Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Wearable Health Monitoring Smart Gloves
For high data rate communication, Orthogonal Frequency Division Multiplexing technique is most effective & it is widely used for various wireless communication for example Digital Video Broadcasting(DVB), Digital Audio Broadcasting (DAB), WiMAX etc. High Peak to Average Power ratio of an OFDM signal is the main drawback of this parallel transmission scheme. Several methods are available to reduce high PAPR of OFDM signal like Clipping, Tone Reservation, Tone Injection, Companding, Partial Transmit Sequence etc. Among these methods PTS is the most powerful method to reduce PAPR of OFDM. In this paper we describe the performance of partial transmit sequence technique applied to the OFDM signal to reduce PAPR, & shows the BER performance improvement after PAPR reduction .This paper also shows that the computational complexity is a drawback of PTS.
The heart is one of the key organs of human bodies and each component of heart sounds reflects important information about the cardiac status. The wavelet shrinkage denosing method can effectively reduce the noise of non-stationary signal but preserve the local regularity and the generalization threshold function is build .We present a method for heart sound segmentation based on the signal’s simplicity and strength .The method has two phases the first phase identifies the timing of s1 and s2 sound limit using simplicity and strength in the wavelet domain. The second phase discripainates among the s1 and s2 using high frequency information. The pre-processing before calculating energy of PCG signal by wavelet ,segment the PCG signal, find different parameters for segmentation .This paper presents an analysis of haar wavelet segmentation of heart sound segmentation techniques and suggested performance measures.
In thermal power plant, the use of boiler unit which produces steam helps to rotate the turbine to produce electricity. The boiler unit which consists of boiler drum converts feedwater into steam. The three types of boiler drum system are single element boiler drum, two element boiler drum and three element boiler drum. In thermal power plant the three element boiler drum is used. In the boiler drum, during the conversion of feedwater into steam, the level should be maintained and controlled properly. For controlling purpose the PID controller is used and the tuning of controller parameter controller is done manually. In the proposed method PID controller is replaced by adaptive controller where the tuning of controller will be made automatically. Thus the performance of the boiler drum level control system will be improved by three element method.
Pre-processing of Speech Signal serves various purposes in any speech processing application. It includes Noise Removal, Endpoint Detection, Pre-emphasis, Framing, Windowing, Echo Canceling etc. Out of these, silence portion removal along with endpoint detection is the fundamental step for applications like Speech and Speaker Recognition. The proposed method uses multi-layerperceptronalong with hierarchal mixturemodel for classification of voiced part of a speech from silence/unvoiced part. The work shows better end point detection as well as silence removal.The study is based on timing-based traffic analysis attacks thatcan be used to reconstruct the communication on end-to-end voip systems by taking advantage of the reduction or suppression of the generation of traffic whenever the sender detects a voice inactivity. Removal of the silence in between the inter packet time in order to obtain a clear communication. The proposed attacks can detect speakers of encrypted speech communications with high probabilities. With the help of different codecs we are going to detect speakers of speech communications In comparison with traditional traffic analysis attacks, the proposed traffic analysis attacks do not require simultaneous accesses to one traffic flow of interest at both sides.
So for we have seen many modulation techniques in which sine wave (carrier)is used to modulate digital data .In this paper digital data are modulated using wavelets (carrier). Initially only one mother wavelet is created and later scaled version of mother wavelet is derived for each bits in digital data. The scaled wavelets is used to perform digital modulation. The Performance of proposed technique is compared with conventional modulation schemes in terms of bit error rate (BER) and power efficiency (PE).