Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging as it is exceedingly prone to high frequency noise and various other artifacts due to its low amplitude. In remote heath care systems, computer based high level understanding of ECG signals is performed using advanced machine learning algorithms. The accuracy of these algorithms relies on the Signal-to-Noise-Ratio (SNR) of the input ECG signal. In this paper, we analyse various methods for removing the high frequency noise components from the ECG signal and evaluate the performance of several adaptive filtering algorithms. The result suggest that the Normalized Least Mean Square (NLMS) algorithm achieves high SNR and Sign LMS is computationally efficient.
Late arrival of the passenger trains has become a routine matter making annoyed commutators switch to other means of transportation. Due to unscheduled timing in these trains, where either a train does not leave a station at fixed time or it stops at undesired station which wastes the time and money of passengers. This work is aimed to design real time system that is used to track monitor the speed, location unauthorized stops of train to update the status of train locations and timings to headquarter. We will establish methodology to interface arduino board, GSM/GPRS and GPS modules. GPS receiver module is interfaced with GSM/GPRS module to give data and provide exact information of train locations to railway headquarter station. PIR sensor is used to detect the movement of driver; if motion is not detected in each five minutes, then the system will send message to headquarter. This work can be provided as a premium service to passengers and provides a low-cost solution and has real-time capability, emerges by putting modern information technologies together and be able to form a real time accurate, effective comprehensive transportation system.
The study of plants is known as botany and for any botanist it is a daily routine work to examine various plants in their research lab. This research efforts an image processingbased algorithm for extracting the region of interest (ROI) from plant leaf in order to classify the specie and to recognize the particular botanical disease as well. Moreover, this paper addresses the implementation of curvelet transform on subdivided leaf images in order to compute the related information and train the support vector machine (SVM) classifier to execute better results. Furthermore, the paper presents a comparative analysis of existing and proposed algorithm for species and botanical diseases recognition over the dataset of leaves. The proposed multi-dimensional curvelet transform based algorithm provides relatively greater accuracy of 93.5% with leaves dataset.
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