Effect of tartrate content on aging and deposition condition of copper-tin electrodeposits from a non-cyanide acid bath

The effect of the presence of tartrate additive on the chemical stability of a Cu-Sn acid bath was evaluated. It was observed that this additive hinders decomposition of the Cu/Sn deposition bath with storage time, since the decrease in electrochemical efficiency was attenuated. In addition, it was observed that optimal galvanostatic deposition with or without tartrate occurs at approximately 11 mA cm(-2). However, in the presence of tartrate the deposition charge was lower, leading to lower energy consumption. SEM analysis showed that the tartrate added to the plating bath caused a marked change in the morphology of the Cu/Sn films obtained gal vanostatic ally. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Classification of histological images based on the stationary wavelet transform

Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.