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Echo Cancellation
Published in Jerry D. Gibson, Mobile Communications Handbook, 2017
Digital signal processing techniques for echo cancellation provide large echo attenuation, and eliminate the need for additional line interfaces and digital-to-analog and analog-to-digital converters that are required by echo cancellation in the analog signal domain. In voiceband modems for data transmission over the telephone network, digital techniques for echo cancellation allow a precise tracking of the carrier phase and frequency shift of far-end echos.
Adaptive Filtering and Signal Analysis
Published in Maurice G. Bellanger, Adaptive Digital Filters, 2001
Echo cancellation or (more accurately) echo control, consists in modeling these unwanted couplings between local emitters and receivers and subtracting a synthetic echo from the real echo. Actually, it is a straight application of adaptive filtering concepts and algorithms. However, the problem may become extremely complex and challenging, depending on the environment, the operational constraints, and the user requirements.
Echo Cancellation
Published in Jerry D. Gibson, The Communications Handbook, 2018
Digital signal processing techniques for echo cancellation provide large echo attenuation and eliminate the need for additional line interfaces and digital-to-analog and analog-to-digital converters that are required by echo cancellation in the analog signal domain.
The design and implementation of folded adaptive lattice filter structures in FPGA for ECG signals
Published in Automatika, 2023
Kalamani C., Kamatchi S., Sasikala S., Murali L.
In the literature, enormous work has been proposed in adaptive filters for the removal of noise in ECG. Some contributions particularly focusing on the VLSI architectures of adaptive filters for high speed, low power consumption and low area have also been proposed. In ref. [3], a combination of Systolic and Folding architectures has been proposed for various adaptive filters such as Recursive Least Square (RLS), Affine Projection (AP) and Kalman filters. In the paper [4] the folding technique for FIR filter has been used for area reduction. Adaptive LMS filter and Folded Adaptive LMS filter have been implemented in Xilinx in [5]. To reduce area and power consumption folded architecture for non-canonical Least Mean Square adaptive digital filters used in echo cancellation were implemented in ref. [6]. The design of the adaptive lattice LMS filter and its implementation has been done, which has an increased area. From the literature, it has been analysed that adaptive lattice LMS filters with the low area folding technique can be designed.
Path-Finder Optimization Based Control of Grid-Tied PV Hybrid Energy Storage System
Published in IETE Journal of Research, 2021
Mukul Chankaya, Aijaz Ahmad, Ikhlaq Hussain
The BCNMCC based VSC control utilizes the biased compensated term for reducing the steady-state misalignment and mean square error (MSE) [19], as shown in Figure 5. The BCNMCC based VSC control provides better echo cancellation and system identification in a noisy input environment. Though the computational burden will increase but outperforms the MCC and LMS with reduced steady-state error and faster convergence rate. The BCNMCC based VSC control effectively manages the Gaussian noises in the input signal. The VSC control receives an accurate as a crucial element for maintaining system stability. The VSC generates precise weight signals of each phase as the component of load current .
Volterra and Wiener Model Based Temporally and Spatio-Temporally Coupled Nonlinear System Identification: A Synthesized Review
Published in IETE Technical Review, 2021
Saurav Gupta, Ajit Kumar Sahoo, Upendra Kumar Sahoo
With the use of inversion lemma [150, pg.571], the recursive update of can be expressed as where The RLS update expression can be given as Some of the important literature on adaptive methods of Volterra model and its applications are discussed as follows. Koh et al. in [151] presented three methods (minimum mean square error, iterative factorization and least mean square) to obtain the solution of second-order Volterra filtering. Article [152] presented the adaptive Volterra filters that utilizes the orthogonalization procedure for Gaussian signals to identify Volterra model. Also, an NLMS-based efficient lattice linear predictor is realized. Authors in article [153] presented nonlinear real-time speech coding using LMS- and RLS-based quadratic-Volterra filters. Other suitable applications of Volterra model are in active noise controller [154–156], nonlinear acoustic echo cancellation [157], and identification of parametric loudspeaker system [158]. Increased parameter complexity, a general limitation of Volterra model is overcome by efficient approximation of Volterra kernels with Laguerre functions [13, 159].