which can be applied to K-band sensing, especially for wind profile radars.
RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model
Authors: Kim Yoon Ji , Ju Woong , Nam Kye Hyun , Kim Soo Nyung , Kim Young Jae , Kim Kwang Gi
Published: May 2022

Cervical cancer is one of the main causes of death from cancer in women. However, it can be treated successfully at an early stage. This study aims to propose an image processing algorithm based on acetowhite, which is an important criterion for diagnosing cervical cancer, to increase the accuracy of the deep learning classification model. Then, we mainly compared the performance of the model, the original image without image processing, a mask image made with acetowhite as the region of interest, and an image using the proposed algorithm. In conclusion, the deep learning classification model based on images with the proposed algorithm achieved an accuracy of 81.31%, which is approximately 9% higher than the model with original images and approximately 4% higher than the model with acetowhite mask images. Our study suggests that the proposed algorithm based on acetowhite could have a better performance than other image processing algorithms for classifying stages of cervical images.
Tri-Band Rectenna Dedicated to UHF RFID, GSM-1800 and UMTS-2100 Frequency Bands
Authors: Assogba Ognadon , Mbodji Abdoul Karim , Bréard Arnaud , Diallo Abdou Karim , Duroc Yvan
Published: May 2022

The omnipresence of connected objects leads to the quasi-permanent presence of electromagnetic waves from different sources in our environment. This article presents a new electromagnetic energy harvesting device, rectenna type, which offers the advantage of being versatile. Indeed, the proposed prototype is compatible with three frequency bands of radio standards widely deployed today (UHF RFID, GSM-1800, and UMTS-2100), and its performances remain good for low to very low ambient power levels as well as for different loads depending on the targeted application. The proposed solution is based on a tri-band antenna with very good efficiency and a bandwidth of at least 80 MHz for each of the operating frequencies. Moreover, the associated rectifier circuit is also tri-band and offers good performance in terms of RF-to-DC conversion efficiency for input levels varying in a rather wide range of power levels. The study is based on a design phase by simulation until the realization of prototypes and their experimental characterization. The designed rectenna is compared with solutions found in the literature.
Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
Authors: Kim Euiho , Song Jaeyoung , Shin Yujin , Kim Saekyul , Son Pyo-Woong , Park Sulgee , Park Sanghyun
Published: May 2022

Centimeter level augmentation system (CLAS) of the quasi-zenith satellite system (QZSS) is the first precise point positioning-real time kinematic (PPP-RTK) augmentation system of the global navigation satellite system (GNSS), which is currently providing services for Japan. CLAS broadcasts the state-space representation of correction messages along with integrity messages regarding satellite faults and the quality index of each correction. In other GNSS augmentation systems, such as the space-based augmentation system (SBAS) of GNSS, the quality indices of correction messages are used to generate fault-free protection levels that represent a position bound containing a true user position with a probability of missed detections. Although the protection level equations are well defined for the SBAS, a protection level equation for the CLAS PPP-RTK service has not been rigorously discussed in the literature. This paper proposes a fault-free protection level equation for the PPP-RTK methods that considers the probability of correct integer ambiguity fixes in the GNSS carrier phase measurements as well as the CLAS correction quality messages. The computed protection levels with position errors were experimentally compared by processing the GNSS measurements from the GNSS Earth Observation Network (GEONET) stations in Japan and the L6 messages from the CLAS broadcast using the virtual reference station-real time kinematic (VRS-RTK) techniques. Our results, based on the GEONET dataset spanning 7 days, showed that the computed protection levels using the proposed equations were larger than the position errors for all epochs. In the dataset, the RMS errors of the CLAS VRS-RTK position were 4.6 and 14 cm in the horizontal and vertical directions, respectively, whereas the horizontal protection levels ranged from 25 cm to 2.3 m and the vertical protection levels ranged from 50 cm to 5.2 m based on fault-free integrity risk of 10−7.
The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI)
Authors: Brinza Crischentian , Floria Mariana , Covic Adrian , Covic Andreea , Scripcariu Dragos-Viorel , Burlacu Alexandru
Published: May 2022

Background: Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations between HRV and adverse outcomes in a contemporary cohort of patients presenting with STEMI undergoing primary percutaneous coronary intervention (PCI). Methods: We will perform a prospective, observational cohort study in a single healthcare center. Adult patients aged ≥18 years presenting with STEMI in sinus rhythm will be enrolled for primary PCI within 12 h from symptoms onset. Time domain, frequency domain, and nonlinear HRV parameters will be measured using a medically approved wrist-wearable device for 5 min segments during myocardial revascularization by primary PCI. Additional HRV measurements will be performed one and six months from the index event. The primary composite outcome will include all-cause mortality and major adverse cardiovascular events (during the hospital stay, one month, and one year following admission). Several secondary outcomes will be analyzed: individual components of the primary composite outcome, target lesion revascularization, hospitalizations for heart failure, ventricular arrhythmias, left ventricular ejection fraction, and left ventricular diastolic function. Conclusions: Our study will enlighten the reliability and usefulness of HRV evaluation as a prognostic marker in contemporary patients with STEMI. The potential validation of HRV as a risk marker for adverse outcomes following STEMI will ensure a background for including HRV parameters in future risk scores and guidelines.
Novel Receive Antenna Selection Scheme for Precoding-Aided Spatial Modulation with Lattice Reduction
Authors: Kim Sangchoon
Published: May 2022

In this paper, a new receive antenna subset (RAS) selection scheme is proposed for precoding-aided spatial modulation (PSM). First, a lattice reduction (LR)-based precoder is employed instead of a conventional zero-forcing (ZF) precoder. It is analytically shown that a full diversity gain can be achieved by the LR-based ZF precoder without RAS selection. Then, an optimal LR-based RAS selection criterion is derived for the over-determined LR-based PSM systems, and a suboptimal selection algorithm is additionally presented. It is also shown that optimal and suboptimal RAS selection algorithms based on LR improve the BER performance of the LR-based PSM system. Further, the overall diversity order of the over-determined LR-based PSM systems with optimal LR-based RAS selection is analyzed. Finally, diversity analysis and simulation results show that the LR-ZF-based PSM system with optimal LR-based RAS selection outperforms the conventional ZF-based PSM system with conventional optimal RAS selection.
A New RF Energy Harvesting System Based on Two Architectures to Enhance the DC Output Voltage for WSN Feeding
Authors: Benkalfate Chemseddine , Ouslimani Achour , Kasbari Abed-Elhak , Feham Mohammed
Published: May 2022

In this paper, a new RF Energy Harvesting (RF-EH) system for Wireless Sensor Network (WSN) feeding is proposed. It is based on two different monitored architectures using switch circuits controlled by the input powers. One architecture is more adapted to high input powers and the other to low input powers. The two different architectures and the system are designed and realized on Teflon glass substrate with a relative permittivity of 2.1 and thickness of 0.67 mm. They are tested separately as a function of the distance from the relay antenna. A new multiband antenna with a size of 40 × 30 mm2 is used for both architectures and the system. The measured antenna gains are 2.7 dB, 2.9 dB, and 2.55 dB for the frequencies of 1.8 GHz, 2.1 GHz, and 2.66 GHz corresponding to the mobile communication networks, respectively. The rectifier consists of two Schottky diodes forming a full-wave rectifier and voltage doubler. The maximum measured RF-to-DC conversion efficiency is 71.5%. The proposed RF-EH system provides a maximum DC output voltage of 5.6 V and 3.15 V for an open and 2 kΩ resistance load, respectively.
Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient
Authors: Gong Hui , Wang Peng , Ni Cui , Cheng Nuo
Published: May 2022

When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile robot path planning, due to the limited observable environment of mobile robots, the training efficiency of the path planning model is low, and the convergence speed is slow. In this paper, Long Short-Term Memory (LSTM) is introduced into the DDPG network, the former and current states of the mobile robot are combined to determine the actions of the robot, and a Batch Norm layer is added after each layer of the Actor network. At the same time, the reward function is optimized to guide the mobile robot to move faster towards the target point. In order to improve the learning efficiency, different normalization methods are used to normalize the distance and angle between the mobile robot and the target point, which are used as the input of the DDPG network model. When the model outputs the next action of the mobile robot, mixed noise composed of Gaussian noise and Ornstein–Uhlenbeck (OU) noise is added. Finally, the simulation environment built by a ROS system and a Gazebo platform is used for experiments. The results show that the proposed algorithm can accelerate the convergence speed of DDPG, improve the generalization ability of the path planning model and improve the efficiency and success rate of mobile robot path planning.
A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning
Authors: Zhang Lejun , Wang Jinlong , Wang Weizheng , Jin Zilong , Zhao Chunhui , Cai Zhennao , Chen Huiling
Published: May 2022

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.
Can the Eight Hop Test Be Measured with Sensors? A Systematic Review
Authors: Pimenta Luís , Garcia Nuno M. , Zdravevski Eftim , Chorbev Ivan , Trajkovik Vladimir , Lameski Petre , Albuquerque Carlos , Pires Ivan Miguel
Published: May 2022

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.
BioTouch: Reliable Re-Authentication via Finger Bio-Capacitance and Touching Behavior
Authors: Zhang Chong , Li Songfan , Song Yihang , Meng Qianhe , Lu Li , Hou Mengshu
Published: May 2022

Re-authentication continuously checks to see if a user is authorized during a whole usage session, enhancing secrecy capabilities for computational devices, especially against insider attacks. However, it is challenging to design a reliable re-authentication scheme with accuracy, transparency and robustness. Specifically, the approaches of using biometric features (e.g., fingerprint, iris) are often accurate in identifying users but not transparent to them due to the need for user cooperation. On the other hand, while the approaches exploiting behavior features (e.g., touch-screen gesture, movement) are often transparent in use, their applications suffer from low accuracy and robustness as behavior information collected is subjective and may change frequently over different use situations and even user’s motion. In this paper, we propose BioTouch, a reliable re-authentication scheme that satisfies all the above requirements. First, BioTouch utilizes multiple features (finger capacitance and touching behavior) to identify the user for better accuracy. Second, BioTouch automatically works during user operation on capacitive-touch devices, achieving transparency without the need for manual assistance. Finally, by applying finger bio-capacitance, BioTouch is also robust to various conditions, as this feature is determined by the user’s physical characteristics and will not change by different user positions and motions. We implement BioTouch for proof-of-concept and conduct comprehensive evaluations. The results show that BioTouch can flag 98% of anomalous behaviors within ten touching operations and achieve up to 99.84% accuracy during usage.
Change in Electrical Resistance of SMA (NiTi) Wires during Cyclic Stretching
Authors: Sławski Sebastian , Kciuk Marek , Klein Wojciech
Published: May 2022

In this article, the use of Nickel Titanium (NiTi) alloy as a sensor is examined. A cyclic stretching test, that has various elongations (0.5 and 1%), is administered to NiTi wires with various diameters and lengths. It is assumed that the elongation enables an observation of the change in electrical resistance of the NiTi wires, due to martensite reorientation. During the test, the stretching force, the displacement, and the electrical resistance of the NiTi wires are measured. Following the test, the resistance of all the tested samples differed compared to the starting position. Conducted research indicates that NiTi wires are very sensitive to resistance change when they are deformed. A large difference in NiTi electrical resistance was visible in all samples during the first stretching cycle. For longer wires, with a smaller diameter, resistance change was visible during almost all of the stretching cycles. However, the observed changes were very small. Based on the obtained results, it can be justifiably stated that NiTi wires could be used to build deformation sensors, which operate both online and offline. Moreover, NiTi wires with a small diameter could be used to create cyclic loading sensors. Such sensors can be used in self-sensing applications or in structural health monitoring.
Estimating Safety Outcomes of Increased Organisational Safety Management in Trucking Companies
Authors: Nævestad Tor-Olav , Phillips Ross , Hovi Inger Beate , Jordbakke Guri Natalie , Elvik Rune
Published: May 2022

The present study investigated possible consequences for the number of killed and seriously injured (KSI) in traffic if trucking companies in Norway introduced the organisational safety management (OSM) measures in the stepwise approach called the “Safety Ladder” for road goods transport. The aim of the paper was to estimate the potential of OSM to prevent KSIs involving HGV drivers in Norwegian companies, given the current prevalence and effect. On the basis of these analytical steps, the present study concluded that OSM measures seem to provide an efficient approach to reduce the number of KSIs involving HGVs, especially as previous research indicates low implementation. The estimates in the example calculations varied between 7 and 52 KSI, which potentially can be avoided per year (retrospectively). Thus, OSM measures may reduce KSIs with a share of up to 51% of the total number of KSIs involving HGVs in Norway, when taking into consideration the known effects in robust studies and current prevalence of OSM.