Deep ultraviolet (UV) light emitting diodes (LEDs) have a wide range of applications such as water treatment, medical diagnostics, medical device sterilization and gas sensing. The internal quantum efficiency of UVB and UVC LEDs is extremely low. Added to this is the high refractive index of the sapphire substrate. The electrical input power is converted to more than 95% to heat. Typically, ceramic packages of alumina with metal core or aluminum nitride are used. These promise a minimized thermal resistance. Comparative thermal simulations show that even Si with slightly lower thermal conductivity of 150 W / mK compared to aluminum nitride with 180 to 200 W / mK does not necessarily impair thermal management. From the thermal and optical calculations, basic information was extracted that forms the basis of the Si package layout. The advantage of the Si packing due to the possibility of integrating functional components has been worked out. An optimized Si package is presented that meets in particular the requirements of the assembly and packaging technology of UVB and UVC LEDs. The process technology was designed and implemented. The first samples with integrated protection diode, an optimized reflector and an optically adjusted single Fresnel lens are presented. The Si packages are designed for the flip-chip technology of UV LEDs with SnAg soldering, thermo-compression or thermosonic bonding and silver sintering. Furthermore, an outlook is given on the possibilities of an encapsulating technology to improve the light extraction.
Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the “salt and pepper” noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.