Biblio

Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.

ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.

As a result of increasing the interest in developing the communication systems that use public channels for transmitting information, many channel problems are raised up. Among these problems, the important one should be addressed is the information security. This paper presents a proposed communication system with high security uses two encryption levels based on chaotic systems. The first level is chaotic scrambling, while the second one is chaotic masking. This configuration increases the information security since the key space becomes too large. The MATLAB simulation results showed that the Segmental Spectral Signal to Noise Ratio (SSSNR) of the first level (chaotic scrambling) is reduced by -5.195 dB comparing to time domain scrambling. Furthermore, in the second level (chaotic masking), the SSSNR is reduced by -20.679 dB. It is also showed that when the two levels are combined, the overall reduction obtained is -21.755 dB.

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-to-noise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.