bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Peat Extraction Range and Vegetation Succession on the Baligówka Degraded Peat Bog (Central Europe) Using the ALS Data and Orthophotomap
Authors: Jucha Witold , Mareczka Paulina , Okupny Daniel
Published: June 2022

The Baligówka peat bog is one of the peat bogs of the Orawa-Nowy Targ Basin—the largest complex of wetlands in the Polish Carpathians. Its area has declined in the past as a result of drainage and peat exploitation, which caused a bad hydrological condition and it is gradually overgrown by non-peat bog medium and high vegetation. The research uses models derived from airborne laser scanning (ALS) and an orthophotomap to delimit the bog and divide it into parts and assess the range of drainage ditches and vegetation. The area of the peat dome along with 3 sites of peat exploitation is currently 159.6 ha, while the ecotone zone is 105.9 ha. Both sections are separated by a steep post-mining slope. The medium and high vegetation areas cover 44% of the peat bog; its location is related to the dense drainage system in the southern part of the dome. The parameters of the Baligówka peat bog: area, size and extent of drainage system, and the degree of overgrowth by high vegetation, are the subject of research towards the protection under the Natura 2000 network (PLH120016) and the establishment of a plan for restoration activities.
On the Reconstruction of Missing Sea Surface Temperature Data from Himawari-8 in Adjacent Waters of Taiwan Using DINEOF Conducted with 25-h Data
Authors: Yang Yi-Chung , Lu Ching-Yuan , Huang Shih-Jen , Yang Thwong-Zong , Chang Yu-Cheng , Ho Chung-Ru
Published: June 2022

Satellite remote sensing sea surface temperature (SST) data are lost due to cloud cover. Missing data often cause inconvenience in subsequent applications and thus need to be reconstructed. In this study, the Data Interpolating Empirical Orthogonal Function (DINEOF) method was used to reconstruct the hourly SST data missing from the Himawari-8 satellite in the waters near Taiwan. The SST characteristics in the waters around Taiwan are quite complex, with high SST at Kuroshio in the east of Taiwan and great variation in the SST west of Taiwan due to the influence of tides. Therefore, the analysis with DINEOF was conducted using 25-h data to match the tidal cycle. The influence of SST characteristics on the accuracy of SST reconstruction is also discussed. The results show that in the western sea area where the variation of SST is large, the average root-mean-square error of SST between the original SST and the reconstructed SST is the lowest and the average coefficient of determination is the highest. The accuracy of the reconstructed SST is positively correlated with the SST variation. Furthermore, the statistical results also show that the DINEOF method can effectively reconstruct the SST regardless of the missing data rate.
Conservation and Selection of Genes Related to Environmental Adaptation in Native Small Ruminant Breeds: A Review
Authors: Demir Eymen , Ceccobelli Simone , Bilginer Umit , Pasquini Marina , Attard George , Karsli Taki
Published: June 2022

Environmental stressors have, over generations, influenced the morphology, anatomy, behaviour, physiology, and genetic structure of small ruminants. The widespread dispersal of small ruminants over vast geographical areas occurred along with human migration, thereby promoting the adaptative process to different environmental conditions mainly through natural selection. Ongoing global warming prompted scientific efforts to deepen the knowledge and understanding of adaptation traits in small ruminants. Compared to other ruminants, sheep and goats seem to have a better adaptation ability to environmental stressors, as evident by their presence across different geographic areas on a global level. Adaptation to a specific environment leads to variations in precise genomic regions, allowing for the identification and selection of animals with a high capacity of adaptation to environmental stressors. Rapid development in sequencing technologies, together with bioinformatics tools, make it possible to analyse the genomic regions related to environmental adaptation. Hence, the aims of this review were (i) to outline the main steps of the evolution process in sheep and goat species, (ii) to summarise candidate genes related to environmental adaptation, and (iii) to evaluate both selection and conservation possibilities of these genes in native small ruminant breeds for future challenges to better face the global warming.
Current Knowledge on the Lagune Cattle Breed in Benin: A State of the Art Review
Authors: Ahozonlin Maurice Cossi , Gbangboche Armand Bienvenu , Dossa Luc Hippolyte
Published: June 2022

This paper summarizes the information on the Lagune cattle breed and its production systems in Benin available in peer-reviewed journals, conference papers and research reports covering the period from 1970 to 2020. Lagune cattle are kept in two production systems in Benin: the improved ranching system established at the Governmental farm of Samiondji, where most of the published studies on this breed have been achieved, and the village traditional production system. This breed has a small size with short horns. Reported adult weight varied from 140 to 180 kg for male and from 100 to 140 kg for female. Lagune cows are bad dairy. The breed plays an important role in the livelihoods of the rural people and has many functions, including cultural, ecological, socio-economic, savings and income provision roles. Most of the reports emphasizes its low productivity, which might rather be more related to its poor management conditions than to its genetic potential. The Lagune breed is reported to have adaptive traits such as tolerance to drought and heat, and resistance to ticks and diseases. In 1975, the Government of Benin, having realized the risk of the disappearance of this breed, devoted the Samiondji Farm to its ex situ conservation and set up a purebred multiplication herd. Since then, there have been very few efforts to comprehensively characterize this breed and its productivity under smallholder production systems. Consequently, the opportunities for its in situ conservation, management and sustainable use by the rural communities are not sufficiently explored.
Sensoring the Neck: Classifying Movements and Actions with a Neck-Mounted Wearable Device
Authors: Lacanlale Jonathan , Isayan Paruyr , Mkrtchyan Katya , Nahapetian Ani
Published: June 2022

Sensor technology that captures information from a user’s neck region can enable a range of new possibilities, including less intrusive mobile software interfaces. In this work, we investigate the feasibility of using a single inexpensive flex sensor mounted at the neck to capture information about head gestures, about mouth movements, and about the presence of audible speech. Different sensor sizes and various sensor positions on the neck are experimentally evaluated. With data collected from experiments performed on the finalized prototype, a classification accuracy of 91% in differentiating common head gestures, a classification accuracy of 63% in differentiating mouth movements, and a classification accuracy of 83% in speech detection are achieved.
Applicability of Magnetic Sensors in Interlocking Systems
Authors: Gheorghiu Răzvan Andrei , Bădău Florin , Iordache Valentin
Published: June 2022

Rail interlocking systems follow the progress of technology and train speeds. Nowadays, new systems are designed based on microcontrollers and reliable software, requiring many hours of testing to ensure their proper functionality and bug-free coding. However, in many countries, there are still older relay solutions implemented that are not envisaged to be upgraded in the near future partially due to costs but mainly due to the fact they function very well, being based on a highly reliable element: the relay. However, specialized maintenance personnel are becoming more difficult to find, so automation systems that check the proper relay functionality are a key element to ensure a longer lifetime of existing systems. In this article, the authors present a monitoring solution based on magnetic sensors that proved capable of provide reliable information about the relays, hence revealing the first step in a relay automated control system.
Lightweight Self-Detection and Self-Calibration Strategy for MEMS Gas Sensor Arrays
Authors: Liu Bing , Zhou Yanzhen , Fu Hongshuo , Fu Ping , Feng Lei
Published: June 2022

With the development of Internet of Things (IoT) and edge computing technology, gas sensor arrays based on Micro-Electro-Mechanical System (MEMS) fabrication technique have broad application prospects in intelligent integrated systems, portable devices, and other fields. In such complex scenarios, the normal operation of a gas sensing system depends heavily on the accuracy of the sensor output. Therefore, a lightweight Self-Detection and Self-Calibration strategy for MEMS gas sensor arrays is proposed in this paper to monitor the working status of sensor arrays and correct the abnormal data in real time. Evaluations on real-world datasets indicate that the strategy has high performance of fault detection, isolation, and data recovery. Furthermore, our method has low computation complexity and low storage resource occupation. The board-level verification on CC1350 shows that the average calculation time and running power consumption of the algorithm are 0.28 ms and 9.884 mW. The proposed strategy can be deployed on most resource-limited IoT devices to improve the reliability of gas sensing systems.
Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions
Authors: Morrone Sarah , Dimauro Corrado , Gambella Filippo , Cappai Maria Grazia
Published: June 2022

Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.
RAEN: Rate Adaptation for Effective Nodes in Backscatter Networks
Authors: Zhao Jumin , Liu Qi , Li Dengao , Wang Qiang , Bai Ruiqin
Published: June 2022

A backscatter network, as a key enabling technology for interconnecting plentiful IoT sensing devices, can be applicable to a variety of interesting applications, e.g., wireless sensing and motion tracking. In these scenarios, the vital information-carrying effective nodes always suffer from an extremely low individual reading rate, which results from both unpredictable channel conditions and intense competition from other nodes. In this paper, we propose a rate-adaptation algorithm for effective nodes (RAEN), to improve the throughput of effective nodes, by allowing them to transmit exclusively and work in an appropriate data rate. RAEN works in two stages: (1) RAEN exclusively extracts effective nodes with an identification module and selection module; (2) then, RAEN leverages a trigger mechanism, combined with a random forest-based classifier, to predict the overall optimal rate. As RAEN is fully compatible with the EPC C1G2 standard, we implement the experiment through a commercial reader and multiple RFID tags. Comprehensive experiments show that RAEN improves the throughput of effective nodes by 3×, when 1/6 of the nodes are effective, compared with normal reading. What is more, the throughput of RAEN is better than traditional rate-adaptation methods.
Direct Measuring Particulate Matters in Smoke Plumes from Chimneys in a Textile Dyeing Industrial Park by a Self-Developed PM Detector on an UAV in Yangtze River Delta of China
Authors: Wu Zhentao , Pang Xiaobing , Han Zhangliang , Yuan Kaibin , Dai Shang , Li Jingjing , Chen Jianmeng , Xing Bo
Published: June 2022

Directly measuring particulate matters (PM) from chimneys in an industrial park is difficult due to it being hard to reach the peak heights. A self-developed PM detector on an unmanned aerial vehicle (UAV) had been deployed to directly measure the PM emissions in smoke plumes from chimneys in a textile dyeing industrial park. Compared with a commercial PM device (LD-5R, SIBATA, Kyoto, Japan), the self-developed detector showed similar performance with a good correlation (R2 varying from 0.911 to 0.951) in simultaneously vertical PM measurements on UAV. The PM emissions from chimneys after different textile treating processes, including pigment printing, dyeing process, and digital printing, were investigated. PM mass concentrations and particle number concentrations (PNC) in different sizes were found to be significantly higher in pigment printing than those in dyeing process and digital printing by 2 or 3 times after electrostatic precipitation. The activated carbon adsorption and electrostatic precipitation were the major PM controlling techniques in the park. The PM mass concentrations and PNC were the highest in the process of dyeing after activated carbon adsorption with the concentrations of PM1 (1000 μg·m−3), PM2.5 (1600 μg·m−3), and PM10 (2000 μg·m−3), respectively. According to the results of PM and PNC, PM2.5 was found to be the dominant particles accounting for 99% of the PM emissions. It may be due to the high temperature in thermo-fixing machine, which is beneficial to the PM2.5 generation. This study revealed PM2.5 was the dominant particles to be reduced in textile dyeing enterprises to mitigate PM pollution.
Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy)
Authors: Vitale Giovanni , D’Alessandro Antonino , Di Benedetto Andrea , Figlioli Anna , Costanzo Antonio , Speciale Stefano , Piattoni Quintilio , Cipriani Leonardo
Published: June 2022

Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implemented in the urban district of Camerino, one of the cities in central Italy with the greatest seismic vulnerability. The technological choices adopted in developing this system as well as the implemented algorithms are shown in the context of their application to the first seismic event recorded by this innovative monitoring infrastructure. This monitoring network is innovative because it implements a distributed computing and statistical earthquake detection algorithm. As such, it is not based on the traces received by the stations from the central server; rather, each station carries out the necessary checks on the signal in real time, sending brief reports to the server in case of anomalies. This approach attempts to shorten the time between event detection and alert, effectively removing the dead times in the systems currently used in the Italian national network. The only limit for an instant alarm is the latency in the tcp/ip packages used to send the short reports to the server. The presented work shows the infrastructure created; however, there is not enough data to draw conclusions on this new early warning approach in the field, as it is currently in the data collection phase.
The Use of Armeo®Spring Device to Assess the Effect of Trunk Stabilization Exercises on the Functional Capabilities of the Upper Limb—An Observational Study of Patients after Stroke
Authors: Olczak Anna , Truszczyńska-Baszak Aleksandra , Stępień Adam
Published: June 2022

Almost half of stroke patients report impaired function of the upper limb and hand. Stability of the trunk is required for the proper movement of the body, including the legs and arms. The aim of the study was to analyze the effect of trunk stabilization exercises on coordinated movement of the affected upper limb in patients after stroke, using an Armoe®Spring device and the “wall” and “abacus” functional tests. Materials and methods: This is a randomized, double-blinded study. The research was carried out in the Rehabilitation Clinic on a group of 60 stroke patients who were randomly assigned to groups differing in the rehabilitation program. The study group had physiotherapy based on the NDT Bobath concept and the control group used classic exercises. The importance of the trunk for upper limb coordination was assessed on the Armeo®Spring device using three evaluation programs, “perpendicular fishing”; “horizontal fishing”; “reaction time”, and two proprietary tests, “wall” and “abacus”. Results: The post-treatment analysis showed significantly better results in the study group for the abacus (p < 0.001), wall (p = 0.003) tests, and a significantly higher percentage of task completion in the vertical fishing (p = 0.036) and reaction time (p = 0.009) tests. Conclusions: Physiotherapy including exercises to stabilize the trunk had a significant effect on increasing the functional efficiency of the affected upper limb and on improving the handgrip strength. The Armeo®Spring device is a good device for the functional assessment of the upper limb before and after therapy.
The Identification of ECG Signals Using Wavelet Transform and WOA-PNN
Authors: Li Ning , He Fuxing , Ma Wentao , Wang Ruotong , Jiang Lin , Zhang Xiaoping
Published: June 2022

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.
Flexible Platform of Acoustofluidics and Metamaterials with Decoupled Resonant Frequencies
Authors: Zahertar Shahrzad , Torun Hamdi , Sun Chao , Markwell Christopher , Dong Yinhua , Yang Xin , Fu Yongqing
Published: June 2022

The key challenge for a lab-on-chip (LOC) device is the seamless integration of key elements of biosensing and actuation (e.g., biosampling or microfluidics), which are conventionally realised using different technologies. In this paper, we report a convenient and efficient LOC platform fabricated using an electrode patterned flexible printed circuit board (FPCB) pressed onto a piezoelectric film coated substrate, which can implement multiple functions of both acoustofluidics using surface acoustic waves (SAWs) and sensing functions using electromagnetic metamaterials, based on the same electrode on the FPCB. We explored the actuation capability of the integrated structure by pumping a sessile droplet using SAWs in the radio frequency range. We then investigated the hybrid sensing capability (including both physical and chemical ones) of the structure employing the concept of electromagnetic split-ring resonators (SRRs) in the microwave frequency range. The originality of this sensing work is based on the premise that the proposed structure contains three completely decoupled resonant frequencies for sensing applications and each resonance has been used as a separate physical or a chemical sensor. This feature compliments the acoustofluidic capability and is well-aligned with the goals set for a successful LOC device.
A Low-Computing-Complexity Touch Signal Detection Method and Analog Front-End Circuits Based on Cross-Correlation Technology for Large-Size Touch Panel
Authors: Guo Xiaoyu , Li Hongge , Chen Yuhao , Sun Wei
Published: June 2022

This paper proposes a low-computing-complexity touch signal detection method and analog front-end (AFE) circuits based on cross-correlation technology for large mutual capacitance touch screen panels (TSPs). To solve the traditional touch signal detection method problem of lots of invalid data being sampled and processed in a large-size TSP, the proposed method only samples and processes the signals around the touch points rather than full-screen data to decrease the computing complexity and analog–digital convertor (ADC) acquisition number. Compared with the traditional method, the proposed touch points search algorithm complexity decreases from MN to M + nN where M, N, and n are the number of RX channels, TX channels, and touch points, respectively. The maximum ADC acquisition number of the proposed method decreases from MN to 18n. Based on the proposed touch signal detection method, the AFE circuits are designed by a 0.11 μm process. The proposed dual cross-correlation AFE achieves detection of the weak touch signal submerged in the large display panel noise. The average channel area and power consumption are decreased to 0.015 mm2 and 0.227 mW, respectively. The maximum frame rate is 384.6 Hz with 10 touch points. The proposed cross-correlation AFE achieves a high frame rate while reducing the die area and power consumption.
An Effective Color Image Encryption Based on Henon Map, Tent Chaotic Map, and Orthogonal Matrices
Authors: Kanwal Shamsa , Inam Saba , Othman Mohamed Tahar Ben , Waqar Ayesha , Ibrahim Muhammad , Nawaz Fariha , Nawaz Zainab , Hamam Habib
Published: June 2022

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.
Validity of Calculating Continuous Relative Phase during Cycling from Measures Taken with Skin-Mounted Electro-Goniometers
Authors: Whittle Chris , Jobson Simon A. , Smith Neal
Published: June 2022

The aim of this study was to assess the validity of electro-goniometers as a tool for recording continuous relative phase data at two joint couplings during cycling tasks at a range of cadences. Seven participants (4 male, 3 female, age: 29 ± 7 years, height: 1.76 ± 0.10 m, mass: 71.97 ± 11.57 kg) performed exercise bouts of 30 s at four prescribed cadences (60, 80, 100, 120 rev·min−1) on a stationary ergometer (Wattbike, Nottingham, UK). Measures were synchronously recorded by bi-axial electro-goniometers (Biometrics, UK) and a 12-camera motion-capture system (Qualisys, Gothenburg, Sweden), with both systems sampling at 500 Hz. Sagittal plane joint angle and joint angular velocity were recorded at the hip, knee and ankle and analysed for ten complete pedal revolutions per participant per condition. Data were interpolated to 100 time points and used to calculate mean continuous relative phase (CRP) per pedal revolution at two intra-limb couplings: (i) knee flexion/extension–ankle plantarflexion/dorsiflexion (KA) and (ii) hip flexion/extension–knee flexion/extension (HK). At the KA coupling, significant differences in mean CRP were found between measurement systems at 120 rev·min−1 (p = 0.006). At the HK coupling, significant differences in mean CRP were found between measurement systems at 80 rev·min−1 (p = 0.043) and 100 rev·min−1 (p = 0.028). ICC values for most comparisons were below 0.5, suggesting poor levels of agreement between systems. Significant differences in mean CRP per pedal revolution and poor levels of agreement between systems suggests that electro-goniometers are not a suitable alternative to motion-capture systems when attempting to record CRP during cycling.
Scene Uyghur Text Detection Based on Fine-Grained Feature Representation
Authors: Wang Yiwen , Mamat Hornisa , Xu Xuebin , Aysa Alimjan , Ubul Kurban
Published: June 2022

Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar texture and text morphology in the complex background noise of natural scene images are prone to false recall and difficult to detect multi-scale texts, a multi-directional scene Uyghur text detection model based on fine-grained feature representation and spatial feature fusion is proposed, and feature extraction and feature fusion are improved to enhance the network’s ability to represent multi-scale features. In this method, the multiple groups of 3 × 3 convolutional feature groups that are connected like the hierarchical residual to build a residual network for feature extraction, which captures the feature details and increases the receptive field of the network to adapt to multi-scale text and long glued dimensional font detection and suppress false positives of text-like objects. Secondly, an adaptive multi-level feature map fusion strategy is adopted to overcome the inconsistency of information in multi-scale feature map fusion. The proposed model achieves 93.94% and 84.92% F-measure on the self-built Uyghur dataset and the ICDAR2015 dataset, respectively, which improves the accuracy of Uyghur text detection and suppresses false positives.
Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics
Authors: Liu Bo , Fu Weiping , Wang Wen , Li Rui , Gao Zhiqiang , Peng Lixia , Du Huilong
Published: June 2022

Recently, the safety of workers has gained increasing attention due to the applications of collaborative robots (cobot). However, there is no quantitative research on the impact of cobot behavior on humans’ psychological reactions, and these results are not applied to the cobot motion planning algorithms. Based on the concept of the gravity field, this paper proposes a model of the psychological safety field (PSF), designs a comprehensive experiment on different speeds and minimum distances when approaching the head, chest, and abdomen, and obtains the ordinary surface equation of psychological stress about speed and minimum distance by using data fitting. By combining social rules and PSF models, we improve the robot motion planning algorithm based on behavioral dynamics. The validation experiment results show that our proposed improved robot motion planning algorithm can effectively reduce psychological stress. Eighty-seven point one percent (87.1%) of the experimental participants think that robot motion planned by improved robot motion planning algorithms is more “friendly”, can effectively reduce psychological stress, and is more suitable for human–robot interaction scenarios.
Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity
Authors: Cho Byeong-Hyo , Kim Yong-Hyun , Lee Ki-Beom , Hong Young-Ki , Kim Kyoung-Chul
Published: June 2022

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.
Editorial: Special Issue “Edge and Fog Computing for Internet of Things Systems”
Authors: Dezfouli Behnam , Liu Yuhong
Published: June 2022

Employing edge and fog computing for building IoT systems is essential, especially because of the massive number of data generated by sensing devices, the delay requirements of IoT applications, the high burden of data processing on cloud platforms, and the need to take immediate actions against security threats.
The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
Authors: Al-qudah Rabiah , Khamayseh Yaser , Aldwairi Monther , Khan Sarfraz
Published: June 2022

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. To highlight the importance and usefulness of the proposed framework, we designed and implemented a smart image handling system targeted at non-technical personnel. The high cost, security, and inconvenience issues may limit the cities’ abilities to adopt such solutions. Therefore, this work also proposes to design and implement a generalized image processing model using deep learning. The proposed model accepts images from users, then performs self-tuning operations to select the best deep network, and finally produces the required insights without any human intervention. This helps in automating the decision-making process without the need for a specialized data scientist.
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
Authors: Javed Abdul Rehman , Hassan Muhammad Abul , Shahzad Faisal , Ahmed Waqas , Singh Saurabh , Baker Thar , Gadekallu Thippa Reddy
Published: June 2022

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.
A Novel Electrochemical Sensor Modified with a Computer-Simulative Magnetic Ion-Imprinted Membrane for Identification of Uranyl Ion
Authors: He Li-Qiong , Wang Zhi-Mei , Li Yu-Jie , Yang Jing , Liao Li-Fu , Xiao Xi-Lin , Liu Yong
Published: June 2022

In this paper, a novel ion-imprinted electrochemical sensor modified with magnetic nanomaterial Fe3O4@SiO2 was established for the high sensitivity and selectivity determination of UO22+ in the environment. Density functional theory (DFT) was employed to investigate the interaction between templates and binding ligands to screen out suitable functional binding ligand for the reasonable design of the ion imprinted sensors. The MIIP/MCPE (magnetic ion imprinted membrane/magnetic carbon paste electrode) modified with Fe3O4@SiO2 exhibited a strong response current and high sensitivity toward uranyl ion comparison with the bare carbon paste electrodes. Meanwhile, the MCPE was fabricated simultaneously under the action of strong magnetic adsorption, and the ion imprinted membrane can be adsorbed stably on the electrode surface, handling the problem that the imprinted membrane was easy to fall off during the process of experimental determination and elution. Based on the uranyl ion imprinting network, differential pulse voltammetry (DPV) was adopted for the detection technology to realize the electrochemical reduction of uranyl ions, which improved the selectivity of the sensor. Thereafter, uranyl ions were detected in the linear concentration range of 1.0 × 10−9 mol L−1 to 2.0 × 10−7 mol L−1, with the detection and quantification limit of 1.08 × 10−9 and 3.23 × 10−10 mol L−1, respectively. In addition, the sensor was successfully demonstrated for the determination of uranyl ions in uranium tailings soil samples and water samples with a recovery of 95% to 104%.
A Fully-Integrated Ambient RF Energy Harvesting System with 423-μW Output Power
Authors: Pakkirisami Churchill Kishore Kumar , Ramiah Harikrishnan , Chong Gabriel , Chen Yong , Mak Pui-In , Martins Rui P.
Published: June 2022

This paper proposes a 2.4-GHz fully-integrated single-frequency multi-channel RF energy harvesting (RFEH) system with increased harvested power density. The RFEH can produce an output power of ~423-μW in harvesting ambient RF energy. The front-end consists of an on-chip impedance matching network with a stacked rectifier concurrently matched to a 50 Ω input source. The circuit mitigates the “dead-zone” by enhancing the pumping efficiency, achieved through the increase of Vgs drivability of the proposed internal gate boosting 6-stage low-input voltage charge pump and the 5-stage shared-auxiliary-biasing ring-voltage-controlled-oscillator (VCO) integrated to improve the start-up. The RFEH system, simulated in 180-nm complementary metal–oxide–semiconductor (CMOS), occupies an active area of 1.02 mm2. Post-layout simulations show a peak power conversion efficiency(PCE) of 21.15%, driving a 3.3-kΩ load at an input power of 0 dBm and sensitivity of −14.1 dBm corresponding to an output voltage, Vout,RFEH of 1.25 V.
A Sentiment Analysis Method Based on a Blockchain-Supported Long Short-Term Memory Deep Network
Authors: Mendi Arif Furkan
Published: June 2022

Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a “Proof of Learning” consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
Performance Analysis of State-of-the-Art CNN Architectures for LUNA16
Authors: Naseer Iftikhar , Akram Sheeraz , Masood Tehreem , Jaffar Arfan , Khan Muhammad Adnan , Mosavi Amir
Published: June 2022

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.
Narrow-Bandpass One-Step Leapfrog Hybrid Implicit-Explicit Algorithm with Convolutional Boundary Condition for Its Applications in Sensors
Authors: Wang Yangjing , Xie Yongjun , Jiang Haolin , Wu Peiyu
Published: June 2022

A large number of sensors work in the narrow bandpass circumstance. Meanwhile, some of them hold fine details merely along one and two dimensions. In order to efficiently simulate these sensors and devices, the one-step leapfrog hybrid implicit-explicit (HIE) algorithm with the complex envelope (CE) method and absorbing boundary condition is proposed in the narrow bandpass circumstance. To be more precise, absorbing boundary condition is implemented by the higher order convolutional perfectly matched layer (CPML) formulation to further enhance the absorption during the entire simulation. Numerical examples and their experiments are carried out to further illustrate the effectiveness of the proposed algorithm. The results show considerable agreement with the experiment and theory resolution. The relationship between the time step and mesh size can break the Courant–Friedrichs–Levy condition which indicates the physical size/selection mesh size. Such a condition indicates that the proposed algorithm behaviors are considerably accurate due to the rational choice in discretized mesh. It also shows decrement in simulation duration and memory consumption compared with the other algorithms. In addition, absorption performance can be improved by employing the proposed higher order CPML algorithm during the whole simulation.
Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting
Authors: Zulfiqar M. , Gamage Kelum A. A. , Kamran M. , Rasheed M. B.
Published: June 2022

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).
Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring
Authors: Yen Chih-Ta , Chen Un-Hung , Wang Guo-Chang , Chen Zong-Xian
Published: June 2022

This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses.
End-to-End Train Horn Detection for Railway Transit Safety
Authors: Tran Van-Thuan , Tsai Wei-Ho , Furletov Yury , Gorodnichev Mikhail
Published: June 2022

The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety.