Motion planning for spatially constrained robots is particularly difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end effector placement for articulated linkages. In fact, for many spatially constrained systems, the probability that a randomly sampled robot configuration satisfies the additional constraints approaches ...

Model-based diagnosis has traditionally operated on hardware systems. However, in most complex systems today, hardware is augmented with software functions that influence the system’s behavior. In this paper hardware models are extended to include the behavior of associated embedded software, resulting in more comprehensive diagnoses. Capturing the behavior of software is much more complex than...

The problem of recursive state estimation for a class of distributed parameter systems with an integral quadratic constraint in Hilbert spaces is addressed. Based on solving the linear tracking problem for time-varying systems in Hilbert spaces, necessary and sufficient conditions for robust state estimation problem involving construction of the set of all possible states at the finite interval...

Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typically employ discrete approximations of these models. This limits the set of actions that can be modeled, and may lead to non-optimal solutions. We introduce the Continuous Action and State Space Learner (CASSL), an int...

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-b...

In this paper, we propose a method for solving constrained optimization problems using Interval Analysis combined with Particle Swarm Optimization. A Set Inverter Via Interval Analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty search spaces, preventing useless executions of th...

The evolution of IoT network is basically the effect of requirement of a system that is having better capabilities compared to existing ones. One main feature of the system that we need to satisfy is long range and low power. The protocols IEEE 802.15.4, 6LoWPAN, IPv6 and CoAP are used for achieving low rate, low power, large address space and reducing the data space respectively. In this paper...

We present a principled spectral approach to the wellstudied constrained clustering problem. It reduces clustering to a generalized eigenvalue problem on Laplacians. The method works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outpe...

Call Blocking Probabilities (CBP) are the key index of the call-level QoS in multirate networks supporting either CBR or elastic traffic. We review the Engset Multirate Loss Model, in a single link, for CBR traffic, and for elastic traffic we propose the Connection Dependent Threshold Model with finite sources (f-CDTM). In f-CDTM, calls may adjust their traffic and bandwidth requirements accord...