Reza Abrishambaf

Path loss exponent analysis in Wireless Sensor Networks: Experimental evaluation

Wireless Sensor Networks are an emerging technology which has been recently adopted in many applications. Due to its wireless nature, the analysis of the radio propagation models plays an important role for performance evaluation in both theoretical and practical aspects. In this regards, path loss exponent is one of the most important parameter which has been considered widely in wireless communications analysis. There are several theoretical evaluations of path loss exponent for wireless sensor networks available in the literature. However there is a lack of experimental evaluation of both path loss exponent and the effect of shadowing. In this paper, three environments (free space, in building and industrial), where wireless sensor nodes are widely deployed, have been chosen in order to evaluate the experimental analysis. Path loss and path loss exponent are measured by means of Received Signal Strength Indicator (RSSI) and based on them, the standard deviation of shadowing effect is also calculated. All the measured parameters are compared with the theoretical analysis available in the literatures.

Multimodal fusion of the finger vein, fingerprint and the finger-knuckle-print using Kernel Fisher analysis

We proposed the fusion of the finger vein, fingerprint and the finger-knuckle-print.We used the fusion of these three biometric modalities in the feature level and also the decision level.The proposed approach contains three phases: enhancement, feature extraction and classification.We used the feature selection for enhance the feature fusion level by reducing the space and also enhancing the recognition performances. Unimodal biometric have improved the possibility to establish systems capable of identifying and managing the flow of individuals according to the available intrinsic characteristics that we have. However, a reliable recognition system requires multiple resources. This is the main objective of the multimodal systems that consists of using different resources. Although multimodality improves the accuracy of the systems, it occupies a large memory space and consumes more execution time considering the collected information from different resources. Therefore we have considered the feature selection, that is, the selection of the best attributes that enhances the accuracy and reduce the memory space as a solution. As a result, acceptable recognition performances with less forge and steal can be guaranteed. In this paper we propose an identification system using multimodal fusion of finger-knuckle-print, fingerprint and fingers venous network by adopting several techniques in different levels for multimodal fusion. A feature level fusion and decision level is proposed for the fusion of these three biological traits. An optimization method for this multimodal fusion system by enhancing the feature level fusion is introduced. The optimization consists of the space reduction by using different methods.

Energy analysis of routing protocols in wireless sensor networks for industrial applications

Wireless sensor network technology is rapidly becoming a feasible solution for monitoring and control applications at the lowest level of manufacturing automation systems. Sensor nodes are energy constrained and therefore designing the communication network should be in such a way that the electronic circuitry consumes minimum energy from the node resource and hence increase the network life time. However, the design of routing protocol of wireless sensor networks is highly application specific and hence the communication protocol should be developed according to the environmental factors of the system. In this paper, an energy analysis of routing protocols is presented and it is shown that for industrial applications such as in a die casting factory, developing routing techniques is highly dependent on the system factors such as the transmission frequency, the number of nodes, the distances between the machines and the message length. The final routing protocol can be determined based on the developed analysis and simulation techniques.

A fully CNN based fingerprint recognition system

In this paper, a fully cellular neural networks (CNN) based fingerprint recognition system is introduced. The system includes a preprocessing phase where the input fingerprint image is enhanced and a recognition phase where the enhanced fingerprint image is matched with the fingerprints in the database. Both preprocessing and recognition phases are realized by means of CNN approaches. A novel application of skeletonization method is used to perform ridgeline thinning which improves the quality of the extracted lines for further processing, and hence increases the overall system performance.

Comparison of wireless sensor network and radio frequency identification for the process control of distributed industrial systems

In today’s demanding market, flexibility and reconfigurability play an important role in manufacturing industries. This can be achieved by employing distributed control system such as International Electrotechnical Committee 61499 function block, which is a new standard adopted for industrial-process measurement and control system. On the other hand, wireless systems have been adopted recently in manufacturing systems as a replacement for conventional wired systems. This includes the wireless sensor network and radio frequency identification, which are emerging technologies adopted recently in a wide range of applications, especially in industry sectors. This article presents and compares the application of wireless sensor network and radio frequency identification in the process control of industrial system on the basis of International Electrotechnical Committee 61499 standard. Two process control architectures are considered for a case of an assembly line in order to compare and measure performance by employing Petri net as a mathematical modelling tool.

Integration of Wireless Sensor Networks into the distributed intelligent manufacturing within the framework of IEC 61499 function blocks

Wireless Sensor Networks (WSNs) is an emerging technology holding a great potential for distributed manufacturing automation. The WSN is not only a replacement of the conventional wired sensors, but is a technology that enhances flexible, distributed sensing, and collaborative information processing, which are vital elements for the distributed intelligent manufacturing control paradigm. Currently, the IEC 61499 function blocks have become an industry standard in distributed control for manufacturing automation. This standard has been empowered with the Multi-Agent Systems in a framework for added intelligence in manufacturing automation. In this paper, we explore the integration of the emerging WSN technology into this framework in order to enhance the flexibility and re-configurability through the wireless sensing and distributed information processing provided by a network of wireless sensors. We propose a three tiered model for integrating the WSN into the function blocks for realization of the distributed intelligent manufacturing control system.

Smart and wearable wireless sensors: Scenario analysis and communication issues

Wireless Sensor Network is an emerging technology that is nowadays adopted in a wide range of applications. In this context, with the recent advancement in the microelectronics, communication and computation, smart and wearable wireless sensors are increasingly getting popular due to the low-cost and easy deployment. In this paper, a review of the main applications of the smart and wearable sensors such as: health, sport and fitness, security, and rescue teams will be given. The aim is to identify the design challenges and issues specifically in the communication domain. The main goal is to provide an analysis of the scenarios followed by performance comparison and available network technologies in order to identify the problems, limitations and classify opportunities for future investigations.

Distributed Control Architecture for Wireless Sensor Networks Using IEC 61499 Function Blocks for Industrial Automation

Wireless Sensor Networks (WSN) technology is rapidly becoming a feasible solution for monitoring and control at the lowest level of manufacturing automation systems. This paper presents a distributed approach for modeling WSN for applications of intelligent manufacturing automation and control. The proposed approach uses IEC 61499 function blocks, which is a new industry standard in the context of manufacturing automation. The distributed modeling of WSN through the function blocks provides flexibility and scalability to the low-level monitoring and control of the manufacturing systems, which are two important challenges in todays conventional manufacturing control systems. The proposed model is a replacement of the conventional centralized control architecture, where local processing is performed rather than having a central control unit.

Finger vein recognition using Gabor filter and Support Vector Machine

Nowadays biometric identification systems are widely spread since the safety of those systems has been proven. They exhibit a large number of advantages when compared to other identification systems such as key and password that are subject to falsification and loss. Among biometric systems, finger vein recognition based on venous network has been considered recently in the literatures. This paper aims to present a finger vein recognition system using Support Vector Machine (SVM) based on a supervised training algorithm. The proposed system is divided in several phases, each performing a specific task. Two pre-processing schemes are employed in order to assess the efficiency in terms of recognition rate. Simulation results show that using Gabor filters in preprocessing for codifying the venous network and SVM for the classification can improve the recognition rate when compared to the existing methods.

Survey and Taxonomy of Transmissions Power Control Mechanisms for Wireless Body Area Networks

Wireless body area networks (WBANs) comprise several sensor nodes equipped with a short range radio-frequency transceiver, implanted and/or attached to the human body. Their low form-factor and localization imply less resources available and limited energy capabilities. Therefore, energy efficiency of WBANs is a critical issue since, in many cases, batteries cannot be replaced or recharged, requiring mechanisms to extend their life-time. The aim of transmission power control (TPC) mechanisms, which can be combined with other energy-saving mechanisms, is to reduce the energy consumption, external interferences, and specific-observation-rate in wireless communications by dynamically adjusting the transmission power output of data transmission, with the minimum effect on other performance aspects, such as reliability and latency. This paper describes and analyzes the TPCs developed as a result of a synergy created by two mechanisms, a link quality estimator and the transmission power level control. Design choices, in terms of the solutions devised to each mechanism, as well as, the strategy adopted to combine them in a TPC, are highlighted. A comparison between the newest and the most relevant research works in this area is provided as a guideline to future research. A taxonomy to classify the different TPC mechanisms developed is proposed.