This paper presents the results of the theoretical and practical analysis of selected features of the function of conditional average value of the absolute value of delayed signal (CAAV). The results obtained with the CAAV method have been compared with the results obtained by method of cross correlation (CCF), which is often used at the measurements of random signal time delay. The paper is divided into five sections. The first is devoted to a short introduction to the subject of the paper. The model of measured stochastic signals is described in Section 2. The fundamentals of time delay estimation using CCF and CAAV are presented in Section 3. The standard deviations of both functions in their extreme points are evaluated and compared. The results of experimental investigations are discussed in Section 4. Computer simulations were used to evaluate the performance of the CAAV and CCF methods. The signal and the noise were Gaussian random variables, produced by a pseudorandom noise generator. The experimental standard deviations of both functions for the chosen signal to noise ratio (SNR) were obtained and compared. All simulation results were averaged for 1000 independent runs. It should be noted that the experimental results were close to the theoretical values. The conclusions and final remarks were included in Section 5. The authors conclude that the CAAV method described in this paper has less standard deviation in the extreme point than CCF and can be applied to time delay measurement of random signals.

Precise measurement of rail vehicle velocities is an essential prerequisite for the implementation of modern train control systems and the improvement of transportation capacity and logistics. Novel eddy current sensor systems make it possible to estimate velocity by using cross-correlation techniques, which show a decline in precision in areas of high accelerations. This is due to signal distortions within the correlation interval. We propose to overcome these problems by employing algorithms from the field of dynamic programming. In this paper we evaluate the application of correlation optimized warping, an enhanced version of dynamic time warping algorithms, and compare it with the classical algorithm for estimating rail vehicle velocities in areas of high accelerations and decelerations.

In order to solve the problem of large error of delay estimation in low SNR environment, a new delay estimation method based on cross power spectral frequency domain weighting and spectrum subtraction is proposed. Through theoretical analysis and MATLAB simulation, among the four common weighting functions, it is proved that the cross-power spectral phase weighting method has a good sharpening effect on the peak value of the cross-correlation function, and it is verified that the improved spectral subtraction method generally has a good noise reduction effect under different SNR environments. Finally, the joint simulation results of the whole algorithm show that the combination of spectrum subtraction and crosspower spectrum phase method can effectively sharpen the peak value of cross-correlation function and improve the accuracy of time delay estimation in the low SNR environment. The results of this paper can provide useful help for sound source localization in complex environments.

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

The paper presents noise measurements in low-resistance photodetectors using a cross-correlation-based transimpedance amplifier. Such measurements usually apply a transimpedance amplifier design to provide a current fluctuation amplification. In the case of low-resistance sources, the measurement system causes additional relevant system noise which can be higher than noise generated in a tested detector. It mainly comes from the equivalent input voltage noise of the transimpedance amplifier. In this work, the unique circuit and a three-step procedure were used to reduce the floor noise, covering the measured infrared detector noise, mainly when operating with no-bias or low-bias voltage. The modified circuit and procedure to measure the noise of unbiased and biased detectors characterized by resistances much lower than 100 Ω were presented. Under low biases, the reference low-resistance resistors tested the measurement system operation and techniques. After the system verification, noise characteristics in low-resistance InAs and InAsSb infrared detectors were also measured.

In order to minimize the receiver complexity and improve the performance of the spectral amplitude coding - optical code division multiple access system, a novel one-dimensional zero cross-correlation code using Pascal’s triangle matrix has been suggested. This research article shows that the position of chip “1” in the code sequences is one of the important factors affecting system performance. In fact, mathematical results show that, for the all-wavelength direct detection, it is possible to reduce the number of filters without sacrificing system performance. In addition, compared to one-wavelength direct detection, the signal-to-noise ratio value is increased with an increasing weight by using wide-bandwidth filters as decoders. Performance of the proposed system in terms of the minimum bit error rate is validated using the OptiSystem software. Compared with the previous systems at 622 Mbps, the suggested system gave the best values of bit error rate of around 10−43, 10−35, and 10−26 for higher, medium, and lower service demand, respectively.