bluuebunny/crossref_metadata_2025 huggingface dataset:
A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study
Authors: Schneider Miroslav , Machacek Zdenek , Martinek Radek , Koziorek Jiri , Jaros Rene
Published: June 2020

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.
Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks
Authors: Marques Junior Ademir , de Souza Eniuce Menezes , Müller Marianne , Brum Diego , Zanotta Daniel Capella , Horota Rafael Kenji , Kupssinskü Lucas Silveira , Veronez Maurício Roberto , Gonzaga Luiz , Cazarin Caroline Lessio
Published: June 2020

Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.
Biosensors—Recent Advances and Future Challenges in Electrode Materials
Authors: Otero Fernando , Magner Edmond
Published: June 2020

Electrochemical biosensors benefit from the simplicity, sensitivity, and rapid response of electroanalytical devices coupled with the selectivity of biorecognition molecules. The implementation of electrochemical biosensors in a clinical analysis can provide a sensitive and rapid response for the analysis of biomarkers, with the most successful being glucose sensors for diabetes patients. This review summarizes recent work on the use of structured materials such as nanoporous metals, graphene, carbon nanotubes, and ordered mesoporous carbon for biosensing applications. We also describe the use of additive manufacturing (AM) and review recent progress and challenges for the use of AM in biosensing applications.
Low-Cost Automated Design of Compact Branch-Line Couplers
Authors: Bekasiewicz Adrian
Published: June 2020

Branch-line couplers (BLCs) are important components of wireless communication systems. Conventional BLCs are often characterized by large footprints which make miniaturization an important pre-requisite for their application in modern devices. State-of-the-art approaches to design compact BLCs are largely based on the use of high-permittivity substrates and multi-layer topologies. Alternative methods involve replacement of transmission-line sections of the circuit, with their composite counterparts, referred to as compact cells (CCs). Due to the efficient use of available space, CC-based couplers are often characterized by small footprints. The design of compact BLCs is normally conducted based on engineering experience. The process is laborious and requires many adjustments of topology followed by manual or, semi-automatic tuning of design parameters. In this work, a framework for low-cost automated design of compact BLCs using pre-defined CCs is proposed. The low cost of the presented design technique is ensured using equivalent-circuit models, space mapping correction methods, and trust-region-based local optimization algorithms. The performance of the framework is demonstrated based on three examples, concerning the design of unequal-power split coupler, comparison of automatically generated compact BLCs, as well as rapid re-design of the coupler for different substrates. Furthermore, the approach has been benchmarked against the state-of-the-art methods for low-cost design of circuits.
Graphene Plasmonics in Sensor Applications: A Review
Authors: Ogawa Shinpei , Fukushima Shoichiro , Shimatani Masaaki
Published: June 2020

Surface plasmon polaritons (SPPs) can be generated in graphene at frequencies in the mid-infrared to terahertz range, which is not possible using conventional plasmonic materials such as noble metals. Moreover, the lifetime and confinement volume of such SPPs are much longer and smaller, respectively, than those in metals. For these reasons, graphene plasmonics has potential applications in novel plasmonic sensors and various concepts have been proposed. This review paper examines the potential of such graphene plasmonics with regard to the development of novel high-performance sensors. The theoretical background is summarized and the intrinsic nature of graphene plasmons, interactions between graphene and SPPs induced by metallic nanostructures and the electrical control of SPPs by adjusting the Fermi level of graphene are discussed. Subsequently, the development of optical sensors, biological sensors and important components such as absorbers/emitters and reconfigurable optical mirrors for use in new sensor systems are reviewed. Finally, future challenges related to the fabrication of graphene-based devices as well as various advanced optical devices incorporating other two-dimensional materials are examined. This review is intended to assist researchers in both industry and academia in the design and development of novel sensors based on graphene plasmonics.
Automatic Cow Location Tracking System Using Ear Tag Visual Analysis
Authors: Zin Thi Thi , Pwint Moe Zet , Seint Pann Thinzar , Thant Shin , Misawa Shuhei , Sumi Kosuke , Yoshida Kyohiro
Published: June 2020

Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow’s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application’s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.
Simulation System of Electric-Powered Wheelchairs for Training Purposes
Authors: Hernandez-Ossa Kevin A. , Montenegro-Couto Eduardo H. , Longo Berthil , Bissoli Alexandre , Sime Mariana M. , Lessa Hilton M. , Enriquez Ivan R. , Frizera-Neto Anselmo , Bastos-Filho Teodiano
Published: June 2020

For some people with severe physical disabilities, the main assistive device to improve their independence and to enhance overall well-being is an electric-powered wheelchair (EPW). However, there is a necessity to offer users EPW training. In this work, the Simcadrom is introduced, which is a virtual reality simulator for EPW driving learning purposes, testing of driving skills and performance, and testing of input interfaces. This simulator uses a joystick as the main input interface, and a virtual reality head-mounted display. However, it can also be used with an eye-tracker device as an alternative input interface and a projector to display the virtual environment (VE). Sense of presence, and user experience questionnaires were implemented to evaluate this version of the Simcadrom in addition to some statistical tests for performance parameters like: total elapsed time, path following error, and total number of commands. A test protocol was proposed and, considering the overall results, the system proved to simulate, very realistically, the usability, kinematics, and dynamics of a real EPW in a VE. Most subjects were able to improve their EPW driving performance in the training session. Furthermore, all skills learned are feasible to be transferred to a real EPW.
Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network
Authors: Curto Belén , Moreno Vidal , García-Esteban Juan Alberto , Blanco Francisco Javier , González Inmaculada , Vivar Ana , Revilla Isabel
Published: June 2020

The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.
Detecting Collagen Molecules at Picogram Level through Electric Field-Induced Accumulation
Authors: Rega Romina , Mugnano Martina , Oleandro Emilia , Tkachenko Volodymyr , del Giudice Danila , Bagnato Gianluca , Ferraro Pietro , Grilli Simonetta , Gangemi Sebastiano
Published: June 2020

The demand for sensors capable of measuring low-abundant collagen in human fluids has highly increased in recent years. Indeed, collagen is expected to be a biomarker for chronic diseases and could monitor their progression. Here we show detection of highly diluted samples of collagen at picogram level thanks to an innovative pyro-electrohydrodynamic jet (p-jet) system. Through the intense electric fields generated by the pyroelectric effect in a ferroelectric crystal, the collagen solution was concentrated on a small area of a slide that was appropriately functionalized to bind proteins. The collagen molecules were labeled by an appropriate fluorophore to show how the number of tiny droplets influences the limit of detection of the technique. The results show that the p-jet is extremely promising for overcoming the current detection limits of collagen-based products in human fluids, performing 10 times better than the enzyme-linked immunosorbent assay (ELISA) and thus paving the way for the early diagnosis of related chronic diseases.
FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning
Authors: Shinohara Takayuki , Xiu Haoyi , Matsuoka Masashi
Published: June 2020

In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.
Sensor Systems for Detecting Dough Properties Fortified with Grape Pomace and Mealworm Powders
Authors: Adamek Martin , Adamkova Anna , Mlcek Jiri , Vojackova Klara , Famera Oldrich , Buran Martin , Hlobilova Veronika , Buckova Martina , Baron Mojmir , Sochor Jiri
Published: June 2020

The present article dealt with the fortification of plain wheat flour by the addition of grape pomace flour and mealworm larvae powder, focusing on the mineral content and selected properties of the dough. The work also analyzed the properties of one mixture in a weight combination of 80% wheat flour, 10% grape pomace, and 10% mealworm. X-ray analysis was used to measure the mineral content of calcium, iron, copper, and zinc. The properties of the individual mixture were monitored using an experimental electronic nose and a thermodynamic sensor system during the leavening. The results showed that a combination of 50% grape pomace and 50% mealworm larvae was advantageous from the viewpoint of the favorable representation of minerals. The analyzed mixture contained a high proportion of calcium (3976.7 ± 362.9 mg·kg−1), iron (209.3 ± 25.7 mg·kg−1), and copper (65.0 ± 100.1 mg·kg−1) for grape pomace as well as a high proportion of zinc (277.0 ± 21.9 mg·kg−1) for the mealworm larvae. However, this mixture showed a small change in the heat flux response when analyzed with thermodynamic sensors (lower yeast activity and worse gas formation properties resulted from the sensor characteristic with a lower response). The 100% wheat flour had the highest response, and the second highest response was recorded for a mixture of wheat flour with 10% grape pomace and 10% mealworm larvae. This combination also often had one of the highest responses when measured with an experimental electronic nose, so this combination was considered as one of the most advantageous options for processing from the mixtures mentioned in the article.
Perspective and Evolution of Gesture Recognition for Sign Language: A Review
Authors: Galván-Ruiz Jesús , Travieso-González Carlos M. , Tejera-Fettmilch Acaymo , Pinan-Roescher Alejandro , Esteban-Hernández Luis , Domínguez-Quintana Luis
Published: June 2020

This review analyses the different gesture recognition systems through a timeline, showing the different types of technology, and specifying which are the most important features and their achieved recognition rates. At the end of the review, Leap Motion sensor possibilities are described in detail, in order to consider its application on the field of sign language. This device has many positive characteristics that make it a good option for sign language. One of the most important conclusions is the ability of the Leap Motion sensor to provide 3D information from the hands for due identification.
Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms
Authors: Brambilla Mattia , Combi Lorenzo , Matera Andrea , Tagliaferri Dario , Nicoli Monica , Spagnolini Umberto
Published: June 2020

This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave (mmW) or Free-Space Optics (FSO). Beam-based V2X communications mainly suffer from blockage and pointing misalignment issues. This work focuses on the latter case, which is addressed by proposing a V2X architecture that enables a sensor-aided beam-tracking strategy to counteract the detrimental effect of vibrations and tilting dynamics. A parallel low-rate, low-latency, and reliable control link, in fact, is used to exchange data on vehicle kinematics (i.e., position and orientation) that assists the beam-pointing along the line-of-sight between V2X transceivers (i.e., the dominant multipath component for mmW, or the direct link for FSO). This link can be based on sub-6 GHz V2X communication, as in 5G frequency range 1 (FR1). Performance assessments are carried out to validate the robustness of the proposed methodology in coping with misalignment induced by vehicle dynamics. Numerical results show that highly directional mmW and/or FSO communications are promising candidates for massive data-rate vehicular communications even in high mobility scenarios.
A Nonparametric SVM-Based REM Recapitulation Assisted by Voluntary Sensing Participants under Smart Contracts on Blockchain
Authors: Park Seung Bum , Lee Won Cheol
Published: June 2020

This paper proposes a blockchain-based automated frequency coordination system (BAFCS) for secure and reliable spectrum sharing without causing any harmful interference to an existing system. For the exact assessment of whether the incumbent is interfered with by the spectrum sharer, the received signal strength (RSS) associated with the incumbent should be measured with sufficient accuracy at every location within the area of interest. However, since it requires brute force to carry out empirical measurements around an entire region, to lessen the burden, only the confined portion of the RSSs associated with the incumbent as a kind of primary user are observed and the omitted residuals are conventionally estimated by carrying out the well-known Kriging interpolation with regard to the geostatistical characteristics. This paper proposes a frequency coordination system capable of identifying whether a requested frequency band can be eligible for spectrum sharing while exchanging adequate information over blockchain network to confirm the usability. This paper proposes the Support Vector Machine (SVM)-based Kriging interpolation for recapitulating the radio environment map (REM) when only a fraction of the RSS measurements is acquired by the voluntary sensing participant (VSP). The nonparametric modeling approach for variograms proposed in this paper was determined to have a vital role in making a confident decision regarding spectrum sharing. The simulation result confirmed the effectiveness and the superiority of the proposed BAFCS with several affirmative features, such as enabling the consensus-based approval of spectrum sharing, the secure transaction of the information, and reliable assurance of no harmful interference.
Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines
Authors: Gómez María Jesús , Castejón Cristina , Corral Eduardo , García-Prada Juan Carlos
Published: June 2020

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.
Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System
Authors: Najm Aws , Ibraheem Ibraheem , Azar Ahmad , Humaidi Amjad
Published: June 2020

A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.
Smoothness of Gait in Healthy and Cognitively Impaired Individuals: A Study on Italian Elderly Using Wearable Inertial Sensor
Authors: Pau Massimiliano , Mulas Ilaria , Putzu Valeria , Asoni Gesuina , Viale Daniela , Mameli Irene , Leban Bruno , Allali Gilles
Published: June 2020

The main purpose of the present study was to compare the smoothness of gait in older adults with and without cognitive impairments, using the harmonic ratio (HR), a metric derived from trunk accelerations. Ninety older adults aged over 65 (age: 78.9 ± 4.8 years; 62% female) underwent instrumental gait analysis, performed using a wearable inertial sensor and cognitive assessment with the Mini Mental State Examination (MMSE) and Addenbrooke’s Cognitive Examination Revised (ACE-R). They were stratified into three groups based on their MMSE performance: healthy controls (HC), early and advanced cognitive decline (ECD, ACD). The spatio-temporal and smoothness of gait parameters, the latter expressed through HR in anteroposterior (AP), vertical (V) and mediolateral (ML) directions, were derived from trunk acceleration data. The existence of a relationship between gait parameters and degree of cognitive impairment was also explored. The results show that individuals with ECD and ACD exhibited significantly slower speed and shorter stride length, as well as reduced values of HR in the AP and V directions compared to HC, while no significant differences were found between ECD and ACD in any of the investigated parameters. Gait speed, stride length and HR in all directions were found to be moderately correlated with both MMSE and ACE-R scores. Such findings suggest that, in addition to the known changes in gait speed and stride length, important reductions in smoothness of gait are likely to occur in older adults, owing to early/prodromal stages of cognitive impairment. Given the peculiar nature of these metrics, which refers to overall body stability during gait, the calculation of HR may result in being useful in improving the characterization of gait patterns in older adults with cognitive impairments.
A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication
Authors: Islam Kh Tohidul , Raj Ram Gopal , Shamsul Islam Syed Mohammed , Wijewickrema Sudanthi , Hossain Md Sazzad , Razmovski Tayla , O’Leary Stephen
Published: June 2020

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.
A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine
Authors: Wang Hong , Wang Hongbin , Jiang Guoqian , Wang Yueling , Ren Shuang
Published: June 2020

Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.
TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud
Authors: Alam Aftab , Lee Young-Koo
Published: June 2020

In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.
Bipolar Optical Code Division Multiple Access Techniques Using a Dual Electro-Optical Modulator Implemented in Free-Space Optics Communications
Authors: Tseng Shin-Pin , Wijanto Eddy , Lai Po-Han , Cheng Hsu-Chih
Published: June 2020

This study developed a bipolar optical code division multiple access (Bi-OCDMA) technique based on spectral amplitude coding for the formation and transmission of optical-polarized and coded signals over wireless optical channels. Compared with conventional Bi-OCDMA schemes, the proposed free-space optics communication system that uses a dual electro-optical modulator design improves the transmission rate. In theory, multiple access interference can be removed by using correlation subtraction schemes. The experiment results revealed that the proposed system can be employed to accurately extract codewords from an M-sequence and subsequently reconstruct the desired original data. Moreover, the proposed architecture can be implemented easily in simple and cost-effective designs and may be beneficial for broadening the use of Bi-OCDMA schemes in wireless optical communications.
Window-Modulated Compounding Nakagami Parameter Ratio Approach for Assessing Muscle Perfusion with Contrast-Enhanced Ultrasound Imaging
Authors: Lin Huang-Chen , Wang Shyh-Hau
Published: June 2020

The assessment of microvascular perfusion is essential for the diagnosis of a specific muscle disease. In comparison with the current available medical modalities, the contrast-enhanced ultrasound imaging is the simplest and fastest means for probing the tissue perfusion. Specifically, the perfusion parameters estimated from the ultrasound time-intensity curve (TIC) and statistics-based time–Nakagami parameter curve (TNC) approaches were found able to quantify the perfusion. However, due to insufficient tolerance on tissue clutters and subresolvable effects, these approaches remain short of reproducibility and robustness. Consequently, the window-modulated compounding (WMC) Nakagami parameter ratio imaging was proposed to alleviate these effects, by taking the ratio of WMC Nakagami parameters corresponding to the incidence of two different acoustic pressures from an employed transducer. The time–Nakagami parameter ratio curve (TNRC) approach was also developed to estimate perfusion parameters. Measurements for the assessment of muscle perfusion were performed from the flow phantom and animal subjects administrated with a bolus of ultrasound contrast agents. The TNRC approach demonstrated better sensitivity and tolerance of tissue clutters than those of TIC and TNC. The fusion image with the WMC Nakagami parameter ratio and B-mode images indicated that both the tissue structures and perfusion properties of ultrasound contrast agents may be better discerned.
A Socio-Spatial Approach to Enable Inclusive Well-Being in Cities: A Case Study of Birmingham, UK
Authors: Rajendran Lakshmi P. , Boyko Christopher T. , Coulton Claire J. , Hale James D. , Cooper Rachel F. D.
Published: June 2020

This article examines density and deprivation, the two important parameters that define health and well-being in cities. Discussions are drawn from a case study conducted in Birmingham in four neighborhoods characterized by their different population density and deprivation levels. Data were collected through questionnaires developed from a set of subjective well-being measures and built environment audits, based on the Irvine Minnesota Inventory that evaluates the quality of streets and walkability in neighborhoods. The inferences from the study support the need for linking health, planning, policy and design research and decision-making to the socio-spatial practices of people, impacting well-being at the everyday level. The findings provide a holistic approach health and well-being research and suggests a conceptual framework for inclusive well-being in cities, which signifies the role of social and spatial parameters in determining peoples’ health and well-being. The study also highlights the lack of interdisciplinary research in understanding the association between well-being and social and behavioral practices in diverse communities.
The Unethical Managerial Behaviours and Abusive Use of Power in Downwards Vertical Workplace Bullying: A Phenomenological Case Study
Authors: Medina Alicia , Lopez Eduardo , Medina Rolf
Published: June 2020

The aim of this article is to introduce an ethical perspective of managerial behaviours to the study of vertical workplace bullying. A framework called the line of impunity was chosen that describes the missuses of power by certain ranks in organizations. Previous research on bullying addresses several perspectives such as the consequences of the bullying situation for the organization, the target and bystanders, the leadership style of the bully, the perceived structural support, and the manifestations of the abusive behaviours. However, to date, the ethical aspects have been poorly outlined. Applying the line of impunity brings light to several aspects of workplace bullying that are connected to an unethical use of power. This study is unusual because it is a phenomenological research based on two case studies that present the field experiences of two of the authors while working in different organizations, one in Sweden and the other in USA, during an extended period of time. The two main contributions of the study are the new concepts power methods and reinforcing, which highlight the connection between abusive behaviour and the ethical aspects that are present in downwards vertical workplace bullying situations.
Requirements for the Construction of New Desalination Plants into a Framework of Sustainability
Authors: Berenguel-Felices Francisco , Lara-Galera Antonio , Muñoz-Medina María Belén
Published: June 2020

Population growth has increased in the last two centuries. In the driest countries, water supply alternatives are scarce, and desalination is an alternative to guarantee water supply. The question is what conditions must be met by the new desalination plants to achieve the objectives of sustainability. The present study is an analysis of the social, economic, and environmental variables that are critical in the development of desalination plants: technology used, energy sources, correction of the generated environmental impacts, and the most appropriate contractual model for its development. These attributes justify at the time of writing why reverse osmosis is the safest and most efficient technology among those available and those that are under investigation. It is proposed to incorporate renewable energy production sources, although it is still necessary to continue depending on the significant contribution of the traditional energy sources. The need will also be demonstrated to adopt corrective measures to mitigate against the impact produced on the environment by energy production and to implement monitoring plans to confirm the validity of these corrective measures. Finally, turnkey contracts are proposed because osmosis technology is complex, although technology should be justified by means of a decision support system. One of the determining factors is proposed in this present analysis.
Harnessing the Power of Mutagenesis and Adaptive Laboratory Evolution for High Lipid Production by Oleaginous Microalgae and Yeasts
Authors: Arora Neha , Yen Hong-Wei , Philippidis George P.
Published: June 2020

Oleaginous microalgae and yeasts represent promising candidates for large-scale production of lipids, which can be utilized for production of drop-in biofuels, nutraceuticals, pigments, and cosmetics. However, low lipid productivity and costly downstream processing continue to hamper the commercial deployment of oleaginous microorganisms. Strain improvement can play an essential role in the development of such industrial microorganisms by increasing lipid production and hence reducing production costs. The main means of strain improvement are random mutagenesis, adaptive laboratory evolution (ALE), and rational genetic engineering. Among these, random mutagenesis and ALE are straight forward, low-cost, and do not require thorough knowledge of the microorganism’s genetic composition. This paper reviews available mutagenesis and ALE techniques and screening methods to effectively select for oleaginous microalgae and yeasts with enhanced lipid yield and understand the alterations caused to metabolic pathways, which could subsequently serve as the basis for further targeted genetic engineering.
Methods for the Sustainable Design of Solar Energy Systems for Industrial Process Heat
Authors: Franco Alessandro
Published: June 2020

In this paper, the production of low to medium temperature water for industrial process heat using solar energy is considered. In particular, the paper outlines the perspective of an optimum design method that takes into account all of the typical variables of the problem (solar irradiation, system architecture, design constraints, load type and distribution, and design and optimization criteria) and also considers the use of the fossil fuel backup system. The key element of the methodology is the definition of a synthetic combined energetic and economic utility function. This considers the attribution of an economic penalty to irreversibility in connection with the use of a fossil fuel backup. This function incorporates the share of the solar system production (solar fraction) as an optimum design variable. This paper shows how, using the proposed criteria, the optimal value of the solar fraction, defined as the share of operation of the solar system with respect to the whole energy demand, can be increased. Current practice considers values in the range between 40 and 60%. However, levels up to 80% can also be obtained with the proposed methodology. Thus, penalizing the use of fossil fuels does not exclude a priori their contribution.
Managing Choice Uncertainties in Life-Cycle Assessment as a Decision-Support Tool for Building Design: A Case Study on Building Framework
Authors: Ylmén Peter , Berlin Johanna , Mjörnell Kristina , Arfvidsson Jesper
Published: June 2020

To establish a circular economy in society, it is crucial to incorporate life-cycle studies, such as life-cycle assessment (LCA), in the design process of products in order to mitigate the well-recognized problem of the design paradox. The aim of the study was to provide means in a structured way to highlight choice uncertainty present in LCA when used as decision support, as well as to mitigate subjective interpretations of the numerical results leading to arbitrary decisions. The study focused on choices available when defining the goal and scope of a life-cycle assessment. The suggested approach is intended to be used in the early design phases of complex products with high levels of uncertainty in the product life-cycle. To demonstrate and evaluate the approach, a life-cycle assessment was conducted of two design options for a specific building. In the case study two types of building frameworks were compared from an environmental perspective by calculating the global warming potential, eutrophication potential, acidification potential, stratospheric ozone depletion potential and photochemical oxidants creation potential. In the study, a procedure named the Decision Choices Procedure (DCP) was developed to improve LCA as an effective tool for decision support concerning design alternatives when less information is available. The advantages and drawbacks of the proposed approach are discussed to spur further improvements in the use of LCA as a decision-support tool.
Shifting from Fragmentation to Integration: A Proposed Framework for Strengthening Agricultural Knowledge and Innovation System in Egypt
Authors: Zahran Yehia , Kassem Hazem S. , Naba Shimaa M. , Alotaibi Bader Alhafi
Published: June 2020

Agricultural knowledge and innovation system (AKIS) has a strong potential to enhance economic performance of farming and contribute to agricultural sustainability, as it may increase synergies and complementarity among actors. This paper is aimed to develop a proposed framework to strengthen AKIS in the study area based on the results of this study. This paper explores perception and views about strengthening AKIS in Dakhalia governorate of Egypt by applying a multi-actor approach. Quantitative and qualitative data were collected through face-to-face interviews and focus group discussion. This paper focuses on three structural dimensions, namely actors, interactions, and technologies, to describe the nature of innovation processes within AKIS. Results indicate that legal and regulatory frameworks, lack of infrastructure, and weak the role of intermediary organizations are the main barriers that AKIS faces. Linkages of contracting and public–private partnerships were viewed as main interactions required to strengthen AKIS. The analysis also explores the availability of innovation requirements at each actor, as well as the distributive technologies, that should be encouraged to build the capacity the agricultural sector. A proposed framework is developed based on the results of this study and the characteristics of the AKIS in the study area. This framework could be used for stimulating innovation and enhancing coordination between actors.
Socio-Economic Benefits Stemming from Bush Clearing and Restoration Projects Conducted in the D’Nyala Nature Reserve and Shongoane Village, Lephalale, South Africa
Authors: Mangani Tshepiso , Coetzee Hendri , Kellner Klaus , Chirima George
Published: June 2020

This study aimed to investigate the socio-economic benefits stemming from bush clearing and restoration projects conducted in the Lephalale municipality, within the Limpopo Province of South Africa. The study was conducted at two sites: the D’Nyala Nature Reserve and a nearby local village, Shongoane. A qualitative thematic content analysis approach and semi-structured interviews were used to gather data from 14 purposively selected participants between the ages of 22 and 55 (male = 9 and female = 5). The results indicated that the nature reserve benefited from the project via the improved visibility of the landscape features and game viewing, which made the reserve more attractive for tourists and resulted in increased revenue. The costs of buying feed for game could also be curbed since the grazing capacity increased. Since the nature reserve sourced temporary labour from the local village to execute the project, the community benefited in terms of members being able to earn a wage, which led to an improvement in their livelihoods. Another indirect benefit was the morale and behavioural changes observed amongst community members. It was obvious that the socio-economic benefits derived from projects such as these far outweigh the negatives and that there is every reason to institute projects of a similar nature elsewhere.