Magnetotransport and Remote Sensing of Microwave Reflection of Two Dimensional Electron Systems under Microwave Excitation

This dissertation summarizes three research projects related to microwave radiation induced electron transport properties in the GaAs/AlGaAs two dimensional electron systems. In chronological order, the projects are: a microwave reflection and electron magneto-transport correlation study, the combined microwave power and polarization dependence on microwave radiation induced magneto-resistance oscillations study, and a comparative study about the effect of circularly polarized and linearly polarized microwaves radiation on magneto-resistance oscillations induced due to the microwave. These three research projects experimentally address many interesting issues in the non-equilibrium low dimensional electron transport under microwave irradiation and provide potential applications of utilizing microwave radiation induced magneto-resistance oscillations in two dimensional electron systems as a method to detect different qualities of microwaves or terahertz waves

Comparative study of microwave radiation-induced magnetoresistive oscillations induced by circularly- and linearly- polarized photo-excitation

A comparative study of the radiation-induced magnetoresistance oscillations in the high mobility GaAs/AlGaAs heterostructure two dimensional electron system (2DES) under linearly- and circularlypolarized microwave excitation indicates a profound difference in the response observed upon rotating the microwave launcher for the two cases, although circularly polarized microwave radiation induced magnetoresistance oscillations observed at low magnetic fields are similar to the oscillations observed with linearly polarized radiation. For the linearly polarized radiation, the magnetoresistive response is a strong sinusoidal function of the launcher rotation (or linear polarization) angle, {\theta}. For circularly polarized radiation, the oscillatory magnetoresistive response is hardly sensitive to {\theta}

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications

Molecular cloning and functional analysis of the follicle-stimulating hormone (FSH) receptor gene promoter from the Jintang black goat

A 762 bp fragment of the 5’-flanking region of the FSHR gene from the Jintang black goat was cloned. The putative initial transcript site was the A at 681 bp and there were 7 putative cis-acting elements and 3 AT-rich regions. The sequence of the FSHR promoter from the Jintang black goat is 99.34% homology to Capra hircus, 32.38% to Gallus gallus and 38.55% to mouse. It could promote the EGFP, FSHR transcription in HEK293 cells, the fluorescence intensity was weaker than the CMV promoter, but the expressed FSHR could respond to the FSH signaling with signal intensity much higher than that at 24 h. This indicated that the FSHR promoter of the Jintang black goat is a strong promoter and may be a gene-special promoter.Key words: Follicle-stimulating hormone receptor, gene promoter, Jintang black goat, molecular cloning, functional analysis