82 research outputs found
Low-complexity RLS algorithms using dichotomous coordinate descent iterations
In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N-2) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented
Acceleration of parasitic multistatic radar system using GPGPU
This dissertation details the implementation of PMR [Parasitic Multistatic Radar] signal processing chain in the GPGPU [General Purpose Graphic Processing Units] platform. The primary objective of the project is to accelerate the signal processing chain without compromising the algorithm efficiency and to prove that GPGPUs are a promising platform for parasitic radar signal processing
IMPLEMENTATION OF NOISE CANCELLATION WITH HARDWARE DESCRIPTION LANGUAGE
The objective of this project is to implement noise cancellation technique on an FPGA
using Hardware Description Language. The performance of several adaptive algorithms is
compared to determine the desirable algorithm used for adaptive noise cancellation
system. The project will focus on the implementation of adaptive filter with least-meansquares
(LMS) algorithm or normalized least-mean-squares (NLMS) algorithm to cancel
acoustic noises. This noise consists of extraneous or unwanted waveforms that can
interfere with communication. Due to the simplicity and effectiveness of adaptive noise
cancellation technique, it is used to remove the noise component from the desired signal.
The project is divided into four main parts: research, Matlab simulation, ModelSim
simulation and hardware implementation. The project starts with research on several noise
cancellation techniques, and then with Matlab code, Simulink and FDA tool, the adaptive
noise cancellation system is designed with the implementation of the LMS algorithm,
NLMS algorithm and recursive-least-square algorithm to remove the interference noise.
By using the Matlab code and Simulink, the noise that interfered with a sinusoidal signal
and a record of music can be removed. The original signal in turns can be retrieved from
the noise corrupted signal by changing the coefficient of the filter. Since filter is the
important component in adaptive filtering process, the filter is designed first before adding
adaptive algorithm. A Finite Impulse Response (FIR) filter is designed and the desired
result of functional simulation and timing simulation is obtained through ModelSim and
Integrated Software Environment (ISE) software and FPGA implementation. Finally the
adaptive algorithm is added to the filter, and implemented in the FPGA. The noise is
greatly reduced in Matlab simulation, functional simulation and timing simulation. Hence
the results of this project show that noise cancellation with adaptive filter is feasible
Design of a reusable distributed arithmetic filter and its application to the affine projection algorithm
Digital signal processing (DSP) is widely used in many applications spanning the spectrum from audio processing to image and video processing to radar and sonar processing. At the core of digital signal processing applications is the digital filter which are implemented in two ways, using either finite impulse response (FIR) filters or infinite impulse response (IIR) filters. The primary difference between FIR and IIR is that for FIR filters, the output is dependent only on the inputs, while for IIR filters the output is dependent on the inputs and the previous outputs. FIR filters also do not sur from stability issues stemming from the feedback of the output to the input that aect IIR filters.
In this thesis, an architecture for FIR filtering based on distributed arithmetic is presented. The proposed architecture has the ability to implement large FIR filters using minimal hardware and at the same time is able to complete the FIR filtering operation in minimal amount of time and delay when compared to typical FIR filter implementations. The proposed architecture is then used to implement the fast affine projection adaptive algorithm, an algorithm that is typically used with large filter sizes. The fast affine projection algorithm has a high computational burden that limits the throughput, which in turn restricts the number of applications. However, using the proposed FIR filtering architecture, the limitations on throughput are removed. The implementation of the fast affine projection adaptive algorithm using distributed arithmetic is unique to this thesis. The constructed adaptive filter shares all the benefits of the proposed FIR filter: low hardware requirements, high speed, and minimal delay.Ph.D.Committee Chair: Anderson, Dr. David V.; Committee Member: Hasler, Dr. Paul E.; Committee Member: Mooney, Dr. Vincent J.; Committee Member: Taylor, Dr. David G.; Committee Member: Vuduc, Dr. Richar
IMPLEMENTATION OF NOISE CANCELLATION WITH HARDWARE DESCRIPTION LANGUAGE
The objective of this project is to implement noise cancellation technique on an FPGA
using Hardware Description Language. The performance of several adaptive algorithms is
compared to determine the desirable algorithm used for adaptive noise cancellation
system. The project will focus on the implementation of adaptive filter with least-meansquares
(LMS) algorithm or normalized least-mean-squares (NLMS) algorithm to cancel
acoustic noises. This noise consists of extraneous or unwanted waveforms that can
interfere with communication. Due to the simplicity and effectiveness of adaptive noise
cancellation technique, it is used to remove the noise component from the desired signal.
The project is divided into four main parts: research, Matlab simulation, ModelSim
simulation and hardware implementation. The project starts with research on several noise
cancellation techniques, and then with Matlab code, Simulink and FDA tool, the adaptive
noise cancellation system is designed with the implementation of the LMS algorithm,
NLMS algorithm and recursive-least-square algorithm to remove the interference noise.
By using the Matlab code and Simulink, the noise that interfered with a sinusoidal signal
and a record of music can be removed. The original signal in turns can be retrieved from
the noise corrupted signal by changing the coefficient of the filter. Since filter is the
important component in adaptive filtering process, the filter is designed first before adding
adaptive algorithm. A Finite Impulse Response (FIR) filter is designed and the desired
result of functional simulation and timing simulation is obtained through ModelSim and
Integrated Software Environment (ISE) software and FPGA implementation. Finally the
adaptive algorithm is added to the filter, and implemented in the FPGA. The noise is
greatly reduced in Matlab simulation, functional simulation and timing simulation. Hence
the results of this project show that noise cancellation with adaptive filter is feasible
Active cancellation of noise in acoustic signals
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2020In the acoustic domain, sources of noise can be derived from music, vibration from an
engine or a person shouting. There has been a rise in different technologies to combat the
problem of noise. Passive techniques such as using soundproof materials have been
developed, however they pose the challenge of weight from the bulky materials used. Active
noise cancellation techniques although requiring complex computation, can cancel noise up
to 50 decibels (dB), depending on the efficiency of the technology or algorithm used. In this
project, an active noise cancellation device which has the capability of cancelling
surrounding sound has been built using the filtered NLMS algorithm in a digital signal
processor to create a region of silence. It is based on the principle of Destructive
Interference. It is a more affordable rendition of existing products on the market which
incorporate these same principles.Ashesi Universit
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