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Sensors (ISSN 1424-8220; CODEN: SENSC9) is the leading international peer-reviewed open access journal on the science and technology of sensors and biosensors. Sensors is published monthly online by MDPI.
- Open Access free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed by the Science Citation Index Expanded (Web of Science), MEDLINE (PubMed), Ei Compendex, Inspec (IET) and Scopus.
- CiteScore 2017 (Scopus): 3.23; ranked 9/116 in 'Physics and Astronomy: Instrumentation' and 100/644 in 'Electrical and Electronic Engineering.'
- Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 21 days after submission; acceptance to publication is undertaken in 5 days (median values for papers published in the first half of 2018).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 8 topical sections.
Impact Factor: 2.475 (2017) ; 5-Year Impact Factor: 3.014 (2017)
Latest Articles
Geometric Correction for the Geostationary Ocean Color Imager from a Combination of Shoreline Matching and Frequency Matching
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Sensors 2018, 18(11), 3599; https://doi.org/10.3390/s18113599 (registering DOI) - 23 October 2018
Abstract
Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline
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Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth’s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.
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Remote Sensing Performance Enhancement in Hyperspectral Images
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Sensors 2018, 18(11), 3598; https://doi.org/10.3390/s18113598 (registering DOI) - 23 October 2018
Abstract
Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper,
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Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.
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On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
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Sensors 2018, 18(11), 3597; https://doi.org/10.3390/s18113597 (registering DOI) - 23 October 2018
Abstract
The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due
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The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.
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Direct Methanol (or Ethanol) Fuel Cell as Enzymatic or Non-Enzymatic Device, Used to Check Ethanol in Several Pharmaceutical and Forensic Samples
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Sensors 2018, 18(11), 3596; https://doi.org/10.3390/s18113596 (registering DOI) - 23 October 2018
Abstract
It was already demonstrated by our research group that a direct catalytic methanol (or ethanol) fuel cell (DMFC) device can be used also for analytical purposes, such as the determination of ethanol content in beverages. In the present research we extended the application
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It was already demonstrated by our research group that a direct catalytic methanol (or ethanol) fuel cell (DMFC) device can be used also for analytical purposes, such as the determination of ethanol content in beverages. In the present research we extended the application to the analysis of several ethanol-based pharmaceutical products, i.e., pharmaceutical tinctures (dyes) and disinfectants. In recent work we have also shown that the use of alcohol dehydrogenase enzyme as a component of the anodic section of a direct catalytic methanol (or ethanol) fuel cell significantly improves the performance of a simple DMFC device, making it more suitable to measure ethanol (or methanol) in real samples by this cell. At the same time, we have also shown that DMFC can respond to certain organic compounds that are more complex than methanol and ethanol and having R(R’)CH-OH group in the molecule. Firstly, pharmaceutical dyes were analyzed for their ethanol content using the simple catalytic DMFC device, with good accuracy and precision. The results are illustrated in the present paper. Additionally, a detailed investigation carried out on commercial denatured alcoholic samples evidenced several interferences due to the contained additives. Secondly, we hypothesized that by using the enzymatic fuel cell it would be possible to improve the determination, for instance, of certain antibiotics, such as imipenem, or else carry out determinations of ethanol content in saliva and serum (simulating forensic tests, correlated to drivers “breath test”); even if this has already been hypothesized in previous papers, the present study is the first to perform them experimentally, obtaining satisfactory results. In practice, all of the goals which we proposed were reached, confirming the remarkable opportunities of the enzymatic (or non-enzymatic) DMFC device.
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Automatic Detection of Missing Access Points in Indoor Positioning System †
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Sensors 2018, 18(11), 3595; https://doi.org/10.3390/s18113595 (registering DOI) - 23 October 2018
Abstract
The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be
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The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.
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Active Thermal Sensing for Bonding Structure Damage Detection of Hidden Frame Glass Curtain Wall
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Sensors 2018, 18(11), 3594; https://doi.org/10.3390/s18113594 (registering DOI) - 23 October 2018
Abstract
Adhesive bonded structure damage of hidden frame glass curtain wall will cause falling glass, which threatens the security of people and property. Therefore, the damage detection of the adhesive bonded structure of glass curtain wall has great significance. In this paper, active thermal
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Adhesive bonded structure damage of hidden frame glass curtain wall will cause falling glass, which threatens the security of people and property. Therefore, the damage detection of the adhesive bonded structure of glass curtain wall has great significance. In this paper, active thermal sensing technology for bonding structure damage detection was introduced. Firstly, the thermal wave propagation of bonded structure was analyzed. Second, the simulated annealing algorithm and TracePro simulation were utilized to design the heat source. Thirdly, the platform of active thermal sensing was built, and experiments were conducted. Finally, image fusion enhancement of Laplacian pyramid was utilized to the enhancement process of thermal images. The simulation results showed that the irradiance of the cross-optimization was more uniform, and the uniformity was 17.50% higher than the original design value. The experiments results showed that defects of different sizes and depths can be distinguished. The gray differences of the damages on the depth of 0 mm and 4 mm were 0.025 and 0.045, respectively. The thermal wave testing can detect damage intuitively and rapidly, which is significant for the future research of unmanned detection of bonding structure damage of hidden frame glass curtain wall.
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Virtual FBGs Using Saturable Absorbers for Sensing with Fiber Lasers
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Sensors 2018, 18(11), 3593; https://doi.org/10.3390/s18113593 (registering DOI) - 23 October 2018
Abstract
The spectral narrowing of Fiber Bragg Gratings (FBGs) introduced by unpumped Er-doped fiber (EDF) was analyzed for fiber lasers (FL). Owing to spatial hole burning (SHB), the spectral response of a virtual FBG can be employed for narrowing the band pass filter employed
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The spectral narrowing of Fiber Bragg Gratings (FBGs) introduced by unpumped Er-doped fiber (EDF) was analyzed for fiber lasers (FL). Owing to spatial hole burning (SHB), the spectral response of a virtual FBG can be employed for narrowing the band pass filter employed to determine the lasing wavelength of laser cavities. A common FL was mounted to analyze the spectral stability of the method, and a FL sensor for strain and temperature measurements was experimentally characterized to determine the stability of the narrowing effect achieved by the unpumped EDF, which acts as a virtual FBG. The results exhibited remarkably good narrowing effects of the spectral response of uniform FBGs.
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Double Assurance of Epidural Space Detection Using Fiberoptics-Based Needle Design and Autofluorescence Technologies for Epidural Blockade in Painless Labor
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Sensors 2018, 18(11), 3592; https://doi.org/10.3390/s18113592 (registering DOI) - 23 October 2018
Abstract
Purpose: Technology of reflectance spectroscopy incorporated with auto-fluorescence spectroscopy were employed to increase the safety of epidural placement in regional anesthesia which is generally used for surgery, epidural anesthesia, post-operative pain control and painless childbirth. Method: Ex vivo study of auto-fluorescence spectroscopy was
[...] Read more.Purpose: Technology of reflectance spectroscopy incorporated with auto-fluorescence spectroscopy were employed to increase the safety of epidural placement in regional anesthesia which is generally used for surgery, epidural anesthesia, post-operative pain control and painless childbirth. Method: Ex vivo study of auto-fluorescence spectroscopy was performed for the para-vertebral tissues contained fat, interspinous ligament, supraspinous ligament and ligamentumflavum by multimode microplate reader at wavelength 405 nm for the purpose of tissue differentiation. A specially designed optic-fiber-embedded needle was employed to incorporate with both reflectance and autofluorescence spectroscopies in order to probe the epidural space as double assurance demands. In vivo study was carried out in a Chinese native swine weighted about 30 kg under intubated general anesthesia with ventilation support. The reflective (405 nm) and autofluorescence signals (λ and λ*) were recorded at 5 different sites by an oscilloscope during the needle puncture procedure from skin to epidural space in the back of the swine. Results: Study of either autofluorescence spectroscopy for tissue samples or ex vivo needle puncture in porcine trunk tissues indicates that ligmentumflavum has at least 10-fold higher fluorescence intensity than the other tissues. In the in vivo study, ligamentumflavum shows a double-peak character for both reflectance and autofluorescence signals. The epidural space is located right after the drop from the double-peak. Both peaks of reflectance and fluorescence are coincident which ensures that the epidural space is correctly detected. Conclusions: The fiber-optical technologies of double-assurance demands for tissue discrimination during epidural needle puncture can not only provide an objective visual information in a real-time fashion but also it can help the operator to achieve much higher success rate in this anesthesia procedure.
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A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing
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Sensors 2018, 18(11), 3591; https://doi.org/10.3390/s18113591 (registering DOI) - 23 October 2018
Abstract
The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The
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The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The cooling demand primarily relies on the accurate detection of occupancy. The current researches of demand-based HVAC control tend to detect the occupant count using cameras or other sensors, which often impose high computation and costs with limited real-life applications. Instead of detecting the occupant count, this paper proposes to detect the occupancy density. The occupancy density (estimated by image foreground moving pixels) together with the indoor and outdoor information (acquired from existing sensors) are used as inputs to an artificial neural network model for cooling demand estimation. Experiments have been implemented in a university design studio. Results show that, by adding the occupancy density, the cooling demand estimation error is greatly reduced by 67.4% and the R value is improved from 0.75 to 0.96. The proposed approach also features low-cost, computationally efficient, privacy-friendly and easily implementable. It shows good application potentials and can be readily incorporated into existing building management systems for improving energy efficiency.
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FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization
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Sensors 2018, 18(10), 3590; https://doi.org/10.3390/s18103590 (registering DOI) - 22 October 2018
Abstract
In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor
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In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.
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A New Disaster Information Sensing Mode: Using Multi-Robot System with Air Dispersal Mode
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Sensors 2018, 18(10), 3589; https://doi.org/10.3390/s18103589 (registering DOI) - 22 October 2018
Abstract
This paper presents a novel sensing mode for using mobile robots to collect disaster ground information when the ground traffic from the rescue center to disaster site is disrupted. Traditional sensing modes which use aerial robots or ground robots independently either have limited
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This paper presents a novel sensing mode for using mobile robots to collect disaster ground information when the ground traffic from the rescue center to disaster site is disrupted. Traditional sensing modes which use aerial robots or ground robots independently either have limited ability to access disaster site or are only able to provide a bird’s eye view of the disaster site. To illustrate the proposed sensing mode, the authors have developed a Multi-robot System with Air Dispersal Mode (MSADM) by combining the unimpeded path of aerial robots with the detailed view of ground robots. In the MSADM, an airplane carries some minimal reconnaissance ground robots to overcome the paralyzed traffic problem and deploys them on the ground to collect detailed scene information using parachutes and separation device modules. In addition, the airplane cruises in the sky and relays the control and reported information between the ground robots and the human operator. This means that the proposed sensing mode is able to provide more reliable communication performance when there are obstacles between the human operators and the ground robots. Additionally, the proposed sensing mode can easily make use of different kinds of ground robots, as long as they have a compatible interface with the separation device. Finally, an experimental demonstration of the MSADM is presented to show the effectiveness of the proposed sensing mode.
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Cooperative Sensing Data Collection and Distribution with Packet Collision Avoidance in Mobile Long-Thin Networks
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Sensors 2018, 18(10), 3588; https://doi.org/10.3390/s18103588 (registering DOI) - 22 October 2018
Abstract
Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is
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Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.
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Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network
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Sensors 2018, 18(10), 3587; https://doi.org/10.3390/s18103587 (registering DOI) - 22 October 2018
Abstract
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult
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In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.
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A Combined Ray Tracing Method for Improving the Precision of the USBL Positioning System in Smart Ocean
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Sensors 2018, 18(10), 3586; https://doi.org/10.3390/s18103586 (registering DOI) - 22 October 2018
Abstract
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error
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The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error due to ray tracing, a phase difference error of the USBL, and acoustic noise in the underwater communication. Most of these errors are related to the changes in the sound speed during its propagation through the ocean. Therefore, when using the USBL for underwater detection, it is necessary to correct the sound speed profile in the detection area and optimize the ray tracing. Taking into account the actual conditions, this paper aims at correcting the model of underwater sound speed propagation and improving the tracking method of sound lines when the marine environment in the shallow sea area changes. This paper proposes a combined ray tracing method that can adaptively determine whether to use the constant sound speed ray tracing method or the equal gradient ray tracing method. The theoretical analysis and simulation results show that the proposed method can effectively reduce the error of slant distance in USBL compared with the traditional acoustic tracking method and the constant sound speed ray tracing method. The proposed sound ray correction algorithm solves the contradiction between the number of iterations and the reduction of positioning error and has engineering application value.
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Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks
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Sensors 2018, 18(10), 3585; https://doi.org/10.3390/s18103585 (registering DOI) - 22 October 2018
Abstract
One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and
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One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.
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Survey on Prominent RFID Authentication Protocols for Passive Tags
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Sensors 2018, 18(10), 3584; https://doi.org/10.3390/s18103584 (registering DOI) - 22 October 2018
Abstract
Radio Frequency Identification (RFID) is one of the leading technologies in the Internet of Things (IoT) to create an efficient and reliable system to securely identify objects in many environments such as business, health, and manufacturing areas. Recent RFID authentication protocols have been
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Radio Frequency Identification (RFID) is one of the leading technologies in the Internet of Things (IoT) to create an efficient and reliable system to securely identify objects in many environments such as business, health, and manufacturing areas. Recent RFID authentication protocols have been proposed to satisfy the security features of RFID communication. In this article, we identify and review some of the most recent and enhanced authentication protocols that mainly focus on the authentication between a reader and a tag. However, the scope of this survey includes only passive tags protocols, due to the large scale of the RFID framework. We examined some of the recent RFID protocols in term of security requirements, computation, and attack resistance. We conclude that only five protocols resist all of the major attacks, while only one protocol satisfies all of the security requirements of the RFID system.
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A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
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Sensors 2018, 18(10), 3583; https://doi.org/10.3390/s18103583 (registering DOI) - 22 October 2018
Abstract
Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI
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Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.
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Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations
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Sensors 2018, 18(10), 3582; https://doi.org/10.3390/s18103582 (registering DOI) - 22 October 2018
Abstract
In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a
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In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.
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Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
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Sensors 2018, 18(10), 3581; https://doi.org/10.3390/s18103581 (registering DOI) - 22 October 2018
Abstract
In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve
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In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.
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A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems
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Sensors 2018, 18(10), 3580; https://doi.org/10.3390/s18103580 (registering DOI) - 22 October 2018
Abstract
In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems
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In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.
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