bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Zein as a Potential Biopolymer for Tissue Engineering and Nanotechnological Applications
Authors: Pérez-Guzmán Carlos Joaquín , Castro-Muñoz Roberto
Published: October 2020

Tissue engineering (TE) is one of the most challenging fields of research since it provides current alternative protocols and materials for the regeneration of damaged tissue. The success of TE has been mainly related to the right selection of nano-sized biocompatible materials for the development of matrixes, which can display excellent anatomical structure, functionality, mechanical properties, and histocompatibility. Today, the research community has paid particular attention to zein as a potential biomaterial for TE applications and nanotechnological approaches. Considering the properties of zein and the advances in the field, there is a need to reviewing the current state of the art of using this natural origin material for TE and nanotechnological applications. Therefore, the goal of this review paper is to elucidate the latest (over the last five years) applications and development works in the field, including TE, encapsulations of drugs, food, pesticides and bandaging for external wounds. In particular, attention has been focused on studies proving new breakthroughs and findings. Also, a complete background of zein’s properties and features are addressed.
Reinscribing the Lived Body: A Qualitative Study of Extraordinary Religious Healing Experiences in Norwegian Contexts
Authors: Austad Anne , Nygaard Marianne Rodriguez , Kleiven Tormod
Published: October 2020

Poor health often motivates people to engage in religious and spiritual approaches to healing. However, there is limited research on such experiences from a northern European perspective. This article investigates healing experiences related to Christian faith and practices in Norway by thematic analysis of 25 semi-structured interviews with individuals who have experienced healing of different ailments. In so doing, healing events across diverse contexts are characterised, and the results show that such experiences not only feature practices in which other people are present in prayer, preaching, and the laying on of hands, but also spontaneous extraordinary encounters with a divine being through visions and voices. The healing events are further described as experiences of transformational, powerful touch. In light of the lived body theory, these transformational experiences can be understood as re-inscriptions of health that are manifested in the intertwined bio–psycho–social–spiritual aspects of the body.
From Līlā to Nitya and Back: Śrī Rāmakṛṣṇa and Vedānta
Authors: Mitra Arpita
Published: October 2020

There has been a long-standing academic debate on the religious orientation of Śrī Rāmakṛṣṇa Paramahaṁsa (1836–1886), one of the leading religious figures of modern India. In the light of his teachings, it is possible to accept that Rāmakṛṣṇa’s ideas were Vedāntic, albeit not in a sectarian or exclusive way. This article explores the question of where exactly to place him in the chequered history of Vedāntic ideas. It points out that Rāmakṛṣṇa repeatedly referred to different states of consciousness while explaining the difference in the attitudes towards the Divine. This is the basis of his harmonization of the different streams within Vedānta. Again, it is also the basis of his understanding of the place of śakti. He demonstrated that, as long as one has I-consciousness, one is operating within the jurisdiction of śakti, and has to accept śakti as real. On the other hand, in the state of samādhi, which is the only state in which the I-consciosuness disappears, there is neither One nor many. The article also shows that, while Rāmakṛṣṇa accepted all of the different views within Vedānta, he was probably not as distant from the Advaita Vedānta philosopher Ādi Śaṁkara as he has been made out to be.
Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning
Authors: Tom Manu , Prabha Rajanie , Wu Tianyu , Baltsavias Emmanuel , Leal-Taixé Laura , Schindler Konrad
Published: October 2020

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources capable of monitoring lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores > 93%, for both sensors. It also generalises well across lakes and winters with mIoU scores > 78% and >80% respectively. On average, our webcam approach achieves mIoU values of ≈87% and generalisation scores of ≈71% and ≈69% across different cameras and winters respectively. Additionally, we generate and make available a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras.
Developing IoT Sensing System for Construction-Induced Vibration Monitoring and Impact Assessment
Authors: Meng Qiuhan , Zhu Songye
Published: October 2020

Construction activities often generate intensive ground-borne vibrations that may adversely affect structure safety, human comfort, and equipment functionality. Vibration monitoring systems are commonly deployed to assess the vibration impact on the surrounding environment during the construction period. However, traditional vibration monitoring systems are associated with limitations such as expensive devices, difficult installation, complex operation, etc. Few of these monitoring systems have integrated functions such as in situ data processing and remote data transmission and access. By leveraging the recent advances in information technology, an Internet of Things (IoT) sensing system has been developed to provide a promising alternative to the traditional vibration monitoring system. A microcomputer (Raspberry Pi) and a microelectromechanical systems (MEMS) accelerometer are adopted to minimize the system cost and size. A USB internet dongle is used to provide 4G communication with cloud. Time synchronization and different operation modes have been designed to achieve energy efficiency. The whole system is powered by a rechargeable solar battery, which completely avoids cabling work on construction sites. Various alarm functions, MySQL database for measurement data storage, and webpage-based user interface are built on a public cloud platform. The architecture of the IoT vibration sensing system and its working mechanism are introduced in detail. The performance of the developed IoT vibration sensing system has been successfully validated by a series of tests in the laboratory and on a selected construction site.
An Efficient Resource Allocation Strategy for Edge-Computing Based Environmental Monitoring System
Authors: Fang Juan , Hu Juntao , Wei Jianhua , Liu Tong , Wang Bo
Published: October 2020

The cloud computing and microsensor technology has greatly changed environmental monitoring, but it is difficult for cloud-computing based monitoring system to meet the computation demand of smaller monitoring granularity and increasing monitoring applications. As a novel computing paradigm, edge computing deals with this problem by deploying resource on edge network. However, the particularity of environmental monitoring applications is ignored by most previous studies. In this paper, we proposed a resource allocation algorithm and a task scheduling strategy to reduce the average completion latency of environmental monitoring application, when considering the characteristic of environmental monitoring system and dependency among task. Simulations are conducted, and the results show that compared with the traditional algorithms. With considering the emergency task, the proposed methods decrease the average completion latency by 21.6% in the best scenario.
A Multispectral Camera Development: From the Prototype Assembly until Its Use in a UAV System
Authors: Morales Alejandro , Guerra Raul , Horstrand Pablo , Diaz Maria , Jimenez Adan , Melian Jose , Lopez Sebastian , Lopez Jose F.
Published: October 2020

Multispectral imaging (MI) techniques are being used very often to identify different properties of nature in several domains, going from precision agriculture to environmental studies, not to mention quality inspection of pharmaceutical production, art restoration, biochemistry, forensic sciences or geology, just to name some. Different implementations are commercially available from the industry and yet there is quite an interest from the scientific community to spread its use to the majority of society by means of cost effectiveness and ease of use for solutions. These devices make the most sense when combined with unmanned aerial vehicles (UAVs), going a step further and alleviating repetitive routines which could be strenuous if traditional methods were adopted. In this work, a low cost and modular solution for a multispectral camera is presented, based on the use of a single panchromatic complementary metal oxide semiconductor (CMOS) sensor combined with a rotating wheel of interchangeable band pass optic filters. The system is compatible with open source hardware permitting one to capture, process, store and/or transmit data if needed. In addition, a calibration and characterization methodology has been developed for the camera, allowing not only for quantifying its performance, but also able to characterize other CMOS sensors in the market in order to select the one that best suits the budget and application. The process was experimentally validated by mounting the camera in a Dji Matrice 600 UAV to uncover vegetation indices in a reduced area of palm trees plantation. Results are presented for the normalized difference vegetation index (NDVI) showing a generated colored map with the captured information.
Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
Authors: Qasem Zeyad A. H. , Esmaiel Hamada , Sun Haixin , Qi Jie , Wang Junfeng
Published: October 2020

The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity.
An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis
Authors: Bae Tae Wuk , Lee Sang Hag , Kwon Kee Koo
Published: October 2020

With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.
Mimicking the Martian Hydrological Cycle: A Set-Up to Introduce Liquid Water in Vacuum
Authors: Sobrado Jesús Manuel
Published: October 2020

Liquid water is well known as the life ingredient as a solvent. However, so far, it has only been found in liquid state on this planetary surface. The aim of this experiment and technological development was to test if a moss sample is capable of surviving in Martian conditions. We built a system that simulates the environmental conditions of the red planet including its hydrological cycle. This laboratory facility enables us to control the water cycle in its three phases through temperature, relative humidity, hydration, and pressure with a system that injects water droplets into a vacuum chamber. We successfully simulated the daytime and nighttime of Mars by recreating water condensation and created a layer of superficial ice that protects the sample against external radiation and minimizes the loss of humidity due to evaporation to maintain a moss sample in survival conditions in this extreme environment. We performed the simulations with the design and development of different tools that recreate Martian weather in the MARTE simulation chamber.
A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA
Authors: Cruz-Miguel Edson E. , García-Martínez José R. , Rodríguez-Reséndiz Juvenal , Carrillo-Serrano Roberto V.
Published: October 2020

Servo systems are feedback control systems characterized by position, speed, and/or acceleration outputs. Nowadays, industrial advances make the electronic stages in these systems obsolete compared to the mechanical elements, which generates a recurring problem in technological, commercial and industrial applications. This article presents a methodology for the development of an open-architecture controller that is based on reconfigurable hardware under the open source concept for servo applications. The most outstanding contribution of this paper is the implementation of a Genetic Algorithm for online self tuning with a focus on both high-quality servo control and reduction of vibrations during the positioning of a linear motion system. The proposed techniques have been validated on a real platform and form a novel, effective approach as compared to the conventional tuning methods that employ empirical or analytical solutions and cannot improve their parameter set. The controller was elaborated from the Graphical User Interface to the logical implementation while using free tools. This approach also allows for modification and updates to be made easily, thereby reducing the susceptibility to obsolescence. A comparison of the logical implementation with the manufacturer software was also conducted in order to test the performance of free tools in FPGAs. The Graphical User Interface developed in Python presents features, such as speed profiling, controller auto-tuning, measurement of main parameters, and monitoring of servo system vibrations.
Spoofing Attack on Ultrasonic Distance Sensors Using a Continuous Signal
Authors: Gluck Tomer , Kravchik Moshe , Chocron Samuel , Elovici Yuval , Shabtai Asaf
Published: October 2020

Ultrasonic distance sensors use an ultrasonic pulse’s time of flight to calculate the distance to the reflecting object. Widely used in industry, these sensors are an important component in autonomous vehicles, where they are used for such tasks as object avoidance and altitude measurement. The proper operation of such autonomous vehicles relies on sensor measurements; therefore, an adversary that has the ability to undermine the sensor’s reliability can pose a major risk to the vehicle. Previous attempts to alter the measurements of this sensor using an external signal succeeded in performing a denial-of-service (DoS) attack, in which the sensor’s reading showed a constant value, and a spoofing attack, in which the attacker could control the measurement to some extent. However, these attacks require precise knowledge of the sensor and its operation (e.g., timing of the ultrasonic pulse sent by the sensor). In this paper, we present an attack on ultrasonic distance sensors in which the measured distance can be altered (i.e., spoofing attack). The attack exploits a vulnerability discovered in the ultrasonic sensor’s receiver that results in a fake pulse that is produced by a constant noise in the input. A major advantage of the proposed attack is that, unlike previous attacks, a constant signal is used, and therefore, no prior knowledge of the sensor’s relative location or its timing behavior is required. We demonstrate the attack in both a lab setup (testbed) and a real setup involving a drone to demonstrate its feasibility. Our experiments show that the attack can interfere with the proper operation of the vehicle. In addition to the risk that the attack poses to autonomous vehicles, it can also be used as an effective defensive tool for restricting the movement of unauthorized autonomous vehicles within a protected area.
The Danger of Walking with Socks: Evidence from Kinematic Analysis in People with Progressive Multiple Sclerosis
Authors: Huang Su-Chun , Dalla Costa Gloria , Pisa Marco , Gregoris Lorenzo , Leccabue Giulia , Congiu Martina , Comi Giancarlo , Leocani Letizia
Published: October 2020

Multiple sclerosis (MS) is characterized by gait impairments and severely impacts the quality of life. Technological advances in biomechanics offer objective assessments of gait disabilities in clinical settings. Here we employed wearable sensors to measure electromyography (EMG) and body acceleration during walking and to quantify the altered gait pattern between people with progressive MS (PwPMS) and healthy controls (HCs). Forty consecutive patients attending our department as in-patients were examined together with fifteen healthy controls. All subjects performed the timed 10 min walking test (T10MW) using a wearable accelerator and 8 electrodes attached to bilateral thighs and legs so that body acceleration and EMG activity were recorded. The T10MWs were recorded under three conditions: standard (wearing shoes), reduced grip (wearing socks) and increased cognitive load (backward-counting dual-task). PwPMS showed worse kinematics of gait and increased muscle coactivation than controls at both the thigh and leg levels. Both reduced grip and increased cognitive load caused a reduction in the cadence and velocity of the T10MW, which were correlated with one another. A higher coactivation index at the thigh level of the more affected side was positively correlated with the time of the T10MW (r = 0.5, p < 0.01), Expanded Disability Status Scale (EDSS) (r = 0.4, p < 0.05), and negatively correlated with the cadence (r = −0.6, p < 0.001). Our results suggest that excessive coactivation at the thigh level is the major determinant of the gait performance as the disease progresses. Moreover, demanding walking conditions do not influence gait in controls but deteriorate walking performances in PwPMS, thus those conditions should be prevented during hospital examinations as well as in homecare environments.
An Energy-Efficient Unselfish Spectrum Leasing Scheme for Cognitive Radio Networks
Authors: Bilibashi Denis , Vitucci Enrico M. , Degli-Esposti Vittorio , Giorgetti Andrea
Published: October 2020

Cooperative Communications in Cognitive Radio (CR) have been introduced as an essential and efficient technique to improve the transmission performance of primary users and offer transmission opportunities for secondary users. In a typical multiuser Cooperative Communication in CR, each primary user can choose one secondary user as a relay node. To encourage the cooperative behavior of the secondary users, primary users lease a fraction of their allocated spectrum to the relay secondary users to transmit their data packets. In this work, a novel unselfish spectrum leasing scheme in CR networks is proposed that offers an energy-efficient solution minimizing the environmental impact of our network. A network management architecture is introduced, and resource allocation is proposed as a constrained sum energy efficiency maximization problem. The optimization problem is formulated and solved using non-linear programming methods and based on a modified Kuhn-Munkres bipartite matching algorithm. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.
Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network
Authors: Hu Di , Zhang Chen , Yang Tao , Chen Gang
Published: October 2020

Anomaly detection is of great significance in condition-based maintenance of power plant equipment. The conventional fixed threshold detection method is not able to perform early detection of equipment abnormalities. In this study, a general anomaly detection framework based on a long short-term memory-based autoencoder (LSTM-AE) network is proposed. A normal behavior model (NBM) is established to learn the normal behavior patterns of the operating variables of the equipment in space and time. Based on the similarity analysis between the NBM output distribution and the corresponding measurement distribution, the Mahalanobis distance (MD) is used to describe the overall residual (OR) of the model. The reasonable range is obtained using kernel density estimation (KDE) with a 99% confidence interval, and the OR is monitored to detect abnormalities in real-time. An induced draft fan is chosen as a case study. Results show that the established NBM has excellent accuracy and generalizability, with average root mean square errors of 0.026 and 0.035 for the training and test data, respectively, and average mean absolute percentage errors of 0.027%. Moreover, the abnormal operation case shows that the proposed framework can be effectively used for the early detection of abnormalities.
A Multidomain Approach to Assessing the Convergent and Concurrent Validity of a Mobile Application When Compared to Conventional Methods of Determining Body Composition
Authors: Neufeld Eric V. , Seltzer Ryan A. , Sazzad Tasnim , Dolezal Brett A.
Published: October 2020

Determining body composition via mobile application may circumvent limitations of conventional methods. However, the accuracy of many technologies remains unknown. This investigation assessed the convergent and concurrent validity of a mobile application (LS) that employs 2-dimensional digital photography (LS2D) and 3-dimensional photonic scanning (LS3D). Measures of body composition including circumferences, waist-to-hip ratio (WHR), and body fat percentage (BF%) were obtained from 240 healthy adults using LS and a diverse set of conventional methods—Gulick tape, bioelectrical impedance analysis (BIA), and skinfolds. Convergent validity was consistently high—indicating these methods vary proportionally and can thus reliably detect changes despite individual measurement differences. The span of the Limits of Agreement (LoA) using LS were comparable to the LoA between conventional methods. LS3D exhibited high agreement relative to Gulick tape in the measurement of WHR, despite poor agreement with individual waist and hip circumferences. In BF%, LS2D exhibited high agreement with BIA and skinfold methods, whereas LS3D demonstrated low agreement. Interestingly, the low inferred bias between LS3D and DXA using existing data suggests that LS3D may have high agreement with dual-energy x-ray absorptiometry. Overall, the suitability of LS2D and LS3D to replace conventional methods must be based on an individual user’s criteria.
Machine Learning Based Biomimetic Underwater Covert Acoustic Communication Method Using Dolphin Whistle Contours
Authors: Ahn Jongmin , Lee Hojun , Kim Yongcheol , Kim Wanjin , Chung Jaehak
Published: October 2020

For underwater acoustic covert communications, biomimetic covert communications have been developed using dolphin whistles. The conventional biomimetic covert communication methods transmit slightly different signal patterns from real dolphin whistles, which results in a low degree of mimic (DoM). In this paper, we propose a novel biomimetic communication method that preserves the large DoM with a low bit error rate (BER). For the transmission, the proposed method utilizes the various contours of real dolphin whistles with the link information among consecutive whistles, and the proposed receiver uses machine learning based whistle detectors with the aid of the link information. Computer simulations and practical ocean experiments were executed to demonstrate the better BER performance of the proposed method. Ocean experiments demonstrate that the BER of the proposed method was 0.002, while the BER of the conventional Deep Neural Network (DNN) based detector showed 0.36.
Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network
Authors: Lin Ming-Chang , Han Po-Yu , Fan Yi-Hua , Li Chih-Hung G.
Published: October 2020

Gear transmission is widely used in mechanical equipment. In practice, if the gearbox is damaged, it not only affects the yield rate but also damages other parts of machines; thus, increases the cost and difficulty of maintenance. With the advancement of technology, the concept of unmanned factories has been proposed; an automatic diagnosis system for the health management of gearboxes becomes necessary. In this paper, a compound fault diagnosis system for the gearbox based on convolutional neural network (CNN) is developed. Specifically, three-axis vibration signals measured by accelerometers are used as the input of the one-dimensional CNN; the detection of the existence and type of the fault is directly output. In testing, the model achieved nearly 100% accuracy on the fault samples we captured. Experimental evidence also shows that the frequency-domain data can provide better diagnostic results than the time-domain data due to the stable characteristics in the frequency spectrum. For practical usage, we demonstrated a remote fault diagnosis system through a local area network on an embedded platform. Furthermore, optimization of convolution kernels was also investigated. When moderately reducing the number of convolution kernels, it does not affect the diagnostic accuracy but greatly reduces the training time of the model.
Dynamic Modeling and Experimental Validation of an Impact-Driven Piezoelectric Energy Harvester in Magnetic Field
Authors: Chen Chung-De , Wu Yu-Hsuan , Su Po-Wen
Published: October 2020

In this study, an impact-driven piezoelectric energy harvester (PEH) in magnetic field is presented. The PEH consists of a piezoelectric cantilever beam and plural magnets. At its initial status, the beam tip magnet is attracted by a second magnet. The second magnet is moved away by hand and then the beam tip magnet moves to a third magnet by the guidance of the magnetic fields. The impact occurs when the beam motion is stopped by the third magnet. The impact between magnets produces an impact energy and causes a transient beam vibration. The electric energy is generated by the piezoelectric effect. Based on the energy principle, a multi-DOF (multi-degree of freedom) mathematical model was developed to calculate the displacements, velocities, and voltage outputs of the PEH. A prototype of the PEH was fabricated. The voltages outputs of the beam were monitored by an oscilloscope. The maximum generated energy was about 0.4045 mJ for a single impact. A comparison between numerical and experimental results was presented in detail. It showed that the predictions based on the model agree with the experimental measurements. The PEH was connected to a diode bridge rectifier and a storage capacitor. The charges generated by the piezoelectric beam were stored in the capacitor by ten impacts. The experiments showed that the energy stored in the capacitor can light up the LED.
Portable and Affordable Light Source-Based Photoacoustic Tomography
Authors: Kuniyil Ajith Singh Mithun , Xia Wenfeng
Published: October 2020

Photoacoustic imaging is a hybrid imaging modality that offers the advantages of optical (spectroscopic contrast) and ultrasound imaging (scalable spatial resolution and imaging depth). This promising modality has shown excellent potential in a wide range of preclinical and clinical imaging and sensing applications. Even though photoacoustic imaging technology has matured in research settings, its clinical translation is not happening at the expected pace. One of the main reasons for this is the requirement of bulky and expensive pulsed lasers for excitation. To accelerate the clinical translation of photoacoustic imaging and explore its potential in resource-limited settings, it is of paramount importance to develop portable and affordable light sources that can be used as the excitation light source. In this review, we focus on the following aspects: (1) the basic theory of photoacoustic imaging; (2) inexpensive light sources and different implementations; and (3) important preclinical and clinical applications, demonstrated using affordable light source-based photoacoustics. The main focus will be on laser diodes and light-emitting diodes as they have demonstrated promise in photoacoustic tomography—the key technological developments in these areas will be thoroughly reviewed. We believe that this review will be a useful opus for both the beginners and experts in the field of biomedical photoacoustic imaging.
AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things
Authors: Awan Kamran Ahmad , Ud Din Ikram , Almogren Ahmad , Almajed Hisham
Published: October 2020

Internet of Things (IoT) provides a diverse platform to automate things where smart agriculture is one of the most promising concepts in the field of Internet of Agriculture Things (IoAT). Due to the requirements of more processing power for computations and predictions, the concept of Cloud-based smart agriculture is proposed for autonomic systems. This is where digital innovation and technology helps to improve the quality of life in the area of urbanization expansion. For the integration of cloud in smart agriculture, the system is shown to have security and privacy challenges, and most significantly, the identification of malicious and compromised nodes along with a secure transmission of information between sensors, cloud, and base station (BS). The identification of malicious and compromised node among soil sensors communicating with the BS is a notable challenge in the BS to cloud communications. The trust management mechanism is proposed as one of the solutions providing a lightweight approach to identify these nodes. In this article, we have proposed a novel trust management mechanism to identify malicious and compromised nodes by utilizing trust parameters. The trust mechanism is an event-driven process that computes trust based on the pre-defined time interval and utilizes the previous trust degree to develop an absolute trust degree. The system also maintains the trust degree of a BS and cloud service providers using distinct approaches. We have also performed extensive simulations to evaluate the performance of the proposed mechanism against several potential attacks. In addition, this research helps to create friendlier environments and efficient agricultural productions for the migration of people to the cities.
IoT-Based Electricity Bill for Domestic Applications
Authors: Jiménez Betancourt Ramón Octavio , González López Juan Miguel , Barocio Espejo Emilio , Concha Sánchez Antonio , Villalvazo Laureano Efraín , Sandoval Pérez Sergio , Contreras Aguilar Luis
Published: October 2020

This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review and administrate their energy consumption. Using low-cost sensors and the electronic board Particle® Photon, an energy meter is proposed. The presented prototype is extremely compact and satisfies safety measures to be used by anyone in a domestic installation. The measurement data is displayed and processed in real-time, and an appropriate algorithm determines the accumulated kWh. The energy consumed is displayed using an Html interface of easy interpretation for the customers, given recommendations about their consumption habits and some alarms in case of abnormal or high consumption. As a reinforcement measure for avoiding large consumption bills, the system is programmed to send messages to the user, remembering if the estimated consumption is large.
A Method to Eliminate the Influence of Frequency-Modulating-Induced Auxiliary Amplitude Modulation on the Calibration of 3 × 3 Coupler Asymmetric Parameters
Authors: Zhang Yichi , Wang Jianfei , Chen Mo , Wang Mingyang , Liang Yan , Meng Zhou
Published: October 2020

The 3 × 3 demodulation algorithm has been widely used in retrieving the phase information in the optical fiber sensing system, while the asymmetry of the 3 × 3 coupler can introduce some distortions. In this situation, the parameters of the 3 × 3 coupler can be calibrated by the ellipse fitting method to remove the distortions. Conducting a frequency modulation on the laser, together with an unbalanced Michelson interferometer, makes the ellipse fitting method implemented, which is more appropriate for all-optical sensing. Unexpectedly, the auxiliary amplitude modulation of the laser induced by the frequency modulation is inevitable, leading to the deterioration of the calibration. In this paper, the influence of the auxiliary amplitude modulation on the calibration of the asymmetric parameters is analyzed theoretically and verified experimentally, on the basis of which a convenient but highly efficient method for acquiring the output of the laser and removing related items from the interferometric signals is put forward. The feasibility and robustness of the proposed solution is tested experimentally, and the results show that the mean square errors of the fittings and the variation coefficients of the calibrated parameter sequences are at the scale of 10−5 and 10−4, respectively, indicating that the method performs very well.
Optimal Placement of Social Digital Twins in Edge IoT Networks
Authors: Chukhno Olga , Chukhno Nadezhda , Araniti Giuseppe , Campolo Claudia , Iera Antonio , Molinaro Antonella
Published: October 2020

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.
A Sensor-Based Screening Tool for Identifying High Pelvic Mobility in Patients Due to Undergo Total Hip Arthroplasty
Authors: Wang Xueyang , Qureshi Arham , Vepa Abhinav , Rahman Usama , Palit Arnab , Williams Mark A. , King Richard , Elliott Mark T.
Published: October 2020

There is increasing evidence that pelvic mobility is a critical factor to consider in implant alignment during total hip arthroplasty (THA). Here, we test the feasibility of using an inertial sensor fitted across the sacrum to measure change in pelvic tilt, and hence screen for patients with high pelvic mobility. Patients (n = 32, mean age: 57.4 years) due to receive THA surgery participated in the study. Measures of pelvic tilt were captured simultaneously using the device and radiograph in three functional positions: Standing, flexed-seated, and step-up. We found a strong correlation between the device and radiograph measures for the change in pelvic tilt measure from standing to flexed-seated position (R2 = 0.911); 75% of absolute errors were under 5 degrees. We demonstrated that the device can be used as a screening tool to rapidly identify patients who would benefit from more detailed surgical planning of implant positioning to reduce future risks of impingement and dislocation.
GadgetArm—Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning
Authors: Park JoungMin , Lee SangYoon , Lee JaeWoon , Um Jumyung
Published: October 2020

Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturing system to be autonomous, self-recognized, and adaptable by using artificial intelligence of robot programming dealing with arbitrary shapes of work-in-processes. This paper specifically explores the chain of key technologies, such as 3D object recognition with CAD and point cloud data, reinforcement learning of robot arm, and customized 3D printed gripper, in order to enhance the intelligence of the robot controller system. And it also proposes the integration with 3D point cloud based object recognition and game-engine based reinforcement learning. The result of the prototype of the intelligent robot gripping system developed by the proposed method with a 4 degree-of-freedom robot arm is explained in this paper.
A Non-Array Type Cut to Shape Soft Slip Detection Sensor Applicable to Arbitrary Surface
Authors: Kim Sung Joon , Lee Seung Ho , Moon Hyungpil , Choi Hyouk Ryeol , Koo Ja Choon
Published: October 2020

The presence of a tactile sensor is essential to hold an object and manipulate it without damage. The tactile information helps determine whether an object is stably held. If a tactile sensor is installed at wherever the robot and the object touch, the robot could interact with more objects. In this paper, a skin type slip sensor that can be attached to the surface of a robot with various curvatures is presented. A simple mechanical sensor structure enables the cut and fit of the sensor according to the curvature. The sensor uses a non-array structure and can operate even if a part of the sensor is cut off. The slip was distinguished using a simple vibration signal received from the sensor. The signal is transformed into the time-frequency domain, and the slippage was determined using an artificial neural network. The accuracy of slip detection was compared using four artificial neural network models. In addition, the strengths and weaknesses of each neural network model were analyzed according to the data used for training. As a result, the developed sensor detected slip with an average of 95.73% accuracy at various curvatures and contact points.
Measuring Floodplain Inundation Using Diel Amplitude of Temperature
Authors: Celi Jorge E. , Hamilton Stephen K.
Published: October 2020

Assessment of inundation patterns across large and remote floodplains is challenging and costly. Inexpensive loggers that record the damping of the diel amplitude of temperature (DAT) when submerged compared to overlying air can indirectly indicate inundation. We assessed the efficacy of this approach in tropical, subtropical, and temperate floodplains by comparing direct water level measurements using pressure transducers with the indirect indication of inundation ascertained from the DAT at the same location. The approach worked better in tropical than in subtropical and temperate floodplains. However, the relatively small DATs of air in humid and densely vegetated settings made estimation of inundation more challenging compared to the drier and less vegetated settings, where a large diel range of air temperature was markedly damped beneath the water. The indirect temperature approach must be calibrated for a particular ecosystem using direct water-level measurements to define DAT thresholds that are indicative of submergence of the sensors. Temperature provides an inexpensive indicator of duration of inundation that can be particularly useful in studies of large and remote floodplains, although the development of inexpensive sensors that directly measure submergence (e.g., by resistivity) will likely become a better option in the future.
Influence of the Flow Rate in an Automated Microfluidic Electronic Tongue Tested for Sucralose Differentiation
Authors: Braunger Maria L. , Fier Igor , Shimizu Flávio M. , de Barros Anerise , Rodrigues Varlei , Riul Antonio
Published: October 2020

Incorporating electronic tongues into microfluidic devices brings benefits as dealing with small amounts of sample/discharge. Nonetheless, such measurements may be time-consuming in some applications once they require several operational steps. Here, we designed four collinear electrodes on a single printed circuit board, further comprised inside a straight microchannel, culminating in a robust e-tongue device for faster data acquisition. An analog multiplexing circuit automated the signal’s routing from each of the four sensing units to an impedance analyzer. Both instruments and a syringe pump are controlled by dedicated software. The automated e-tongue was tested with four Brazilian brands of liquid sucralose-based sweeteners under 20 different flow rates, aiming to systematically evaluate the influence of the flow rate in the discrimination among sweet tastes sold as the same food product. All four brands were successfully distinguished using principal component analysis of the raw data, and despite the nearly identical sucralose-based taste in all samples, all brands’ significant distinction is attributed to small differences in the ingredients and manufacturing processes to deliver the final food product. The increasing flow rate improves the analyte’s discrimination, as the silhouette coefficient reaches a plateau at ~3 mL/h. We used an equivalent circuit model to evaluate the raw data, finding a decrease in the double-layer capacitance proportional to improvements in the samples’ discrimination. In other words, the flow rate increase mitigates the formation of the double-layer, resulting in faster stabilization and better repeatability in the sensor response.
The Degradation of Automotive Radar Sensor Signals Caused by Vehicle Vibrations and Other Nonlinear Movements
Authors: Hau Florian , Baumgärtner Florian , Vossiek Martin
Published: October 2020

As the demands on modern radar systems with respect to accuracy, reliability, and availability increase, a detailed assessment of the influence of nonlinear movements has become necessary. In particular, from the point of view of radar, different types of movements, such as any kind of acceleration, braking situation, or vehicle vibration, are essential parts of any traffic scenario. These unavoidable motions, in which the relative velocity changes within one measurement cycle, are called nonlinear movements. These nonlinearities contribute to intermediate frequencies, which are comparable to the extensively described nonlinearities of a frequency ramp. This additional contribution to the intermediate signal has a direct effect on the signal-to-noise ratio and thus on the accuracy and probability of target detection. This paper presents a study of various types of nonlinear motion and a detailed definition of the resulting parameters based on a variety of vehicle-based measurements. An advanced signal model of frequency-modulated continuous wave (FMCW) radar is introduced and verified in addition to a detailed mathematical description of spectral signal behaviour in sinusoidal motions and linear acceleration. The theoretical and experimental results in idealised point targets are transferred to real complex road users. Furthermore, by applying established automotive signal processing steps in the form of an ordered statistical constant false alarm rate (OS CFAR), the consequences of determining the noise level are also shown. In combination with the already introduced signal behaviour, these results enabled general description of the signal-to-noise ratio of nonlinear movements in complex traffic scenarios.
Devising a Distributed Co-Simulator for a Multi-UAV Network
Authors: Park Seongjoon , La Woong Gyu , Lee Woonghee , Kim  Hwangnam
Published: October 2020

Practical evaluation of the Unmanned Aerial Vehicle (UAV) network requires a lot of money to build experiment environments, which includes UAVs, network devices, flight controllers, and so on. To investigate the time-sensitivity of the multi-UAV network, the influence of the UAVs’ mobility should be precisely evaluated in the long term. Although there are some simulators for UAVs’ physical flight, there is no explicit scheme for simulating both the network environment and the flight environments simultaneously. In this paper, we propose a novel co-simulation scheme for the multiple UAVs network, which performs the flight simulation and the network simulation simultaneously. By considering the dependency between the flight status and networking situations of UAV, our work focuses on the consistency of simulation state through synchronization among simulation components. Furthermore, we extend our simulator to perform multiple scenarios by exploiting distributed manner. We verify our system with respect to the robustness of time management and propose some use cases which can be solely simulated by this.