The largest Lyapunov exponent and the Lyapunov spectrum of a coupled map lattice are studied when the system state is desynchronous chaos. In the large system size limit a scaling region is found in the parameter spac...The largest Lyapunov exponent and the Lyapunov spectrum of a coupled map lattice are studied when the system state is desynchronous chaos. In the large system size limit a scaling region is found in the parameter space where the largest Lyapunov exponent is independent of the system size and the coupling strength. Some scaling relation between the Lyapunov spectrum distributions for different coupling strengths is found when the coupling strengths are taken in the scaling parameter region. The existence of the scaling domain and the scaling relation of Lyapunov spectra there are heuristically explained.展开更多
A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescop...A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.展开更多
文摘The largest Lyapunov exponent and the Lyapunov spectrum of a coupled map lattice are studied when the system state is desynchronous chaos. In the large system size limit a scaling region is found in the parameter space where the largest Lyapunov exponent is independent of the system size and the coupling strength. Some scaling relation between the Lyapunov spectrum distributions for different coupling strengths is found when the coupling strengths are taken in the scaling parameter region. The existence of the scaling domain and the scaling relation of Lyapunov spectra there are heuristically explained.
文摘A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.
