A novel and integrated architecture for identification and cancellation of noise from GSM signal

There are multiple reasons for the evolution as well as the presence of noise over transmitted GSM signal. In spite of various approaches towards noise cancellation techniques, there are less applicable techniques for controlling noise in acoustic GSM signal. Therefore, the proposed manuscript presents an integrated modelling which performs modelling of noise identification that could significantly assist in successful noise cancellation. The proposed system uses three different approach viz. i) stochastic based approach for noise modelling, ii) analytical-based approach where allocated power acts as one of the prominent factors of noise, and iii) wavelet-based approach for effective decomposition of GSM signal for assisting better noise cancellation technique followed by better retention of signal quality. Simulated in MATLAB, the study outcome shows that it offers a cost-effective implementation, A Practical Approach for Noise identification, and Effective Noise Cancellation with Signal quality retention. The proposed system offers approximately 24% of enhancement in noise reduction as compared to any existing digital filters with 1.6 seconds faster in processing speed

An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation

In this research an advance variable structure adaptive Multiple Sub-Filters (MSF) based algorithm for single channel Acoustic Echo Cancellation (AEC) is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE) and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers

In vivo MRI with Concurrent Excitation and Acquisition using Automated Active Analog Cancellation

Magnetic resonance imaging (MRI) provides excellent cross-sectional images of the soft tissues in patients. Unfortunately, MRI is intrinsically slow, it exposes patients to severe acoustic noise levels, and is limited in the visualization of certain tissues such as bone. These limitations are partly caused by the timing structure of the MRI exam which first generates the MR signal by a strong radio-frequency excitation and later acquires the weak MRI signal. Concurrent excitation and acquisition (CEA) can overcome these limitations, but is extremely challenging due to the huge intensity difference between transmit and receive signal (up to 100 dB). To suppress the strong transmit signals during signal reception, a fully automated analog cancellation unit was designed. On a 3 Tesla clinical MRI system we achieved an on-resonance analog isolation of 90 dB between the transmit and receive path, so that CEA images of the head and the extremities could be acquired with an acquisition efficiency of higher than 90% at sound pressure levels close to background noise. CEA with analog cancellation might provide new opportunities for MRI in tissues with very short T2 relaxation times, and it offers a silent and time-efficient MRI acquisition

Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems.    Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platfor

Interference cancellation and network coding for underwater communication systems

It is widely believed that wider access to the aquatic environment will enhance human knowledge and understanding of the world's oceans which constitute the major part of our planet. Hence, the current development of underwater sensing and communication systems will produce scientific, economic and social benefits. New applications will be enabled, such as deeper ocean observation, environmental monitoring, surveying or search and rescue missions. Underwater communications differ from terrestrial communications due to the unpredictable and complex ocean conditions, relying on acoustic waves which are affected by many factors like large propagation losses, long latency, limited bandwidth capacity and channel stability, posing great challenges for reliable data transport in this kind of networks. The aim of this project is to design a future underwater acoustic communication system for dense traffic situations investigating the possibility of Medium Access with Interference Cancellation and Network Coding. The main efforts focus on reliability, low energy consumption, storage capacity, throughput and scalabilit

Residual echo signal in critically sampled subband acoustic echo cancellers based on IIR and FIR filter banks

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