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Sensors, Volume 12, Issue 9 (September 2012) , Pages 11435-12869

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Open AccessArticle A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks
Sensors 2012, 12(9), 12844-12869; https://doi.org/10.3390/s120912844
Received: 25 July 2012 / Revised: 27 August 2012 / Accepted: 6 September 2012 / Published: 21 September 2012
Cited by 18 | Viewed by 4538 | PDF Full-text (1445 KB) | HTML Full-text | XML Full-text
Abstract
Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification [...] Read more.
Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient’s ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. Full article
(This article belongs to the Special Issue Body Sensor Networks for Healthcare and Pervasive Applications)
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Open AccessArticle Restoration of Reflection Spectra in a Serial FBG Sensor Array of a WDM/TDM Measurement System
Sensors 2012, 12(9), 12836-12843; https://doi.org/10.3390/s120912836
Received: 12 June 2012 / Revised: 12 September 2012 / Accepted: 13 September 2012 / Published: 20 September 2012
Cited by 5 | Viewed by 2607 | PDF Full-text (371 KB) | HTML Full-text | XML Full-text
Abstract
A restoration method for reflection spectra in a serial FBG sensor array with spectral shadowing is proposed and experimentally demonstrated in a WDM/TDM combined multiplexing system. The SNR of each FBG sensor is formulated and analyzed as a function of the number and [...] Read more.
A restoration method for reflection spectra in a serial FBG sensor array with spectral shadowing is proposed and experimentally demonstrated in a WDM/TDM combined multiplexing system. The SNR of each FBG sensor is formulated and analyzed as a function of the number and reflectivities of serial FBG sensors. The maximum number of FBG sensors in a single fiber line can be determined by the approximate formula. In the test using two FBG sensors, the restored reflection spectrum of second FBG sensor is shown to be very well matched with the original reflection spectrum. Using the proposed restoration method, the maximum peak detection error in a strain experiment is suppressed drastically by almost seven-fold, from 0.074 nm to 0.011 nm. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field
Sensors 2012, 12(9), 12814-12835; https://doi.org/10.3390/s120912814
Received: 25 June 2012 / Revised: 14 August 2012 / Accepted: 10 September 2012 / Published: 19 September 2012
Cited by 50 | Viewed by 4346 | PDF Full-text (1612 KB) | HTML Full-text | XML Full-text
Abstract
The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser [...] Read more.
The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Tree Height Growth Measurement with Single-Scan Airborne, Static Terrestrial and Mobile Laser Scanning
Sensors 2012, 12(9), 12798-12813; https://doi.org/10.3390/s120912798
Received: 16 July 2012 / Revised: 23 August 2012 / Accepted: 10 September 2012 / Published: 19 September 2012
Cited by 18 | Viewed by 3579 | PDF Full-text (804 KB) | HTML Full-text | XML Full-text
Abstract
This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can [...] Read more.
This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can the new technique characterize the height growth for each individual tree?” and “Can this technique refine the minimum growth-discernable temporal interval further?” To solve these two puzzles, the sampling principles of the three laser scanning modes were first examined, and their error sources against the task of tree-top capturing were also analyzed. Next, the three-year growths of 58 Nordic maple trees (Crimson King) for test were intermittently surveyed with one type of laser scanning each time and then analyzed by statistics. The evaluations show that the height growth of each individual tree still cannot be reliably characterized even by single-scan terrestrial laser scanning, and statistical analysis is necessary in this scenario. After Gaussian regression, it is found that the minimum temporal interval with distinguishable tree height growths can be refined into one month based on terrestrial laser scanning, far better than the two years deduced in the previous works based on airborne laser scanning. The associated mean growth was detected to be about 0.12 m. Moreover, the parameter of tree height generally under-estimated by airborne and even mobile laser scanning can be relatively revised by means of introducing static terrestrial laser scanning data. Overall, the effectiveness of the proposed technique is primarily validated. Full article
(This article belongs to the Special Issue Laser Sensing and Imaging)
Open AccessArticle A Behavior-Based Strategy for Single and Multi-Robot Autonomous Exploration
Sensors 2012, 12(9), 12772-12797; https://doi.org/10.3390/s120912772
Received: 13 July 2012 / Revised: 6 September 2012 / Accepted: 6 September 2012 / Published: 18 September 2012
Cited by 16 | Viewed by 4914 | PDF Full-text (1651 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data [...] Read more.
In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data structure to store previously visited regions. This allows robots to safely navigate, disperse and efficiently explore the environment. A series of experiments performed using a realistic robotic simulator and a real testbed scenario demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission in large open spaces, narrow cluttered environments, dead-end corridors, as well as rooms with minimum exits. Full article
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
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Open AccessReview Effect-Based Tools for Monitoring and Predicting the Ecotoxicological Effects of Chemicals in the Aquatic Environment
Sensors 2012, 12(9), 12741-12771; https://doi.org/10.3390/s120912741
Received: 31 July 2012 / Revised: 28 August 2012 / Accepted: 30 August 2012 / Published: 18 September 2012
Cited by 94 | Viewed by 6133 | PDF Full-text (886 KB) | HTML Full-text | XML Full-text
Abstract
Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water [...] Read more.
Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water quality standards; currently applied effect-based tools are presented based on whether exposure occurs in a controlled laboratory environment or in the field. With increasing ecological relevance the reproducibility, specificity and thus suitability for standardisation of methods tends to diminish. We discuss the use of biomarkers in ecotoxicology including ecotoxicogenomics-based endpoints, which are becoming increasingly important for the detection of sublethal effects. Carefully selected sets of biomarkers allow an assessment of exposure to and effects of toxic chemicals, as well as the health status of organisms and, when combined with chemical analysis, identification of toxicant(s). The promising concept of “adverse outcome pathways (AOP)” links mechanistic responses on the cellular level with whole organism, population, community and potentially ecosystem effects and services. For most toxic mechanisms, however, practical application of AOPs will require more information and the identification of key links between responses, as well as key indicators, at different levels of biological organization, ecosystem functioning and ecosystem services. Full article
(This article belongs to the Special Issue Bioassays)
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Open AccessArticle A One ppm NDIR Methane Gas Sensor with Single Frequency Filter Denoising Algorithm
Sensors 2012, 12(9), 12729-12740; https://doi.org/10.3390/s120912729
Received: 16 July 2012 / Revised: 10 September 2012 / Accepted: 11 September 2012 / Published: 18 September 2012
Cited by 20 | Viewed by 3608 | PDF Full-text (632 KB) | HTML Full-text | XML Full-text
Abstract
A non-dispersive infrared (NDIR) methane gas sensor prototype has achieved a minimum detection limit of 1 parts per million by volume (ppm). The central idea of the design of the sensor is to decrease the detection limit by increasing the signal to noise [...] Read more.
A non-dispersive infrared (NDIR) methane gas sensor prototype has achieved a minimum detection limit of 1 parts per million by volume (ppm). The central idea of the design of the sensor is to decrease the detection limit by increasing the signal to noise ratio (SNR) of the system. In order to decrease the noise level, a single frequency filter algorithm based on fast Fourier transform (FFT) is adopted for signal processing. Through simulation and experiment, it is found that the full width at half maximum (FWHM) of the filter narrows with the extension of sampling period and the increase of lamp modulation frequency, and at some optimum sampling period and modulation frequency, the filtered signal maintains a noise to signal ratio of below 1/10,000. The sensor prototype provides the key techniques for a hand-held methane detector that has a low cost and a high resolution. Such a detector may facilitate the detection of leakage of city natural gas pipelines buried underground, the monitoring of landfill gas, the monitoring of air quality and so on. Full article
(This article belongs to the Section Chemical Sensors)
Open AccessReview Validation Processes of Protein Biomarkers in Serum—A Cross Platform Comparison
Sensors 2012, 12(9), 12710-12728; https://doi.org/10.3390/s120912710
Received: 31 August 2012 / Revised: 6 September 2012 / Accepted: 10 September 2012 / Published: 18 September 2012
Cited by 26 | Viewed by 3600 | PDF Full-text (963 KB) | HTML Full-text | XML Full-text
Abstract
Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and [...] Read more.
Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and validation of serum biomarkers, as well as their implementation in diagnostic routine requires an application of independent immunoassay platforms with the possibility of high-throughput. This review will focus on three main multi-analyte immunoassay platforms: planar microarrays, multiplex bead systems and, array-based surface plasmon resonance (SPR) chips. Recent developments of each platform will be discussed for application in clinical proteomics, principles, detection methods, and performance strength. The requirements for specific surface functionalization of assay platforms are continuously increasing. The reasons for this increase is the demand for highly sensitive assays, as well as the reduction of non-specific adsorption from complex samples, and with it high signal-to-noise-ratios. To achieve this, different support materials were adapted to the immobilized biomarker/ligand, allowing a high binding capacity and immobilization efficiency. In the case of immunoassays, the immobilized ligands are proteins, antibodies or peptides, which exhibit a diversity of chemical properties (acidic/alkaline; hydrophobic/hydrophilic; secondary or tertiary structure/linear). Consequently it is more challenging to develop immobilization strategies necessary to ensure a homogenous covered surface and reliable assay in comparison to DNA immobilization. New developments concerning material support for each platform are discussed especially with regard to increase the immobilization efficiency and reducing the non-specific adsorption from complex samples like serum and cell lysates. Full article
(This article belongs to the Special Issue Biochips)
Open AccessArticle Optimal Filter Estimation for Lucas-Kanade Optical Flow
Sensors 2012, 12(9), 12694-12709; https://doi.org/10.3390/s120912694
Received: 7 June 2012 / Revised: 3 September 2012 / Accepted: 4 September 2012 / Published: 17 September 2012
Cited by 10 | Viewed by 4630 | PDF Full-text (479 KB) | HTML Full-text | XML Full-text
Abstract
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In [...] Read more.
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Design, Performance and Optimization for Multimodal Radar Operation
Sensors 2012, 12(9), 12673-12693; https://doi.org/10.3390/s120912673
Received: 19 July 2012 / Revised: 29 August 2012 / Accepted: 6 September 2012 / Published: 17 September 2012
Cited by 5 | Viewed by 2541 | PDF Full-text (1140 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the underlying methodology behind an adaptive multimodal radar sensor that is capable of progressively optimizing its range resolution depending upon the target scattering features. It consists of a test-bed that enables the generation of linear frequency modulated waveforms of various [...] Read more.
This paper describes the underlying methodology behind an adaptive multimodal radar sensor that is capable of progressively optimizing its range resolution depending upon the target scattering features. It consists of a test-bed that enables the generation of linear frequency modulated waveforms of various bandwidths. This paper discusses a theoretical approach to optimizing the bandwidth used by the multimodal radar. It also discusses the various experimental results obtained from measurement. The resolution predicted from theory agrees quite well with that obtained from experiments for different target arrangements. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle Multispectral Image Feature Points
Sensors 2012, 12(9), 12661-12672; https://doi.org/10.3390/s120912661
Received: 6 July 2012 / Revised: 24 August 2012 / Accepted: 30 August 2012 / Published: 17 September 2012
Cited by 64 | Viewed by 4238 | PDF Full-text (2197 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both [...] Read more.
This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle On the Optimization of Wireless Multimedia Sensor Networks: A Goal Programming Approach
Sensors 2012, 12(9), 12634-12660; https://doi.org/10.3390/s120912634
Received: 7 June 2012 / Revised: 3 September 2012 / Accepted: 6 September 2012 / Published: 17 September 2012
Cited by 7 | Viewed by 3022 | PDF Full-text (965 KB) | HTML Full-text | XML Full-text
Abstract
Network lifetime is a crucial concern for Wireless Multimedia Sensor Networks (WMSNs), particularly due to the energy constraints of their nodes and the significant bitrate required by multimedia applications. This paper deals with this issue, studying how to achieve the maximum network lifetime, [...] Read more.
Network lifetime is a crucial concern for Wireless Multimedia Sensor Networks (WMSNs), particularly due to the energy constraints of their nodes and the significant bitrate required by multimedia applications. This paper deals with this issue, studying how to achieve the maximum network lifetime, and simultaneously satisfying the best aggregate throughput for the multimedia services offered. To this end, we propose a planning model that results in a more accurate solution for an arbitrary network deployment than using the current optimization techniques applied both to WMSNs and traditional Wireless Sensor Networks (WSNs). Our model is based on multi-objective formulation and goal programming, which, to the best of our knowledge, have never been employed in the WSN field. This paper also proposes a load balancing algorithm which ensures a fair traffic load distribution per link during the network operation and matches the values returned by the mathematical planning model for the set lifetime and throughput. Simulation results are presented and further discussed to show the effectiveness of the numerical solutions. Finally, a test-bed deployed in a trial environment validates the theoretical contributions of this work. Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle Efficient Security Mechanisms for mHealth Applications Using Wireless Body Sensor Networks
Sensors 2012, 12(9), 12606-12633; https://doi.org/10.3390/s120912606
Received: 15 February 2012 / Revised: 27 February 2012 / Accepted: 12 March 2012 / Published: 17 September 2012
Cited by 31 | Viewed by 4083 | PDF Full-text (742 KB) | HTML Full-text | XML Full-text
Abstract
Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be [...] Read more.
Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be monitored from a distant location using tiny biomedical wireless sensors. Hence, it is highly essential to combine the ubiquitous computing with mobile health technology using wireless sensors and smart phones to monitor the well-being of chronic patients such as cardiac, Parkinson and epilepsy patients. Since physiological data of a patient are highly sensitive, maintaining its confidentiality is highly essential. Hence, security is a vital research issue in mobile health (mHealth) applications, especially if a patient has an embarrassing disease. In this paper a three tier security architecture for the mHealth application is proposed, in which light weight data confidentiality and authentication protocols are proposed to maintain the privacy of a patient. Moreover, considering the energy and hardware constraints of the wireless body sensors, low complexity data confidential and authentication schemes are designed. Performance evaluation of the proposed architecture shows that they can satisfy the energy and hardware limitations of the sensors and still can maintain the secure fabrics of the wireless body sensor networks. Besides, the proposed schemes can outperform in terms of energy consumption, memory usage and computation time over standard key establishment security scheme. Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone
Sensors 2012, 12(9), 12588-12605; https://doi.org/10.3390/s120912588
Received: 24 July 2012 / Revised: 10 September 2012 / Accepted: 13 September 2012 / Published: 17 September 2012
Cited by 43 | Viewed by 4534 | PDF Full-text (725 KB) | HTML Full-text | XML Full-text
Abstract
Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware [...] Read more.
Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware applications using smartphones, these applications are currently only able to recognize simple contexts using a single type of sensor. Therefore, in this work, we introduce a comprehensive approach for context aware applications that utilizes the multimodal sensors in smartphones. The proposed system is not only able to recognize different kinds of contexts with high accuracy, but it is also able to optimize the power consumption since power-hungry sensors can be activated or deactivated at appropriate times. Additionally, the system is able to recognize activities wherever the smartphone is on a human’s body, even when the user is using the phone to make a phone call, manipulate applications, play games, or listen to music. Furthermore, we also present a novel feature selection algorithm for the accelerometer classification module. The proposed feature selection algorithm helps select good features and eliminates bad features, thereby improving the overall accuracy of the accelerometer classifier. Experimental results show that the proposed system can classify eight activities with an accuracy of 92.43%. Full article
(This article belongs to the Special Issue Ubiquitous Sensing)
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Open AccessArticle Modular Architecture of a Non-Contact Pinch Actuation Micropump
Sensors 2012, 12(9), 12572-12587; https://doi.org/10.3390/s120912572
Received: 13 July 2012 / Revised: 18 August 2012 / Accepted: 28 August 2012 / Published: 13 September 2012
Cited by 16 | Viewed by 3544 | PDF Full-text (1252 KB) | HTML Full-text | XML Full-text
Abstract
This paper demonstrates a modular architecture of a non-contact actuation micropump setup. Rapid hot embossing prototyping was employed in micropump fabrication by using printed circuit board (PCB) as a mold material in polymer casting. Actuator-membrane gap separation was studied, with experimental investigation of [...] Read more.
This paper demonstrates a modular architecture of a non-contact actuation micropump setup. Rapid hot embossing prototyping was employed in micropump fabrication by using printed circuit board (PCB) as a mold material in polymer casting. Actuator-membrane gap separation was studied, with experimental investigation of three separation distances: 2.0 mm, 2.5 mm and 3.5 mm. To enhance the micropump performance, interaction surface area between plunger and membrane was modeled via finite element analysis (FEA). The micropump was evaluated against two frequency ranges, which comprised a low driving frequency range (0–5 Hz, with 0.5 Hz step increments) and a nominal frequency range (0–80 Hz, with 10 Hz per step increments). The low range frequency features a linear relationship of flow rate with the operating frequency function, while two magnitude peaks were captured in the flow rate and back pressure characteristic in the nominal frequency range. Repeatability and reliability tests conducted suggest the pump performed at a maximum flow rate of 5.78 mL/min at 65 Hz and a backpressure of 1.35 kPa at 60 Hz. Full article
(This article belongs to the Special Issue Microfluidic Devices)
Open AccessArticle Species Discrimination among Three Kinds of Puffer Fish Using an Electronic Nose Combined with Olfactory Sensory Evaluation
Sensors 2012, 12(9), 12562-12571; https://doi.org/10.3390/s120912562
Received: 11 May 2012 / Revised: 31 July 2012 / Accepted: 2 August 2012 / Published: 13 September 2012
Cited by 12 | Viewed by 3138 | PDF Full-text (367 KB) | HTML Full-text | XML Full-text
Abstract
Species discrimination among three kinds of puffer fish, Takifugu obscurus, Takifugu flavidus and Takifugu rubripes, was conducted using an electronic nose combined with olfactory sensory evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA) and [...] Read more.
Species discrimination among three kinds of puffer fish, Takifugu obscurus, Takifugu flavidus and Takifugu rubripes, was conducted using an electronic nose combined with olfactory sensory evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA) and discriminant factor analysis (DFA). The results showed the discriminant model by PCA method and DFA method. Using PCA and DFA, it was shown that the electronic nose was able to reasonably distinguish between each of the eleven puffer fish groups, with a discrimination index of 85. The olfactory sensory evaluation was undertaken in accordance to Sensory analysis—Methodology—Initiation and training of assessors in the detection and recognition of odors (BS ISO 5496-2006), and the results showed that the evaluation was able to identify puffer fish samples according to their species, geographical origin and age. Results from this analysis demonstrate that the E-nose can be used to complement the discrimination of odors by sensory evaluation from the three species of puffer fish studied here. Full article
Open AccessArticle Sunglint Detection for Unmanned and Automated Platforms
Sensors 2012, 12(9), 12545-12561; https://doi.org/10.3390/s120912545
Received: 14 July 2012 / Revised: 20 August 2012 / Accepted: 29 August 2012 / Published: 13 September 2012
Cited by 11 | Viewed by 4143 | PDF Full-text (1299 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture [...] Read more.
We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture sea surface and sky images of the investigated points. The quality control consists of meteorological flags, to mask dusk, dawn, precipitation and low light conditions, utilizing incoming solar irradiance (ES) spectra. Using 629 from a total of 3,121 spectral measurements that passed the test conditions of the meteorological flagging, a new sunglint flag was developed. To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented. Applying this algorithm, two sets of data, one with (having too much or detectable white pixels or sunglint) and one without sunglint (having least visible/detectable white pixel or sunglint), were derived. To identify the most effective sunglint flagging criteria we evaluated the spectral characteristics of these two data sets using water leaving radiance (LW) and remote sensing reflectance (RRS). Spectral conditions satisfying ‘mean LW (700–950 nm) < 2 mW∙m−2∙nm−1∙Sr−1’ or alternatively ‘minimum RRS (700–950 nm) < 0.010 Sr−1’, mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control. Full article
(This article belongs to the Section Remote Sensors)
Open AccessReview Fiber-Optical Sensors: Basics and Applications in Multiphase Reactors
Sensors 2012, 12(9), 12519-12544; https://doi.org/10.3390/s120912519
Received: 30 July 2012 / Revised: 7 September 2012 / Accepted: 7 September 2012 / Published: 13 September 2012
Cited by 18 | Viewed by 4572 | PDF Full-text (1067 KB) | HTML Full-text | XML Full-text
Abstract
This work presents a brief introduction on the basics of fiber-optical sensors and an overview focused on the applications to measurements in multiphase reactors. The most commonly principle utilized is laser back scattering, which is also the foundation for almost all current probes [...] Read more.
This work presents a brief introduction on the basics of fiber-optical sensors and an overview focused on the applications to measurements in multiphase reactors. The most commonly principle utilized is laser back scattering, which is also the foundation for almost all current probes used in multiphase reactors. The fiber-optical probe techniques in two-phase reactors are more developed than those in three-phase reactors. There are many studies on the measurement of gas holdup using fiber-optical probes in three-phase fluidized beds, but negative interference of particles on probe function was less studied. The interactions between solids and probe tips were less studied because glass beads etc. were always used as the solid phase. The vision probes may be the most promising for simultaneous measurements of gas dispersion and solids suspension in three-phase reactors. Thus, the following techniques of the fiber-optical probes in multiphase reactors should be developed further: (1) online measuring techniques under nearly industrial operating conditions; (2) corresponding signal data processing techniques; (3) joint application with other measuring techniques. Full article
(This article belongs to the Special Issue Optical Fiber Sensors 2012)
Open AccessArticle A SPR Aptasensor for Detection of Avian Influenza Virus H5N1
Sensors 2012, 12(9), 12506-12518; https://doi.org/10.3390/s120912506
Received: 30 July 2012 / Revised: 27 August 2012 / Accepted: 28 August 2012 / Published: 13 September 2012
Cited by 74 | Viewed by 4542 | PDF Full-text (387 KB) | HTML Full-text | XML Full-text
Abstract
Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable [...] Read more.
Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable affinity for target viruses and better thermal stability than monoclonal antibodies. The objective of this research was to use a DNA-aptamer as the specific recognition element in a portable Surface Plasmon Resonance (SPR) biosensor for rapid detection of AIV H5N1 in poultry swab samples. A SPR biosensor was fabricated using selected aptamers that were biotinylated and then immobilized on the sensor gold surface coated with streptavidin via streptavidin-biotin binding. The immobilized aptamers captured AIV H5N1 in a sample solution, which caused an increase in the refraction index (RI). After optimizing the streptavidin and aptamer parameters, the results showed that the RI value was linearly related (R2 = 0.99) to the concentration of AIV in the range of 0.128 to 1.28 HAU. Negligible signal ( < 4% of H5N1) was observed from six non-target AIV subtypes. The AIV H5N1 in poultry swab samples with concentrations of 0.128 to 12.8 HAU could be detected using this aptasensor in 1.5 h. Full article
(This article belongs to the Section Biosensors)
Open AccessArticle Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine
Sensors 2012, 12(9), 12489-12505; https://doi.org/10.3390/s120912489
Received: 16 July 2012 / Revised: 7 September 2012 / Accepted: 7 September 2012 / Published: 13 September 2012
Cited by 95 | Viewed by 9694 | PDF Full-text (712 KB) | HTML Full-text | XML Full-text
Abstract
Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate [...] Read more.
Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest. Full article
(This article belongs to the Special Issue Medical & Biological Imaging)
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Open AccessArticle Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring
Sensors 2012, 12(9), 12473-12488; https://doi.org/10.3390/s120912473
Received: 16 July 2012 / Revised: 5 September 2012 / Accepted: 10 September 2012 / Published: 13 September 2012
Cited by 17 | Viewed by 4147 | PDF Full-text (983 KB) | HTML Full-text | XML Full-text
Abstract
The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital [...] Read more.
The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users’ quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users’ daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN’s nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user. Full article
(This article belongs to the Special Issue Body Sensor Networks for Healthcare and Pervasive Applications)
Open AccessArticle Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices
Sensors 2012, 12(9), 12455-12472; https://doi.org/10.3390/s120912455
Received: 10 July 2012 / Revised: 10 September 2012 / Accepted: 11 September 2012 / Published: 13 September 2012
Cited by 12 | Viewed by 2913 | PDF Full-text (793 KB) | HTML Full-text | XML Full-text
Abstract
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on [...] Read more.
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO)-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates
Sensors 2012, 12(9), 12437-12454; https://doi.org/10.3390/s120912437
Received: 28 July 2012 / Revised: 27 August 2012 / Accepted: 29 August 2012 / Published: 12 September 2012
Cited by 11 | Viewed by 2568 | PDF Full-text (356 KB) | HTML Full-text | XML Full-text
Abstract
In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model [...] Read more.
In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models. Full article
(This article belongs to the Section Remote Sensors)
Open AccessArticle A Nonlinear Adaptive Beamforming Algorithm Based on Least Squares Support Vector Regression
Sensors 2012, 12(9), 12424-12436; https://doi.org/10.3390/s120912424
Received: 28 July 2012 / Revised: 21 August 2012 / Accepted: 30 August 2012 / Published: 12 September 2012
Cited by 8 | Viewed by 2735 | PDF Full-text (317 KB) | HTML Full-text | XML Full-text
Abstract
To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this [...] Read more.
To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this approach, the conventional linearly constrained minimum variance cost function used by minimum variance distortionless response (MVDR) beamformer is replaced by a squared-loss function to increase robustness in complex scenarios and provide additional control over the sidelobe level. Gaussian kernels are also used to obtain better generalization capacity. This novel approach has two highlights, one is a recursive regression procedure to estimate the weight vectors on real-time, the other is a sparse model with novelty criterion to reduce the final size of the beamformer. The analysis and simulation tests show that the proposed approach offers better noise suppression capability and achieve near optimal signal-to-interference-and-noise ratio (SINR) with a low computational burden, as compared to other recently proposed robust beamforming techniques. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision
Sensors 2012, 12(9), 12405-12423; https://doi.org/10.3390/s120912405
Received: 6 August 2012 / Revised: 28 August 2012 / Accepted: 30 August 2012 / Published: 12 September 2012
Cited by 44 | Viewed by 4176 | PDF Full-text (7118 KB) | HTML Full-text | XML Full-text
Abstract
Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this [...] Read more.
Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2012)
Open AccessArticle Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle
Sensors 2012, 12(9), 12386-12404; https://doi.org/10.3390/s120912386
Received: 31 July 2012 / Revised: 20 August 2012 / Accepted: 23 August 2012 / Published: 12 September 2012
Cited by 15 | Viewed by 3583 | PDF Full-text (2163 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic [...] Read more.
This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system. Full article
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
Open AccessArticle Spatial Frequency Multiplexing of Fiber-Optic Interferometric Refractive Index Sensors Based on Graded-Index Multimode Fibers
Sensors 2012, 12(9), 12377-12385; https://doi.org/10.3390/s120912377
Received: 30 July 2012 / Revised: 4 September 2012 / Accepted: 4 September 2012 / Published: 12 September 2012
Cited by 10 | Viewed by 3050 | PDF Full-text (773 KB) | HTML Full-text | XML Full-text
Abstract
Fiber-optic interferometric sensors based on graded-index multimode fibers have very high refractive-index sensitivity, as we previously demonstrated. In this paper, spatial-frequency multiplexing of this type of fiber-optic refractive index sensors is investigated. It is estimated that multiplexing of more than 10 such sensors [...] Read more.
Fiber-optic interferometric sensors based on graded-index multimode fibers have very high refractive-index sensitivity, as we previously demonstrated. In this paper, spatial-frequency multiplexing of this type of fiber-optic refractive index sensors is investigated. It is estimated that multiplexing of more than 10 such sensors is possible. In the multiplexing scheme, one of the sensors is used to investigate the refractive index and temperature responses. The fast Fourier transform (FFT) of the combined reflective spectra is analyzed. The intensity of the FFT spectra is linearly related with the refractive index and is not sensitive to the temperature. Full article
(This article belongs to the Section Physical Sensors)
Open AccessCorrection Correction: Darwish, A. and Hassanien, A.E. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring. Sensors 2011, 11, 5561-5595
Sensors 2012, 12(9), 12375-12376; https://doi.org/10.3390/s120912375
Received: 10 September 2012 / Accepted: 11 September 2012 / Published: 12 September 2012
Cited by 4 | Viewed by 2770 | PDF Full-text (237 KB) | HTML Full-text | XML Full-text
Abstract
A reference is missing in our paper [1]. Figure 2 was adapted from Reference [2] with permission. The figure is listed and described as below: [...] Full article
(This article belongs to the Section Sensor Networks)
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Open AccessCorrection Correction: Chen, S. et al. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors 2008, 8, 520-528
Sensors 2012, 12(9), 12374; https://doi.org/10.3390/s120912374
Received: 10 September 2012 / Accepted: 11 September 2012 / Published: 11 September 2012
Viewed by 2607 | PDF Full-text (113 KB) | HTML Full-text | XML Full-text
Abstract
There are two mistakes in this article [1]. On page 523, lines 10–11, the sentence “the value of the SAM is 1, but the value of the ESAM is less than 1” should be “the value of the SAM is 0, but the [...] Read more.
There are two mistakes in this article [1]. On page 523, lines 10–11, the sentence “the value of the SAM is 1, but the value of the ESAM is less than 1” should be “the value of the SAM is 0, but the value of the ESAM is great than 0”. Line 12, “but the value of the ESAM is less than 1” should be “but the value of the ESAM is great than 0 for even n”. [...] Full article
(This article belongs to the Section Remote Sensors)
Open AccessArticle Pt-TiO2/MWCNTs Hybrid Composites for Monitoring Low Hydrogen Concentrations in Air
Sensors 2012, 12(9), 12361-12373; https://doi.org/10.3390/s120912361
Received: 24 August 2012 / Revised: 5 September 2012 / Accepted: 7 September 2012 / Published: 10 September 2012
Cited by 26 | Viewed by 3316 | PDF Full-text (622 KB) | HTML Full-text | XML Full-text
Abstract
Hydrogen is a valuable fuel for the next energy scenario. Unfortunately, hydrogen is highly flammable at concentrations higher than 4% in air. This aspect makes the monitoring of H2 leaks an essential issue for safety reasons, especially in the transportation field. In [...] Read more.
Hydrogen is a valuable fuel for the next energy scenario. Unfortunately, hydrogen is highly flammable at concentrations higher than 4% in air. This aspect makes the monitoring of H2 leaks an essential issue for safety reasons, especially in the transportation field. In this paper, nanocomposites based on Pt-doped TiO2/multiwalled carbon nanotubes (MWCNTs) have been introduced as sensitive materials for H2 at low temperatures. Pt-TiO2/MWNTs nanocomposites with different composition have been prepared by a simple wet chemical procedure and their morphological, microstructural and electrical properties were investigated. Resistive thick-film devices have been fabricated printing the hybrid nanocomposites on alumina substrates provided with Pt interdigitated electrodes. Electrical tests in air have shown that embedding MWCNTs in the TiO2 matrix modify markedly the electrical conductivity, providing a means to decrease the resistance of the sensing layer. Pt acts as a catalytic additive. Pt-TiO2/MWNTs-based sensors were found to be sensitive to hydrogen at concentrations between 0.5 and 3% in air, satisfying the requisites for practical applications in hydrogen leak detection devices. Full article
(This article belongs to the Special Issue Organics and Metal Oxide Hybrid Sensors)
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