Tuesday, October 4, 2011
Research by Dr. Moustafa Youssef
Today I had the opportunity to attend a session given by Dr. Moustafa Youssef on his research. The key point in his talk was about detecting the presence of humans inside a wireless network area by detecting variations in the wireless signal strength (Received Signal Strength Indicator, RSSI) at the receiver side. Dr. Moustafa introduced his Horus system based on this concept! Imagine the scenario in which your home wireless network acts as an intrusion detection system while you are sleeping at night. Dr. Moustafa and his group developed an all-software solution that provides this functionality by utilizing the hardware of the deployed wireless network and your laptop! If you are really interested, you can watch his Google Tech Talk here. The same concept is eventually being applied in hospital settings as well as discussed in this paper.
Monday, October 3, 2011
The Adaptive Noise Canceller as a High-Pass Filter
In 1975 Widrow et. al. introduced a scientific paper about Adaptive Noise Cancellers (ANCs). Since then, ANCs have been used extensively in different applications. This article is about the use of an ANC as a simple, yet powerful, high-pass filter.
As shown in the figure, a reference DC signal x(n)=1 is fed to a single-coefficient ANC. The noise estimate y(n) is the multiplication of the DC signal x(n) times the single coefficient, or weight, w(n). The noise estimate is then subtracted from the input signal d(n) to obtain a noise-free sample e(n) which can be considered the output of the ANC. e(n) is also fed back into the Least Mean Squares (LMS) Algorithm block, which calculates a new value for the weight w(n+1) based on the correlation between the reference signal x(n) and the error signal e(n) using the equation
Widrow et. al. proved in their 1975 paper that this structure is equivalent to a high-pass filter with a corner frequency determined by mu.
The ANC is indeed a very simple, yet powerful, structure that has many applications in different disciplines. Recently, I've used the ANC as proposed by Widrow et. al. with some modifications for adaptive cancellation of power-line interference signals from the electrocardiogram (ECG), as shown in this paper.
As shown in the figure, a reference DC signal x(n)=1 is fed to a single-coefficient ANC. The noise estimate y(n) is the multiplication of the DC signal x(n) times the single coefficient, or weight, w(n). The noise estimate is then subtracted from the input signal d(n) to obtain a noise-free sample e(n) which can be considered the output of the ANC. e(n) is also fed back into the Least Mean Squares (LMS) Algorithm block, which calculates a new value for the weight w(n+1) based on the correlation between the reference signal x(n) and the error signal e(n) using the equation
w(n+1) = w(n) + mu * e(n) * x(n)where mu is the adaptation step, usually a very small factor in the order of 0.01.
Widrow et. al. proved in their 1975 paper that this structure is equivalent to a high-pass filter with a corner frequency determined by mu.
The ANC is indeed a very simple, yet powerful, structure that has many applications in different disciplines. Recently, I've used the ANC as proposed by Widrow et. al. with some modifications for adaptive cancellation of power-line interference signals from the electrocardiogram (ECG), as shown in this paper.
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