Sensors

Muthugala, M. A. Viraj J.;Samarakoon, S. M. Bhagya P.;Mohan Rayguru, Madan;Ramalingam, Balakrishnan;Elara, Mohan Rajesh

doi: 10.3390/s20164445pmid: 32784888

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

Xu, Shiwu;Chen, Chih-Cheng;Wu, Yi;Wang, Xufang;Wei, Fen

doi: 10.3390/s20164432pmid: 32784420

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

Giardino, Claudia;Bresciani, Mariano;Braga, Federica;Fabbretto, Alice;Ghirardi, Nicola;Pepe, Monica;Gianinetto, Marco;Colombo, Roberto;Cogliati, Sergio;Ghebrehiwot, Semhar;Laanen, Marnix;Peters, Steef;Schroeder, Thomas;Concha, Javier A.;Brando, Vittorio E.

doi: 10.3390/s20164553pmid: 32823847

Yang, Heekyung;Han, Jongdae;Min, Kyungha

doi: 10.3390/s20164543pmid: 32823741

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography (EEG) signals. As a result, we conclude that the enhancement of contrast induces the increase of valence, while the reduction of contrast induces the decrease. Meanwhile, the contrast control affects arousal on a very minute scale.

de Oliveira Júnior, Gildásio Antonio;de Oliveira Albuquerque, Robson;Borges de Andrade, César Augusto;de Sousa, Rafael Timóteo;Sandoval Orozco, Ana Lucila;García Villalba, Luis Javier

doi: 10.3390/s20164557pmid: 32824014

Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

Al-Yasir, Yasir I. A.;Ojaroudi Parchin, Naser;Tu, Yuxiang;Abdulkhaleq, Ahmed M.;Elfergani, Issa T. E.;Rodriguez, Jonathan;Abd-Alhameed, Raed A.

doi: 10.3390/s20164538pmid: 32823654

A very compact microstrip reconfigurable filter for fourth-generation (4G) and sub-6 GHz fifth-generation (5G) systems using a new hybrid co-simulation method is presented in this manuscript. The basic microstrip design uses three coupled line resonators with λ/4 open-circuited stubs. The coupling coefficients between the adjacent and non-adjacent resonators are used to tune the filter at the required center frequency to cover the frequency range from 2.5 to 3.8 GHz. The coupling coefficient factors between the adjacent resonators are adjusted to control and achieve the required bandwidth, while the input and output external quality factors are adjusted to ensure maximum power transfer between the input and output ports. Two varactor diodes and biasing circuit components are selected and designed to meet the targeted performance for the tunable filter. The impedance bandwidth is maintained between 95 and 115 MHz with measured return losses of more than 17 dB and measured insertion loss of less than 1 dB. Computer simulation technology (CST) is utilized to design and optimize the presented reconfigurable filter, with hybrid co-simulation technique, using both CST microwave studio (MWS) and CST design studio (DS), is applied to build the model by considering the SPICE representation for the varactor switches and all electronic elements of the biasing circuit. The introduced reconfigurable microstrip filter is also fabricated using a Rogers RO3010 material with a relative dielectric constant of 10.1 and it is printed on a very compact size of 13 × 8 × 0.81 mm3. An excellent agreement is obtained between the simulation and measurement performance.

Garcia-Gonzalez, Daniel;Rivero, Daniel;Fernandez-Blanco, Enrique;Luaces, Miguel R.

doi: 10.3390/s20164650pmid: 32824766

The authors wish to make the following corrections to this paper [...]

Yang, Tianwei;Nan, Xinyuan;Jin, Weixu

doi: 10.3390/s20164533pmid: 32823567

The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers; the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.

Ortega, Fernando;González-Prieto, Ángel;Bobadilla, Jesús;Gutiérrez, Abraham

doi: 10.3390/s20164628pmid: 32824579

Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods.

Jodko-Władzińska, Anna;Wildner, Krzysztof;Pałko, Tadeusz;Władziński, Michał

doi: 10.3390/s20164563pmid: 32823964

Magnetography with superconducting quantum interference device (SQUID) sensor arrays is a well-established technique for measuring subtle magnetic fields generated by physiological phenomena in the human body. Unfortunately, the SQUID-based systems have some limitations related to the need to cool them down with liquid helium. The room-temperature alternatives for SQUIDs are optically pumped magnetometers (OPM) operating in spin exchange relaxation-free (SERF) regime, which require a very low ambient magnetic field. The most common two-layer magnetically shielded rooms (MSR) with residual magnetic field of 50 nT may not be sufficiently magnetically attenuated and additional compensation of external magnetic field is required. A cost-efficient compensation system based on square Helmholtz coils was designed and successfully used for preliminary measurements with commercially available zero-field OPM. The presented setup can reduce the static ambient magnetic field inside a magnetically shielded room, which improves the usability of OPMs by providing a proper environment for them to operate, independent of initial conditions in MSR.