The recording of electrocardiogram (ECG) signals for the purpose of finding arrhythmias takes 24 hours. Generally speaking, changes in R-R intervals are used to detect arrhythmias. Our purpose is to develop an algorithm which efficiently detects R-R intervals. This system uses the R-wave position to calculate R-R intervals and then detects any arrhythmias. The algorithm searches for only the short time duration estimated from the most recent R-wave position in order to detect the next R-wave efficiently. We call this duration a WINDOW. A WINDOW is decided according to a proposed search algorithm so that the next R-wave can be expected in the WINDOW. In a case in which an S-wave is enhanced for some reason such as the manner in which the electrodes are installed in the system, the S-wave positions are taken to calculate the peak intervals instead of the R-wave. However, baseline wander and noise contained in the ECG signal have a deterrent effect on the accuracy with which the R-wave or the S-wave position is determined. In order to improve detection, the ECG signal is preprocessed using a Band-Pass Filter (BPF) which is composed of simple Cascaded Integrator Comb (CIC) filters. The American Heart Association (AHA) database was used in the simulation with the proposed algorithm. Accurate detection of the R-wave position was achieved in 99% of cases and efficient extraction of R-R intervals was possible.

A model of a single neuron with chaotic dynamics is implemented with current-mode circuit design technique. The existence of chaotic dynamics in the circuit is demonstrated by simulation with SPICE3. The proposed circuit is suitable for implementing a chaotic neural network composed of such neuron models on a VLSI chip.

This paper presents a method to optimize 2-D sparse array configurations along with a technique to interpolate holes to accurately estimate the direction of arrival (DOA). Conventional 2-D sparse arrays are often defined using a closed-form representation and have the property that they can create hole-free difference co-arrays that can estimate DOAs of incident signals that outnumber the physical elements. However, this property restricts the array configuration to a limited structure and results in a significant mutual coupling effect between consecutive sensors. In this paper, we introduce an optimization-based method for designing 2-D sparse arrays that enhances flexibility of array configuration as well as DOA estimation accuracy. We also propose a method to interpolate holes in 2-D co-arrays by nuclear norm minimization (NNM) that permits holes and to extend array aperture to further enhance DOA estimation accuracy. The performance of the proposed optimum arrays is evaluated through numerical examples.

This paper presents an improved current-mode circuit for implementation of a chaotic neuron model. The proposed circuit uses a switched-current integrator and a nonlinear output function circuit, which is based on an operational transconductance amplifier, as building blocks. Is is shown by SPICE simulations and experiments using discrete elements that the proposed circuit well replicates the behavior of the chaotic neuron model.

This paper presents a weighted spatial filter (WSF) design method based on direction of arrival (DOA) estimates for a novel array configuration called a sum and difference composite co-array. A sum and difference composite co-array is basically a combination of sum and difference co-arrays. Our configuration can realize higher degrees of freedom (DOF) with the sum co-array part at a calculation cost lower than those of the other sparse arrays. To further enhance the robustness of our proposed sum and difference composite co-array we design an optimal beam pattern by WSF based on the information of estimated DOAs. Performance of the proposed system and the DOA estimation accuracy of close-impinging waves are evaluated through computer simulations.