Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually reali...

Qualitative simulation is a key inference technique of model-based reasoning and is successfully demonstrated in areas like monitoring, fault-diagnosis and design. For industrial applications, embedded qualitative simulation is required, i.e., the qualitative simulator is coupled with the physical process by a set of sensors and actuators. Often the qualitative simulator must additionally satis...

A number of Denial of Service (DoS) attacks in IEEE 802.11 are due to unauthenticated/ unencrypted management and control frames. Current IEEE 802.11 simulators deal with Physical and MAC layers and do not include the exchange of management and control frames, thus making it difficult to simulate an attack (DoS) and its possible solution. A basic IEEE 802.11 network simulator using Verilog is p...

Often the most practical way to define a Markov Decision Process (MDP) is as a simulator that, given a state and an action, produces a resulting state and immediate reward sampled from the corresponding distributions. Simulators in natural resource management can be very expensive to execute, so that the time required to solve such MDPs is dominated by the number of calls to the simulator. This...

We describe a new methodology for modelling key microarchitectural features in an advanced undergraduate computer architecture course and demonstrate the specific application of this methodology to branch predictors. The proposed approach consists of two separate, but synergistic programming assignments. In the first part, students implement the branch prediction logic as an independent softwar...

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable co...

We design a Nakagami multi-channel simulator by modeling the received combined signal-to-noise ratio as a finite-state Markov chain, following a previously proposed approach. Our model generates directly the error process at the output of a diversity receiver and can emulate selection, maximal-ratio and equal-gain combining. As the order of diversity increases, the savings in computational comp...