In this paper, a compression system with high synthetic architect is introduced, it is based on wavelet transform, polynomial representation and quadtree coding. The bio-orthogonal (tap 9/7) wavelet transform is used to decompose the image signal, and 2D polynomial representation is utilized to prune the existing high scale variation of image signal. Quantization with quadtree coding are followed by shift coding are applied to compress the detail band and the residue part of approximation subband. The test results indicate that the introduced system is simple and fast and it leads to better compression gain in comparison with the case of using first order polynomial approximation.

     Data transmission in public communication system is not safe since of interception and improper manipulation by attacker. So, the attractive solution for these problems is to design high secure system that reduce the ability of attacker from getting sensitive information such as (account ID, passwords, etc.). The best way is combine two high secure techniques: steganography technique, which is the method of hiding any secret information like data, password and image behind any cover file and cryptography, which is convert the data to unreadable data. This paper suggests a crypto-stego authentication method to provide a highly secured authentication. The proposed method is utilized audio steganography and AES Cryp

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

Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp

This investigation deals with the use of orange peel (OP) waste as adsorbent for removal of nitrate (NO₃) from simulated wastewater. Orange peel prepared in two conditions dried at 60C° (OPD) and burning at 500 °C (OPB). The effect of pH: 2-10, contact time: 30- 180 min, sorbent weight: 0.5- 3.0 g were considered. The optimal pH value for NO₃ adsorption was found to be 2.0 for both adsorbents. The equilibrium data were analyzed using Langmuir and  Freundlich isotherm models. Freundlich model was found to fit the equilibrium data very well with high-correlation coefficient (R²). The adsorption kinetics was found to follow pseudo-second-order rate kinetic model, with a good correlation (R²

     This study observed the formation of struvite crystals in wastewater using natural zeolite activated with Mg²⁺ ions. Mg²⁺ ions released from natural zeolite would react with PO₄^3- and NH₄⁺ ions from in wastewater to form struvite crystals. The results showed that at pH 8.5, the removal of PO₄^3- and NH₄⁺ ions was more effective using the modified zeolite than the natural zeolite. Adding 40 g/L Zeo-Mg (1) produced the best results, with PO4^3- (93.32%) and NH₄ (40%) adsorption. Meanwhile, 40g/L Zeo-Mg (2) adsorbed 81% PO₄^3- ions and 27.12% NH₄⁺ ions. The eqil

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

Abstract

In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.

A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between

     Improving the performance of visual computing systems is achieved by removing unwanted reflections from a picture captured in front of a glass. Reflection and transmission layers are superimposed in a linear form at the reflected photographs. Decomposing an image into these layers is often a difficult task. Plentiful classical separation methods are available in the literature which either works on a single image or requires multiple images. The major step in reflection removal is the detection of reflection and background edges. Separation of the background and reflection layers is depended on edge categorization results. In this paper a wavelet transform is used as a prior estimation of background edges to sepa

This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.