Comments on "Gang EDF Schedulability Analysis"

This short report raises a correctness issue in the schedulability test presented in Kato et al., "Gang EDF Scheduling of Parallel Task Systems", 30th IEEE Real-Time Systems Symposium, 2009, pp. 459-468

Moment-based Kalman Filter: Nonlinear Kalman Filtering with Exact Moment Propagation

This paper develops a new nonlinear filter, called Moment-based Kalman Filter (MKF), using the exact moment propagation method. Existing state estimation methods use linearization techniques or sampling points to compute approximate values of moments. However, moment propagation of probability distributions of random variables through nonlinear process and measurement models play a key role in the development of state estimation and directly affects their performance. The proposed moment propagation procedure can compute exact moments for non-Gaussian as well as non-independent Gaussian random variables. Thus, MKF can propagate exact moments of uncertain state variables up to any desired order. MKF is derivative-free and does not require tuning parameters. Moreover, MKF has the same computation time complexity as the extended or unscented Kalman filters, i.e., EKF and UKF. The experimental evaluations show that MKF is the preferred filter in comparison to EKF and UKF and outperforms both filters in non-Gaussian noise regimes.Comment: Accepted at the IEEE Conference on Robotics and Automation (ICRA), 202

Semi-partitioned Fixed-Priority Scheduling on Multiprocessors

This paper presents a new algorithm for fixed-priority scheduling of sporadic task systems on multiprocessors. The algorithm is categorized to such a scheduling class that qualifies a few tasks to migrate across processors, while most tasks are fixed to particular processors. We design the algorithm so that a task is qualified to migrate, only if it cannot be assigned to any individual processors, in such a way that it is never returned to the same processor within the same period, once it is migrated from one processor to another processor. The scheduling policy is then conformed to Deadline Monotonic. According to the simulation results, the new algorithm significantly outperforms the traditional fixed-priority algorithms in terms of schedulability.

CADLIVE Optimizer: Web-based Parameter Estimation for Dynamic Models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models

CADLIVE optimizer: web-based parameter estimation for dynamic models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models

APEX: Autonomous Vehicle Plan Verification and Execution

Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV\u27s decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. In contrast, consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces state estimation errors and disturbances. Third, verification is restricted to a discretized state space with fixed-size cells; this assumption can artificially limit the set of available trajectories if the discretization is too coarse. Conversely, too fine of a discretization renders the problem intractable for automated analysis. This work presents a new verification tool, APEX, which investigates the combined action of a behavioral planner and state lattice-based motion planner to guarantee a safe vehicle trajectory is chosen. In APEX, decisions made at the behavioral layer can be traced through to the spatio-temporal evolution of the AV and verified. Thus, there is no need to create abstractions of the AV\u27s controllers, and aggressive trajectories required for evasive maneuvers can be accurately investigated