Phonocardiograms (PCG) Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the cardiac system. This makes PCG an effective method for tracking the progress of heart diseases. A PCG signal, in the healthy case, consists of two fundamental sounds s1 and s2. These two elements are derived from the mechanical functioning of the heart. A triple rhythm in diastole is called a gallop and results from the presence of a heart sound s3, s4 or both. An Extra Heart Sound  (EHS) may not be a sign of disease. However, in some situations it is an important sign of disease, which, if detected early, could save lives. The major aim of this study is to propose cyclostationary and Gabor kernel based mathematical model for extra heart sounds. The ambition behind it is to present a framework, making use of cyclic statistics for robustness to low SNR conditions, which allow the detection of EHS s3 and s4 and hence the early identification of some heart diseases. For this reason, the proposed model is compared with the one of normal PCG signal [17] in order to set up the differences allowing the early detection of EHS. Lastly, this research is proved on experimental data sets.

In this work, an autonomous technique of power gating is introduced at coarse level in Field Programmable Gate Array (FPGA) architecture to minimize leakage power. One of the major disadvantages of FPGA is the unnecessary power dissipation associated with the unused logic/inactive blocks. These inactive blocks in a FPGA are automatically cut-off from the power supply in this approach, based on a CLB priority algorithm. Our method focuses on introducing gating into both the logic blocks and routing resources of an FPGA at the same time, contrary to previous approaches. The proposed technique divides the FPGA fabric into clusters of CLBs and associated routing resources and introduces power gating separately for each cluster during runtime. The FPGA prototype has been developed in Cadence virtuoso spectrum at 45 nm technology and the layout of the proposed power gated FPGA is developed also. Simulation has been carried out for a ‘4 CLB’ prototype and results in a maximum of 55 % power reduction. The area overhead is 1.85 % for the ‘4 CLB’ FPGA prototype and tends to reduce with the increase in number of CLBs. The area overhead of a ‘128 CLB’ FPGA prototype is only 0.058 %, considering 4 sleep transistors. As an extension to the proposed gating in ‘4 CLB’ prototype, two techniques for an ‘8 CLB’ prototype are also evaluated in this paper, each having its own advantages. Due to the wake up time associated with power gated blocks, delay tends to increase. The wake-up time however, reduces with the increase in sleep transistor width.

An electrocardiogram (ECG) machine is a device that checks the patient’s heart rhythm and electrical activity. This is done by attaching sensors on the skin of the patients. But the problem with these machines is that, these are expensive and not portable. Thus it is difficult to use these machines in the rural or remote areas of developing countries like Bangladesh where the issue of portability and cost arises. In this paper, the problem of cost and portability is addressed. We propose a complete solution for a low-cost portable ECG monitoring from recording to report generation for patients including real-time ECG traces on screen with storage options and calculation of all necessary diagnostics parameters for helping the doctors to make decision. This type of ECG machines could be used in hospitals, homes, villages or even in a disaster area. The system designed in this paper includes a PC/Laptop, as these devices are now widely available, at least available at hospitals and health care centers, even in rural/remote areas of Bangladesh. With this, a significant difference can be made against heart diseases. 

This paper seeks to evaluate the appropriateness of various univariate forecasting techniques for providing accurate and statistically significant forecasts for manufacturing industries using natural gas. The term "univariate time series" refers to a time series that consists of single observation recorded sequentially over an equal time interval. A forecasting technique to predict natural gas requirement is an important aspect of an organization that uses natural gas in form of input fuel as it will help to predict future consumption of organization.We report the results from the seven most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the Naive method. Naïve method, Drift method, Simple Exponential Smoothing (SES), Holt method, ETS(Error, trend, seasonal) method, ARIMA,  and Neural Network (NN) have been studied and compared.Forecasting accuracy measures used for performance checking are MSE, RMSE,  and MAPE. Comparison of forecasting performance shows that ARIMA model gives a better performance. 

Electroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to movements of eye, heart, muscles and power line interference. Owing to eye movement, Ocular Activity creates significant artifacts and makes the analysis difficult.  In this paper, a new threshold is presented for correction of Ocular Artifacts (OA) from EEG signal using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Unlike the conventional EMD based EEG denoising techniques, which neglects the higher order low-frequency Intrinsic Mode Functions (IMFs), IMF Interval thresholding is opted to correct the artifacts. Obtained the noisy IMFs based on MI scores and perform interval thresholding to the noisy IMFs gives a relatively cleaner EEG signal. Extensive computations are carried out using EEG Motor Movement/Imagery (eegmmidb) dataset and compare the performance of Proposed Threshold (PT) with current threshold functions i.e., Universal Threshold (UT), Minimax Threshold (MT) and Statistical Threshold (ST) using several standard performance metrics: change in SNR (ΔSNR), Artifact Rejection Ratio (ARR), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). Results of these studies reveal that CEEMD+PT is efficient to correct OAs in EEG signals and maintaining the background neural activity in non-artifact zones.

This In the present study, a new type of ship is designed based on the theory of magnetic levitation (Maglev). The structural layout of the proposed ship is modified to adopt the maglev technology. The objective of designing this ship is to assist merchandise and travelers over a long distance in a short period of time.  The design and size of this ship is made in such a manner that it has got airplanes landing and take-off facility. According to the theory of magnetic levitation, it travels with negligible friction (ideally frictionless) on a magnetic guideway with high speed and expected safety. An electro-dynamic suspension (EDS) system for magnetic levitation has been used and it would reduce friction on a large scale and generate strong force for both lifting and propulsion purposes and it would also allow the very high speed of travel. High-temperature superconductor (HTS) wires with cryogenic structure are utilized as a part of the configuration of a couple of segments of the maglev ship. The attraction and repulsion force between superconductor magnets and permanent electromagnets are the key factors behind such operation. Computer vision study helps to track the moving object to find whether the object is moving or not. The proposed method has been found to give the acceptable result to identify the moving object.

Wavelet Transform (WT) has widely been used in signal processing. WT breaks a signal into its wavelets that are scaled and shifted versions of given signal. Thus wavelets are able represent graphical objects. The irregular shape and compact support of wavelets made them ideal for analyzing non-stationary signals. They are useful in analysis in both temporal and frequency domains. In contract, the Fourier transform provides information in frequency domain lacking in information in time domain. Thus wavelets became popular for signal processing and image processing applications. Nevertheless, wavelets suffer from a drawback as they cannot effectively represent images at different angles and different scales. To overcome this problem, of late, Curvelet Transform (CT) came into existence. CT is nothing but the higher dimensional generalization of WT which can effectively represent images at different angles and different scales. In this paper we proposed a CT method that is used to represent textures and classify them. The methodology used in this paper has an underlying approach that exploits statistical features of curvelets that resulted in curvelet decomposition. We built a prototype application using MATLAB to demonstrate proof of the concept.