d array造句

• Even higher D arrays are equally simple to create.
• A 2-D array of photodetectors may be used as an image sensor to form images from the pattern of light before it.
• In terms of Array DBMS implementations, the rasdaman system has the longest implementation track record of n-D arrays with full query support.
• Scanning arrays were developed and used because of historical difficulties in fabricating 2-D arrays of sufficient size and quality for direct 2-D imaging.
• A 1-D array of photodetectors, as in a spectrophotometer or a Line scanner, may be used to measure the distribution of light along a line.
• However, it may be more difficult to create a dynamic n-D array ( e . g ., from 3 by 4 to 100 by 999 by 30000 ) using multidimensional array.
• First significant work in going beyond BLOBs has been established with PICDMS . This system offers the precursor of a 2-D array query language, albeit still procedural and without suitable storage support.
• Rather than store a big 2-D array of every pixel in the image, a quadtree can capture the same information potentially many divisive levels higher than the pixel-resolution sized cells that we would otherwise require.
• Scanning arrays are constructed from linear arrays ( or very narrow 2-D arrays ) that are rastered across the desired field of view using a rotating or oscillating mirror to construct a 2-D image over time.
• Obviously, no matter what approaches you take, querying EAV will not be as fast as querying standard column-modeled relational data for certain types of query, in much the same way that access of elements in sparse matrices are not as fast as those on non-sparse matrices if the latter fit entirely into main memory . ( Sparse matrices, represented using structures such as linked lists, require list traversal to access an element at a given X-Y position, while access to elements in matrices represented as 2-D arrays can be performed using fast CPU register operations . ) If, however, you chose the EAV approach correctly for the problem that you were trying to solve, this is the price that you pay; in this respect, EAV modeling is an example of a space ( and schema maintenance ) versus CPU-time tradeoff.