In this paper we propose a rapidly converging LMS algorithm for crosstalk cancellation. The architecture is similar to deep neural networks, where multiple layers are adapted sequentially. The application motivating this approach is gigabit rate transmission over unshielded twisted pairs using a vectored system. The crosstalk cancellation algorithm uses an adaptive non-diagonal preprocessing ma...

The image compression in telecommunication is very demanding application to control the distortion in image in channel after transmitting the image. The Differential pulse code modulation (DPCM) may be used to remove the unused bit in the image for image compression. In this thesis we compare the compressed image for 1, 2, 3, bits (2, 4 and 8 quantization levels, respectively), estimation error...

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing -norm penalty...

Sparse channel estimation problem is one of challenge technical issues in stable broadband wireless communications. Based on square error criterion (SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting least mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS) algorithm, have been proposed to mitigate noise interferences as well as to exploit the inhere...

| Based on the least mean squares (LMS) algorithm , the LMS spectrum analyzer can be used to re-cursively calculate the discrete Fourier transform (DFT) of a sliding window of data. In this paper, we compare the LMS spectrum analyzer with the straightforward non-adaptive implementation of the recursive DFT. In particular , we demonstrate the robustness of the LMS spectrum analyzer to the propag...

We present an efficient architecture for the implementation of delayed least mean square adaptive filter. We use a novel partial product generator and propose a strategy for optimized balanced pipelining across the time consuming combinational blocks of the structure .An efficient systolic architecture of the delayed least mean square adaptive filter based on the processing element We propose a...

BACKGROUND A biomedical signal can be defined by its extrinsic features (x-axis and y-axis shift and scale) and intrinsic features (shape after normalization of extrinsic features). In this study, an LMS algorithm utilizing the method of differential steepest descent is developed, and is tested by normalization of extrinsic features in complex fractionated atrial electrograms (CFAE). METHOD E...

The voice communication between humans and machines is the idea people have been thinking about a long time. For high level of the voice communication with the control system it is important to ensure good quality of the speech signal processing with additive noise in real environments. This paper describes a proposed method for optimal adjustment parameters of the adaptive filter with an LMS a...

In this paper we designed a least mean square (LMS) adaptive filter to remove the unwanted noise which might occur during music recordings, echo in telephone networks, etc. Generally all LMS algorithm starts with an assumption of weight vector as zero initially and iteration continues till the error is minimized till its optimum level. This takes much more time to compute the optimized coeffici...

We present an efficient architecture for the implementation of delayed least mean square adaptive filter. We use a novel partial product generator and propose a strategy for optimized balanced pipelining across the time consuming combinational blocks of the structure .An efficient systolic architecture of the delayed least mean square adaptive filter based on the processing element .We propose ...