Abstract
Micro-solder joint (MSJ) lifetime prediction methodology and failure analysis (FA) are to assess reliability by fatigue model with a series of theoretical calculations, numerical simulation and experimental method. Due to shortened time of solder joints on high-temperature, high-frequency sampling error that is not allowed in productions may exist in various models, including round-off error. Combining intermetallic compound (IMC) growth theory and the FA technology for the magnetic head in actual production, this thesis puts forward a new growth model to predict life expectancy for solder joint of the magnetic head. And the impact of IMC, generating from interface reaction between slider (magnetic head, usually be called slider) and bonding pad, on mechanical performance during aging process is analyzed in it. By further researching on FA of solder ball bonding, thesis chooses AuSn4 growth model that affects least to solder joint mechanical property to indicate that the IMC methodology is suitable to forecast the solder lifetime. And the diffusion constant under work condition 60 A degrees C is 0.015354; the solder lifetime t is 14.46 years .

Abstract
Reject option is a technique used to improve classifier's reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the development of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view, which are based on the self-organizing map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world datasets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers.

Abstract
Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Scorecards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplicity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.

Abstract
In recent years, periocular recognition has become a popular alternative to face and iris recognition in less ideal acquisition scenarios. An interesting example of such scenarios is the usage of mobile devices for recognition purposes. With the growing popularity and easy access to such devices, the development of robust biometric recognition algorithms to work under such conditions finds strong motivation. In the present work we assess the performance of extended versions of two state-of-the-art periocular recognition algorithms on the publicly available CSIP database, a recent dataset composed of images acquired under highly unconstrained and multi-sensor mobile scenarios. The achieved results show each algorithm is better fit to tackle different scenarios and applications of the biometric recognition problem.

Abstract
Deep Transfer Learning (DTL) emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. Even though DTL offers a greater flexibility in extracting high-level features and enabling feature transference from a source to a target task, the DTL solution might get stuck at local minima leading to performance degradation-negative transference-, similar to what happens in the classical machine learning approach. In this paper, we propose the Source-Target-Source (STS) methodology to reduce the impact of negative transference, by iteratively switching between source and target tasks in the training process. The results show the effectiveness of such approach.

Abstract