Figure 6.Schematic view of the flow + temperature sensor sections when mounted on a fluidic PCB, depicting the bypass and thermistors.The flow sensing circuit is a Wheatstone bridge, with one branch consisting in thick-film thermistors in the channel (one heating/sensing resistor R+hi + 10 third identical ones in parallel forming R+lo, as reference, cf. Figure 3) and the other branch consisting of fixed setpoint resistors R?hi and R?lo. The use of a single thermistor geometry optimizes the match between the sensing and reference resistors.We aimed to regulate the central heating resistor R+hi ca. 40 K above the reference one R+lo, estimating this was a good compromise between sensitivity and power consumption.

This is done by introducing a co

Approximate nearest neighbor search (ANN) is proposed to tackle the curse of the dimensionality problem [1,2] in exact nearest neighbor (NN) searching. The key idea is to find the nearest neighbor with high probability. ANN is a fundamental primitive in computer vision applications such as keypoint Inhibitors,Modulators,Libraries matching, object retrieval, image classification and scene recognition [3]. In many computer vision applications, the data-points are high-dimensional vectors that are embedded in Euclidean space, and the memory usage for storing and searching high-dimensional vectors is a key criterion for problems Inhibitors,Modulators,Libraries involving large amount of data.The state-of-the-art approaches such as tree-based methods (e.g., KD-tree [4], hierarchical k-means (HKM) [5], FLANN [6]) and hash-based methods (e.g., Exact Euclidean Locality-Sensitive Hashing (E2LSH) [7,8]) involve indexing structures to improve the performance.

The memory usage of indexing structure may even be higher than the original data when processing large scale data. Moreover, FLANN and E2LSH need a final re-ranking based on exact Euclidean distance, which means the original vector should be stored in main memory, this requirement Inhibitors,Modulators,Libraries seriously limits the databases�� scale. Binary index methods such as [9�C11] simplify the indexing structure by using binary code to index the space partitions. However, these methods also need the original vector for final re-ranking.Recently proposed hamming embedding methods compress the vectors into short codes and approximate the Euclidiean distance between two vectors by the hamming distance between their codes.

Inhibitors,Modulators,Libraries These methods include hamming embedding [12], miniBOF [13], small hashing code [14], small binary code [15] and spectral hashing [16]. These methods make it possible to store large scale data in main memory. One weakness Batimastat of these methods is the discrimination limitation of hamming distance as the total number of possible hamming distance is limited by code length. [17] introduced product quantization to compress the vector into several protocol bytes and proposed a more accurate distance approximation. However, its search quality is limited on unstructured vector data.