Baseline removal in spectrometry gamma by observation of local minima

This paper presents a Baseline Removal method in the context of spectrometry gamma. The method implements an estimator for the full continuum based on the observation of local minima. This estimator is constructed from the statistical properties of the signal and is therefore easily explainable. The method involves a limited number of fixed parameters, which allows the automation of the process. Moreover, the method is adaptable to any peaks width, which makes it suitable for both HPGe spectrometers and scintillators. Application to real gamma spectrometry measurements are presented, as well as a discussion about the choice of the parameters, for which an adjustment is proposed.

Ergonomics analysis based on intention inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

Radio telescope total power mode: improving observation efficiency

Aims. Radio observing efficiency can be improved by calibrating and reducing the observations in total power mode rather than in frequency, beam, or position-switching modes. Methods. We selected a sample of spectra obtained from the Institut de Radio-Astronomie Millimétrique (IRAM) 30-m telescope and the Green Bank Telescope (GBT) to test the feasibility of the method. Given that modern front-end amplifiers for the GBT and direct Local Oscillator injection for the 30 m telescope provide smooth pass bands that are a few tens of megahertz in width, the spectra from standard observations can be cleaned (baseline removal) separately and then co-added directly when the lines are narrow enough (a few km s−1), instead of performing the traditional ON minus OFF data reduction. This technique works for frequency-switched observations as well as for position- and beam-switched observations when the ON and OFF data are saved separately. Results. The method works best when the lines are narrow enough and not too numerous so that a secure baseline removal can be achieved. A signal-to-noise ratio improvement of a factor of √2 is found in most cases, consistent with theoretical expectations. Conclusions. By keeping the traditional observing mode, the fallback solution of the standard reduction technique is still available in cases of suboptimal baseline behavior, sky instability, or wide lines, and to confirm the line intensities. These techniques of total-power-mode reduction can be applied to any radio telescope with stable baselines as long as they record and deliver the ONs and OFFs separately, as is the case for the GBT.