Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd preventing chronic diseases a vital investment to read.

Preventing Chronic Diseases: A Vital Investment

Control Systems Engineering

A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

Book review: Applied cryptography: Protocols, algorithms, and source code in C

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

https://cyberleninka.org/article/n/1305079.pdf

Horse Riding & Hiding in Image for Data Guarding

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

Performance comparison of artificial neural network models for daily rainfall prediction

In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.

http://koreascience.or.kr:80/article/JAKO201315463253971.pdf

Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks

In this work, a novel and adaptive visible/invisible watermarking scheme for embedding and extracting a digital watermark into/from an image is proposed. The proposed method uses an adaptive procedure for calculating scaling factor or scaling strength using sigmoid function in Hadamard transform domain. The value of scaling factor is governed by a control parameter. The control parameter can be adjusted to make the watermarking scheme as either visible or invisible. The proposed methodology facilitates in preserving ownership rights and prevents the piracy of digital data which are considered to be the basic needs of digital watermarking. As proposed watermarking process is carried out in Hadamard transform domain, it is more robust to image/signal processing attacks. The experimental results and performance analysis confirm the efficiency of the proposed scheme.

Hadamard transform based adaptive visible/invisible watermarking scheme for digital images

Steganography is used to hide a secret message within a cover image, thereby yielding a stego image such that even the trace of the presence of secret information is wiped out. The purpose of steganography is to maintain secret communication between two users. Steganography has several technical challenges namely high hiding capacity and imperceptibility. In this paper we propose a modern steganographic technique with Integer Wavelet transform (IWT) and double key to achieve high hiding capacity, high security and good visual quality. Here cover image is converted in to wavelet transform co-efficients and the coefficients are selected randomly by using Key-1 for embedding the data. Key-2 is used to calculate the number of bits to be embedded in the randomly selected coefficients. Finally the Optimum Pixel Adjustment Process (OPAP) is applied to the stego image to reduce the data embedding error.

Pachiyappan Arulmozhivarman

Papers

Horse Riding & Hiding in Image for Data Guarding

Performance comparison of artificial neural network models for daily rainfall prediction

Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks

Hadamard transform based adaptive visible/invisible watermarking scheme for digital images

Wave (let) decide choosy pixel embedding for stego