bluuebunny/crossref_metadata_2025 huggingface dataset:
A Model for Estimating Tactile Sensation by Machine Learning Based on Vibration Information Obtained while Touching an Object
Authors: Ito Fumiya , Takemura Kenjiro
Published: December 2021

The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.
Human Behavior Recognition Model Based on Feature and Classifier Selection
Authors: Gao Ge , Li Zhixin , Huan Zhan , Chen Ying , Liang Jiuzhen , Zhou Bangwen , Dong Chenhui
Published: December 2021

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.
Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT
Authors: K Arumugam , J Srimathi , Maurya Sudhanshu , Joseph Senoj , Asokan Anju , M Poongodi , Algethami Abdullah A. , Hamdi Mounir , Rauf Hafiz Tayyab
Published: December 2021

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.
Feasibility and First Results of Heart Failure Monitoring Using the Wearable Cardioverter–Defibrillator in Newly Diagnosed Heart Failure with Reduced Ejection Fraction
Authors: Hillmann Henrike Aenne Katrin , Hohmann Stephan , Mueller-Leisse Johanna , Zormpas Christos , Eiringhaus Jörg , Bauersachs Johann , Veltmann Christian , Duncker David
Published: December 2021

The wearable cardioverter–defibrillator (WCD) is used in patients with newly diagnosed heart failure and reduced ejection fraction (HFrEF). In addition to arrhythmic events, the WCD provides near-continuous telemetric heart failure monitoring. The purpose of this study was to evaluate the clinical relevance of additionally recorded parameters, such as heart rate or step count. We included patients with newly diagnosed HFrEF prescribed with a WCD. Via the WCD, step count and heart rate were acquired, and an approximate for heart rate variability (HRV5) was calculated. Multivariate analysis was performed to analyze predictors for an improvement in left ventricular ejection fraction (LVEF). Two hundred and seventy-six patients (31.9% female) were included. Mean LVEF was 25.3 ± 8.5%. Between the first and last seven days of usage, median heart rate fell significantly (p < 0.001), while median step count and HRV5 significantly increased (p < 0.001). In a multivariate analysis, a delta of HRV5 > 23 ms was an independent predictor for LVEF improvement of ≥10% between prescription and 3-month follow-up. Patients with newly diagnosed HFrEF showed significant changes in heart rate, step count, and HRV5 between the beginning and end of WCD prescription time. HRV5 was an independent predictor for LVEF improvement and could serve as an early indicator of treatment response.
Development of a Dengue Virus Serotype-Specific Non-Structural Protein 1 Capture Immunochromatography Method
Authors: Poltep Kanaporn , Nakayama Emi E. , Sasaki Tadahiro , Kurosu Takeshi , Takashima Yoshiki , Phadungsombat Juthamas , Kosoltanapiwat Nathamon , Hanboonkunupakarn Borimas , Suwanpakdee Sarin , Imad Hisham A. , Srimark Narinee , Kitamura Chiaki , Yamanaka Atsushi , Okubo Akio , Shioda Tatsuo , Leaungwutiwong Pornsawan
Published: December 2021

Four serotypes of dengue virus (DENV), type 1 to 4 (DENV-1 to DENV-4), exhibit approximately 25–40% of the difference in the encoded amino acid residues of viral proteins. Reverse transcription of RNA extracted from specimens followed by PCR amplification is the current standard method of DENV serotype determination. However, since this method is time-consuming, rapid detection systems are desirable. We established several mouse monoclonal antibodies directed against DENV non-structural protein 1 and integrated them into rapid DENV detection systems. We successfully developed serotype-specific immunochromatography systems for all four DENV serotypes. Each system can detect 104 copies/mL in 15 min using laboratory and clinical isolates of DENV. No cross-reaction between DENV serotypes was observed in these DENV isolates. We also confirmed that there was no cross-reaction with chikungunya, Japanese encephalitis, Sindbis, and Zika viruses. Evaluation of these systems using serum from DENV-infected individuals indicated a serotype specificity of almost 100%. These assay systems could accelerate both DENV infection diagnosis and epidemiologic studies in DENV-endemic areas.
Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion
Authors: Xing Xiaoxue , Luo Cong , Zhou Jian , Yan Minghan , Liu Cheng , Xu Tingfa
Published: December 2021

To get more obvious target information and more texture features, a new fusion method for the infrared (IR) and visible (VIS) images combining regional energy (RE) and intuitionistic fuzzy sets (IFS) is proposed, and this method can be described by several steps as follows. Firstly, the IR and VIS images are decomposed into low- and high-frequency sub-bands by non-subsampled shearlet transform (NSST). Secondly, RE-based fusion rule is used to obtain the low-frequency pre-fusion image, which allows the important target information preserved in the resulting image. Based on the pre-fusion image, the IFS-based fusion rule is introduced to achieve the final low-frequency image, which enables more important texture information transferred to the resulting image. Thirdly, the ‘max-absolute’ fusion rule is adopted to fuse high-frequency sub-bands. Finally, the fused image is reconstructed by inverse NSST. The TNO and RoadScene datasets are used to evaluate the proposed method. The simulation results demonstrate that the fused images of the proposed method have more obvious targets, higher contrast, more plentiful detailed information, and local features. Qualitative and quantitative analysis results show that the presented method is superior to the other nine advanced fusion methods.
Real-Time Monitoring of Doxorubicin Release from Hybrid Nanoporous Anodic Alumina Structures
Authors: Kapruwan Pankaj , Ferré-Borrull Josep , Marsal Lluis F.
Published: December 2021

This work demonstrates an advanced approach to fabricate Hybrid nanoporous anodic alumina gradient-index filters (Hy-NAA-GIFs) through a heterogeneous anodization process combining sinusoidal current-density anodization and constant potential anodization. As a result, the hybrid structure obtained reveals a single photonic stopband (PSB), which falls within the absorption region of the drug molecule and the intensity of the spectrum that are far from such absorption range. The prepared structures were loaded with the doxorubicin (DOX) drug through the drop-casting method, which allows for evaluating the maximum reflectance of the relative height of the PSB with the average reflectance of the spectrum intensity. Thereafter, this property has been applied in a flow cell setup connected to a reflectance spectrophotometer where different drug-loaded samples were placed to study the behavior and kinetics of the drug release in real-time by varying two parameters, i.e., different pore length and flow rates. As such, obtained results were analyzed with a model that includes a sum of two inverted exponential decay functions with two different characteristic time releases. Overall, this study opens up several possibilities for the Hy-NAA-GIFs to study the drug kinetics from nanoporous structures.
Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure
Authors: Kang Changhee , Kang Sang-ug
Published: December 2021

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.
The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults
Authors: Lopes Tiago Drummond , Raizer Adroaldo , Valente Júnior Wilson
Published: December 2021

Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage to machines. Usually, fault diagnosis is carried out by analyzing the characteristic effects caused by the faults. Thus, it is necessary to know and understand the behavior during the operation of the faulty machine. In general, monitoring these characteristics is complex, as it is necessary to acquire signals from the same motor with and without failures for comparison purposes. Whether in an industrial environment or in laboratories, the experimental characterization of failures can become unfeasible for several reasons. Thus, computer simulation of faulty motors digital twins can be an important alternative for failure analysis, especially in large motors. From this perspective, this paper presents and discusses several limitations found in the technical literature that can be minimized with the implementation of digital twins. In addition, a 3D finite element model of an induction motor with broken rotor bars is demonstrated, and motor current signature analysis is used to verify the fault effects. Results are analyzed in the time and frequency domain. Additionally, an artificial neural network of the multilayer perceptron type is used to classify the failure of broken bars in the 3D model rotor.
Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation
Authors: Li Dongqian , Fan Cien , Zou Lian , Zuo Qi , Jiang Hao , Liu Yifeng
Published: December 2021

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder–decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.
Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
Authors: Abdel-Aziz Mostafa M. , Hosny Khalid M. , Lashin Nabil A. , Fouda Mostafa M.
Published: December 2021

This paper proposes a new blind, color image watermarking method using fast Walsh–Hadamard transformation (FWHT) and multi-channel fractional Legendre–Fourier moments (MFrLFMs). The input host color image is first split into 4 × 4 non-interfering blocks, and the MFrLFMs are computed for each block, where proper MFrLFMs coefficients are selected and FWHT is applied on the selected coefficients. The scrambled binary watermark has been inserted in the quantized selected MFrLFMs coefficients. The proposed method is a blind extraction, as the original host image is not required to extract the watermark. The proposed method is evaluated over many visual imperceptibility terms such as peak signal-to-noise ratio (PSNR), normalized correlation (NC), and bit error rate. The robustness of the proposed method is tested over several geometrical attacks such as scaling, rotation, cropping, and translation with different parameter values. The most widely recognized image processing attacks are also considered, e.g., compressing and adding noise attacks. A set of combination attacks are also tested to analyze the robustness of the proposed scheme versus several attacks. The proposed model’s experimental and numerical results for invisibility and robustness were superior to the results of similar watermarking methods.
Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood
Authors: Shabalina Anastasiia V. , Sharko Darya O. , Glazyrin Yury E. , Bolshevich Elena A. , Dubinina Oksana V. , Kim Anastasiia M. , Veprintsev Dmitry V. , Lapin Ivan N. , Zamay Galina S. , Krat Alexey V. , Zamay Sergey S. , Svetlichnyi Valery A. , Kichkailo Anna S. , Berezovski Maxim V.
Published: December 2021

We describe the preparation and characterization of an aptamer-based electrochemical sensor to lung cancer tumor markers in human blood. The highly reproducible aptamer sensing layer with a high density (up to 70% coverage) on the gold electrode was made. Electrochemical methods and confocal laser scanning microscopy were used to study the stability of the aptamer layer structure and binding ability. A new blocking agent, a thiolated oligonucleotide with an unrelated sequence, was applied to fill the aptamer layer’s defects. Electrochemical aptasensor signal processing was enhanced using deep learning and computer simulation of the experimental data array. It was found that the combinations (coupled and tripled) of cyclic voltammogram features allowed for distinguishing between the samples from lung cancer patients and healthy candidates with a mean accuracy of 0.73. The capacitive component from the non-Faradic electrochemical impedance spectroscopy data indicated the tumor marker’s presence in a sample. These findings allowed for the creation of highly informative aptasensors for early lung cancer diagnostics.
HARTH: A Human Activity Recognition Dataset for Machine Learning
Authors: Logacjov Aleksej , Bach Kerstin , Kongsvold Atle , Bårdstu Hilde Bremseth , Mork Paul Jarle
Published: December 2021

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
A CMOS PSR Enhancer with 87.3 mV PVT-Insensitive Dropout Voltage for Sensor Circuits
Authors: Zhang Jianyu , Chan Pak Kwong
Published: December 2021

A new power supply rejection (PSR) based enhancer with small and stable dropout voltage is presented in this work. It is implemented using TSMC-40 nm process technology and powered by 1.2 V supply voltage. A number of circuit techniques are proposed in this work. These include the temperature compensation for Level-Shifted Flipped Voltage Follower (LSFVF) and the Complementary-To-Absolute Temperature (CTAT) current reference. The typical output voltage and dropout voltage of the enhancer is 1.1127 V and 87.3 mV, respectively. The Monte-Carlo simulation of this output voltage yields a mean T.C. of 29.4 ppm/°C from −20 °C and 80 °C. Besides, the dropout voltage has been verified with good immunity against Process, Temperature and Process (PVT) variation through the worst-case simulation. Consuming only 4.75 μA, the circuit can drive load up to 500 μA to yield additional PSR improvement of 36 dB and 20 dB of PSR at 1 Hz and 1 MHz, respectively for the sensor circuit of interest. This is demonstrated through the application of an enhancer on the instrumentation Differential Difference Amplifier (DDA) for sensing floating bridge sensor signal. The comparative Monte-Carlo simulation results on a respective DDA circuit have revealed that the process sensitivity of output voltage of this work has achieved 14 times reduction in transient metrics with respect to that of the conventional counterpart over the operation temperature range in typical operation condition. Due to simplicity without voltage reference and operational amplifier(s), low power and small consumption of supply voltage headroom, the proposed work is very useful for supply noise sensitive analog or sensor circuit applications.
Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data
Authors: Bae Chulhee , Lee Yu-Cheol , Yu Wonpil , Lee Sejin
Published: December 2021

Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2’s main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes’ rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.
FEM Analysis of Various Multilayer Structures for CMOS Compatible Wearable Acousto-Optic Devices
Authors: Hanif Mehwish , Jeoti Varun , Ahmad Mohamad Radzi , Aslam Muhammad Zubair , Qureshi Saima , Stojanovic Goran
Published: December 2021

Lately, wearable applications featuring photonic on-chip sensors are on the rise. Among many ways of controlling and/or modulating, the acousto-optic technique is seen to be a popular technique. This paper undertakes the study of different multilayer structures that can be fabricated for realizing an acousto-optic device, the objective being to obtain a high acousto-optic figure of merit (AOFM). By varying the thicknesses of the layers of these materials, several properties are discussed. The study shows that the multilayer thin film structure-based devices can give a high value of electromechanical coupling coefficient (k2) and a high AOFM as compared to the bulk piezoelectric/optical materials. The study is conducted to find the optimal normalised thickness of the multilayer structures with a material possessing the best optical and piezoelectric properties for fabricating acousto-optic devices. Based on simulations and studies of SAW propagation characteristics such as the electromechanical coupling coefficient (k2) and phase velocity (v), the acousto-optic figure of merit is calculated. The maximum value of the acousto-optic figure of merit achieved is higher than the AOFM of all the individual materials used in these layer structures. The suggested SAW device has potential application in wearable and small footprint acousto-optic devices and gives better results than those made with bulk piezoelectric materials.
Differential Microstrip Sensor for Complex Permittivity Characterization of Organic Fluid Mixtures
Authors: al-Behadili Amer Abbood , Mocanu Iulia Andreea , Petrescu Teodor Mihai , Elwi Taha A.
Published: December 2021

A microstrip highly sensitive differential sensor for complex permittivity characterization of urine samples was designed, fabricated and tested. The sensing area contains two pairs of open-stub resonators, and the working frequency of the unloaded sensor is 1.25 GHz. The sensor is easily implemented on an affordable substrate FR-4 Epoxy with a thickness of 1.6 mm. A Teflon beaker is mounted on the sensor without affecting the measurements. Numerically, liquid mixtures of water and urine at different percentages were introduced to the proposed sensor to evaluate the frequency variation. The percentage of water content in the mixture varied from 0% (100% urine) to 100% (0% urine) with a step of 3.226%, thus giving 32 data groups of the simulated results. Experimentally, the mixtures of: 0% urine (100% water), 20% urine (80% water), 33% urine (66% water), 50% urine (50% water), 66% urine (33% water), and 100% urine (0% water) were considered for validation. The complex permittivity of the considered samples was evaluated using a nonlinear least square curve fitting in MATLAB in order to realize a sensing sensitivity of about 3%.
Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review
Authors: Horvers Anne , Tombeng Natasha , Bosse Tibor , Lazonder Ard W. , Molenaar Inge
Published: December 2021

There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.
A Real-Time Distance Measurement System for a Digital Twin Using Mixed Reality Goggles
Authors: Lalik Krzysztof , Flaga Stanisław
Published: December 2021

This paper presents a new system architecture for controlling industrial devices using Mixed Reality (MR) applications and a new method based upon them for measuring the distance between real and virtual points. The research has been carried out using a physical robot and its Digital Twin (DT). The possibility of controlling them using gestures recognized by Mixed Reality goggles has been presented. The extension of the robot’s environment with a 3D model capable of following its movements and positions was also analyzed. The system was supervised by an industrial Programmable Logic Controller (PLC) serving as an end point for the data sent by the goggles and controlling the movements of the real robot by activating the corresponding control. The results of the preliminary measurements presented here concerned the responsiveness of the system and showing the influence of system parameters in the accuracy of distance estimation between measured points.
Application of a Validated Innovative Smart Wearable for Performance Analysis by Experienced and Non-Experienced Athletes in Boxing
Authors: Menzel Tobias , Potthast Wolfgang
Published: December 2021

An athlete’s sporting performance depends to a large extent on the technical execution of the athletic motion in order to achieve maximum effectiveness in physical performance. Performance analysis provides an important means of classifying and quantifying athletic prowess in terms of the significant performance aspects of the sport to provide objective feedback. This study aimed to analyze technical execution in terms of punch trajectory, force, velocity and time, considering the expert-novice paradigm by investigating the technical execution of 31 experienced and non-experienced athletes for the four main punching techniques of the cross, jab, uppercut and hook strike. The kinetic and kinematic data were collected by means of a boxing monitoring system developed and validated for in-field use. The research revealed significant correlation for executed punching trajectory and punch force in intragroup comparison and significant differences in intergroup comparison. No significant differences were detected for punch velocity in either inter- or intra-group paradigms. This study, through use of the sensor system, aligns with the results of existing publications conducted in laboratory conditions, in the assessment of punch force, punch speed and punch time and thus extends the state of research by use of a smart wearable in field method.
Implementing Replication of Objects in DOORS—The Object-Oriented Runtime System for Edge Computing
Authors: Palanciuc Dorin , Pop Florin
Published: December 2021

Aiming for simplicity and efficiency in the domain of edge computing, DOORS is a distributed system expected to scale up to hundreds of nodes, which encapsulates application state and behavior into objects and gives them the ability to exchange asynchronous messages. DOORS offers semi-synchronous replication and the ability to explicitly move objects from one node to another, as methods to achieve scalability and resilience. The present paper gives an outline of the system structure, describes how DOORS implements object replication, and describes a basic set of measurements, yielding an initial set of conclusions for the improvements of the design.
A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure
Authors: Sott Michele Kremer , Nascimento Leandro da Silva , Foguesatto Cristian Rogério , Furstenau Leonardo B. , Faccin Kadígia , Zawislak Paulo Antônio , Mellado Bruce , Kong Jude Dzevela , Bragazzi Nicola Luigi
Published: December 2021

The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.
Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
Authors: Diógenes do Rego Iago , de Sousa Vicente A.
Published: December 2021

This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (MIMO) and small cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case.
A Wearable Electrochemical Gas Sensor for Ammonia Detection
Authors: Serafini Martina , Mariani Federica , Gualandi Isacco , Decataldo Francesco , Possanzini Luca , Tessarolo Marta , Fraboni Beatrice , Tonelli Domenica , Scavetta Erika
Published: December 2021

The next future strategies for improved occupational safety and health management could largely benefit from wearable and Internet of Things technologies, enabling the real-time monitoring of health-related and environmental information to the wearer, to emergency responders, and to inspectors. The aim of this study is the development of a wearable gas sensor for the detection of NH3 at room temperature based on the organic semiconductor poly(3,4-ethylenedioxythiophene) (PEDOT), electrochemically deposited iridium oxide particles, and a hydrogel film. The hydrogel composition was finely optimised to obtain self-healing properties, as well as the desired porosity, adhesion to the substrate, and stability in humidity variations. Its chemical structure and morphology were characterised by infrared spectroscopy and scanning electron microscopy, respectively, and were found to play a key role in the transduction process and in the achievement of a reversible and selective response. The sensing properties rely on a potentiometric-like mechanism that significantly differs from most of the state-of-the-art NH3 gas sensors and provides superior robustness to the final device. Thanks to the reliability of the analytical response, the simple two-terminal configuration and the low power consumption, the PEDOT:PSS/IrOx Ps/hydrogel sensor was realised on a flexible plastic foil and successfully tested in a wearable configuration with wireless connectivity to a smartphone. The wearable sensor showed stability to mechanical deformations and good analytical performances, with a sensitivity of 60 ± 8 μA decade−1 in a wide concentration range (17–7899 ppm), which includes the safety limits set by law for NH3 exposure.
Level-K Classification from EEG Signals Using Transfer Learning
Authors: Mizrahi Dor , Zuckerman Inon , Laufer Ilan
Published: December 2021

Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.
Design of a CANFD to SOME/IP Gateway Considering Security for In-Vehicle Networks
Authors: Zuo Zheng , Yang Shichun , Ma Bin , Zou Bosong , Cao Yaoguang , Li Qiangwei , Zhou Sida , Li Jichong
Published: December 2021

In recent years, Ethernet has been introduced into vehicular networks to cope with the increasing demand for bandwidth and complexity in communication networks. To exchange data between controller area network (CAN) and Ethernet, a gateway system is required to provide a communication interface. Additionally, the existence of networked devices exposes automobiles to cyber security threats. Against this background, a gateway for CAN/CAN with flexible data-rate (CANFD) to scalable service-oriented middleware over IP (SOME/IP) protocol conversion is designed, and security schemes are implemented in the routing process to provide integrity and confidentiality protections. Based on NXP-S32G, the designed gateway is implemented and evaluated. Under most operating conditions, the CPU and the RAM usage are less than 5% and 20 MB, respectively. Devices running a Linux operating system can easily bear such a system resource overhead. The latency caused by the security scheme accounts for about 25% of the entire protocol conversion latency. Considering the security protection provided by the security scheme, this overhead is worthwhile. The results show that the designed gateway can ensure a CAN/CANFD to SOME/IP protocol conversion with a low system resource overhead and a low latency while effectively resisting hacker attacks such as frame forgery, tampering, and sniffing.
Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization
Authors: Lu Jianqiang , Lin Weize , Chen Pingfu , Lan Yubin , Deng Xiaoling , Niu Hongyu , Mo Jiawei , Li Jiaxing , Luo Shengfu
Published: December 2021

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.
Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications
Authors: Silva António , Fernandes Duarte , Névoa Rafael , Monteiro João , Novais Paulo , Girão Pedro , Afonso Tiago , Melo-Pinto Pedro
Published: December 2021

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training. This paper investigates the use of a deep learning-based method in edge devices for onboard real-time inference that is power-effective and low in terms of space-constrained demand. A methodology is proposed for deploying high-end GPU-specific models in edge devices for onboard inference, consisting of a two-folder flow: study model hyperparameters’ implications in meeting application requirements; and compression of the network for meeting the board resource limitations. A hybrid FPGA-CPU board is proposed as an effective onboard inference solution by comparing its performance in the KITTI dataset with computer performances. The achieved accuracy is comparable to the PC-based deep learning method with a plus that it is more effective for real-time inference, power limited and space-constrained purposes.
Detection and Classification System for Rail Surface Defects Based on Eddy Current
Authors: Alvarenga Tiago A. , Carvalho Alexandre L. , Honorio Leonardo M. , Cerqueira Augusto S. , Filho Luciano M. A. , Nobrega Rafael A.
Published: December 2021

The prospect of growth of a railway system impacts both the network size and its occupation. Due to the overloaded infrastructure, it is necessary to increase reliability by adopting fast maintenance services to reach economic and security conditions. In this context, one major problem is the excessive friction caused by the wheels. This contingency may cause ruptures with severe consequences. While eddy’s current approaches are adequate to detect superficial damages in metal structures, there are still open challenges concerning automatic identification of rail defects. Herein, we propose an embedded system for online detection and location of rails defects based on eddy current. Moreover, we propose a new method to interpret eddy current signals by analyzing their wavelet transforms through a convolutional neural network. With this approach, the embedded system locates and classifies different types of anomalies, enabling an optimization of the railway maintenance plan. Field tests were performed, in which the rail anomalies were grouped in three classes: squids, weld and joints. The results showed a classification efficiency of ~98%, surpassing the most commonly used methods found in the literature.
The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing
Authors: Chaparro Javier Dorado , Ruiz Jesus Fernandez-Bermejo , Romero Maria J. Santofimia , Peño Cristina Bolaños , Irurtia Luis Unzueta , Perea Meritxell Garcia , Garcia Xavier del Toro , Molina Felix J. Villanueva , Grigoleit Sonja , Lopez Juan C.
Published: December 2021

The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based on a smart mirror, which is equipped with a set of digital solutions whose main focus is to overcome older adults’ reluctance to use technology at home and wearable devices on the move. The system has been developed in the context of two use cases: the support of independent living for older individuals with neurodegenerative diseases and the promotion of physical rehabilitation activities at home. Aspects such as reliability, usability, consumption of computational resources, performance and accuracy of the proposed platform and digital solutions have been evaluated in the initial stages of the pilots within the SHAPES project, an EU-funded innovation action. It can be concluded that the SHAPES smart mirror has the potential to contribute as a technological breakthrough to overcome the barriers that prevent older adults from engaging in the use of assistive technologies.
Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion
Authors: Zhu Yinlong , Zhang Fujie , Li Lixia , Lin Yuhao , Zhang Zhongxiong , Shi Lei , Tao Huan , Qin Tao
Published: December 2021

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.