bluuebunny/crossref_metadata_2025 huggingface dataset:
An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines
Authors: Cao Ruifeng , Yunusa-Kaltungo Akilu
Published: April 2021

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.
Smart Video Surveillance System Based on Edge Computing
Authors: Cob-Parro Antonio Carlos , Losada-Gutiérrez Cristina , Marrón-Romera Marta , Gardel-Vicente Alfredo , Bravo-Muñoz Ignacio
Published: April 2021

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.
Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators
Authors: Zheng Tianyi , Yang Wuhao , Sun Jie , Xiong Xingyin , Wang Zheng , Li Zhitian , Zou Xudong
Published: April 2021

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.
A Complete Key Management Scheme for LoRaWAN v1.1
Authors: Chen Xingda , Lech Margaret , Wang Liuping
Published: April 2021

Security is one of the major concerns of the Internet of Things (IoT) wireless technologies. LoRaWAN is one of the emerging Low Power Wide Area Networks being developed for IoT applications. The latest LoRaWAN release v.1.1 has provided a security framework that includes data confidentiality protection, data integrity check, device authentication and key management. However, its key management part is only ambiguously defined. In this paper, a complete key management scheme is proposed for LoRaWAN. The scheme addresses key updating, key generation, key backup, and key backward compatibility. The proposed scheme was shown not only to enhance the current LoRaWAN standard, but also to meet the primary design consideration of LoRaWAN, i.e., low power consumption.
Feedback from HTC Vive Sensors Results in Transient Performance Enhancements on a Juggling Task in Virtual Reality
Authors: Borglund Filip , Young Michael , Eriksson Joakim , Rasmussen Anders
Published: April 2021

Virtual reality headsets, such as the HTC Vive, can be used to model objects, forces, and interactions between objects with high perceived realism and accuracy. Moreover, they can accurately track movements of the head and the hands. This combination makes it possible to provide subjects with precise quantitative feedback on their performance while they are learning a motor task. Juggling is a challenging motor task that requires precise coordination of both hands. Professional jugglers throw objects so that the arc peaks just above head height, and they time their throws so that the second ball is thrown when the first ball reaches its peak. Here, we examined whether it is possible to learn to juggle in virtual reality and whether the height and the timing of the throws can be improved by providing immediate feedback derived from the motion sensors. Almost all participants became better at juggling in the ~30 min session: the height and timing of their throws improved and they dropped fewer balls. Feedback on height, but not timing, improved performance, albeit only temporarily.
Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer
Authors: Oh Yunkwang , Kwon Oh-Seok , Min Sun-Seek , Shin Yong-Beom , Oh Min-Kyu , Kim Moonil
Published: April 2021

Early detection is critical to successfully eradicating a variety of cancers, so the development of a new cancer primary screening system is essential. Herein, we report an animal nose sensor system for the potential primary screening of lung cancer. To establish this, we developed an odor discrimination training device based on operant conditioning paradigms for detection of toluene, an odor indicator component of lung cancer. The rats (N = 15) were trained to jump onto a floating ledge in response to toluene-spiked breath samples. Twelve rats among 15 trained rats reached performance criterion in 12 consecutive successful tests within a given set, or over 12 sets, with a success rate of over 90%. Through a total of 1934 tests, the trained rats (N = 3) showed excellent performance for toluene detection with 82% accuracy, 83% sensitivity, 81% specificity, 80% positive predictive value (PPV) and 83% negative predictive value (NPV). The animals also acquired considerable performance for odor discrimination even in rigorous tests, validating odor specificity. Since environmental and long-term stability are important factors that can influence the sensing results, the performance of the trained rats was studied under specified temperature (20, 25, and 30 °C) and humidity (30%, 45%, and 60% RH) conditions, and monitored over a period of 45 days. At given conditions of temperature and humidity, the animal sensors showed an average accuracy within a deviation range of ±10%, indicating the excellent environmental stability of the detection rats. Surprisingly, the trained rats did not differ in retention of last odor discrimination when tested 45 days after training, denoting that the rats’ memory for trained odor is still available over a long period of time. When taken together, these results indicate that our odor discrimination training system can be useful for non-invasive breath testing and potential primary screening of lung cancer.
Punch Trackers: Correct Recognition Depends on Punch Type and Training Experience
Authors: Omcirk Dan , Vetrovsky Tomas , Padecky Jan , Vanbelle Sophie , Malecek Jan , Tufano James
Published: April 2021

To determine the ability of different punch trackers (PT) (Corner (CPT), Everlast (EPT), and Hykso (HPT)) to recognize specific punch types (lead and rear straight punches, lead and rear hooks, and lead and rear uppercuts) thrown by trained (TR, n = 10) and untrained punchers (UNTR, n = 11), subjects performed different punch combinations, and PT data were compared to data from video recordings to determine how well each PT recognized the punches that were actually thrown. Descriptive statistics and multilevel modelling were used to analyze the data. The CPT, EPT and HPT detected punches more accurately in TR than UNTR, evidenced by a lower percentage error in TR (p = 0.007). The CPT, EPT, and HPT detected straight punches better than uppercuts and hooks, with a lower percentage error for straight punches (p < 0.001). The recognition of punches with CPT and HPT depended on punch order, with earlier punches in a sequence recognized better. The same may or may not have occurred with EPT, but EPT does not allow for data to be exported, meaning the order of individual punches could not be analyzed. The CPT and HPT both seem to be viable options for tracking punch count and punch type in TR and UNTR.
Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram
Authors: Augustyniak Piotr
Published: April 2021

We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100–500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.
On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes
Authors: Huang Wei , Zhou Jianzhong , Zhang Dongying
Published: April 2021

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.
Managing the Cloud Continuum: Lessons Learnt from a Real Fog-to-Cloud Deployment
Authors: Masip-Bruin Xavi , Marín-Tordera Eva , Sánchez-López Sergi , Garcia Jordi , Jukan Admela , Juan Ferrer Ana , Queralt Anna , Salis Antonio , Bartoli Andrea , Cankar Matija , Cordeiro Cristovao , Jensen Jens , Kennedy John
Published: April 2021

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.
Bhattacharyya Parameter of Monomial Codes for the Binary Erasure Channel: From Pointwise to Average Reliability
Authors: Drăgoi Vlad-Florin , Cristescu Gabriela
Published: April 2021

Monomial codes were recently equipped with partial order relations, a fact that allowed researchers to discover structural properties and efficient algorithm for constructing polar codes. Here, we refine the existing order relations in the particular case of the binary erasure channel. The new order relation takes us closer to the ultimate order relation induced by the pointwise evaluation of the Bhattacharyya parameter of the synthetic channels, which is still a partial order relation. To overcome this issue, we appeal to a related technique from network theory. Reliability network theory was recently used in the context of polar coding and more generally in connection with decreasing monomial codes. In this article, we investigate how the concept of average reliability is applied for polar codes designed for the binary erasure channel. Instead of minimizing the error probability of the synthetic channels, for a particular value of the erasure parameter p, our codes minimize the average error probability of the synthetic channels. By means of basic network theory results, we determine a closed formula for the average reliability of a particular synthetic channel, that recently gain the attention of researchers.
3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution
Authors: Zhang Hongtao , Shinomiya Yuki , Yoshida Shinichi
Published: April 2021

The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.
Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm
Authors: Řezník Tomáš , Herman Lukáš , Klocová Martina , Leitner Filip , Pavelka Tomáš , Leitgeb Šimon , Trojanová Kateřina , Štampach Radim , Moshou Dimitrios , Mouazen Abdul M. , Alexandridis Thomas K. , Hrádek Jakub , Lukas Vojtěch , Širůček Petr
Published: April 2021

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.
Deep Residual Networks for User Authentication via Hand-Object Manipulations
Authors: Choi Kanghae , Ryu Hokyoung , Kim Jieun
Published: April 2021

With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 > ResNet-101 > ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively.
Quantization and Deployment of Deep Neural Networks on Microcontrollers
Authors: Novac Pierre-Emmanuel , Boukli Hacene Ghouthi , Pegatoquet Alain , Miramond Benoît , Gripon Vincent
Published: April 2021

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity Recognition. However, there is still room for optimization of deep neural networks onto embedded devices. These optimizations mainly address power consumption, memory and real-time constraints, but also an easier deployment at the edge. Moreover, there is still a need for a better understanding of what can be achieved for different use cases. This work focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. The quantization methods, relevant in the context of an embedded execution onto a microcontroller, are first outlined. Then, a new framework for end-to-end deep neural networks training, quantization and deployment is presented. This framework, called MicroAI, is designed as an alternative to existing inference engines (TensorFlow Lite for Microcontrollers and STM32Cube.AI). Our framework can indeed be easily adjusted and/or extended for specific use cases. Execution using single precision 32-bit floating-point as well as fixed-point on 8- and 16 bits integers are supported. The proposed quantization method is evaluated with three different datasets (UCI-HAR, Spoken MNIST and GTSRB). Finally, a comparison study between MicroAI and both existing embedded inference engines is provided in terms of memory and power efficiency. On-device evaluation is done using ARM Cortex-M4F-based microcontrollers (Ambiq Apollo3 and STM32L452RE).
Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments
Authors: Ghaleb Mustafa , Azzedin Farag
Published: April 2021

The Internet of Services (IoS) is gaining ground where cloud environments are utilized to create, subscribe, publish, and share services. The fast and significant evolution of IoS is affecting various aspects in people’s life and is enabling a wide spectrum of services and applications ranging from smart e-health, smart homes, to smart surveillance. Building trusted IoT environments is of great importance to achieve the full benefits of IoS. In addition, building trusted IoT environments mitigates unrecoverable and unexpected damages in order to create reliable, efficient, stable, and flexible smart IoS-driven systems. Therefore, ensuring trust will provide the confidence and belief that IoT devices and consequently IoS behave as expected. Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. In this article, we propose scalable and efficient Chord-based horizontal architecture. We also show how trust modeling can be mapped to our proposed architecture. Extensive performance evaluation experiments have been conducted to evaluate the performance and the feasibility and also to verify the behavior of our proposed architecture.
Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model
Authors: Vogado Luis , Veras Rodrigo , Aires Kelson , Araújo Flávio , Silva Romuere , Ponti Moacir , Tavares João Manuel R. S.
Published: April 2021

Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.
Progressive von Mises–Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation
Authors: Li Kailai , Pfaff Florian , Hanebeck Uwe D.
Published: April 2021

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.
Non-Contact Inspection of Railhead via Laser-Generated Rayleigh Waves and an Enhanced Matching Pursuit to Assist Detection of Surface and Subsurface Defects
Authors: Ghafoor Imran , Tse Peter W. , Rostami Javad , Ng Kim-Ming
Published: April 2021

Laser ultrasonic technology can provide a non-contact, reliable and efficient inspection of train rails. However, the laser-generated signals measured at the railhead are usually contaminated with a high level of noise and unwanted wave components that complicate the identification of defect echoes in the signal. This study explores the possibility of combining laser ultrasonic technology (LUT) and an enhanced matching pursuit (MP) to achieve a fully non-contact inspection of the rail track. A completely non-contact laser-based inspection system was used to generate and sense Rayleigh waves to detect artificial surface horizontal, surface edge, subsurface horizontal and subsurface vertical defects created at railheads of different dimensions. MP was enhanced by developing two novel dictionaries, which include a finite element method (FEM) simulation dictionary and an experimental dictionary. The enhanced MP was used to analyze the experimentally obtained laser-generated Rayleigh wave signals. The results show that the enhanced MP is highly effective in detecting defects by suppressing noise, and, further, it could also overcome the deficiency in the low repeatability of the laser-generated signals. The comparative analysis of MP with both the FEM simulation and experimental dictionaries shows that the enhanced MP with the FEM simulation dictionary is highly efficient in both noise removal and defect detection from the experimental signals captured by a laser-generated ultrasonic inspection system. The major novelty contributed by this research work is the enhanced MP method with the developments of, first, an FEM simulation dictionary and, second, an experimental dictionary that is especially suited for Rayleigh wave signals. Third, the enhanced MP dictionaries are created to process the Rayleigh wave signals generated by laser excitation and received using a 3D laser scanner. Fourth, we introduce a pioneer application of such laser-generated Rayleigh waves for inspecting surface and subsurface detects occurring in train rails.
Finite-Time Dynamic Tracking Control of Parallel Robots with Uncertainties and Input Saturation
Authors: Ye Mengyang , Gao Guoqin , Zhong Junwen , Qin Qiuyue
Published: April 2021

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method.
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
Authors: Khan Aamir , Jin Weidong , Haider Amir , Rahman MuhibUr , Wang Desheng
Published: April 2021

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.
Experimentation Platforms as Bridges to Urban Sustainability
Authors: Rehm Sven-Volker , McLoughlin Shane , Maccani Giovanni
Published: April 2021

Despite immense efforts to realize diverse visions of the ‘smart city,’ municipalities still face manifold uncertainties of how governance and the tools of governance can best support public and regional value creation for achieving urban sustainability. To this end, Urban Living Labs have become a known enabling mechanism. In this paper, we extend the lab idea and formulate the concept of Urban Experimentation Platform that focuses on developing urban innovation ecosystems for urban sustainability. We use action design research and participant observation across multiple case studies enacting Urban Experimentation Platforms in order to investigate how the tie-in between governance and the local lab’s innovation process unfolds. Our analysis distills three facets that are instrumental in institutionalizing these platforms as resilient organizational models. With the help of the case studies, we illustrate the three facets, concerning issues of urban ecosystem governance, empowering co-creation, and qualifying local innovation. The facets reinforce the roles of digital instruments and digital capabilities for effective urban governance and platform management. We draw some conclusions for future research and formulate policy recommendations for implementing and operating Urban Experimentation Platforms.
Cooperatives of Mexico: Their Social Benefits and Their Contribution to Meeting the Sustainable Development Goals
Authors: Díaz de León Denise , Díaz Fragoso Omar , Rivera Igor , Rivera Gibrán
Published: April 2021

Cooperatives are considered an organizational alternative based on principles and values. These organizations seek the fulfillment of social and economic benefits for their members as well as for the community in which they develop. Their nature allows them to solve problems such as social and labor exclusion as well as environmental issues. However, although their benefits to society have been extensively studied, little is known about how these organizations contribute to the attainment of the Sustainable Development Goals (SDGs). The aim of this study is to analyze the way in which the social benefits generated by cooperatives contribute to the fulfillment of the SDGs in Mexico City. This qualitative study uses interviews and thematic analysis to identify and define the different social benefits generated by 134 cooperatives in Mexico City. These benefits were analyzed to identify whether they contribute to the fulfillment of the SDGs. The results stimulate the theoretical and empirical discussion about the role of cooperatives and their contribution to solving current social problems, as established by the United Nations. The article presented is of scientific interest and it is also relevant for the elaboration of public policies aimed at producing better results for these types of organizations.
Immersive Robotic Telepresence for Remote Educational Scenarios
Authors: Botev Jean , Rodríguez Lera Francisco J.
Published: April 2021

Social robots have an enormous potential for educational applications and allow for cognitive outcomes that are similar to those with human involvement. Remotely controlling a social robot to interact with students and peers in an immersive fashion opens up new possibilities for instructors and learners alike. Using immersive approaches can promote engagement and have beneficial effects on remote lesson delivery and participation. However, the performance and power consumption associated with the involved devices are often not sufficiently contemplated, despite being particularly important in light of sustainability considerations. The contributions of this research are thus twofold. On the one hand, we present telepresence solutions for a social robot’s location-independent operation using (a) a virtual reality headset with controllers and (b) a mobile augmented reality application. On the other hand, we perform a thorough analysis of their power consumption and system performance, discussing the impact of employing the various technologies. Using the QTrobot as a platform, direct and immersive control via different interaction modes, including motion, emotion, and voice output, is possible. By not focusing on individual subsystems or motor chains, but the cumulative energy consumption of an unaltered robot performing remote tasks, this research provides orientation regarding the actual cost of deploying immersive robotic telepresence solutions.
Information and Communication Technologies as a Source of Customer Value in the Context of Balancing the Positions of Younger and Older Consumers
Authors: Frąckiewicz Ewa
Published: April 2021

Information and communication technologies (ICT, new technologies) have revolutionized every aspect of consumers’ lives and become an important value creation tool for them. It is commonly believed that this process concerns young and adult persons (younger than 60, 60−) to a much greater extent than older persons (60 and older, 60+, seniors). It is therefore assumed that calendar age is the key determinant of customer behavior. Meanwhile, the qualities of ICT, by their very nature, act as a source of value for purchasers regardless of their chronological age. In this sense, ICT has the potential to balance the positions between younger and older consumers. The aim of this article is to illustrate the similarities and differences between the two age groups (60− and 60+) with respect to the manner of approaching and using ICT as a source of customer value. This article has been written on the basis of literature research, existing data, and the findings of quantitative and qualitative research conducted among Polish respondents. The author’s own research was conducted in three stages. The quantitative surveys were conducted using the direct survey method and a questionnaire-based random survey among 827 respondents (340 young and adults, and 487 seniors). The two qualitative studies made it possible to expand the scope of information gathered. They were conducted using targeted direct interviews in three mini groups using interview scripts and questionnaire-based observation of the three social media channels managed by seniors, with the most popular themes in Poland taken into account. The conducted studies prove that, despite the discrepancies in the percentage share of younger and older ICT users, individuals utilizing the same solutions speak of the same advantages and see the same risks. Moreover, respondents believe that, in the future, all consumers will be included in the digital world. The results may be useful for practitioners whose increasingly digitized offerings will be targeted at younger as well as a growing number of older consumers.
The Beautiful Risk of Collaborative and Interdisciplinary Research. A Challenging Collaborative and Critical Approach toward Sustainable Learning Processes in Academic Profession
Authors: Christensen Jonas , Ekelund Nils , Melin Margareta , Widén Pär
Published: April 2021

In this article, we aim to identify and explore possibilities and challenges of academic interdisciplinary capacities and ethos. The objective is that this knowledge could be used both in future interdisciplinary research projects and in educational settings. We achieve this through self-reflective learning processes among a group of interdisciplinary scholars from four distinctly different subjects. The method used is an autoethnographic and empirical self-reflective approach to data collection, analysis and deconstruction of professional learning processes. This also serves to establish research methodological trustworthiness and authenticity. The results show that interdisciplinarity is undervalued by grant-giving institutions and the academic system, in general. It also entails time-consuming and risky research practices. However, interdisciplinary and collaborative research creates a more innovative and stimulating learning environment and enforces new ways of thinking and doing, in ascertaining each individual’s knowledge and competences. We argue that a long-term interdisciplinary and collaborative research process could enhance and raise a critical thinking and creative consciousness among scholars, contributing to a more holistic, sustainable and socially robust learning in research and higher education. Finally, we conclude that this academic interdisciplinary capacity and ethos could be framed and enhanced by the notion of Challenge-Based Learning.
Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning
Authors: Mashaba-Munghemezulu Zinhle , Chirima George Johannes , Munghemezulu Cilence
Published: April 2021

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.
Outdoors Evacuation Routes Algorithm Using Cellular Automata and Graph Theory for Uphills and Downhills
Authors: Velasquez Washington , Alvarez-Alvarado Manuel S.
Published: April 2021

This paper proposes an innovative algorithm for the determination of the optimal outdoor evacuation routes in hills. The algorithm uses web services to obtain the Google Image Tile (a portion of the area to evaluate) and each area’s geographic information. Then, the routes are calculated using cellular automata in 3D and graph theory with geographic information obtained from Google Maps (latitude, longitude, and elevation). The calculation of evacuation routes per study area is performed in an adequate time, revealing the potential of the proposed approach for different applications in emergency scenarios such as wildfires, smart transport, and climbing.
The Green Image in the Spanish Hotel Sector: Analysis of Its Consequences from a Relational Perspective
Authors: Sarmiento-Guede José Ramón , Antonovica Arta , Antolín-Prieto Rebeca
Published: April 2021

The main objective of this research is to propose a hierarchy of effects model to study three antecedents of green brand image, such as trust, satisfaction and loyalty, and to examine the relationships between these variables. In this context, we investigated how these variables have been applied in the Spanish hotel management strategies for influencing consumer attitude and behavior. To test the proposed model empirically, personal surveys of 732 hotel customers were conducted in five Spanish cities using a structured questionnaire. A structural equations model was developed to test the research hypothesis. The main results indicate positive relationships between the green image and loyalty, the green image and satisfaction, the green image and trust. In addition, there are positive relationships between trust and loyalty, and finally between satisfaction and loyalty. From a practical point of view, hotel chains and their marketers must improve their green image through the correct design of activities, actions, and strategies to increase their clients’ trust, satisfaction, and loyalty. In addition, the hotel chains must design green communication campaigns through their own and paid media to ensure that customers talk about them, and as a result, be able to influence the attitude and behavior of their customers. The results of our research provide a better understanding of the relationships between the green image, trust, satisfaction, and loyalty, following the framework of the hierarchy of effects model. By complementing previous studies on green image creation, this study offers an assessment of how green marketing strategies in the hotel industry increase trust, loyalty, and customer satisfaction.
Representing the Under-Represented: Labor Unions as Urbanists
Authors: Inam Aseem
Published: April 2021

Persistent precarity is a fundamental, yet usually hidden and often overlooked condition of urbanism, particularly for those who represent the human labor that produces and reproduces the capitalist city. The question, then, is how do those who represent this under-represented human labor, unions, engage with and influence the underlying power structure that actually shapes the city? Labor unions simultaneously shape and are shaped by the spatial political economy of the contemporary city. This article examines this phenomenon through analysis of an illuminating case study, the powerful Culinary Union in Las Vegas. Drawing from different primary and secondary sources, this article offers several valuable insights: organized labor is significant in the spatial production of the city, urban precarity can be mitigated by advocating for the public realm, and asserting agency in the power dynamics of the city can be an effective way of influencing its urbanism.