Special Issue "Neural Networks and Sensors"

Guest Editor
Dr. Michael W. Retsky
Harvard Medical School, Childrens Hospital, Karp Family Research Labs 12, 300 Longwood Ave., Boston, MA 02115, USA
Website: http://hms.harvard.edu/WhitePagesPublic.asp?task=showperson&id=EVQyYGBFPzc=&a=hms&r=1&kw=
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Related Special Issue

Neural Networks and Sensors to be published in Algorithms.

Submission

Sensors is a highly rated journal with a 1.573 impact factor in 2007. Sensors is indexed and abstracted very quickly by Chemical Abstracts, Analytical Abstracts, Science Citation Index Expanded, Chemistry Citation Index, Scopus and Google Scholar.

All papers should be submitted to sensors@mdpi.org with copy to the guest editors. To be published continuously until the deadline and papers will be listed together at the special websites.

Please visit the Instructions for Authors page before submitting a paper. Open Access publication fees are 1050 CHF per paper. English correction fees (250 CHF) will be added in certain cases (1300 CHF per paper for those papers that require extensive additional formatting and/or English corrections.).

neural networks, sensors, sensing

Submitted Papers

Title: Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System
Authors: Raúl Vicen-Bueno *, Rubén Carrasco-Álvarez, Manuel Rosa-Zurera and José Carlos Nieto-Borge
Signal Theory and Communications Department, Superior Politechnic School, University of Alcalá, Alcalá de Henares, 28805, Madrid, Spain
Abstract: The presence of sea clutter in marine radar signals is sometimes not desired. So, efficient radar signal processing techniques are needed to reduce it. In this way, nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system. The developed experiments show promising results characterized by different subjective (visual analysis of the processed radar images) and objective (clutter reduction, target enhancement and signal-to-clutter ratio improvement) criteria. Moreover, a deep study of the NN structure is done, where the low computational cost and the high processing speed of the proposed NN structure are emphasized.
Keywords: Neural Networks; Non-linear Signal Procesing; Radar; Remote Sensing; Clutter Reduction; Target Enhancement; SCR Improvement

Title: Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Network
Authors: Amir Jabbari *, Reiner Jedermann, Ramanan Muthuraman and Walter Lang
Department of Electrical Engineering, Institute of Micro sensors, Actuators and Systems (IMSAS), University of Bremen, NW1 Building, D-28359 Bremen, Germany
Abstract: In this paper, a new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems.
Keywords: Radial basis function; back propagation; wireless sensor network; distributed Data approximation and classification

Title: Artificial Neural Network Based-on Smart Pixel Colors Identification
Authors: Mohamed Lamine Hafiane ^1,*,Zohir Dibi ¹ and Otto Manck ^1
1 Laboratoire d’Electronique Avancée, Département d’Electronique, Université de Batna, 05 avenue Chahid Boukhlouf 05000 Batna, Algeria. E-Mail:zohirdibi@yahoo.fr
² Institut für Technische Informatik und Mikroelektronik, Technische Universität Berlin, Germany
* Author to whom correspondence should be addressed: hafiane_lamine@yahoo.fr
Abstract: An intelligent sensor for colors identification, suitable for visible range optical applications, has been developed. Using buried triple photo junctions as basic pixel sensing element in combination with artificial neural network (ANN) , the pixel color wavelength readout can be achieved with a full-scale error less than 1.5 % over the rang of 400 to 780nm. Through this work, the applicability of ANNs approach in optical sensing is investigated and compared with conventional methods, thus a good compromise between accuracy and the possibility for on-chip implementation was founded. Indeed, this technique can serve different purposes and may replace conventional methods.
Keywords: buried photo PN junctions, image sensor, Artificial Neural Network, wavelength measurement

Planned Papers

Title: Vehicle Signal Analysis using Artificial Neural Networks for Bridge Weigh in Motion System
Authors: Jungwhee Lee (Researcher, Research Institute of Industrial Science and Technology, KOREA); Sungkon Kim (Corresponding author) (Professor, Seoul National University of Technology, KOREA); Minseok Park (Researcher, Korea Highway Corporation, KOREA)
Abstract: This paper describes the development procedures of signal analysis algorithm using artificial neural networks for Bridge Weigh-in-Motion (BWIM) systems. Through the analysis procedure, the extraction of information on heavy traffic such as weight, speed, and number of axles from the time domain strain data of the BWIM system was attempted. As one of the several pattern recognition techniques, Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interaction. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for crosschecking and validation of the performance of developed algorithm. The extracted traffic information can be utilized in developing a quantitative database of the loading effect. Likewise, it can contribute to the estimation of fatigue life or current health condition. The design truck can also be revised based on the database reflecting recent traffic trends.