Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Texture features for image classification and retrieval.

Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVM is used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest for the same application.

Fault Detection in Wireless Sensor Networks Through SVM Classifier

The implementation of a monitoring and control system for the induction motor based on programmable logic controller (PLC) technology is described. Also, the implementation of the hardware and software for speed control and protection with the results obtained from tests on induction motor performance is provided. The PLC correlates the operational parameters to the speed requested by the user and monitors the system during normal operation and under trip conditions. Tests of the induction motor system driven by inverter and controlled by PLC prove a higher accuracy in speed regulation as compared to a conventional V/f control system. The efficiency of PLC control is increased at high speeds up to 95% of the synchronous speed. Thus, PLC proves themselves as a very versatile and effective tool in industrial control of electric drives.

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Design and implementation of PLC-based monitoring control system for induction motor

Vehicular ad hoc networks are a special type of MANET providing vehicle to vehicle and vehicle to roadside wireless communications. Vehicular ad-hoc network (VANET) have been designed in order to assist drivers on the road with a variety of applications especially in preventing danger and saving lives. For such ends, broadcasting is a suitable scheme to convey emergency messages dissemination to the entire network. Broadcasting in VANETs is a challenging task due to the specific VANET features such as nodes mobility and frequent topology changes. VANET applications, especially those related with human life saving, are delay sensitive and have specific requirements in terms of performances and QoS. A QoS aware broadcasting scheme relies on different factors and has to deal with hard constraints. In this paper, we introduce a survey of broadcasting in vehicular networks and discussion of different performance and QoS related to broadcasting issues. Furthermore, a comparative study of QoS aware broadcasting protocols classifying them according to different taxonomies is elaborated. This survey specifies QoS requirements and performance metrics of VANET services. Furthermore, this survey focuses on QoS aware broadcasting as a challenging problem regrading VANET characteristics.

A survey and comparative study of QoS aware broadcasting techniques in VANET

This paper proposes the recognition and classification of three dominant patterns of woven fabrics such as twill, satin and plain. The proposed classifier is based on the texture analysis of woven fabric images for the recognition. In the pattern recognition phase, three methods are tested and compared: Gabor wavelet, local binary pattern operators and gray-level co-occurrence matrices (GLCM). Taking advantage of the differences between the woven fabric textures, we adopt a technique which is based on the texture of the images in the pattern recognition phase. For the classification phase we used a support vector machine, which we have proven is a suitable classifier for this type of problem. The experimental results show that some of the studied methods are more compatible with this classification problem than others. Although it is the oldest method, GLCM always remains accurate (97.2%). The fusion of the Gabor wavelet and GLCM gives the best result (98%), but GLCM have the better running time.

Automatic recognition of woven fabrics based on texture and using SVM

The authors main concern is to efficiently integrate video and audio devices alongside robots, programmable logic controllers (PLCs), numerical control (NC), manufacturing computers and process control systems. They present an experimental platform that implements their manufacturing communication system. This platform has been developed in the laboratory for the support of high bandwidth and real-time distributed applications. In this paper, they have chosen two challenging communication protocols to define their communication system. These protocols are the asynchronous transfer mode (ATM) and the manufacturing message specification (MMS) application layer protocols. They give the specification of an MMS-ATM communication system that guarantees high quality of services for real-time multimedia applications. This guarantees results from an ATM intelligent communication board and a reduced OSI stack.

Communication system for industrial automation

This paper proposes the classification of three mean woven textile fabrics. The classifier is based on the texture analysis of woven fabric images for the recognition.

Texture classification of woven fabric based on a GLCM method and using multiclass support vector machine

In the last decade, daily lifestyles have been invaded by mobile networks which added a huge exchange of information. This exchange among vehicular nodes as a novel paradigm in smart cities, rapidly became one of the most important topics for industrial investigations and academic research in the same time. This technology covers several domains which impacts the daily human life making emerging mobile social network services to be projected on vehicular networks. Moreover, and especially in big cities, vehicular ad hoc networks (VANET) as a form of intelligent transportation systems have to have a cooperative driver assistance. Such applications generally call out broadcasting techniques for data dissemination. Many approaches were introduced in literature in the aim of improving broadcast performance, one of these approaches is mobility prediction. Since mobility has various aspects such as node position, direction, velocity and also density many broadcast algorithms were founded on one or more different mobility parameter. Multi point relay (MPR) a traditional heuristic for broadcasting protocols, were extended based on kinetic aspect of the network to Kinetic multi point relay (KMPR) heuristic. In this paper we introduce a modification of KMPR main formula by setting a new more accurate coverage density parameter. Then we endorse the original algorithm by other constraints to manage more performance metrics. We finally illustrate that our contribution improves the forwarding process quality compared to MPR and KMPR by reducing the relay set size and duplicate information.

Salem Nasri

Papers

A survey and comparative study of QoS aware broadcasting techniques in VANET

Automatic recognition of woven fabrics based on texture and using SVM

Communication system for industrial automation

Texture classification of woven fabric based on a GLCM method and using multiclass support vector machine

Enhancing VANETs Broadcasting Performance with Mobility Prediction for Smart Road