𝔖 Bobbio Scriptorium
✦   LIBER   ✦

comparison between geometric and functional method for the estimation of the glenohumeral rotation center

✍ Scribed by A. Levasseur; P. Tétreault; J.A. de Guise; N. Nuño; N. Hagemeister


Book ID
108333307
Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
163 KB
Volume
39
Category
Article
ISSN
0021-9290

No coin nor oath required. For personal study only.


📜 SIMILAR VOLUMES


Interval estimation for the difference b
✍ Robert G. Newcombe 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 223 KB 👁 2 views

Several existing unconditional methods for setting confidence intervals for the difference between binomial proportions are evaluated. Computationally simpler methods are prone to a variety of aberrations and poor coverage properties. The closely interrelated methods of Mee and Miettinen and Nurmine

Comparison between equations-of-motion a
✍ Francis S.M. Tsui; Karl F. Freed 📂 Article 📅 1975 🏛 Elsevier Science 🌐 English ⚖ 787 KB

We show that, apart from a few differences;the equations-of-motion method of h ¶cKoy et al. provides the Ieading COTrection to the random phase approximation (with exchange). in ths fully renkmalized responx function (density-density correlation function). Thus, their equations-of-motion method is s

Comparison between the Matrix Pencil Met
✍ José Enrique Fernández del Rı́o; Tapan K. Sarkar 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 616 KB

where j is 01 , K is the number of frequency com-Ferna ´ndez del RıB o, J. E., and Sarkar, T. K., Comparison ponents, and A m is the complex amplitude at frebetween the Matrix Pencil Method and the Fourier Transquency f m . form Technique for High-Resolution Spectral Estimation, The time function is

Performance comparison between the train
✍ Chen-San Chen; Ching-Shiow Tseng 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 355 KB

The orthogonal neural network is a recently developed neural network based on the properties of orthogonal functions. It can avoid the drawbacks of traditional feedforward neural networks such as initial values of weights, number of processing elements, and slow convergence speed. Nevertheless, it n