Laurent George

FP/FIFO feasibility conditions with kernel overheads for periodic tasks on an event driven OSEK system

In this paper we show how to take into account kernel overheads in classical real-time feasibility conditions for fixed priority (FP) scheduling where tasks having the same fixed priority are scheduled FP/FIFO. We consider the periodic task model with arbitrary deadlines and an event driven OSEK kernel. The feasibility conditions are based on the worst case response time computation of the tasks. We identify the sources of kernel overheads that influence the response time of the tasks. In such a system the overheads are due to the context switching that activates/terminates and reschedules tasks and to the granularity of the periodic timer used to implement the periodic task model. We show how to take into account those overheads in the classical FP/FIFO feasibility conditions. We compare the worst case response time obtained with kernel overhead to the response time obtained on a real event driven OSEK implementation. We show that the kernel overheads cannot be neglected and that the theoretical results are valid and can be used for a real-time dimensioning

Toward Adaptive VR Simulators Combining Visual, Haptic, and Brain-Computer Interfaces

The next generation of VR simulators could take into account a novel input: the users mental state, as measured with electrodes and a brain-computer interface. One illustration of this promising path is a project that adapted a guidance systems force feedback to the users mental workload in real time. A first application of this approach is a medical training simulator that provides virtual assistance that adapts to the trainees mental activity. Such results pave the way to VR systems that will automatically reconfigure and adapt to their users mental states and cognitive processes.

Using scalp electrical biosignals to control an object by concentration and relaxation tasks: Design and evaluation

In this paper we explore the use of electrical biosignals measured on scalp and corresponding to mental relaxation and concentration tasks in order to control an object in a video game. To evaluate the requirements of such a system in terms of sensors and signal processing we compare two designs. The first one uses only one scalp electroencephalographic (EEG) electrode and the power in the alpha frequency band. The second one uses sixteen scalp EEG electrodes and machine-learning methods. The role of muscular activity is also evaluated using five electrodes positioned on the face and the neck. Results show that the first design enabled 70% of the participants to successfully control the game, whereas 100% of the participants managed to do it with the second design based on machine learning. Subjective questionnaires confirm these results: users globally felt to have control in both designs, with an increased feeling of control in the second one. Offline analysis of face and neck muscle activity shows that this activity could also be used to distinguish between relaxation and concentration tasks. Results suggest that the combination of muscular and brain activity could improve performance of this kind of system. They also suggest that muscular activity has probably been recorded by EEG electrodes.

Combining brain-computer interfaces and haptics: detecting mental workload to adapt haptic assistance

In this paper we introduce the combined use of Brain-Computer Interfaces (BCI) and Haptic interfaces. We propose to adapt haptic guides based on the mental activity measured by a BCI system. This novel approach is illustrated within a proof-of-concept system: haptic guides are toggled during a path-following task thanks to a mental workload index provided by a BCI. The aim of this system is to provide haptic assistance only when the users brain activity reflects a high mental workload. A user study conducted with 8 participants shows that our proof-of-concept is operational and exploitable. Results show that activation of haptic guides occurs in the most difficult part of the path-following task. Moreover it allows to increase task performance by 53% by activating assistance only 59% of the time. Taken together, these results suggest that BCI could be used to determine when the user needs assistance during haptic interaction and to enable haptic guides accordingly.

Schedulability analysis for a combination of non-preemptive strict periodic tasks and preemptive sporadic tasks

We consider the problem of fixed priority scheduling of non-preemptive strict periodic tasks in conjunction with sporadic preemptive tasks. There are few studies about the scheduling problem combining these two kinds of tasks. Moreover, only few results are available on scheduling non-preemptive strict periodic tasks since their performance analysis gives low success ratios, except in the case of harmonic tasks. Also, strict periodic tasks are of great importance since they are in charge for example of sensors/actuators or feedback control functions which are all critical in feedback control systems. Such tasks must have the highest priorities in order to guarantee a correct behavior of the control system. Preemptive sporadic tasks can be used for non critical functions and have lower priorities. We first investigate the scheduling problem of non-preemptive strict periodic tasks by recalling an existing schedulability condition. This results in defining the first release times of strict periodic tasks that preserves the strict periodicity constraints. We show that the schedule of strict periodic tasks can have transient and permanent phases. Then, assuming that some non-preemptive strict periodic tasks have been scheduled, we characterize the release times of the sporadic tasks that maximize their worst case response times. We prove that these release times can be restricted to the permanent phase. For preemptive sporadic tasks, we extend the classical worst case response time computation to take into account non-preemptive strict periodic tasks. Finally, we consider the particular case where some of the sporadic tasks are alternate tasks to primary strict periodic tasks for fault-tolerance.

Can we use a brain-computer interface and manipulate a mouse at the same time?

Brain-Computer Interfaces (BCI) introduce a novel way of interacting with real and virtual environments by directly exploiting cerebral activity. However in most setups using a BCI, the user is explicitly asked to remain as motionless as possible, since muscular activity is commonly admitted to add noise and artifacts in brain electrical signals. Thus, as for today, people have been rarely let using other classical input devices such as mice or joysticks simultaneously to a BCI-based interaction. In this paper, we present an experimental study on the influence of manipulating an input device such as a standard computer mouse on the performance of a BCI system. We have designed a 2-class BCI which relies on Alpha brainwaves to discriminate between focused versus relaxed mental activities. The study uses a simple virtual environment inspired by the well-known Pac-Man videogame and based on BCI and mouse controls. The control of mental activity enables to eat pellets in a simple 2D virtual maze. Different levels of motor activity achieved with the mouse are progressively introduced in the gameplay: 1) no motor activity (control condition), 2) a semi-automatic motor activity, and 3) a highly-demanding motor activity. As expected the BCI performance was found to slightly decrease in presence of motor activity. However, we found that the BCI could still be successfully used in all conditions, and that relaxed versus focused mental activities could still be significantly discriminated even in presence of a highly-demanding mouse manipulation. These promising results pave the way to future experimental studies with more complex mental and motor activities, but also to novel 3D interaction paradigms that could mix BCI and other input devices for virtual reality and videogame applications.

Passive Brain–Computer Interfaces

Passive brain–computer interfaces (passive BCI), also named implicit BCI, provide information from user mental activity to a computerized application without the need for the user to control his brain activity. Passive BCI seem particularly relevant in the context of music creation where they can provide novel information to adapt the music creation process (e.g., user mental concentration state to adapt the music tempo). In this chapter, we present an overview of the use of passive BCI in different contexts. We describe how passive BCI are used and the commonly employed signal processing schemes.

Feasibility conditions with kernel overheads for mixed preemptive FP/FIFO Scheduling with priority ceiling protocol on an event driven OSEK system

In this paper, we propose to extend real-time feasibility conditions taking into account kernel overheads for mixed preemptive periodic tasks with shared resources when tasks are scheduled with fixed priority FP/FIFO scheduling, where FIFO is used to arbitrate tasks having the same fixed priority. The kernel considered in this paper is an event driven OSEK kernel which uses the priority ceiling protocol to avoid deadlocks. The overheads are due to context switching, task activations and terminations, and task rescheduling. Periodic timers used to create the time base to implement periodic tasks and to the OSEKs services used to request and release resources also introduce kernel overheads. We compare the theoretical worst case response time obtained with kernel overheads to the response time obtained on a real event driven OSEK implementation.

Deterministic Scheduling in Network-on-Chip Using the Trajectory Approach

In this paper, we consider the problem of guaranteeing real-time end-to-end transmission time for flows sent on a Network-on-Chip (NoC) with First-in First-out (FIFO) scheduling on each node. We show how to adapt the Trajectory approach, used in the context of Avionics Full DupleX switched Ethernet (AFDX) networks to characterize end-to-end transmission delays, to the context of NoC-based Systems-on-Chip (SoCs). We characterize the benefit of the Trajectory approach on an example.

On the conception of an autonomous and modular robot based on an Event Driven OSEK System with deterministic real-time behavior

In this paper, we are interested in the design of an autonomous and modular self-reconfigurable robot having self-assembly capability and deterministic behavior. The ability of a modular robot to meet its mission strongly depends on the artificial intelligence software and on the underlying hardware and software architecture. The artificial intelligence software of a robot is mapped into several elementary tasks with different real-time constraints. We propose in this paper a real-time analysis taking into account kernel overheads for the validation of the real-time behavior of an artificial intelligence software. We study the OSEK operating system that requires few hardware resources and is cost effective. The overheads are due to the context switching mechanism which activates, terminates, and reschedules tasks, and to the periodic timer used to create the time base which is necessary for the periodic tasks model. We show how to take into account those overheads in the feasibility conditions. We compare the theoretical worst case response time obtained with kernel overheads to the response time obtained on a task set, on a real robot, based on the event driven OSEK implementation. We show that the kernel overheads cannot be neglected and that the theoretical results are valid and can be used to ensure a deterministic behavior of the robot