Concurrent multi-task pre-processing method for LEO mega-constellation based on dynamic spatio-temporal grids

The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.

Thoughts on neurophysiological signal analysis and classification

Neurophysiological signals are crucial intermediaries,through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed.In particular,non-invasive neurophysiological signals,such as electroencephalogram(EEG)and functional magnetic resonance imaging(f MRI),are welcomed and frequently utilised in various studies since these signals can be non-invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity.The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns(e.g.,different cognitive states,brain diseases versus healthy controls).To date,remarkable progress has been made in both the analysis and classification of neurophysiological signals,but scholars are not feeling complacent.Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals.In this paper,I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments.I will elucidate the thoughts after brief summaries of relevant backgrounds,accomplishments,and tendencies.According to my personal selection and preference,I mainly focus on brain connectivity,multidimensional array(tensor),multi-modality,multiple task classification,deep learning,big data,and naturalistic experiment.Hopefully,my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.