Speech Signals have high range of variation in amplitudes and frequency. These acoustic signals with diverse properties are hard to recognize and filter if mixed with noise. To separate noise from original signal, the artifact peaks are separated from original signal and discarded. In this paper, the ICA method of signal denoising is used to differentiate the speech signal from periodic noise and Empirical Mode Decomposition method is proposed to generate the components of signal. The IMF(s) of signal is the non-linear descending order of frequency components that have been filtered for better SNR. Filtering with wiener filter has amended output but also results in loss of information. The selection of IMF(s) for signal regeneration when optimized using objective function of PSO, the information of original signal was dramatically preserved with suppressed noise. The system is tested on 4 example signals and proposed technique illustrates lower mean square error and higher SNR compared to wiener and ICA.

No abstract

Wireless Sensor Network (WSN) is used in multifarious applications like environment monitor, battle based systems, enemy vehicle track determination and many more. It is also limited by various constraints like cost, bandwidth, and energy consumption patterns along with network lifetime. When the data packets have to be sent to the destination node or control center after detection, the path is established between the detected node and the destination node [1]. When the number of paths is more and nodes repeatedly participate in those paths then residual energy value is also reduced of the specific nodes which lead to holes in the network and reduces the network lifetime. This paper presents an overview of WSN, Lifetime ratio effects, a numerical survey of the energy-efficient routing protocol. The methods namely Destination Sequence Distance Vector (DSDV), Ad hoc On-Demand Distance Vector (AODV), Zone Routing Protocol (ZRP) and Energy Efficient Distance Routing (EEDR) are discussed in detail along with the implementation of these methods in MATLAB. Comparison is performed in terms of various parameters namely delay, hops, energy consumption, alive nodes, dead nodes, lifetime ratio, overhead ratio, residual energy as well as throughput [10] and it is proved that EEDR algorithm works in an optimized fashion.

No abstract

No abstract