Hillside region accounts for 73.6% of the land in Taiwan. The mountain region consists of high mountain valley of deep
and faults-knit environment, fragile geological, abrupt slopes, and steep rivers. With the rapid development in recent
years, there has been not only great change in land use, but the destruction of the natural environment, the improper use
of soil and water resources also. It is prudent to effectively build and renew the existing land use information as soon as
possible. Among various land use status investigation and monitoring technology, the remote sensing has the advantages
in getting data covering wide-range and in richness of spectral and spatial information. In this study, hybrid land use
classification methods combining with an edge-based segmentation and three kinds of supervised classification methods,
means Maximum Likelihood, Decision Tree, and Support Vector Machine, were conducted to automatically recognize
land use types for Yi-Lan area using multi-resource data, e.g. satellite images and DTM. The second land use
investigation result of Taiwan in 2006 by the Ministry of the Interior is assumed as the ground truth.
The higher classification accuracy results indicate that the proposed methods can be used to automatic classify
agricultural and forest land use types. Moreover, the results of object-based DT and object-based SVM are better than
the ones for the object-based ML methods. However, adequate training is not easy to select the appropriate samples for
the transportation, hydrology, and built-up land classes.
Total internal reflection fluorescence microscopy (TIRFM) induces the evanescent field from an incident light with an incident angle greater than the critical angle selectively to excite fluorescent molecules on or near a surface. The TIRFM not only provides enhanced understanding of cellular function but also improves signal-to-noise ratio of detecting signal in real time. However, fluorescent emission need to be increased when a dynamic biomolecular image is requested at the frame rate of greater than 100 frames/s. Therefore, the fluorescent signal is enhanced via surface plasmons to match the requirements of better efficiency and larger quantity. In this study, a plasmon-enhanced TIRFM whose operation is based on the electromagnetic field enhancement via surface and particle plasmon effects offered by a nano-scalar silver thin film and particles has been presented. The developed microscopy has been successfully used in the real-time observation of the enhanced fluorescence from the thrombomodulin protein of a living cell membrane. The simulated and
experimental results demonstrate that the plasmon-enhanced TIRFM can provide brighter living cell images through surface plasmon enhanced fluorescence.
A promising contact-type linear image sensor, 16 bit/mm high resolution Schottky a-Si:H photodiodes have been fabricated, and its temperature effect on I-V characteristics has been clarified. To achieve excellent small darkcurrent and improve Ip/Id ratio, the a-Si:H photodiodes with the passivation layer being annealed at 200 degree(s)C in air for 30 min is found essentially necessary. In the reverse bias mode, the darkcurrent increases with temperature and doubles for every 8.89 degree(s)C rise, while the photocurrent exhibits little affect below 100 degree(s)C.