Figure 3 shows the optical images of the flip chips before bonding captured using an imaging instrument (MC001-YR2010), where the white circles show the distribution of the missing solder bumps in the flip chips.Figure 3.The optical images of the flip chip specimens.The flip chips were bonded by use of a flexible sub-micron die bonder (FINEPLACER? lambda). After that, an image acquisition system, SAM (Sonoscan D9500) as shown in Figure 4, was used to inspect the flip chips. The flip chip was fixed on the wafer stage and immersed in the de-ionized water which acted as the coupling medium for the acoustic wave propagation. The transducer transmitted signals and scanned the entire flip chip.

Then the computer processed the received signals and generated the image of the flip chip
In the past several decades, satellite remote sensing has played a vital role in providing up-to-date and detailed information for monitoring atmospheric and terrestrial environments at the regional, continental, and global scales. Such information is typically generated based on remotely sensed images processed into spectral vegetation indices [1,2]. Among the various spectral vegetation indices derived from remotely sensed imagery, one of the most widely used vegetation indices is the normalized difference vegetation index (NDVI), which is defined as the difference between the red and near-infrared (NIR) reflectance divided by their sum [3,4]. Previous studies showed that NDVI is strongly related to the fraction of absorbed photosynthetically active radiation (FPAR) [5,6], leaf area index (LAI) [7], and net primary production (NPP) [8�C11].

NDVI has Drug_discovery also been used in a range of applications including the study of vegetation�Cclimate interactions [12�C14], detection of long-term vegetation changes [15,16], assessment of vegetation functional characteristics [17�C19] and modeling of the global carbon balance [10,20]. Furthermore, NDVI time series data has been successfully used in a variety of applications, including global change investigations, phenological studies, crop growth monitoring and yield prediction, drought and desertification monitoring, wildfire assessment, and climatic and biogeochemical modeling.Since the launch of the National Oceanic and Atmospheric Administration (NOAA) satellites in 1970s, a large amount of invaluable and irreplaceable data sets have been available for global vegetation monitoring [21]. The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard the NOAA satellites have provided one of the most extensive time series of remotely sensed data and continue to produce daily information regarding surface and atmospheric conditions [22].