Biosignal recordings are useful for extracting information about the functional state of an organism. For this reason, such recordings are widely used as tools for supporting medical decision. Nevertheless, reaching a diagnostic decision based on biosignal recordings normally requires analysis of long data records by specialized medical personnel. In several cases, specialized medical attention...

Biomedical signals are nonstationary in nature, namely, their statistical properties are time-dependent. Such changes in the underlying statistical properties of the signal and the effects of external noise often affect the performance and applicability of automatic signal processing methods that require stationarity. A number of methods have been proposed to address the problem of finding stat...

The empirical mode decomposition (EMD) is a powerful tool for non-stationary signal analysis. It has been used successfully for sound and vibration signals separation and time-frequency representation. Linear time-frequency analysis (TFA) is another powerful tool for non-stationary signal. Linear TFAs, e.g. short-time Fourier transform (STFT) and wavelet transform (WT), depend linearly upon the...

The most widely used acoustic feature extraction methods of current automatic speech recognition (ASR) systems are based on the assumption of stationarity. In this paper we extensively evaluate a recently introduced filter stable, non-stationary signal processing method, which relies on an adaptive parttone decomposition of voiced speech to obtain alternative feature vectors for ASR. The non-st...

A macrotile estimation algorithm is introduced to estimate the covariance of locally stationary processes. A macrotile algorithm uses a penalized method to optimize the partition of the space in orthogonal subspaces, and the estimation is computed with a projection operator. It is implemented by searching for a best basis among a dictionary of orthogonal bases and by constructing an adaptive se...

Abstract: A new finite impulse response (FIR) prediction is presented for a state space signal model. The linear predictor proposed in this paper uses the finite number of inputs and outputs on the recent time interval while the infinite impulse response (IIR) predictor with feedback and recursion uses all inputs and outputs from the initial time to the current time. The Yule-Walker equations f...

The spectral characteristics of multimedia signals typically vary with time. Preferably, the sampling density of them would comply with instantaneous bandwidth of signal. The paper discusses the level-crossing sampling principle, which provides such capability for analog-to-digital conversion. As the captured samples are spaced non-uniformly, the appropriate digital signal processing is require...

When communicating with chaos, the greatest challenge over the past 30 years has been achieving a robust form of signal synchronization that can survive the dynamics of a practical communications channel [1,2,3]. The ideal chaotic signal characteristic in AWGN channels for satisfying security, channel capacity [4] and anti-jam performance [5] requirements is that of a bandlimited white Gaussian...

Decomposition of non-stationary signals such as electroencephalogram (EEG) and electrocardiogram (ECG) into stationary or quasi-stationary, signal segmentation, is a wellknown problem in many signal processing applications. Previous methods for segmenting a signal had problems such as slow speed, low performance, and several parameters which must be defined experimentally. In this paper a new m...

Abstract We consider the integrate-and-fire model with non-stationary, stochastic inputs and address the following issue: what are the conditions on the input currents that make the input signal undetectable? A novel theoretical approach to tackle the problem for the model with non-stationary inputs is introduced. When the noise strength is independent of the deterministic component of the syna...