bluuebunny/crossref_metadata_2025 huggingface dataset:
A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar Algorithm
Authors: Liu Chengjie , Du Yuan , Du Li
Published: May 2022

To reduce the pain and the probability of cross-infection caused by the invasive blood glucose testing instruments, the ex vivo glucose measurement is of high significance. The electrical property of blood varies with the density of the glucose, which can be sensed by measuring its reflected coefficient in millimeter-wave. In this article, we built a contactless glucose solution concentration measurement system based on 77-GHz FMCW radar. Several preliminary signal processing algorithms are cascaded with a deep neural network to improve the accuracy of glucose solution concentration measurement. Our experiment shows that the resolution of this ex vivo glucose measurement can achieve up to 0.1 mg/mL.
Photoemission of Plasmonic Gold Nanostars in Laser-Controlled Electron Current Devices for Technical and Biomedical Applications
Authors: Yakunin Alexander N. , Avetisyan Yury A. , Akchurin Garif G. , Zarkov Sergey V. , Aban’shin Nikolay P. , Khanadeev Vitaly A. , Tuchin Valery V.
Published: May 2022

The main goal of this work was to modify the previously developed blade-type planar structure using plasmonic gold nanostars in order to stimulate photofield emission and provide efficient laser control of the electron current. Localization and enhancement of the field at the tips of gold nanostars provided a significant increase in the tunneling electron current in the experimental sample (both electrical field and photofield emission). Irradiation at a wavelength in the vicinity of the plasmon resonance (red laser) provided a gain in the photoresponse value of up to 5 times compared to irradiation far from the resonance (green laser). The prospects for transition to regimes of structure irradiation by femtosecond laser pulses at the wavelength of surface plasmon resonance, which lead to an increase in the local optical field, are discussed. The kinetics of the energy density of photoinduced hot and thermalized electrons is estimated. The proposed laser-controlled matrix current source is promising for use in X-ray computed tomography systems.
Comparative Study of Structural Anomaly Diagnosis Based on ANN Model Using Random Displacement and Acceleration Responses with Incomplete Measurements
Authors: Ruan Zhi-Gang , Ying Zu-Guang
Published: May 2022

Structural anomaly diagnosis, such as damage identification, is a continuously interesting issue. Artificial neural networks have an excellent ability to model complex structure dynamics. In this paper, an artificial neural network model is used to describe the relationship between structural responses and anomalies such as stiffness reduction due to damages. Random acceleration and displacement responses as generally measured data are used as the input to the artificial neural network, and the output of the artificial neural network is the anomaly severity. The artificial neural network model is set up by training and then validated using random vibration responses with different structural anomalies. The structural anomaly diagnosis method based on the artificial neural network model using random acceleration and displacement responses is applied to a five-story building structure under random base excitations (seismic loading). Anomalies in the structure are denoted by stiffness reduction. Structural anomaly diagnosis using random acceleration responses is compared with that using random displacement responses. The numerical results show the effects of different random vibration responses used on the accuracy of predicting stiffness reduction. The actual incomplete measurements include intensive noise, finite sampling time length, and limited measurement points. The effects of the incomplete measurements on the accuracy of predicting results are also discussed.
Weld Feature Extraction Based on Semantic Segmentation Network
Authors: Wang Bin , Li Fengshun , Lu Rongjian , Ni Xiaoyu , Zhu Wenhan
Published: May 2022

Laser welding is an indispensable link in most types of industrial production. The realization of welding automation by industrial robots can greatly improve production efficiency. In the research and development of the welding seam tracking system, information on the position of the weld joint needs to be obtained accurately. For laser welding images with strong and complex interference, a weld tracking module was designed to capture real-time images of the weld, and a total of 737, 1920 × 1200 pixel weld images were captured using the device, of which 637 were used to create the dataset, and the other 100 were used as images to test the segmentation success rate. Based on the pixel-level segmentation capability of the semantic segmentation network, this study used an encoder–decoder architecture to design a lightweight network structure and introduced a channel attention mechanism. Compared to ERF-Net, SegNet, and DFA-Net, the network model in this paper has a fast segmentation speed and higher segmentation accuracy, with a success rate of 96% and remarkable segmentation results.
Improved Multimedia Object Processing for the Internet of Vehicles
Authors: Bhatia Surbhi , Alsuwailam Razan Ibrahim , Roy Deepsubhra Guha , Mashat Arwa
Published: May 2022

The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. An automated car that acts as the data-source node of an intelligent Internet of vehicles or IoV system is one of these examples. Our motivation is to obtain more accurate and rapid object detection using the intelligent cameras of a smart car. The competent supervision camera of the smart automobile model utilizes multimedia data for real-time automation in real-time threat detection. The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. These actions confront real-time delays during data offloading to the cloud and synchronizing with the other nodes. The proposed model follows a cooperative machine learning technique, distributes the computational load by slicing real-time object data among analogous intelligent Internet of Things nodes, and parallel vision processing between connective edge clusters. As a result, the system increases the computational rate and improves accuracy through responsible resource utilization and active–passive learning. We achieved low latency and higher accuracy for object identification through real-time multimedia data objectification.
Sensitivity Improvement of Multi-Slot Subwavelength Bragg Grating Refractive Index Sensors by Increasing the Waveguide Height or Suspending the Sensor
Authors: Heinsalu Siim , Utaka Katsuyuki
Published: May 2022

We present two methods of improving wavelength sensitivity for multi-slot sub-wavelength Bragg grating (MS-SW BG) refractive index sensors. The sensor structure is designed to have high optical mode confinement in the gaps between the silicon pillars whereby the surrounding medium interaction is high, thus improving the sensitivity. Further sensitivity improvements are achieved by increasing the waveguide height or suspending the sensor. The second option, sensor suspension, additionally requires supporting modifications in which case various configurations are considered. After the optimization of the parameters the sensors were fabricated. For the case of a waveguide height increase to 500 nm, the sensitivity of 850 nm/RIU was obtained; for sensor suspension with fully etched holes, 922 nm/RIU; for the case of not fully etched holes, 1100 nm/RIU; with the sensor lengths of about 10 µm for all cases. These values show improvements by 16.5%, 25%, and 50.5%, respectively, compared to the previous result where the height was fixed to 340 nm.
Object Perceptibility Prediction for Transmission Load Reduction in Vehicle-Infrastructure Cooperative Perception
Authors: Lv Pin , Han Jinlei , He Yuebin , Xu Jia , Li Taoshen
Published: May 2022

Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.
Sensing and Analysis of Greenhouse Gas Emissions from Rice Fields to the Near Field Atmosphere
Authors: Rajasekar Panneerselvam , Selvi James Arputha Vijaya
Published: May 2022

Greenhouse gas (GHG) emissions from rice fields have huge effects on climate change. Low-cost systems and management practices to quantify and reduce GHGs emission rates are needed to achieve a better climate. The typical GHGs estimation processes are expensive and mainly depend on high-cost laboratory equipment. This study introduces a low-cost sensor-based GHG sampling and estimation system for rice fields. For this, a fully automatic gas chamber with a sensor-integrated gas accumulator and quantifier unit was designed and implemented to study its performance in the estimation efficiency of greenhouse gases (CH4, N2O, and CO2) from rice fields for two crop seasons. For each crop season, three paddy plots were prepared at the experimental site and then subjected to different irrigation methods (continuous flooding (CF), intermittent flooding (IF), and controlled intermittent flooding (CIF)) and fertilizer treatments to study the production and emission rates of GHGs throughout the crop growing season at regular intervals. A weather station was installed on the site to record the seasonal temperature and rainfall events. The seasonal total CH4 emission was affected by the effects of irrigation treatments. The mean CH4 emission in the CIF field was smaller than in other treatments. CH4 and N2O emission peaks were high during the vegetative and reproductive phases of rice growth, respectively. The results indicated that CIF treatment is most suitable in terms of rice productivity and higher water use efficiency. The application of nitrogen fertilizers produced some peaks in N2O emissions. On the whole, the proposed low-cost GHGs estimation system performed well during both crop seasons and it was found that the adaption of CIF treatment in rice fields could significantly reduce GHG emissions and increase rice productivity. The research results also suggested some mitigation strategies that could reduce the production of GHGs from rice fields.
Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant
Authors: Tancredi Giovanni Paolo , Vignali Giuseppe , Bottani Eleonora
Published: May 2022

This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been designed to be suitable for implementation in industrial plants not directly designed for Industry 4.0 applications. More precisely, this manuscript delineates an approach for implementing three machine-learning algorithms into a digital twin environment and then applying them to a real plant. This paper is based on two previous studies in which the digital twin environment was first developed for the industrial plant under investigation, and then used for monitoring selected plant parameters. Findings from the previous studies are exploited in this work and advanced by implementing and testing the machine-learning algorithms. The results show that two out of the three machine-learning algorithms are effective enough in predicting anomalies, thus suggesting their implementation for enhancing the safety of employees working at industrial plants.
Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
Authors: Oh Seoyeon , Yu Minseok , Cho Seonghyeon , Noh Song , Chun Hyunchae
Published: May 2022

With the remarkable advances in vertical-cavity surface-emitting lasers (VCSELs) in recent decades, VCSELs have been considered promising light sources in the field of optical wireless communications. However, off-the-shelf VCSELs still have a limited modulation bandwidth to meet the multi-Gb/s data rate requirements imposed on the next-generation wireless communication system. Recently, employing machine learning (ML) techniques as a method to tackle such issues has been intriguing for researchers in wireless communication. In this work, through a systematic analysis, it is shown that the ML technique is also very effective in VCSEL-based visible light communication. Using a commercial VCSEL and bidirectional long short-term memory (Bi-LSTM)-based ML scheme, a high-speed visible light communication (VLC) link with a data rate of 13.5 Gbps is demonstrated, which is the fastest single channel result from a cost-effective, off-the-shelf VCSEL device, to the best of the authors’ knowledge.
Dermoscopic Image Classification Method Using an Ensemble of Fine-Tuned Convolutional Neural Networks
Authors: Shen Xin , Wei Lisheng , Tang Shaoyu
Published: May 2022

Aiming at the problems of large intra-class differences, small inter-class differences, low contrast, and small and unbalanced datasets in dermoscopic images, this paper proposes a dermoscopic image classification method based on an ensemble of fine-tuned convolutional neural networks. By reconstructing the fully connected layers of the three pretrained models of Xception, ResNet50, and Vgg-16 and then performing transfer learning and fine-tuning the three pretrained models with the ISIC 2016 Challenge official skin dataset, we integrated the outputs of the three base models using a weighted fusion ensemble strategy in order to obtain a final prediction result able to distinguish whether a dermoscopic image indicates malignancy. The experimental results show that the accuracy of the ensemble model is 86.91%, the precision is 85.67%, the recall is 84.03%, and the F1-score is 84.84%, with these four evaluation metrics being better than those of the three basic models and better than some classical methods, proving the effectiveness and feasibility of the proposed method.
Quantum-Safe Group Key Establishment Protocol from Lattice Trapdoors
Authors: Gebremichael Teklay , Gidlund Mikael , Hancke Gerhard P. , Jennehag Ulf
Published: May 2022

Group communication enables Internet of Things (IoT) devices to communicate in an efficient and fast manner. In most instances, a group message needs to be encrypted using a cryptographic key that only devices in the group know. In this paper, we address the problem of establishing such a key using a lattice-based one-way function, which can easily be inverted using a suitably designed lattice trapdoor. Using the notion of a bad/good basis, we present a new method of coupling multiple private keys into a single public key, which is then used for encrypting a group message. The protocol has the apparent advantage of having a conjectured resistance against potential quantum-computer-based attacks. All functions—key establishment, session key update, node addition, encryption, and decryption—are effected in constant time, using simple linear-algebra operations, making the protocol suitable for resource-constrained IoT networks. We show how a cryptographic session group key can be constructed on the fly by a user with legitimate credentials, making node-capture-type attacks impractical. The protocol also incorporates a mechanism for node addition and session-key generation in a forward- and backward-secrecy-preserving manner.
Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications
Authors: Vansovits Vitali , Petlenkov Eduard , Tepljakov Aleksei , Vassiljeva Kristina , Belikov Juri
Published: May 2022

In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant.
The New Era of Long-Range “Zero-Interception” Ambient Backscattering Systems: 130 m with 130 nA Front-End Consumption
Authors: Daskalakis Spyridon Nektarios , Georgiadis Apostolos , Tentzeris Manos M. , Goussetis George , Deligeorgis George
Published: May 2022

Internet of Things applications based on backscatter radio principles have appeared to address the limitations of high cost and high power consumption. While radio-frequency identification (RFID) sensor nodes are among the most commonly utilized state-of-the-art technologies, their range for passive implementations is typically short and well below 10 m being impractical for “rugged” applications where approaching the tag at such proximity, is not convenient or safe. In this work, we propose a long-range “zero interception” ambient backscatter (LoRAB) communication system relying on low power sensor (tag) deployments. Without employing a dedicated radio transmission, our technology enables the “zero interception” communication of the tags with portable receivers over hundreds of meters. This enables low-cost and low-power communications across a wide range of missions by using chirp spread spectrum (CSS) modulation on ambient FM signals. A laboratory prototype exploiting commercial components (laptops, DAQ, software-defined radios (SDR) platform) have demonstrated the potential by achieving 130 m tag-to-reader distance for a low bit rate of 88 bps with the modulator current consumption at around 103 nA.
De-Bonding Numerical Characterization and Detection in Aeronautic Multi-Element Spars
Authors: Concilio Antonio , Ciminello Monica , Galasso Bernardino , Pellone Lorenzo , Mercurio Umberto , Apuleo Gianvito , Cozzolino Aniello , Kressel Iddo , Shoham Shay , Bardenstein David
Published: May 2022

Structural health monitoring has multifold aims. Concerning composite structures, the main objectives are perhaps reducing costs by shifting from scheduled to on-demand maintenance and reducing weight by removing redundant precautions as the insertion of chicken fasteners to for ensuring joint safety in cases of bonding layer fail. Adhesion defects may be classified along different types, for instance distinguishing between glue deficiency or de-bonding. This paper deals with a preliminary numerical characterization of adhesive layer imperfections on a representative aircraft component. The multipart composite spar is made of two plates and two corresponding C-beams, bonded together to form an almost squared boxed section beam. A numerical test campaign was devoted to extract relevant information from different defect layouts and to try to assess some parameters that could describe their peculiarities. A focus was then given to macroscopic evidence of fault effects behavior, as localization, reciprocal interference, impact on structural response, and so on. A proprietary code was finally used to retrieve the presence and size of the imperfections, correlating numerical outcomes with estimations. Activities were performed along OPTICOMS, a European project funded within the Clean Sky 2 Joint Technology Initiative (JTI).
Transmission Power Influence on WSN-Based Indoor Localization Efficiency
Authors: Elkenawy Amr , Judvaitis Janis
Published: May 2022

Performing a good-quality indoor localization of a mobile target is a challenging task, which can be affected by many factors such as radio wave behavior, the nature of the experimental environment, and available infrastructure. (1) Background: An indoor localization experiment using an Internet of Things (IoT) wireless sensor network (WSN) testbed is performed, in order to study the influence of transmission power level on the quality of received signals, and consequently, the estimated target positional coordinates. (2) Methods: A received signal strength indicator (RSSI) range-based localization system using a geometrical constrained weighted least squares (WLS) estimator multilateration technique is selected to validate the influence of the transmission power level on the performance of the localization algorithm. (3) Results: Fair localization quality was obtained at the highest transmission power level instead of the proposed transmission power level. (4) Conclusion: Additional factors are discussed to fully represent the required operational transmission power for a better localization quality, along with suggested improvements of the infrastructure configuration as a future work.
Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
Authors: Prottasha Nusrat Jahan , Sami Abdullah As , Kowsher Md , Murad Saydul Akbar , Bairagi Anupam Kumar , Masud Mehedi , Baz Mohammed
Published: May 2022

The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT’s transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms.
A Universal Testbed for IoT Wireless Technologies: Abstracting Latency, Error Rate and Stability from the IoT Protocol and Hardware Platform
Authors: Saavedra Edgar , Mascaraque Laura , Calderon Gonzalo , del Campo Guillermo , Santamaria Asuncion
Published: May 2022

IoT applications rely strongly on the performance of wireless communication networks. There is a wide variety of wireless IoT technologies and choosing one over another depends on the specific use case requirements—be they technical, implementation-related or functional factors. Among the technical factors, latency, error rate and stability are the main parameters that affect communication reliability. In this work, we present the design, development and validation of a Universal Testbed to experimentally measure these parameters, abstracting them from the wireless IoT technology protocols and hardware platforms. The Testbed setup, which is based on a Raspberry Pi 4, only requires the IoT device under test to have digital inputs. We evaluate the Testbed’s accuracy with a temporal characterisation—accumulated response delay—showing an error less than 290 µs, leading to a relative error around 3% for the latencies of most IoT wireless technologies, the latencies of which are usually on the order of tens of milliseconds. Finally, we validate the Testbed’s performance by comparing the latency, error and stability measurements with those expected for the most common IoT wireless technologies: 6LoWPAN, LoRaWAN, Sigfox, Zigbee, Wi-Fi, BLE and NB-IoT.
Multi-Floor Indoor Localization Based on Multi-Modal Sensors
Authors: Zhou Guangbing , Xu Shugong , Zhang Shunqing , Wang Yu , Xiang Chenlu
Published: May 2022

High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation.
Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals
Authors: Chen Kunru , Rögnvaldsson Thorsteinn , Nowaczyk Sławomir , Pashami Sepideh , Johansson Emilia , Sternelöv Gustav
Published: May 2022

Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor.
Age Estimation of Faces in Videos Using Head Pose Estimation and Convolutional Neural Networks
Authors: Zhang Beichen , Bao Yue
Published: May 2022

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.
A Wavelength Modulation Spectroscopy-Based Methane Flux Sensor for Quantification of Venting Sources at Oil and Gas Sites
Authors: Festa-Bianchet Simon A. , Seymour Scott P. , Tyner David R. , Johnson Matthew R.
Published: May 2022

An optical sensor employing tunable diode laser absorption spectroscopy with wavelength modulation and 2f harmonic detection was designed, prototyped, and tested for applications in quantifying methane emissions from vent sources in the oil and gas sector. The methane absorption line at 6026.23 cm–1 (1659.41 nm) was used to measure both flow velocity and methane volume fraction, enabling direct measurement of the methane emission rate. Two configurations of the sensor were designed, tested, and compared; the first used a fully fiber-coupled cell with multimode fibers to re-collimate the laser beams, while the second used directly irradiated photodetectors protected by Zener barriers. Importantly, both configurations were designed to enable measurements within regulated Class I / Zone 0 hazardous locations, in which explosive gases are expected during normal operations. Controlled flows with methane volume fractions of 0 to 100% and a velocity range of 0 to 4 m/s were used to characterize sensor performance at a 1 Hz sampling rate. The measurement error in the methane volume fraction was less than 10,000 ppm (1%) across the studied range for both configurations. The short-term velocity measurement error with pure methane was <0.3 m/s with a standard deviation of 0.14 m/s for the fiber-coupled configuration and <0.15 m/s with a standard deviation of 0.07 m/s for the directly irradiated detector configuration. However, modal noise in the multimode fibers of the first configuration contributed to an unstable performance that was highly sensitive to mechanical disturbances. The second configuration showed good potential for an industrial sensor, successfully quantifying methane flow rates up to 11 kg/h within ±2.1 kg/h at 95% confidence over a range of methane fractions from 25–100%, and as low as ±0.85 kg/h in scenarios where the source methane fraction is initially unknown within this range and otherwise invariant.
Dynamical Synergies of Multidigit Hand Prehension
Authors: Pei Dingyi , Olikkal Parthan , Adali Tülay , Vinjamuri Ramana
Published: May 2022

Hand prehension requires highly coordinated control of contact forces. The high-dimensional sensorimotor system of the human hand operates at ease, but poses several challenges when replicated in artificial hands. This paper investigates how the dynamical synergies, coordinated spatiotemporal patterns of contact forces, contribute to the hand grasp, and whether they could potentially capture the force primitives in a low-dimensional space. Ten right-handed subjects were recruited to grasp and hold mass-varied objects. The contact forces during this multidigit prehension were recorded using an instrumented grip glove. The dynamical synergies were derived using principal component analysis (PCA). The contact force patterns during the grasps were reconstructed using the first few synergies. The significance of the dynamical synergies, the influence of load forces and task configurations on the synergies were explained. This study also discussed the contribution of biomechanical constraints on the first few synergies and the current challenges and possible applications of the dynamical synergies in the design and control of exoskeletons. The integration of the dynamical synergies into exoskeletons will be realized in the near future.
Collaborative Filler Network for Enhancing the Performance of BaTiO3/PDMS Flexible Piezoelectric Polymer Composite Nanogenerators
Authors: Bouhamed Ayda , Jöhrmann Nathanael , Naifar Slim , Böhm Benny , Hellwig Olav , Wunderle Bernhard , Kanoun Olfa
Published: May 2022

Wearable sensors are gaining attention in human health monitoring applications, even if their usability is limited due to battery need. Flexible nanogenerators (NGs) converting biomechanical energy into electrical energy offer an interesting solution, as they can supply the sensors or extend the battery lifetime. Herein, flexible generators based on lead-free barium titanate (BaTiO3) and a polydimethylsiloxane (PDMS) polymer have been developed. A comparative study was performed to investigate the impact of multiwalled carbon nanotubes (MWCNTs) via structural, morphological, electrical, and electromechanical measurements. This study demonstrated that MWCNTs boosts the performance of the NG at the percolation threshold. This enhancement is attributed to the enhanced conductivity that promotes charge transfer and enhanced mechanical property and piezoceramics particles distribution. The nanogenerator delivers a maximum open-circuit voltage (VOC) up to 1.5 V and output power of 40 nW, which is two times higher than NG without MWCNTs. Additionally, the performance can be tuned by controlling the composite thickness and the applied frequency. Thicker NG shows a better performance, which enlarges their potential use for harvesting biomechanical energy efficiently up to 11.22 V under palm striking. The voltage output dependency on temperature was also investigated. The results show that the output voltage changes enormously with the temperature.
All-Optical Parametric-Resonance Magnetometer Based on 4He Atomic Alignment
Authors: Wang Bowen , Peng Xiang , Wang Haidong , Xiao Wei , Guo Hong
Published: May 2022

Parametric-resonance magnetometer is a high-sensitivity quantum sensor characterized by applying the non-resonant radio-frequency (RF) fields to the atomic ensemble. The RF fields lead to crosstalk in the multi-sensor design, thus disturbing the magnetic-field measurement results. We propose an optically modulated alignment-based 4He parametric-resonance magnetometer. By using the fictitious field generated by the modulated light shift, parametric resonance is realized, and crosstalk caused by the magnetic RF field is prevented. The relative intensity noise of the lasers is suppressed to optimize the sensitivity of the magnetometer. Our magnetometer experimentally demonstrates a magnetic-field noise floor of 130 fT/Hz1/2 in both open- and closed-loop operations and has the potential to reach 70 fT/Hz1/2 when compared with the optimized magnetic RF scheme. It provides near-zero magnetic-field measurements with a 2 kHz bandwidth at room temperature, which is useful for high-bandwidth measurements in biomagnetic applications.
Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems
Authors: Kang Xiao-Fei , Liu Zi-Hui , Yao Meng
Published: May 2022

In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning scheme for joint pilot design and channel estimation is proposed. This new hybrid network structure is named CAGAN, which is composed of a concrete autoencoder (concrete AE) and a conditional generative adversarial network (cGAN). We first use concrete AE to find and select the most informative position in the time-frequency grid to achieve pilot optimization design and then input the optimized pilots to cGAN to complete channel estimation. Simulation experiments show that the CAGAN scheme outperforms the traditional LS and MMSE estimation methods with fewer pilots, and has good robustness to environmental noise.
Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
Authors: Idehen Igbinigie Philip , You Qingyu , Xu Xiqiang , Li Shaoqing , Zhang Yan , Hu Yaoxing , Wang Yuan
Published: May 2022

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.
HiVTac: A High-Speed Vision-Based Tactile Sensor for Precise and Real-Time Force Reconstruction with Fewer Markers
Authors: Quan Shengjiang , Liang Xiao , Zhu Hairui , Hirano Masahiro , Yamakawa Yuji
Published: May 2022

Although they have been under development for years and are attracting a lot of attention, vision-based tactile sensors still have common defects—the use of such devices to infer the direction of external forces is poorly investigated, and the operating frequency is too low for them to be applied in practical scenarios. Moreover, discussion of the deformation of elastomers used in vision-based tactile sensors remains insufficient. This research focuses on analyzing the deformation of a thin elastic layer on a vision-based tactile sensor by establishing a simplified deformation model, which is cross-validated using the finite element method. Further, this model suggests a reduction in the number of markers required by a vision-based tactile sensor. In subsequent testing, a prototype HiVTac is fabricated, and it demonstrates superior accuracy to its vision-based tactile sensor counterparts in reconstructing an external force. The average error of inferring the direction of external force is 0.32∘, and the root mean squared error of inferring the magnitude of the external force is 0.0098 N. The prototype was capable of working at a sampling rate of 100 Hz and a processing frequency of 1.3 kHz, even on a general PC, allowing for real-time reconstructions of not only the direction but also the magnitude of an external force.
Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications
Authors: Cornago Simone , Tan Yee Shee , Brondi Carlo , Ramakrishna Seeram , Low Jonathan Sze Choong
Published: May 2022

Life cycle assessment (LCA) is a well-established methodology to quantify the environmental impacts of products, processes, and services. An advanced branch of this methodology, dynamic LCA, is increasingly used to reflect the variation in such potential impacts over time. The most common form of dynamic LCA focuses on the dynamism of the life cycle inventory (LCI) phase, which can be enabled by digital models or sensors for a continuous data collection. We adopt a systematic literature review with the aim to support practitioners looking to apply dynamic LCI, particularly in Industry 4.0 applications. We select 67 publications related to dynamic LCI studies to analyze their goal and scope phase and how the dynamic element is integrated in the studies. We describe and discuss methods and applications for dynamic LCI, particularly those involving continuous data collection. Electricity consumption and/or electricity technology mixes are the most used dynamic components in the LCI, with 39 publications in total. This interest can be explained by variability over time and the relevance of electricity consumption as a driver of environmental impacts. Finally, we highlight eight research gaps that, when successfully addressed, could benefit the diffusion and development of sound dynamic LCI studies.
Moderating Effects of Leadership and Innovation Activities on the Technological Innovation, Market Orientation and Corporate Performance Model
Authors: Hwangbo Yun , Shin Wang-Jae , Kim Youngjun
Published: May 2022

This paper aims to examine the relations between market orientation, technological innovation and corporate performance with the moderating roles of leadership, exploitation and exploration by an empirical study targeting 414 Korean responses in diverse industries. The results of the analysis reveal that technological innovation partially mediates the relationship between market orientation and the perceived financial and non-financial performance of a company. Additionally, when technological innovation affects corporate performance, transformational leadership and transactional leadership have a moderating effect on both the perceived financial and non-financial performance, but the exploitation and exploration of innovation activity had a moderating effect only on non-financial performance. Finally, when market orientation affects corporate performance, transformational leadership and exploration of innovative activities have a moderating effect only on non-financial performance. The novelty of this study lies in that it is more effective to carry out market-oriented activities with a focus on the customers rather than to pursue technological innovation alone in order to improve corporate performance. The contribution of this research is to expand the scope of research related to the results of technological innovation academically and to support how to apply corporate management elements in order to improve corporate performance in the field of business.
Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato
Authors: Xie Jingxin , Zhou Zhongrui , Zhang Hongduo , Zhang Liang , Li Ming
Published: May 2022

Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.
Developing and Validating an Instrument for Measuring Teachers’ Informatization Teaching Ability in Primary and Secondary Schools in China for the Sustainable Development of Education Informatization
Authors: Yi Suping , Shadiev Rustam , Yun Ruwei , Lu Yefeng
Published: May 2022

Teachers’ informatization teaching ability (TITA) is the core ability of teachers to engage in educational activities in the informatization environment. It is an essential indicator of the quality of education and affects teachers’ professional development in the technological age. To get a precise teachers’ informatization teaching ability scale and to measure TITA more accurately, the quality of existing scales needs to be improved. This study analyzed and generalized definitions, models and frameworks for TITA, proposed a four-dimension model (teachers’ informatization teaching metacognitive ability, informatization teaching design ability, informatization teaching implementation ability and informatization teaching evaluation ability), and developed the TITA scale. Three experts were involved in the content validity of the TITA scale process. A total of 403 valid questionnaires answered by Chinese primary and secondary school teachers were used to test the reliability and convergent validity of the scale. The results showed that the TITA scale has high reliability and good validity, and it can be used to evaluate TITA in future studies. The TITA scale also provided a theoretical framework to help teachers consider how to transfer knowledge and skills to students by various technologies more effectively.