This paper proposes a feedback-controlled adaptive method for detecting signals in diffusion-based molecular communication (MC) systems. Signal detection via receiver nanomachine is critical challenge the exchange of information MC Incorrect estimations or small errors signal can lead to high data errors. Existing methods improving performance require time costs computational complexity. simple...

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The is represented by a process wherein texture obeys inverse distribution. lies known subspace, which independent target signal subspace. By resorting to two-step generalized likelihood ratio test, Rao, and Wald design criteria, three adaptiv...

In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different fr...

Multi-user detection is an effective method to reduce multiple access interference in code division multiple access (CDMA) systems. This paper discusses a signal subspace based blind adaptive multiuser detector and a Kalman filtering blind adaptive multiuser detector. Combining them together, a new Kalman filtering blind adaptive multiuser detector based on a tracking algorithm of the signal su...

In this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (ANFIS) for canceling maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG).This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. P...

Many statistical signal processing problems found in wireless communications involves making inference about the transmitted information data based on the received signals in the presence of various unknown channel distortions. The optimal solutions to these problems are often too computationally complex to implement by conventional signal processing methods. The recently emerged Bayesian Monte...

in this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. this adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. in addition, an important feature of the proposed structure is that the conventional bias existing ...