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An auroral substorm is an important physical process of energy accumulation and explosive release in the Earth's magnetosphere, and is an important research object of space environment monitoring and space weather warnings. A westward traveling surge (WTS) is a typical auroral physical process of an auroral substorm. Its static characteristic is auroral folding at the polar boundary of an auroral oval and its dynamic characteristic is the westward motion of auroral folding. According to the static characteristic of a WTS, we defined a set of feature parameters based on the morphology and designed a set of automatic detection and discrimination methods; that is, the WTS was identified by using the extracted features and pattern recognition approaches. This approach was tested by using the aurora data of the ultraviolet auroral spectral imager of the Defense Meteorological Satellite Program (DMSP) satellite. The results showed that the accuracy rate of automatic recognition was 61.39%~63.61% and the precision rate was 55.52%~57.92%. The experimental results showed that the approach was effective at detecting the typical characteristics of an auroral substorm (WTS). [ABSTRACT FROM AUTHOR]

Two important phenomena of the solar wind–magnetosphere–ionosphere coupling are auroral particle precipitation and the formation of ions flowing upward from the ionosphere. They have opposite transport directions of energy and substance. Based on the observations of particle precipitation and ion drift from the DMSP F13 satellite in January and July 2005, the ionospheric ion upflows in dayside auroral oval (0600–1800 MLT) can be divided into five types according to the velocity of ion upflows and the spectrum characteristics of auroral particle precipitation, and the distribution for different types of ion upflows is studied. The results show that the ion upflows mainly occur in the geomagnetic latitude (MLAT) range of 70–80°.The main magnetospheric source region of ion upflows (type A and D) caused by the accelerated electron (mainly the soft electron) corresponds to Low Latitude Boundary Layer (LLBL) and Cusp, and ion upflows of type B and C (related to the process of ambipolar diffusion caused by electron acceleration) mainly occur in LLBL and Boundary Plasma Sheet (BPS), while ion upflows of type E without electron acceleration mainly occur in the central plasma sheet (CPS).The dawn–dusk asymmetry is obvious in the winter season, with the ion upflows mainly occurring on the dawn/dusk side ionosphere. However, the ion upflows in summer mainly occur at the magnetic noon, with a symmetric distribution centered at the magnetic noon. The occurrence of ion upflow in winter is significantly higher than that in summer, and it is significantly enhanced during the period of moderate geomagnetic activity. The upward region expands to the lower latitude when the geomagnetic activity is enhanced. The effect of interplanetary magnetic field (IMF) components has also been studied in this paper. When IMF Bx is negative, the upflow occurrence increases in the region of 1500–1800 MLT and 0600–0900 MLT, with the MLAT range below 70°. The direction of IMF By may lead to the high-incidence area reverse at the prenoon or postnoon region. The occurrence of ion upflows with the MLAT range below 75° increases significantly when IMF is southward. Type A ion upflow has the highest velocity of ion upflows, followed by type E, and type D has the lowest. The average velocity of ion upflows in winter is significantly higher than that in summer. [ABSTRACT FROM AUTHOR]

A peak component latency-corrected average (PC-LCA) method consisting of three steps is presented for estimating the evoked potentials (EP) in the human brain. The present PC-LCA is essentially an alternative version of the original LCA method developed by McGillem et al. It first uses a time-varying adaptive filter as a preprocessor to suppress the ongoing EEG and to enhance the EP. Then, the statistics of mean latencies and amplitudes of the EP peak components are estimated by a peak detection and alignment procedure. The final waveform estimate is obtained by superimposing the estimated mean peak components on background information and subsequently fitting a truncated Fourier series using the fast Fourier transform (FFT). Consistent estimates of the EP peak component information are established based on asymptotic analysis of the PC-LCA. Finally, real-time implementation of the PC-LCA is considered, and the superior performance of the PC-LCA method is confirmed by detailed numerical results.

Adaptive implementation of an optimal time-varying filter (TVF) for evoked potential (EP) estimation is addressed in this paper. A data-adaptive scheme is used, which converges asymptotically to the optimal TVF solution. Two basic adaptive TVF's (ATVF's) are first introduced, namely least mean square (LMS) ATVF and recursive least-squares (RLS) ATVF. The latter converges much faster than the former. Since the basic ATVF's usually require a relatively large set of response trials to get a meaningful solution, a reduced-order ATVF is further presented and the corresponding LMS and RLS (including a fast RLS) adaptive algorithms are developed. To save memory, a truncated Fourier; expansion is suggested to express approximately the time-sequenced weight-vectors of the ATVF's, resulting in a simplified reduced-order ATVF. Finally, extensive simulations are provided to confirm the superior performance of the ATVF's. The present ATVF's can be used as prefilters for latency-corrected average (LCA) processing to obtain more informative estimates of EP signals.