Les macroures marcheurs / par E. L. Bouvier

Stomatopod Crustacea from Madagascar

“…-Serene, 1954, p. G. -Stephenson andMcNeill, 1955, p. 241. Squilla ambigua Hansen, 1926, p. 6, pi Bouvier (1914Bouvier ( , 1915 and the detailed account of S. ambigua given by Hansen (1926). Their accounts differ only in the extent of carination of the telson and the armature of the marginal carinae of the abdomen.…”

“…Their accounts differ only in the extent of carination of the telson and the armature of the marginal carinae of the abdomen. Bouvier's figure (1915) (Bouvier, 1914(Bouvier, , 1915, Madagascar, Dangar Besaar, Indo-Malaya (Hansen, 1926), Viet Nam (Serene 1954), New South Wales, Australia (Stephenson and McNeill, 1955) (Hansen) in most features but shows several important differences, as follows: (a) the eyes are of different shape, the stalk is not as long and is less expanded laterally, and the broader cornea is set transversely on the stalk; (b) Haan, 1884) Figure 4…”

Brachyuran crabs (Crustacea: Decapoda) from the Canary Islands (eastern Atlantic): checklist, zoogeographic considerations and conservation

“…2. Bouvier (1940) but the collection was placed at a depth of 40 m. Forest (1964) reassigned this specimen to the genus Macropodia. Forest (1978) demonstrated that H. Milne-Edwards had based the species' description on material of M. longirostris (Fabricius, 1775 (Spalding et al 2007).…”

A Modified Method for Radar Clutter Recognition Based on a Truncation Set

“…With the complexity of the radar backgrounds, different clutter models are required for different regions. These models have to fulfill certain constraints in both amplitude distribution and correlation properties, i.e., spectral characteristics [2]. As a result, the conventional assumption of clutter amplitude's Rayleigh distribution has become difficult to correctly describe the real clutter distribution, and more other distribution models were presented.…”

“…Many recognition techniques are used for the classification purposes: the spectrum analysis, autoregressive simulation, amplitude distribution analysis, neural network and methods based on α truncation set and multi-α truncation set [2][3][4][5][6] , etc, among which the α truncation set like method can overcome the drawbacks of long samples requirement, and has high recognition rate with low computational complexity. However, this kind of method has the common drawbacks, i.e., the optimal value of α have to be properly decided to maximize the recognition rate.…”