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ISSN / EISSN : 14248220 / 14248220
Current Publisher: MDPI (10.3390)
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Moustafa Saleh, Yahya Abbass, Ali Ibrahim, Maurizio Valle
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204437

Abstract:Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system.
Xin Tian, Guoliang Wei, Jianhua Wang, Dianchen Zhang
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204438

Abstract:In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.
T. Matos, C. L. Faria, M. S. Martins, Renato Henriques, P. A. Gomes, L. M. Goncalves
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204439

Abstract:A cost-effective optical sensor for continuous in-situ monitoring of turbidity and suspended particulate matter concentration (SPM), with a production cost in raw materials less than 20 €, is presented for marine or fluvial applications. The sensor uses an infrared LED and three photodetectors with three different positions related to the light source—135º, 90º and 0º—resulting in three different types of light detection: backscattering, nephelometry and transmitted light, respectively. This design allows monitoring in any type of environment, offering a wide dynamic range and accuracy for low and high turbidity or SPM values. An ultraviolet emitter–receiver pair is also used to differentiate organic and inorganic matter through the differences in absorption at different wavelengths. The optical transducers are built in a watertight structure with a radial configuration where a printed circuit board with the electronic signal coupling is assembled. An in-lab calibration of the sensor was made to establish a relation between suspended particulate matter (SPM) or the turbidity (NTU) to the photodetectors’ electrical output value in Volts. Two different sizes of seashore sand were used (180 µm and 350 µm) to evaluate the particle size susceptibility. The sensor was tested in a fluvial environment to evaluate SPM change during sediment transport caused by rain, and a real test of 22 days continuous in-situ monitoring was realized to evaluate its performance in a tidal area. The monitoring results were analysed, showing the SPM change during tidal cycles as well as the influence of the external light and biofouling problems.
Hanming Sun, Bin Duo, Zhengqiang Wang, Xiaochen Lin, Changchun Gao
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204440

Abstract:To improve the secrecy performance of cellular-enabled unmanned aerial vehicle (UAV) communication networks, this paper proposes an aerial cooperative jamming scheme and studies its optimal design to achieve the maximum average secrecy rate. Specifically, a base station (BS) transmits confidential messages to a UAV and meanwhile another UAV performs the role of an aerial jammer by cooperatively sending jamming signals to oppose multiple suspicious eavesdroppers on the ground. As the UAVs have the advantage of the controllable mobility, the objective is to maximize the worst-case average secrecy rate by the joint optimization of the two UAVs’ trajectories and the BS’s/UAV jammer’s transmit/jamming power over a given mission period. The objective function of the formulated problem is highly non-linear regarding the optimization variables and the problem has non-convex constraints, which is, in general, difficult to achieve a globally optimal solution. Thus, we divide the original problem into four subproblems and then solve them by applying the successive convex approximation (SCA) and block coordinate descent (BCD) methods. Numerical results demonstrate that the significantly better secrecy performance can be obtained by using the proposed algorithm in comparison with benchmark schemes.
Jaekwang Cha, Jinhyuk Kim, Shiho Kim
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204441

Abstract:Developing a user interface (UI) suitable for headset environments is one of the challenges in the field of augmented reality (AR) technologies. This study proposes a hands-free UI for an AR headset that exploits facial gestures of the wearer to recognize user intentions. The facial gestures of the headset wearer are detected by a custom-designed sensor that detects skin deformation based on infrared diffusion characteristics of human skin. We designed a deep neural network classifier to determine the user’s intended gestures from skin-deformation data, which are exploited as user input commands for the proposed UI system. The proposed classifier is composed of a spatiotemporal autoencoder and deep embedded clustering algorithm, trained in an unsupervised manner. The UI device was embedded in a commercial AR headset, and several experiments were performed on the online sensor data to verify operation of the device. We achieved implementation of a hands-free UI for an AR headset with average accuracy of 95.4% user-command recognition, as determined through tests by participants.
Yung-Yao Chen, Yu-Hsiu Lin
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204443

Abstract:Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.
Khouloud Jlassi, Mostafa Sliem, Kamel Eid, Igor Krupa, Mohamed Chehimi, Aboubakr Abdullah
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204442

Abstract:Precise designs of low-cost and efficient catalysts for the detection of hydrogen peroxide (H2O2) over wide ranges of pH are important in various environmental applications. Herein, a versatile and ecofriendly approach is presented for the rational design of ternary bentonite-silylpropyl-polypyrrole/silver nanoarchitectures (denoted as BP-PS-PPy/Ag) via the in-situ photo polymerization of pyrrole with salinized bentonite (BP-PS) in the presence of silver nitrate. The Pyrrolyl-functionalized silane (PS) is used as a coupling agent for tailoring the formation of highly exfoliated BP-PS-PPy sheet-like nanostructures ornamented with monodispersed Ag nanoparticles (NPs). Taking advantage of the combination between the unique physicochemical properties of BP-PS-PPy and the outstanding catalytic merits of Ag nanoparticles (NPs), the as-synthesized BP-PS-PPy/Ag shows a superior electrocatalytic reduction and high-detection activity towards H2O2 under different pH conditions (from 3 to 10). Intriguingly, the UV-light irradiation significantly enhances the electroreduction activity of H2O2 substantially, compared with the dark conditions, due to the high photoelectric response properties of Ag NPs. Moreover, BP-PS-PPy/Ag achived a quick current response with a detection limit at 1 μM within only 1 s. Our present approach is green, facile, scalable and renewable.
Gaurav Deep, Rajni Mohana, Anand Nayyar, P. Sanjeevikumar, Eklas Hossain
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204444

Abstract:Cloud computing has made the software development process fast and flexible but on the other hand it has contributed to increasing security attacks. Employees who manage the data in cloud companies may face insider attack, affecting their reputation. They have the advantage of accessing the user data by interacting with the authentication mechanism. The primary aim of this research paper is to provide a novel secure authentication mechanism by using Blockchain technology for cloud databases. Blockchain makes it difficult to change user login credentials details in the user authentication process by an insider. The insider is not able to access the user authentication data due to the distributed ledger-based authentication scheme. Activity of insider can be traced and cannot be changed. Both insider and outsider user’s are authenticated using individual IDs and signatures. Furthermore, the user access control on the cloud database is also authenticated. The algorithm and theorem of the proposed mechanism have been given to demonstrate the applicability and correctness.The proposed mechanism is tested on the Scyther formal system tool against denial of service, impersonation, offline guessing, and no replay attacks. Scyther results show that the proposed methodology is secure cum robust.
Chikondi Chisenga, Jianguo Yan, Jiannan Zhao, Qingyun Deng, Jean-Pierre Barriot
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204445

Abstract:The Von Kármán Crater, within the South Pole-Aitken (SPA) Basin, is the landing site of China’s Chang’E-4 mission. To complement the in situ exploration mission and provide initial subsurface interpretation, we applied a 3D density inversion using the Gravity Recovery and Interior Laboratory (GRAIL) gravity data. We constrain our inversion method using known geological and geophysical lunar parameters to reduce the non-uniqueness associated with gravity inversion. The 3D density models reveal vertical and lateral density variations, 2600–3200 kg/m3, assigned to the changing porosity beneath the Von Kármán Crater. We also identify two mass excess anomalies in the crust with a steep density contrast of 150 kg/m3, which were suggested to have been caused by multiple impact cratering. The anomalies from recovered near surface density models, together with the gravity derivative maps extending to the lower crust, are consistent with surface geological manifestation of excavated mantle materials from remote sensing studies. Therefore, we suggest that the density distribution of the Von Kármán Crater indicates multiple episodes of impact cratering that resulted in formation and destruction of ancient craters, with crustal reworking and excavation of mantle materials.
Guanyuan Shuai, Rafael Martinez-Feria, Jinshui Zhang, Shiming Li, Richard Price, Bruno Basso
Published: 14 October 2019
by MDPI
Sensors, Volume 19; doi:10.3390/s19204446

Abstract:Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability.