With wireless sensor network (WSN) wide applications in popularity, securing its data becomes a requirement. This can be accomplished by encrypting sensor node data. In this paper a new an efficient symmetric cryptographic algorithm is presented. This algorithm is called wireless sensor network wavelet curve ciphering system (WSN-WCCS).  The algorithm idea based on discrete wavelet transformation to generate keys for each node in WSN.  It implements on hierarchical clustering WSN using LEACH protocol. Python programming language version 2.7 was used to create the simulator of WSN framework and implement a WSN-WCCS algorithm. The simulation result of the proposed WSN-WCCS with other symmetric algorithms has show

The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

In petroleum reservoir engineering, history matching refers to the calibration process in which a reservoir simulation model is validated through matching simulation outputs with the measurement of observed data. A traditional history matching technique is performed manually by engineering in which the most uncertain observed parameters are changed until a satisfactory match is obtained between the generated model and historical information. This study focuses on step by step and trial and error history matching of the Mishrif reservoir to constrain the appropriate simulated model. Up to 1 January 2021, Buzurgan Oilfield, which has eighty-five producers and sixteen injectors and has been under production for 45 years when it started

        The secure data transmission over internet is achieved using Steganography. It is the art and science of concealing information in unremarkable cover media so as not to arouse an observer’s suspicion. In this paper the color cover image is divided into equally four parts, for each part select one channel from each part( Red, or Green, or  Blue), choosing one of these channel depending on the high color ratio in that part. The chosen part is decomposing into four parts {LL, HL, LH, HH} by using discrete wavelet transform. The hiding image is divided into four part n*n then apply DCT on each part. Finally the four DCT coefficient parts embedding in four high frequency sub-bands {HH} in

FG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database