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Integrated circuit (IC) testing presents complex problems that for large circuits are exceptionally difficult to solve by traditional computing techniques. To deal with unmanageable time complexity, engineers often rely on human "hunches" and "heuristics" learned through experience. Training computers to adopt these human skills is referred to as machine intelligence (MI) or machine learning (ML). This survey examines applications of such methods to test analog, radio frequency (RF), digital, and memory circuits. It also summarizes ML applications to hardware security and emerging technologies, highlighting challenges and potential research directions. The present work is an extension of a recent paper from IEEE VLSI Test Symposium (VTS'21), and includes recent applications of artificial neural network (ANN) and principal component analysis (PCA) to automatic test pattern generation (ATPG). [ABSTRACT FROM AUTHOR]

This paper aims to introduce efficient computer-aided techniques for automated epileptic diagnosis using the features based on improved local pattern transformation methods (LPT). To analyze electroencephalographic (EEG) signal, three techniques, namely one-dimensional local neighbor descriptive count, one-dimensional local gradient count and one-dimensional local binary count, are proposed in this work. Further, a signature point-based improved LPT approach is introduced for effectual classification of EEG signals. The features are computed at the signature points of the EEG signals, which are detected by using the difference of Gaussian pyramid. The features extracted from the signature points of the EEG signals are fed into artificial neural network (ANN) classifier for the discrimination of EEG signals. In this paper, seventeen different classification cases based on six different experimental cases are evaluated using the University of Bonn EEG database. Experimental results show that high classification accuracy for all the cases is achieved using the proposed approach and it also compares favorably to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Uncertainty in the behavior of geotechnical materials (e.g. soil and rock) is the result of imprecise physical processes associated with their formation. This uncertainty provides complexity in modeling the behavior of such materials. The same condition is applied to the behavior of the structural elements dealing with them. In this regard, pile foundations, as the structural elements used to transfer superstructure loads deep into the ground, are subjected to these material uncertainties and modeling complexity. Artificial Intelligence (AI) has demonstrated superior predictive ability compared to traditional methods in modeling the complex behavior of materials. This ability has made AI a popular and particularly amenable option in geotechnical engineering applications. Genetic Programming (GP) and Artificial Neural Network (ANN) are two of the most common examples of AI techniques. This paper provides a review of GP and ANN applications in estimation of the pile foundations bearing capacity. [ABSTRACT FROM AUTHOR]