A Survey of Desynchronization in a Polychronous Model of Computation

AbstractThe synchronous hypothesis arose in the late Eighties as a conceptual framework for the computer-aided design of embedded systems. Along with this framework, the issue of desynchronization was simultaneously raised as the major topic of mapping the ideal communication and computation model of synchrony on realistic and distributed computer architectures.The aim of the present article is to survey the development of this topics in the particular yet promising model of one of the prominent environments that were build along these principles: Signal and its polychronous (synchronous multi-clocked) model of computation, before to give some hints and ideas about ongoing research addressing this issue

Compositional design of isochronous systems

International audienceThe synchronous modeling paradigm provides strong correctness guarantees for embedded system design while requiring minimal environmental assumptions. In most related frameworks, global execution correctness is achieved by ensuring the insensitivity of (logical) time in the program from (real) time in the environment. This property, called endochrony or patience, can be statically checked, making it fast to ensure design correctness. Unfortunately, it is not preserved by composition, which makes it difficult to exploit with component-based design concepts in mind. Compositionality can be achieved by weakening this objective, but at the cost of an exhaustive state-space exploration. This raises a trade-off between performance and precision. Our aim is to balance it by proposing a formal design methodology that adheres to a weakened global design objective: the non-blocking composition of weakly endochronous processes, while preserving local design objectives for synchronous modules. This yields an effective and cost-efficient approach to compositional synchronous modeling

Event related (de-)synchronization patterns in actual and imagined hand movements

Projecte final de carrera realitzat en col.laboració amb Philips ResearchThis project presents different signal processing techniques, such as Principal Component Analysis (PCA) and Common Spatial Patterns (CSP), applied to characterize the reactivity of central rhythms in the alpha and beta bands during self paced voluntary and imaginary movement. The idea is to allow people to control devices, or interact with machines by simply thinking. To do so, we monitor the brain activity using electroencephalogram (EEG) measurements which record the signals from electrodes positioned on the scalp. The objective is to use motor imagery signals to build a brain computer interface, able to learn from data analyzed before, using the properties of neural networks. The possibility of designing an intuitive communication system between a brain and a computer, available to be operated by everyone, even by people with severe motor impairments, is the main objective of this stud

Code generation for multi-phase tasks on a multi-core distributed memory platform

International audienceEnsuring temporal predictability of real-time systems on a multi-core platform is difficult, mainly due to hard to predict delays related to shared access to the main memory. Task models where computation phases and communication phases are separated (such as the PRedictable Execution Model), have been proposed to both mitigate these delays and make them easier to analyze. In this paper we present a compilation process, part of the Prelude compiler, that automatically translates a high-level synchronous data-flow system specification into a PREM-compliant C program. By automating the production of the PREM-compliant C code, low-level implementation concerns related to task communications become the responsibility of the compiler, which saves tedious and error-prone development efforts

Multi-task implementation of multi-periodic synchronous programs

International audienceThis article presents a complete scheme for the integration and the development of multi-periodic critical embedded systems. A system is formally specified as a modular and hierarchical assembly of several locally mono-periodic synchronous functions into a globally multi-periodic synchronous system. To support this, we introduce a real-time software architecture description language, named \prelude, which is built upon the synchronous languages and which provides a high level of abstraction for describing the functional and the real-time architecture of a multi-periodic control system. A program is translated into a set of real-time tasks that can be executed on a monoprocessor real-time platform with an on-line priority-based scheduler such as Deadline-Monotonic or Earliest-Deadline-First. The compilation is formally proved correct, meaning that the generated code respects the real-time semantics of the original program (respect of periods, deadlines, release dates and precedences) as well as its functional semantics (respect of variable consumption)

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Event related (de-)synchronization patterns in actual and imagined hand movements

Projecte final de carrera realitzat en col.laboració amb Philips ResearchThis project presents different signal processing techniques, such as Principal Component Analysis (PCA) and Common Spatial Patterns (CSP), applied to characterize the reactivity of central rhythms in the alpha and beta bands during self paced voluntary and imaginary movement. The idea is to allow people to control devices, or interact with machines by simply thinking. To do so, we monitor the brain activity using electroencephalogram (EEG) measurements which record the signals from electrodes positioned on the scalp. The objective is to use motor imagery signals to build a brain computer interface, able to learn from data analyzed before, using the properties of neural networks. The possibility of designing an intuitive communication system between a brain and a computer, available to be operated by everyone, even by people with severe motor impairments, is the main objective of this stud

Investigating the effects of neuromodulatory training on autistic traits: a multi-methods psychophysiological study.

Autism spectrum disorder (ASD) is characterized by noticeable difficulties with social interaction and communication. Building on past research in this area and with the aim of improving methodological perspectives, a multi method approach to the study of ASD, mirror neurons and neurofeedback was taken. This thesis is made up of three main experiments: 1) A descriptive study of the resting state electroencephalography (EEG) across the spectrum of autistic traits in neurotypical individuals, 2) A comparison of 3 EEG protocols on MNs activation (mu suppression) and its difference according to self-reported traits of autism in neurotypical individuals, and 3) Neurofeedback training (NFT) on individuals with high autistic traits. In chapters 3 and 4 we employed simultaneous monitoring of physiological data. For chapter 3 EEG and eye-tracking was used, In the case of chapter 4, EEG and eye-tracking as well functional near infrared spectroscopy (fNIRS). Overall the findings revealed differences in mu rhythm reactivity associated to AQ traits. In chapter 2, the rEEG showed that individuals with high AQ scores showed less activation of frontal and fronto-central regions combined with higher levels of complexity in fronto-temporal, temporal, parietal and parieto-occipital areas. In chapter 3, EEG protocols that elicited Mu reactivity in individuals with different AQ traits suggested that as the AQ traits become more pronounced in neurotypical population, the event-related desynchronization (ERD) in low alpha declines. Chapter 3 was also the basis for the choice of pre/post assessment for chapter 4. In chapter 4 the multi-method physiological approach provided parallel physiological evidence for the effects of NFT in sensorimotor reactivity, namely, an increase in ERD in high alpha, higher levels of oxygenated haemoglobin and changes to the amplitude and frequency in the microstructure of mu for participants who underwent active training as opposed to a sham group