Driven by the rising demand for underwater operations in the fields of dam structure monitoring, ecosystems of reservoir lakes from Hydropower Plants (HPP) and mining and oil, underwater robotics is increasing rapidly. The increase in exploration, prospecting, monitoring and security in lakes, rivers and sea, both in commercial applications such as scientific applications, has led large companies and research centers to invest in the development of underwater vehicles. The purpose of this work is to develop and evaluate the performance of a dedicated expert system for an Autonomous Underwater Vehicle (AUV) to inspect hydroelectric dams, focusing efforts on mechatronic project based on dimensioning structural elements and machinery and elaborating the sensory part, which includes navigation sensors and sensors of environment conditions, as well as its vision system to detect and measure cracks on hydroelectric dams. The integration of sensors in an intelligent platform provides a satisfactory control of the vehicle, allowing the movement of the submarine on the three spatial axes. Because of the satisfactory fast response of the sensors, it is possible to determine the acceleration and inclination besides his attitude in relation to the trajectory instantaneously taken, and geometry and depth of the cracks. This vehicle will be able to monitor the physical integrity of dams, making acquisition and storage of environment parameter such as temperature, dissolved oxygen, pH and conductivity as well as document images of the biota from reservoir lakes HPP, with minimized cost, high availability and low dependence on a skilled workforce to operate it.
To improve the effectiveness and QoS of service in a sensor network there are number of communication and localization architectures followed by sensor network. One of such architecture is Leader Selection Architecture. This architecture restrict the communication to short distances so that the energy consumption of a node is reduces. In this paper, an improved approach is defined to perform the selection of Leader. This leader selection architecture approach is defined under multiple parameters including the energy, connectivity analysis and the balancing over the network. The improvement is also performed to generate the safe communication over the network controlled by the leader node. The obtained results show that the work has improved the network communication as well as network life.
Direction of arrival (DOA) estimation is one of the focal problems in the fields of Wireless communications, Radar, Sonar, Radio Astronomy and Seismology. The main objective of the DOA estimation is to obtain the desired signals direction as well as the interference signals direction based on the data received from the array sensor at the base station. In literature various techniques are researched for DOA estimation. Among which are two high resolution algorithms, viz. MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique, MUSIC algorithm is implemented in this paper with Empirical Mode Decomposition technique. The key feature of EMD is to decompose a signal into sum of intrinsic mode functions (IMF) with a final residue of non linear and non stationary signals. The work presented in this paper deals with EMD application to the problem of DOA estimation as a preprocessing technique. This technique separately de-noises the rows of the array data matrix where each row corresponds to the output of a particular sensor array. Especially in low-SNR conditions, the estimation performance of MUSIC algorithm is enhanced significantly when de-noising is given to array data matrix prior to DOA estimation stage.
Existing security methods rely on knowledge based on approaches like password or token based on approaches like access cards. Such method is not very secure, biometrics such as fingerprint, face and voice offer means of personal identification and provide increased security because they rely on who we are. In this paper, algorithm fingerprint identification is introduced. The proposed algorithm has used 196 fingerprint image back to the twenty-eight individual 140 from them has been used for training and 56 image has been used for testing .Discrete Cosine Transform has been used to extract distinctive features from fingerprint image and genetic algorithm has been used as feature selection technique .Genetic algorithm has helped to produce GA filter in order to select subset of features out of DCT. When testing the proposed system by using Probabilistic Neural Network has found the identification rate reaching to 91%. This rate has emboldened on attempted using more one filter of genetic algorithm , the result that reached to 98% as identification rate with more reduction in number features.
computing provides end users with computing resources based on virtualization technologies at the data center. This allowed us to optimize data centers utilization by using techniques and algorithms that optimize the use of cloud computing resources. By taking advantage of some useful proprieties of routing algorithm proposed model is presented in the field of cloud computing that makes data centers more flexible and scalable. Our experimental results indicate that proposed model increases utilization of data centers resources and reduce waiting time. Keywordscomputing; Queuing models; Routing algorithms.
ne of the important techniques in digital image processing is to enhance images. Contrast enhancement is a method that is used to enhance images for viewing process or for further analysis of images. Main idea behind contrast enhancement techniques is to increase contrast and to preserve original brightness of images. In this paper a contrast enhancement technique is proposed that first segments histogram of image recursively and then applies Adaptive Gamma Correction with Weighting Distribution (AGCWD) Technique. The proposed technique is basically an improvement over AGCWD technique and aims to get better contrast enhancement and brightness preservation than AGCWD technique.