DETERMINATION OF RF SOURCE POWER IN WPSN USING MODULATED BACKSCATTERING

DETERMINATION OF RF SOURCE POWER IN WPSN USING MODULATED BACKSCATTERING 

K.Sreedhar1 and Prof.Y.Sreenivasulu2 

1Department of Electronics and Communication Engineering, VITS (N9) Karimnagar, Andhra Pradesh, India sreedhar_kallem@yahoo.com 2Head of the Department of Electronics and Communication Engineering, VITS (N9) Karimnagar, Andhra Pradesh, India yerraboina@yahoo.com 

ABSTRACT 

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. During RF transmission energy consumed by critically energy-constrained sensor nodes in a WSN is related to the life time system, but the life time of the system is inversely proportional to the energy consumed by sensor nodes. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints. In this we are going to investigate the system analytically. To obtain interference-free communication connectivity with the WPSN nodes number of RF sources is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics. The results of this paper reveal that communication coverage and RF Source Power can be practically maintained in WPSN through careful selection of design parameters 

KEYWORDS 

WPSN (wireless passive sensor net works), MB (Modulated Back Scattering), Interference free communication, Communication Coverage, RF Sources (K).  

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/2112ijsc04.pdf

http://airccse.org/journal/ijsc/current2012.html

Bayesian system reliability and availability analysis underthe vague environment based on Exponential distribution

Bayesian system reliability and availability analysis underthe vague environment based on Exponential distribution 

RaminGholizadeh 1 , Aliakbar M. Shirazi2 and Maghdoode Hadian3 1Young Researchers Club, Ayatollah Amoli Branch, Islamic Azad University, Amol , Iran. 2 Islamic Azad University, Ayatollah Amoli Branch, Mathematic Department,Iran. 3Young Researchers Club, Mashhad Branch, Islamic Azad University, Mashhad,Iran Islamic Azad University, Iran 

Abstract 

Reliability modeling is the most important discipline of reliable engineering. The main purpose of this paper is to provide a methodology for discussing the vague environment. Actually we discuss on Bayesian system reliability and availability analysis on the vague environment based on Exponential distribution under squared error symmetric and precautionary asymmetric loss functions. In order to apply the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to create the vague Bayes estimate of system reliability and availability by introducing and applying a theorem called “Resolution Identity” for vague sets. For this purpose, the original problem is transformed into a nonlinear programming problem which is then divided up into eight subproblems to simplify computations. Finally, the results obtained for the subproblems can be used to determine the membership functions of the vague Bayes estimate of system reliability. Finally, the sub problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO. 

Keywords: 

Bayes point estimators, Vague environment, Nonlinear programming; System reliability, System availability, Exponential distribution, Precautionary loss function. 

Open source URL 

http://airccse.org/journal/ijsc/papers/2112ijsc03.pdf

http://airccse.org/journal/ijsc/current2012.html

Submission Link 

http://coneco2009.com/submissions/imagination/home.html

Enhancement of Improved Balanced LEACH for Heterogeneous Wireless Sensor Networks

Enhancement of Improved Balanced LEACH for Heterogeneous Wireless Sensor Networks 

Yogesh Kumar1 and Kanwaljit Singh2 

1 ECE Department, Guru Nanak Dev Engg. College, Ludhiana kumar_yogesh1087@rediffmail.com 2Associate Professor ECE Department, Guru Nanak Dev Engg. College, Ludhiana, India kjitsingh@gndec.ac.in 

Abstract 

Wireless sensor networks consists of thousands of tiny, low cost, low power and multifunctional sensor nodes where each sensor node has very low battery life. Purpose is to conserve the transmitted energy from various sensor nodes. Various energy efficient algorithms have been designed for this. LEACH uses distributed cluster formation & randomized rotation of the cluster head to minimize the network energy consumption. Our paper is proposing an algorithm which is the enhancement of existing IB-LEACH. It reduces the energy consumption by using energy bank. This energy bank stores the energy after each round in both routing and clustering phase which overall increases the life time of the network. In this approach, ACTIVE_ROUTE_TIMEOUT is also enhanced by shamming the static parameters of HELLO_INTERVAL, RREQ_RETRIES and NET_DIAMETER. Results are compared through MATLAB and provide better approach than previous ones.

Keywords 

Enhanced, IB-LEACH, Clustering, AODV protocol, Simulation

Original Source URL - http://airccse.org/journal/acij/papers/3512acij04.pdf

http://airccse.org/journal/acij/vol3.html

Texture Segmentation Based on Multifractal Dimension

Texture Segmentation Based on Multifractal Dimension 

AzmiTawfik Alrawi1 , Ali makki Sagheer2 and Dheyaa Ahmed Ibrahim3 

1Department of physics, University of Anbar, Ramadi, Iraq  2Department of Information System, University of Anbar,Ramadi,Iraq  3Department of Computer Science, University of Anbar, Ramadi, Iraq 

ABSTRACT 

Texture segmentation can be considered the most important problem, since human can distinguish different textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract vector feature for each block to classification these block based on these feature. These feature extract using Box Counting Method (BCM). BCM generate single feature for each block and this feature not enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for the image based on new method produce multithresolding, after this use BCM to generate single feature for each slide.

KEYWORDS

 Texture Segmentation; MultifractalDiension; Fractal Dimension; Box Counting 

Original Source URL : http://airccse.org/journal/ijsc/papers/2112ijsc01.pdf