Gate Delay Fault Test Generation for Non-Scan Circuits

This article presents a technique for the extension of delay fault test pattern generation to synchronous sequential circuits without making use of scan techniques. The technique relies on the coupling of TDgen, a robust combinational test pattern generator for delay faults, and SEMILET, a sequential test pattern generator for several static fault models. The approach uses a forward propagation-backward justification technique: The test pattern generation is started at the fault location, and after successful ¿local¿ test generation fault effect propagation is performed and finally a synchronising sequence to the required state is computed. The algorithm is complete for a robust gate delay fault model, which means that for every testable fault a test will be generated, assuming sufficient time. Experimental results for the ISCAS'89 benchmarks are presented in this pape

Telerobot task planning and reasoning: Introduction to JPL artificial intelligence research

A view of the capabilities and areas of artificial intelligence research which are required for autonomous space telerobotics extending through the year 2000 is given. In the coming years, JPL will be conducting directed research to achieve these capabilities, as well as drawing heavily on collaborative efforts conducted with other research laboratories

In-Silico Proportional-Integral Moment Control of Stochastic Gene Expression

The problem of controlling the mean and the variance of a species of interest in a simple gene expression is addressed. It is shown that the protein mean level can be globally and robustly tracked to any desired value using a simple PI controller that satisfies certain sufficient conditions. Controlling both the mean and variance however requires an additional control input, e.g. the mRNA degradation rate, and local robust tracking of mean and variance is proved to be achievable using multivariable PI control, provided that the reference point satisfies necessary conditions imposed by the system. Even more importantly, it is shown that there exist PI controllers that locally, robustly and simultaneously stabilize all the equilibrium points inside the admissible region. The results are then extended to the mean control of a gene expression with protein dimerization. It is shown that the moment closure problem can be circumvented without invoking any moment closure technique. Local stabilization and convergence of the average dimer population to any desired reference value is ensured using a pure integral control law. Explicit bounds on the controller gain are provided and shown to be valid for any reference value. As a byproduct, an explicit upper-bound of the variance of the monomer species, acting on the system as unknown input due to the moment openness, is obtained. The results are illustrated by simulation.Comment: 28 pages; 9 Figures. arXiv admin note: substantial text overlap with arXiv:1207.4766, arXiv:1307.644

H∞ filtering for uncertain stochastic time-delay systems with sector-bounded nonlinearities

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier Ltd.In this paper, we deal with the robust H∞ filtering problem for a class of uncertain nonlinear time-delay stochastic systems. The system under consideration contains parameter uncertainties, Itô-type stochastic disturbances, time-varying delays, as well as sector-bounded nonlinearities. We aim at designing a full-order filter such that, for all admissible uncertainties, nonlinearities and time delays, the dynamics of the filtering error is guaranteed to be robustly asymptotically stable in the mean square, while achieving the prescribed H∞ disturbance rejection attenuation level. By using the Lyapunov stability theory and Itô’s differential rule, sufficient conditions are first established to ensure the existence of the desired filters, which are expressed in the form of a linear matrix inequality (LMI). Then, the explicit expression of the desired filter gains is also characterized. Finally, a numerical example is exploited to show the usefulness of the results derived.This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor Tongwen Chen under the direction of Editor Ian Petersen. This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, an International Joint Project sponsored by the Royal Society of the UK and the NSFC of China, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the Natural Science Foundation of Jiangsu Education Committee of China under Grant 06KJD110206, the National Natural Science Foundation of China under Grants 60774073 and 10671172, and the Scientific Innovation Fund of Yangzhou University of China under Grant 2006CXJ002

Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in \emph{zero-shot image retrieval and clustering}(ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this 'good' embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this 'good' embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to 'confuse' the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to 'confuse' the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-the-art performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. \textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh