This paper presents a method combining array signal processing and adaptive noise cancellation to suppress unwanted ambient interferences in in situ measurement of radiated emissions of equipment. First, the signals received by the antenna array are processed to form a main data channel and an auxiliary data channel. The main channel contains the radiated emissions of the equipment under test and the attenuated ambient interferences. The auxiliary channel only contains the attenuated ambient interferences. Then, the adaptive noise cancellation technique is used to suppress the ambient interferences based on the correlation of the interferences in the main and auxiliary channels. The proposed method overcomes the problem that the ambient interferences in the two channels of the virtual chamber method are not correlated, and realizes the suppression of multi-source ambient noises in the use of fewer array elements. The results of simulation and experiment show that the proposed method can effectively extract radiated emissions of the equipment under test in complex electromagnetic environment. Finally, discussions on the effect of the beam width of the main channel and the generalization of the proposed method to three dimensionally distributed signals are addressed.

In this paper, a single-channel adaptive noise canceller (SCANC) is proposed to enhance heart sounds during auscultation. Heart sounds provide important information about the condition of the heart, but other sounds interfere with heart sounds during auscultation. The adaptive noise canceller (ANC) is widely used to reduce noises from biomedical signals, but it is not suitable for enhancing auscultatory sounds acquired by a stethoscope. While the ANC needs two inputs, a stethoscope provides only one input. Other approaches, such as ECG gating and wavelet de-noising, are rather complex and difficult to implement as real-time systems. The proposed SCANC uses a single-channel input based on Heart Sound Inherency Indicator and reference generator. The architecture is simple, so it can be easily implemented in real-time systems. It was experimentally confirmed that the proposed SCANC is efficient for heart sound enhancement and is robust against the heart rate variations.

This paper proposes the IMADF (improved multiplication-free adaptive digital filter) algorithm to cancel the background noise that spans nearly all frequency bandwidths in biomedical signals. Under conditions of zero-mean, wide-sense stationary and white Gaussian noise, we analyze the convergence characteristics of the IMADF with a FSE (fractionally-spaced equalizer). In the experimental results, the IMADF algorithm has the advantage in which has superior to a condition of low-frequency and slow data speed. This application gives an important significance in ensuring the objectivity of clinical information and in promoting the representation and the disease diagnosis.

This paper proposes a low-distortion noise canceller and its learning algorithm which is robust against crosstalk and is applicable for continuous sounds. The proposed canceller consists of two stages: cancellation of the crosstalk and cancellation of the noise. A recursive filter reduces the number of computations for noise cancellation stage. Separate filters for the adaptation and the filtering are introduced for crosstalk cancellation. Computer simulations show 10 dB improvement of the error power.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.