Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their timefrequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.
Internet of things (IoT) technology plays a vital role in the current technologies because IoT develops a network by integrating different kinds of objects and sensors to create the communication among objects directly without human interaction. With the presence of internet of things technology in our daily comes smart thinking and various advantages. At the same time, secure systems have been a most important concern for the protection of information systems and networks. However, adopting traditional security management systems in the internet of things leads several issues due to the limited privacy and policies like privacy standards, protocol stacks, and authentication rules. Usually, IoT devices has limited network capacities, storage, and computing processors. So they are having more chances to attacks. Data security, privacy, and reliability are three main challenges in the IoT security domain. To address the solutions for the above issues, IoT technology has to provide advanced privacy and policies in this large incoming data source. Blockchain is one of the trending technologies in the privacy management to provide the security. So this chapter is focused on the blockchain technologies which can be able to solve several IoT security issues. This review mainly focused on the state-of-the-art IoT security issues and vulnerabilities by existing review works in the IoT security domains. The taxonomy is presented about security issues in the view of communication, architecture, and applications. Also presented are the challenges of IoT security management systems. The main aim of this chapter is to describe the importance of blockchain technology in IoT security systems. Finally, it highlights the future directions of blockchain technology roles in IoT systems, which can be helpful for further improvements.
An Electrocardiogram is a significant recording for measuring the electrical activity of the heart. The Feature Extraction of an ECG signal plays a significant role in diagnosing most of the cardiac diseases. This paper concentrates on the algorithm for extraction of the fiducial points of an ECG signal and its performance analysis. To analyze these signals, time-frequency domain is the best suited method. In order to analyze these physiological and non-stationary signals, a Wavelet Transform in which Discrete Wavelet Transform is preferred to Fast Fourier and Short time -Fourier transform. A Discrete Wavelet Transform (DWT) is employed for the better understanding of the ECG signal in Time -Frequency plane with (Haar) as a Mother Wavelet function. The Samples are taken from the available ECG databases PTBDB & QTDB and are checkedagainst the CSE tolerance limits. Since, real time signals like ECG can be easily processed using a DSP processor, the algorithm is targeted onto a ADSP -2181 to reduce the complexity of the code and obtain optimized power levels.
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