SUMMARY. The analytical performance of a new, whole blood glucose and lactate

-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a

the objective to present the different techniques for data collection as input in GIS, as well as a brief discussion on the cost associated with the collection of data. Furthermore, comments on precision, accuracy and the quality of database are given. 1.

in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too

uOPSpe#{174} ” refers to the Operational Process Specifications needed to detect allowable imprecision and inaccuracy for an analytical determination to achieve acceptable quality. The purpose of the OPSpecs is to determine the standard deviation, allowable bias, number of con-trol measurements, and control rules to achieve acceptable analytical perfor-mance for clinical laboratory measure-ments. The OPSpecs Manual is a statis-tically sophisticated approach to selecting appropriate quality-control (QC) methods. The author points out that good labo-

implementation, a significant challenge remains in precisely measuring time-dependent properties such as radio channel utilization. One promising approach, first demonstrated by ATEMU, is to simulate the behavior of sensor network programs at the machine code level with cycle-accuracy, but poor performance has

, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we

the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and ...

for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms

(how well do you know your measurement of g = 9.7 m/sec2. Do you know it exactly 9.7 m/sec2?) Theory: g = 9.8 m/sec2