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		<title>Sensors: State-of-the-Art Sensors Technologies: State-of-the-Art Sensors Technology in The Netherlands</title>
		<link>http://www.mdpi.com/journal/sensors/special_issues/state-of-the-art-nl/</link>
		<description>Dear Colleagues,
The aim of this special issue is to provide a comprehensive view on the state-of-the-art sensors technology in The Netherlands. Research articles are solicited which will provide a consolidated state-of-the-art in this area. The Special Issue will publish those full research, review and high rated manuscripts addressing the above topic.
Dr. Monica  WachowiczGuest Editor{snippet name="submission_info"}</description>
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							<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/9/8504/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/9/8198/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/9/7991/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/9/7979/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/8/7561/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/8/7496/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/8/7421/" />
            				<rdf:li rdf:resource="http://www.mdpi.com/1424-8220/10/3/1599/" />
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	<item rdf:about="http://www.mdpi.com/1424-8220/10/9/8504/">
	<title>Sensors, Vol. 10, Pages 8504-8525: Sensor Networks in the Low Lands</title>
	<link>http://www.mdpi.com/1424-8220/10/9/8504/</link>
	<description>This paper provides an overview of scientific and industrial developments of the last decade in the area of sensor networks in The Netherlands (Low Lands). The goal is to highlight areas in which the Netherlands has made most contributions and is currently a dominant player in the field of sensor networks. On the one hand, motivations, addressed topics, and initiatives taken in this period are presented, while on the other hand, special emphasis is given to identifying current and future trends and formulating a vision for the coming five to ten years. The presented overview and trend analysis clearly show that Dutch research and industrial efforts, in line with recent worldwide developments in the field of sensor technology, present a clear shift from sensor node platforms, operating systems, communication, networking, and data management aspects of the sensor networks to reasoning/cognition, control, and actuation.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/9/8504/</guid>
	<pubDate>Fri, 10 Sep 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-09-10</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>9</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>8504</prism:startingPage>
		<prism:endingPage>8525</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>Sensor Networks in the Low Lands</dc:title>
	<dc:date>2010-09-10</dc:date>
	<dc:identifier>doi: 10.3390/s100908504</dc:identifier>
		<dc:creator>Nirvana Meratnia</dc:creator>
		<dc:creator>Berend Jan Van der Zwaag</dc:creator>
		<dc:creator>Hylke W. Van Dijk</dc:creator>
		<dc:creator>Dennis J.A. Bijwaard</dc:creator>
		<dc:creator>Paul J.M. Havinga</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/9/8198/">
	<title>Sensors, Vol. 10, Pages 8198-8214: Assessment of Relative Accuracy of AHN-2 Laser Scanning Data Using Planar Features</title>
	<link>http://www.mdpi.com/1424-8220/10/9/8198/</link>
	<description>AHN-2 is the second part of the Actueel Hoogtebestand Nederland project, which concerns the acquisition of high-resolution altimetry data over the entire Netherlands using airborne laser scanning. The accuracy assessment of laser altimetry data usually relies on comparing corresponding tie elements, often points or lines, in the overlapping strips. This paper proposes a new approach to strip adjustment and accuracy assessment of AHN-2 data by using planar features. In the proposed approach a transformation is estimated between two overlapping strips by minimizing the distances between points in one strip and their corresponding planes in the other. The planes and the corresponding points are extracted in an automated segmentation process. The point-to-plane distances are used as observables in an estimation model, whereby the parameters of a transformation between the two strips and their associated quality measures are estimated. We demonstrate the performance of the method for the accuracy assessment of the AHN-2 dataset over Zeeland province of The Netherlands. The results show vertical offsets of up to 4 cm between the overlapping strips, and horizontal offsets ranging from 2 cm to 34 cm.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/9/8198/</guid>
	<pubDate>Wed, 01 Sep 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-09-01</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>9</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>8198</prism:startingPage>
		<prism:endingPage>8214</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>Assessment of Relative Accuracy of AHN-2 Laser Scanning Data Using Planar Features</dc:title>
	<dc:date>2010-09-01</dc:date>
	<dc:identifier>doi: 10.3390/s100908198</dc:identifier>
		<dc:creator>Corné van der Sande</dc:creator>
		<dc:creator>Sylvie Soudarissanane</dc:creator>
		<dc:creator>Kourosh Khoshelham</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/9/7991/">
	<title>Sensors, Vol. 10, Pages 7991-8009: Sensors and Clinical Mastitis—The Quest for the Perfect Alert</title>
	<link>http://www.mdpi.com/1424-8220/10/9/7991/</link>
	<description>When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/9/7991/</guid>
	<pubDate>Fri, 27 Aug 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-08-27</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>9</prism:number>
	<prism:section>Review</prism:section>
	<prism:startingPage>7991</prism:startingPage>
		<prism:endingPage>8009</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>Sensors and Clinical Mastitis—The Quest for the Perfect Alert</dc:title>
	<dc:date>2010-08-27</dc:date>
	<dc:identifier>doi: 10.3390/s100907991</dc:identifier>
		<dc:creator>Henk Hogeveen</dc:creator>
		<dc:creator>Claudia Kamphuis</dc:creator>
		<dc:creator>Wilma Steeneveld</dc:creator>
		<dc:creator>Herman Mollenhorst</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/9/7979/">
	<title>Sensors, Vol. 10, Pages 7979-7990: A Fluorescent Thermometer Based on a Pyrene-Labeled Thermoresponsive Polymer</title>
	<link>http://www.mdpi.com/1424-8220/10/9/7979/</link>
	<description>Thermoresponsive polymers that undergo a solubility transition by variation of the temperature are important materials for the development of ‘smart’ materials. In this contribution we exploit the solubility phase transition of poly(methoxy diethylene glycol methacrylate), which is accompanied by a transition from hydrophilic to hydrophobic, for the development of a fluorescent thermometer. To translate the polymer phase transition into a fluorescent response, the polymer was functionalized with pyrene resulting in a change of the emission based on the microenvironment. This approach led to a soluble polymeric fluorescent thermometer with a temperature range from 11 °C to 21 °C. The polymer phase transition that occurs during sensing is studied in detail by dynamic  light scattering.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/9/7979/</guid>
	<pubDate>Fri, 27 Aug 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-08-27</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>9</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>7979</prism:startingPage>
		<prism:endingPage>7990</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>A Fluorescent Thermometer Based on a Pyrene-Labeled Thermoresponsive Polymer</dc:title>
	<dc:date>2010-08-27</dc:date>
	<dc:identifier>doi: 10.3390/s100907979</dc:identifier>
		<dc:creator>Christian Pietsch</dc:creator>
		<dc:creator>Antje Vollrath</dc:creator>
		<dc:creator>Richard Hoogenboom</dc:creator>
		<dc:creator>Ulrich S. Schubert</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/8/7561/">
	<title>Sensors, Vol. 10, Pages 7561-7575: Bayesian Model for Matching the Radiometric Measurements of Aerospace and Field Ocean Color Sensors</title>
	<link>http://www.mdpi.com/1424-8220/10/8/7561/</link>
	<description>A Bayesian model is developed to match aerospace ocean color observation tofield measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R2 &gt; 0.88 and relative error &lt; 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/8/7561/</guid>
	<pubDate>Wed, 11 Aug 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-08-11</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>8</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>7561</prism:startingPage>
		<prism:endingPage>7575</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>Bayesian Model for Matching the Radiometric Measurements of Aerospace and Field Ocean Color Sensors</dc:title>
	<dc:date>2010-08-11</dc:date>
	<dc:identifier>doi: 10.3390/s100807561</dc:identifier>
		<dc:creator>Mhd. Suhyb Salama</dc:creator>
		<dc:creator>Zhongbo Su</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/8/7496/">
	<title>Sensors, Vol. 10, Pages 7496-7513: T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data</title>
	<link>http://www.mdpi.com/1424-8220/10/8/7496/</link>
	<description>The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/8/7496/</guid>
	<pubDate>Tue, 10 Aug 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-08-10</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>8</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>7496</prism:startingPage>
		<prism:endingPage>7513</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data</dc:title>
	<dc:date>2010-08-10</dc:date>
	<dc:identifier>doi: 10.3390/s100807496</dc:identifier>
		<dc:creator>Albert Ali Salah</dc:creator>
		<dc:creator>Eric Pauwels</dc:creator>
		<dc:creator>Romain Tavenard</dc:creator>
		<dc:creator>Theo Gevers</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/8/7421/">
	<title>Sensors, Vol. 10, Pages 7421-7433: High Accuracy Acoustic Relative Humidity Measurement inDuct Flow with Air</title>
	<link>http://www.mdpi.com/1424-8220/10/8/7421/</link>
	<description>An acoustic relative humidity sensor for air-steam mixtures in duct flow is designed and tested. Theory, construction, calibration, considerations on dynamic response and results are presented. The measurement device is capable of measuring line averaged values of gas velocity, temperature and relative humidity (RH) instantaneously, by applying two ultrasonic transducers and an array of four temperature sensors. Measurement ranges are: gas velocity of 0–12 m/s with an error of ±0.13 m/s, temperature 0–100 °C with an error of ±0.07 °C and relative humidity 0–100% with accuracy better than 2 % RH above 50 °C. Main advantage over conventional humidity sensors is the high sensitivity at high RH at temperatures exceeding 50 °C, with accuracy increasing with increasing temperature. The sensors are non-intrusive and resist highly humid environments.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/8/7421/</guid>
	<pubDate>Mon, 09 Aug 2010 00:00:00 CEST</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-08-09</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>8</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>7421</prism:startingPage>
		<prism:endingPage>7433</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>High Accuracy Acoustic Relative Humidity Measurement inDuct Flow with Air</dc:title>
	<dc:date>2010-08-09</dc:date>
	<dc:identifier>doi: 10.3390/s100807421</dc:identifier>
		<dc:creator>Wilhelm van Schaik</dc:creator>
		<dc:creator>Mart Grooten</dc:creator>
		<dc:creator>Twan Wernaart</dc:creator>
		<dc:creator>Cees van der Geld</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
</item>
	<item rdf:about="http://www.mdpi.com/1424-8220/10/3/1599/">
	<title>Sensors, Vol. 10, Pages 1599-1618: Mobile Sensor Networks for Inspection Tasks in Harsh Industrial Environments</title>
	<link>http://www.mdpi.com/1424-8220/10/3/1599/</link>
	<description>Recent advances in sensor technology have enabled the fast development of mobile sensor networks operating in various unknown and sometimes hazardous environments. In this paper, we introduce one integrative approach to design, analyze and test distributed control algorithms to coordinate a network of autonomous mobile sensors by utilizing both simulation tools and a robotic testbed. The research has been carried out in the context of the mobile sensing project, PicoSmart, in the northern provinces of the Netherlands for the inspection of natural gas pipelines.</description>
	
	<guid>http://www.mdpi.com/1424-8220/10/3/1599/</guid>
	<pubDate>Mon, 01 Mar 2010 00:00:00 CET</pubDate>
	
	<prism:publicationName>Sensors</prism:publicationName>
	<prism:publicationDate>2010-03-01</prism:publicationDate>
	<prism:volume>10</prism:volume>
	<prism:number>3</prism:number>
	<prism:section>Article</prism:section>
	<prism:startingPage>1599</prism:startingPage>
		<prism:endingPage>1618</prism:endingPage>
		<prism:issn>1424-8220</prism:issn>
	
	<dc:title>Mobile Sensor Networks for Inspection Tasks in Harsh Industrial Environments</dc:title>
	<dc:date>2010-03-01</dc:date>
	<dc:identifier>doi: 10.3390/s100301599</dc:identifier>
		<dc:creator>Jacob Mulder</dc:creator>
		<dc:creator>Xinyu Wang</dc:creator>
		<dc:creator>Franke Ferwerda</dc:creator>
		<dc:creator>Ming Cao</dc:creator>
	
	<cc:license rdf:resource="http://creativecommons.org/licenses/by/3.0/" />
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