bluuebunny/crossref_metadata_2025 huggingface dataset:
An Architecture for Managing Data Privacy in Healthcare with Blockchain
Authors: de Moraes Rossetto Anubis Graciela , Sega Christofer , Leithardt Valderi Reis Quietinho
Published: October 2022

With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.
Facial Expression Recognition with Geometric Scattering on 3D Point Clouds
Authors: He Yi , Fu Keren , Cheng Peng , Zhang Jianwei
Published: October 2022

As one of the pioneering data representations, the point cloud has shown its straightforward capacity to depict fine geometry in many applications, including computer graphics, molecular structurology, modern sensing signal processing, and more. However, unlike computer graphs obtained with auxiliary regularization techniques or from syntheses, raw sensor/scanner (metric) data often contain natural random noise caused by multiple extrinsic factors, especially in the case of high-speed imaging scenarios. On the other hand, grid-like imaging techniques (e.g., RGB images or video frames) tend to entangle interesting aspects with environmental variations such as pose/illuminations with Euclidean sampling/processing pipelines. As one such typical problem, 3D Facial Expression Recognition (3D FER) has been developed into a new stage, with remaining difficulties involving the implementation of efficient feature abstraction methods for high dimensional observations and of stabilizing methods to obtain adequate robustness in cases of random exterior variations. In this paper, a localized and smoothed overlapping kernel is proposed to extract discriminative inherent geometric features. By association between the induced deformation stability and certain types of exterior perturbations through manifold scattering transform, we provide a novel framework that directly consumes point cloud coordinates for FER while requiring no predefined meshes or other features/signals. As a result, our compact framework achieves 78.33% accuracy on the Bosphorus dataset for expression recognition challenge and 77.55% on 3D-BUFE.
Measuring Suite for Vascular Response Monitoring during Hyperbaric Oxygen Therapy by Means of Pulse Transit Time (PTT) Analysis
Authors: Wandel Theresa , Hausherr Daniel Pascal , Berben Dirk
Published: October 2022

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present for 6.25% of the time. To investigate possible prevailing vascular changes caused by HBOT, the presented device monitors the vascular response during therapy by Pulse-Transit-Time analysis. The device allows synchronous 1 kHz ECG and PPG measurements. The data are stored in a 1 GBit flash drive and retrieved post-therapy. Normoxic measurements on the authors with and without nicotine validate the device’s functionality. Measurements during HBO therapy have been successfully performed.
Ultrasonic Guided Waves for Liquid Water Localization in Fuel Cells: An Ex Situ Proof of Principle
Authors: Sablowski Jakob , Zhao Ziwen , Kupsch Christian
Published: October 2022

Water management is a key issue in the design and operation of proton exchange membrane fuel cells (PEMFCs). For an efficient and stable operation, the accumulation of liquid water inside the flow channels has to be prevented. Existing measurement methods for localizing water are limited in terms of the integration and application of measurements in operating PEMFC stacks. In this study, we present a measurement method for the localization of liquid water based on ultrasonic guided waves. Using a sparse sensing array of four piezoelectric wafer active sensors (PWAS), the measurement requires only minor changes in the PEMFC cell design. The measurement method is demonstrated with ex situ measurements for water drop localization on a single bipolar plate. The wave propagation of the guided waves and their interaction with water drops on different positions of the bipolar plate are investigated. The complex geometry of the bipolar plate leads to complex guided wave responses. Thus, physical modeling of the wave propagation and tomographic methods are not suitable for the localization of the water drops. Using machine learning methods, it is demonstrated that the position of a water drop can be obtained from the guided wave responses despite the complex geometry of the bipolar plate. Our results show standard deviations of 4.2 mm and 3.3 mm in the x and y coordinates, respectively. The measurement method shows high potential for in situ measurements in PEMFC stacks as well as for other applications that require deposit localization on geometrically complex waveguides.
Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy
Authors: An Minhui , Cao Chengmao , Wu Zhengmin , Luo Kun
Published: October 2022

In this study, Near-infrared (NIR) spectroscopy was adopted for the collection of 1200 spectra of three types of walnut materials after breaking the shells. A detection model of the walnut shell-kernel separation accuracy was established. The preprocessing method of de-trending (DT) was adopted. A classification model based on a support vector machine (SVM) and an extreme learning machine (ELM) was established with the principal component factor as the input variable. The effect of the penalty value (C) and kernel width (g) on the SVM model was discussed. The selection criteria of the number of hidden layer nodes (L) in the ELM model were studied, and a genetic algorithm (GA) was used to optimize the input layer weight (W) and the hidden layer threshold value (B) of the ELM. The results revealed that the classification accuracy of SVM and ELM models for the shell, kernel, and chimera was 97.78% and 97.11%. The proposed method can serve as a reference for the detection of walnut shell-kernel separation accuracy.
Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
Authors: Chuang Chia-Chun , Lee Chien-Ching , So Edmund-Cheung , Yeng Chia-Hong , Chen Yeou-Jiunn
Published: October 2022

Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.
Exploration of Semantic Label Decomposition and Dataset Size in Semantic Indoor Scenes Synthesis via Optimized Residual Generative Adversarial Networks
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: October 2022

In this paper, we revisit the paired image-to-image translation using the conditional generative adversarial network, the so-called “Pix2Pix”, and propose efficient optimization techniques for the architecture and the training method to maximize the architecture’s performance to boost the realism of the generated images. We propose a generative adversarial network-based technique to create new artificial indoor scenes using a user-defined semantic segmentation map as an input to define the location, shape, and category of each object in the scene, exactly similar to Pix2Pix. We train different residual connections-based architectures of the generator and discriminator on the NYU depth-v2 dataset and a selected indoor subset from the ADE20K dataset, showing that the proposed models have fewer parameters, less computational complexity, and can generate better quality images than the state of the art methods following the same technique to generate realistic indoor images. We also prove that using extra specific labels and more training samples increases the quality of the generated images; however, the proposed residual connections-based models can learn better from small datasets (i.e., NYU depth-v2) and can improve the realism of the generated images in training on bigger datasets (i.e., ADE20K indoor subset) in comparison to Pix2Pix. The proposed method achieves an LPIPS value of 0.505 and an FID value of 81.067, generating better quality images than that produced by Pix2Pix and other recent paired Image-to-image translation methods and outperforming them in terms of LPIPS and FID.
A Robust Parallel Initialization Method for Monocular Visual-Inertial SLAM
Authors: Zhong Min , Yao Yiqing , Xu Xiaosu , Wei Hongyu
Published: October 2022

In order to improve the initialization robustness of visual inertial SLAM, the complementarity of the optical flow method and the feature-based method can be used in vision data processing. The parallel initialization method is proposed, where the optical flow inertial initialization and the monocular feature-based initialization are carried out at the same time. After the initializations, the state estimation results are jointly optimized by bundle adjustment. The proposed method retains more mapping information, and correspondingly is more adaptable to the initialization scene. It is found that the initialization map constructed by the proposed method features a comparable accuracy to the one constructed by ORB-SLAM3 in monocular inertial mode. Since the online extrinsic parameter estimation can be realized by the proposed method, it is considered better than ORB-SLAM3 in the aspect of portability. By the experiments performed on the benchmark dataset EuRoC, the effectiveness and robustness of the proposed method are validated.
Evaluation of Temperature Sensors for Detection of Heat Sources Using Additive Printing Method
Authors: Ahn Ju-Hun , Kim Han-Na , Cho Jin Yeon , Kim Jeong Ho , Lee Chang-Yull
Published: October 2022

Electrohydrodynamic (EHD) inkjet printing is an efficient technique for printing multiple sensors in a multifaceted area. It can be applied to various fields according to the shape of the printing result and the algorithm employed. In this study, temperature sensors capable of detecting heat sources were fabricated. Inks suitable for EHD inkjet printing were produced, and optimal parameters for printing were determined. Printing was performed using the corresponding parameters, and various printing results were obtained. Furthermore, an experiment was conducted to confirm the temperature measurement characteristics of the results and the tolerance of the sensor. Grid-type sensors were fabricated based on the results, and the sensor characteristics were confirmed in an orthogonal form. Heat was applied to arbitrary positions. Resistance to changes due to heat was measured, and the location at which the heat was generated was detected by varying the change in resistance. Through this study, efficient heat control can be achieved, as the location of the heat source can be identified quickly.
Machine Learning and Swarm Optimization Algorithm in Temperature Compensation of Pressure Sensors
Authors: Wang Hexing , Li Jia
Published: October 2022

The main temperature compensation method for MEMS piezoresistive pressure sensors is software compensation, which processes the sensor data using various algorithms to improve the output accuracy. However, there are few algorithms designed for sensors with specific ranges, most of which ignore the operating characteristics of the sensors themselves. In this paper, we propose three temperature compensation methods based on swarm optimization algorithms fused with machine learning for three different ranges of sensors and explore the partitioning ratio of the calibration dataset on Sensor A. The results show that different algorithms are suitable for pressure sensors of different ranges. An optimal compensation effect was achieved on Sensor A when the splitting ratio was 33.3%, where the zero-drift coefficient was 2.88 × 10−7/°C and the sensitivity temperature coefficient was 4.52 × 10−6/°C. The algorithms were compared with other algorithms in the literature to verify their superiority. The optimal segmentation ratio obtained from the experimental investigation is consistent with the sensor operating temperature interval and exhibits a strong innovation.
A Micron-Range Displacement Sensor Based on Thermo-Optically Tuned Whispering Gallery Modes in a Microcapillary Resonator
Authors: Wang Zhe , Mallik Arun Kumar , Wei Fangfang , Wang Zhuochen , Rout Anuradha , Wu Qiang , Semenova Yuliya
Published: October 2022

A novel micron-range displacement sensor based on a whispering-gallery mode (WGM) microcapillary resonator filled with a nematic liquid crystal (LC) and a magnetic nanoparticle- coated fiber half-taper is proposed and experimentally demonstrated. In the proposed device, the tip of a fiber half-taper coated with a thin layer of magnetic nanoparticles (MNPs) moves inside the LC-filled microcapillary resonator along its axis. The input end of the fiber half-taper is connected to a pump laser source and due to the thermo-optic effect within the MNPs, the fiber tip acts as point heat source increasing the temperature of the LC material in its vicinity. An increase in the LC temperature leads to a decrease in its effective refractive index, which in turn causes spectral shift of the WGM resonances monitored in the transmission spectrum of the coupling fiber. The spectral shift of the WGMs is proportional to the displacement of the MNP-coated tip with respect to the microcapillary’s light coupling point. The sensor’s operation is simulated considering heat transfer in the microcapillary filled with a LC material having a negative thermo-optic coefficient. The simulations are in a good agreement with the WGMs spectral shift observed experimentally. A sensitivity to displacement of 15.44 pm/µm and a response time of 260 ms were demonstrated for the proposed sensor. The device also shows good reversibility and repeatability of response. The proposed micro-displacement sensor has potential applications in micro-manufacturing, precision measurement and medical instruments.
Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine Intelligence
Authors: Ed-Driouch Chadia , Mars Franck , Gourraud Pierre-Antoine , Dumas Cédric
Published: October 2022

Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human–machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians’ knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician–algorithm method showed that the models based on the latter can perform better. Physicians’ engagement is the most promising condition for the safe and innovative use of ML in healthcare.
LZER0: A Cost-Effective Multi-Purpose GNSS Platform
Authors: Zuliani David , Tunini Lavinia , Severin Marco , Bertoni Michele , Ponton Cristian , Parolai Stefano
Published: October 2022

Recent advances in Global Navigation Satellite System (GNSS) technology have made low-cost sensors available to the mass market, opening up new opportunities for real-time ground deformation and structure monitoring. In this paper, we present a new product developed in this framework by the National Institute of Oceanography and Applied Geophysics–OGS in collaboration with a private company (SoluTOP SAS): a cost-effective, multi-purpose GNSS platform called LZER0, suitable not only for surveying measurements, but also for monitoring tasks. The LZER0 platform is a complete system that includes the GNSS equipment (M8T single-frequency model produced by u-blox) and the web portal where the results are displayed. The GNSS data are processed using the RTKLIB software package, and the processed results are made available to the end user. The relative positioning mode was adopted both with real-time and post-processing RTKLIB engines. We present three applications of LZER0—cadastral, monitoring, and automotive—which demonstrate that it is a flexible, multi-purpose platform that is easy to use in terms of both hardware and software, and can be easily deployed to perform various tasks in the research, educational, or professional sectors.
Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning
Authors: Meneses-Cime Karina , Aksun Guvenc Bilin , Guvenc Levent
Published: October 2022

Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers.
Gradient Index Metasurface Lens for Microwave Imaging
Authors: Datta Srijan , Tamburrino Antonello , Udpa Lalita
Published: October 2022

This paper presents the design, simulation and experimental validation of a gradient-index (GRIN) metasurface lens operating at 8 GHz for microwave imaging applications. The unit cell of the metasurface consists of an electric-LC (ELC) resonator. The effective refractive index of the metasurface is controlled by varying the capacitive gap at the center of the unit cell. This allows the design of a gradient index surface. A one-dimensional gradient index lens is designed and tested at first to describe the operational principle of such lenses. The design methodology is extended to a 2D gradient index lens for its potential application as a microwave imaging device. The metasurface lenses are designed and analyzed using full-wave finite element (FEM) solver. The proposed 2D lens has an aperture of size 119 mm (3.17λ) × 119 mm (3.17λ) and thickness of only 0.6 mm (0.016λ). Horn antenna is used as source of plane waves incident on the lens to evaluate the focusing performance. Field distributions of the theoretical designs and fabricated lenses are analyzed and are shown to be in good agreement. A microwave nondestructive evaluation (NDE) experiment is performed with the 2D prototype lens to image a machined groove in a Teflon sample placed at the focal plane of the lens.
Embedded Sensors for Structural Health Monitoring: Methodologies and Applications Review
Authors: Ferreira Pedro M. , Machado Miguel A. , Carvalho Marta S. , Vidal Catarina
Published: October 2022

Sensing Technology (ST) plays a key role in Structural Health-Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems, or smart materials that monitor a wide variety of materials’ properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and enabling continuous and permanent measurements of their structural integrity. The integration of ESs is limited to the processing technology used to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material’s composition, which comprises either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM systems employing ESs, focusing on their actual developments and innovation, as well as analysing the challenges that these technologies present, in order to build a path that allows for a connected world through distributed measurement systems.
Assistive Technology to Improve Collaboration in Children with ASD: State-of-the-Art and Future Challenges in the Smart Products Sector
Authors: Cañete Raquel , Peralta Estela
Published: October 2022

Within the field of products for autism spectrum disorder, one of the main research areas is focused on the development of assistive technology. Mid and high-tech products integrate interactive and smart functions with multisensory reinforcements, making the user experience more intuitive, adaptable, and dynamic. These products have a very significant impact on improving the skills of children with autism, including collaboration and social skills, which are essential for the integration of these children into society and, therefore, their well-being. This work carried out an exhaustive analysis of the scientific literature, as well as market research and trends, and patent analysis to explore the state-of-the-art of assistive technology and smart products for children with ASD, specifically those aimed at improving social and communication skills. The results show a reduced availability of products that act as facilitators of the special needs of children with ASD, which is even more evident for products aimed at improving collaboration skills. Products that allow the participation of several users simultaneously through multi-user interfaces are required. On top of this, the trend toward virtual environments is leading to a loss of material aspects in the design that are essential for the development of these children.
Wavelet LSTM for Fault Forecasting in Electrical Power Grids
Authors: Branco Nathielle Waldrigues , Cavalca Mariana Santos Matos , Stefenon Stefano Frizzo , Leithardt Valderi Reis Quietinho
Published: October 2022

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.
Combination of VMD Mapping MFCC and LSTM: A New Acoustic Fault Diagnosis Method of Diesel Engine
Authors: Yan Hao , Bai Huajun , Zhan Xianbiao , Wu Zhenghao , Wen Liang , Jia Xisheng
Published: October 2022

Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals.
Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review
Authors: Czajkowska Joanna , Borak Martyna
Published: October 2022

Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.
The Development of a Built-In Shoe Plantar Pressure Measurement System for Children
Authors: De Guzman Sarah , Lowe Andrew , Williams Cylie , Kalra Anubha , Anand Gautam
Published: October 2022

There is a rapid increase in plantar pressure from the infant to toddler stage, yet little is known about the reasons for this change. More information about plantar pressure distribution can help clinicians identify early-stage foot-related diseases that may occur during transitions from childhood to adulthood. This information also helps designers create shoes that adapt to different needs. This research describes the development of a low-cost, built-in shoe plantar pressure measurement system that determines foot pressure distribution in toddlers. The study aimed to improve and provide data on pressure distribution during foot growth. This was accomplished by implementing a plantar pressure capacitive measurement system within shoes. The capacitive sensors were laminated using a copper tape sheet on plastic backing with adhesive, elastomer layers, and a combination of conductive and non-conductive fabrics. Constructed sensors were characterized using compression tests with repeated loads. Results demonstrated that the sensors exhibited rate-independent hysteresis in the estimation of pressure. This enabled a calibration model to be developed. The system can mimic more expensive plantar pressure measurement systems at lower fidelity. This emerging technology could be utilized to aid clinicians, researchers, and footwear designers interested in how pressure distribution changes from infants to toddlers.
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
Authors: Wu Guoguo , Yan Tanyi , Yang Guolai , Chai Hongqiang , Cao Chuanchuan
Published: October 2022

As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.
G2O-Pose: Real-Time Monocular 3D Human Pose Estimation Based on General Graph Optimization
Authors: Sun Haixun , Zhang Yanyan , Zheng Yijie , Luo Jianxin , Pan Zhisong
Published: October 2022

Monocular 3D human pose estimation is used to calculate a 3D human pose from monocular images or videos. It still faces some challenges due to the lack of depth information. Traditional methods have tried to disambiguate it by building a pose dictionary or using temporal information, but these methods are too slow for real-time application. In this paper, we propose a real-time method named G2O-pose, which has a high running speed without affecting the accuracy so much. In our work, we regard the 3D human pose as a graph, and solve the problem by general graph optimization (G2O) under multiple constraints. The constraints are implemented by algorithms including 3D bone proportion recovery, human orientation classification and reverse joint correction and suppression. When the depth of the human body does not change much, our method outperforms the previous non-deep learning methods in terms of running speed, with only a slight decrease in accuracy.
A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model
Authors: Lucà Francescantonio , Manzoni Stefano , Cerutti Francesco , Cigada Alfredo
Published: October 2022

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion.
IoT Device Identification Using Directional Packet Length Sequences and 1D-CNN
Authors: Liu Xiangyu , Han Yi , Du Yanhui
Published: October 2022

With the large-scale application of the Internet of Things (IoT), security issues have become increasingly prominent. Device identification is an effective way to secure IoT environment by quickly identifying the category or model of devices in the network. Currently, the passive fingerprinting method used for IoT device identification based on network traffic flow mostly focuses on protocol features in packet headers but does not consider the direction and length of packet sequences. This paper proposes a device identification method for the IoT based on directional packet length sequences in network flows and a deep convolutional neural network. Each value in a packet length sequence represents the size and transmission direction of the corresponding packet. This method constructs device fingerprints from packet length sequences and uses convolutional layers to extract deep features from the device fingerprints. Experimental results show that this method can effectively recognize device identity with accuracy, recall, precision, and f1-score over 99%. Compared with methods using traditional machine learning and feature extraction techniques, our feature representation is more intuitive, and the classification model is effective.
Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy
Authors: Almaviva Salvatore , Artuso Florinda , Giardina Isabella , Lai Antonia , Pasquo Alessandra
Published: October 2022

Fast monitoring of water quality is a fundamental part of environmental management and protection, in particular, the possibility of qualitatively and quantitatively determining its contamination at levels that are dangerous for human health, fauna and flora. Among the techniques currently available, Raman spectroscopy and its variant, Surface-Enhanced Raman Spectroscopy (SERS), have several advantages, including no need for sample preparation, quick and easy operation and the ability to operate on the field. This article describes the application of the Raman and SERS technique to liquid samples contaminated with different classes of substances, including nitrates, phosphates, pesticides and their metabolites. The technique was also used for the detection of the air pollutant polycyclic aromatic hydrocarbons and, in particular, benzo(a)pyrene, considered as a reference for the carcinogenicity of the whole class of these compounds. To pre-concentrate the analytes, we applied a methodology based on the well-known coffee-ring effect, which ensures preconcentration of the analytes without any pretreatment of the sample, providing a versatile approach for fast and in-situ detection of water pollutants. The obtained results allowed us to reveal these analytes at low concentrations, close to or lower than their regulatory limits.
Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals
Authors: Hsu Chih-Yu , Chen Chih-Cheng , Liu Chun-You , Chen Shuo-Tsung , Tu Shu-Yi
Published: October 2022

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients’ confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients’ confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients’ confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients’ confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients’ confidential data. In addition, the embedded patients’ confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.
A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network
Authors: Yang Xu , Yuan Rui , Lv Yong , Li Li , Song Hao
Published: October 2022

Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the dynamical information of tool wear conditions. This paper proposes a novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network (1D CNN). Firstly, multivariate variational mode decomposition (MVMD) is used to process the multivariate cutting force signals. The multivariate band-limited intrinsic mode functions (BLIMFs) are obtained, which contain a large number of nonlinear and nonstationary tool wear characteristics. Afterwards, the proposed modified multiscale permutation entropy (MMPE) is used to measure the complexity of multivariate BLIMFs. The entropy values on multiple scales are calculated as condition indicators in tool wear condition monitoring. Finally, the one-dimensional feature vectors are constructed and employed as the input of 1D CNN to achieve accurate and stable tool wear condition monitoring. The results of the research in this paper demonstrate that the proposed approach has broad prospects in tool wear condition monitoring.
Two Eco-Friendly Chromatographic Methods Evaluated by GAPI for Simultaneous Determination of the Fluoroquinolones Moxifloxacin, Levofloxacin, and Gemifloxacin in Their Pharmaceutical Products
Authors: Abdel Hameed Eman A. , Abd El-Naby Zaitona A. , El Gindy Alaa , Saraya Roshdy E. , Al balawi Aisha Nawaf , Zaitone Sawsan A. , Khairy Gasser M.
Published: October 2022

In this paper, novel green HPLC and HPTLC chromatographic methods were developed for the concurrent determination of moxifloxacin, levofloxacin, and gemifloxacin in bulk and pharmaceutical products. The green HPLC method was used on Thermo C18 (4.6 × 250 mm, 5 µm). By mixing ethanol and 20 mM sodium dihydrogen phosphate dihydrate (pH 5) in a ratio of 25:75, v/v, the mobile phase was created using isocratic elution. The flow rate was 1 mLmin−1. The studied antibiotics were separated well within 9.5 min. The green HPTLC method was used on coated HPTLC aluminum sheets with Silica gel 60 F254 using a mobile phase mixture of water: acetone: ammonia (8:1:1, v/v/v). Compact and well-resolved peaks were obtained under chamber-saturation circumstances for the standard fluoroquinolone antibiotics. Both methods were optimized individually, validated by ICH, and assessed using the Green analytical procedure index (GAPI). The methods were applied to pharmaceutical products and compared with the published methods for the determination of each of these antibiotics individually, using Student’s t-test. They can be used by quality-control laboratories in pharmaceutical factories as sensitive eco-friendly methods for the analysis of these drugs and for the detection of cross-contamination during manufacturing processes.
Impact of Short-Term Cultivation on Some Selected Properties of Sandy Soil in an Arid Environment
Authors: Selmy Salman A. H. , Abd Al-Aziz Salah H. , Ibrahim Ahmed G. , Jiménez-Ballesta Raimundo
Published: October 2022

Soil management is recognized to have an impact on soil quality attributes. Depending on the management approach, this impact can either degrade or improve soil quality. There is a severe shortage of information on the impacts of cultivation on sandy soil properties in arid desert regions. Therefore, the objective of this study was to investigate the short-term cultivation effects (5 years) on the properties’ changes of coarse-textured soil in an arid desert region in western Assiut Governorate, Egypt. The current study was conducted on soils sampled at four depth intervals, namely 0–10, 10–20, 20–30, and 30–40 cm, from both cultivated and uncultivated soils, using a systematic sampling grid (10 × 10 m), to investigate the potential impacts of the cultivation process on six soil attributes. Each land use was represented by an area of 0.5 ha (50 × 100 m). A total of 160 composite soil samples (at all depths) were collected from both soils and analyzed for their physical and chemical properties, employing standard laboratory procedures. The data were statistically and geostatistically analyzed to compare the results and map the spatial distributions of the selected soil properties. The results revealed that cultivation had a considerable positive impact on most of the properties of cultivated soil compared to those of uncultivated soil (virgin land). The findings also showed that the available phosphorus levels in cultivated soil were higher than in virgin soil by 16, 9, 8.5, and 6 folds, with increases in organic matter content of 16.8, 12.4, 11.9, and 7.9 times at depths of 0–10, 10–20, 20–30, and 30–40 cm, respectively. Furthermore, compared to virgin soil, cultivated soil exhibited a salinity reduction of −8.9%, −56.4%, −66.3%, and −71.8%, at depths of 0–10, 10–20, 20–30, and 30–40 cm, respectively. Moreover, some other properties of the cultivated soil improved, particularly in the surface soil layers, such as pH reduction, CaCO3 decline, and CEC increase, while the soil texture grade did not change. Therefore, continuous monitoring of the effects of diverse soil management strategies in the short term assists in the understanding of the ongoing changes in soil physical and chemical characteristics, which is critical for maintaining satisfactory soil quality and sustainable soil productivity in arid lands.
Participatory Historical Village Landscape Analysis Using a Virtual Globe-Based 3D PGIS: Guizhou, China
Authors: Yu Linjun , Zhang Xiaotong , He Feng , Wang Xiaojun
Published: October 2022

The analysis of historical village landscape characteristics and the influential driving factors of their evolutions can provide an essential decision-making basis for rural sustainable development strategies and landscape planning. How to obtain historical village landscape data at a time when objectively recorded data, such as remote sensing images, were unavailable is a key problem that restricts the analysis of village landscape evolution characteristics. As local villagers are important knowledge sources regarding historical village landscapes, a participatory data collection and analysis approach was used for village historical landscape data in this paper using a virtual globe-based three-dimensional participatory geographic information system (3D PGIS). Taking Duimengshan village, Guizhou, China, as a case study, the 3D landscape of the Duimengshan village and corresponding major historical events in four historical periods, 1958, 1980, 1995, and 2015, were collected in an on-site, interactive way by researchers with the participation of local villagers, and its land-use structure, ecosystem service values, and landscape pattern were analyzed. The results show that the historical landscapes in the four periods were strongly related to important local historical events. The 3D PGIS greatly mobilized the enthusiasm of villagers to participate with its intuitive 3D display form and simple and easy-to-use operation mode. It can be concluded that the historical landscape memory of local villagers and the oral inheritance information handed down from generation to generation can be utilized to make up for the lack of remote sensing and other objective data in the collection and acquisition of historical village landscape data. The obtained historical village landscape characteristics and their evolution laws can be used in future participatory rural planning and landscape design.