Bleaching studies on Al-hole ([AlO₄/h]⁰) electron spin resonance (ESR) signal in sedimentary quartz

Electron spin resonance (ESR) dating of sediments using quartz is most commonly used for older sediments (>100 ka), since large residuals render the ESR signal unsuitable for dating young sediments. The multiple-centre approach (utilising both Ti and [AlO4/h]0 signals) is usually used to test the resetting of the signals used for ESR dating. Here we work towards a better understanding of, and correction for, the residual signal in ESR samples of sedimentary quartz. We undertook multiple-centre ESR measurements using quartz [AlO4/h]0 and Ti signals on young aeolian samples of different grain sizes which have been independently dated using optically stimulated luminescence (OSL). Our results demonstrate that [AlO4/h]0 signal yields residuals indicating equivalent doses of about 500 Gy, substantially older than expected for the known OSL equivalent doses in the range of 8–37 Gy. The decay of [AlO4/h]0 signal as function of bleaching time can be represented by an exponential function. We investigate the dependence of the residual magnitude of the ESR signal as a function of the previous given dose and observe an exponential increase in the residual signal with dose. Such observations are consistent with the results of luminescence process modelling conducted for a model comprising two luminescence centres and several traps, one of which is a so-called deep disconnected trap that cannot be emptied during optical stimulation. We propose that bleaching occurs through an electron-hole recombination process with electrons released from optically sensitive traps. In addition to our new insights into the bleaching mechanisms of the [AlO4/h]0 ESR signal, we discuss the implications for the procedures used for performing residual dose corrections in ESR dating. We recommend that modern analogues be used in addition to laboratory-bleached samples when performing residual dose corrections

Lossless compression schemes for ECG signals using neural network predictors

This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes. Copyright (c) 2007 R. Kannan and C. Eswaran