Sökning: "test vectors"
Visar resultat 1 - 5 av 40 avhandlingar innehållade orden test vectors.
1. System-on-Chip Test Scheduling and Test Infrastructure Design
Sammanfattning : There are several challenges that have to be considered in order to reduce the cost of System-on-Chip (SoC) testing, such as test application time, chip area overhead due to hardware introduced to enhance the testing, and the price of the test equipment. In this thesis the test application time and the test infrastructure hardware overhead of multiple-core SoCs are considered and two different problems are addressed. LÄS MER
2. High-Level Test Generation and Built-In Self-Test Techniques for Digital Systems
Sammanfattning : The technological development is enabling production of increasingly complex electronic systems. All those systems must be verified and tested to guarantee correct behavior. As the complexity grows, testing is becoming one of the most significant factors that contribute to the final product cost. LÄS MER
3. Statistical Properties of Preliminary Test Estimators
Sammanfattning : This thesis investigates the statistical properties of preliminary test estimators of linear models with normally distributed errors. Specifically, we derive exact expressions for the mean, variance and quadratic risk (i.e. the Mean Square Error) of estimators whose form are determined by the outcome of a statistical test. LÄS MER
4. Hybrid Built-In Self-Test and Test Generation Techniques for Digital Systems
Sammanfattning : The technological development is enabling the production of increasingly complex electronic systems. All such systems must be verified and tested to guarantee their correct behavior. As the complexity grows, testing has become one of the most significant factors that contribute to the total development cost. LÄS MER
5. Vector Representations of Idioms in Data-Driven Chatbots for Robust Assistance
Sammanfattning : This thesis presents resources capable of enhancing solutions of some Natural Language Processing (NLP) tasks, demonstrates the learning of abstractions by deep models through cross-lingual transferability, and shows how deep learning models trained on idioms can enhance open-domain conversational systems. The challenges of open-domain conversational systems are many and include bland repetitive utterances, lack of utterance diversity, lack of training data for low-resource languages, shallow world-knowledge and non-empathetic responses, among others. LÄS MER