4,867 research outputs found
Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata
To analyze complex and heterogeneous real-time embedded systems, recent works
have proposed interface techniques between real-time calculus (RTC) and timed
automata (TA), in order to take advantage of the strengths of each technique
for analyzing various components. But the time to analyze a state-based
component modeled by TA may be prohibitively high, due to the state space
explosion problem. In this paper, we propose a framework of granularity-based
interfacing to speed up the analysis of a TA modeled component. First, we
abstract fine models to work with event streams at coarse granularity. We
perform analysis of the component at multiple coarse granularities and then
based on RTC theory, we derive lower and upper bounds on arrival patterns of
the fine output streams using the causality closure algorithm. Our framework
can help to achieve tradeoffs between precision and analysis time.Comment: QAPL 201
Response Time Densities in Generalised Stochastic Petri Net Models.
Generalised Stochastic Petri nets (GSPNs) have been widely used to analyse the performance of hardware and software systems. This paper presents a novel technique for the numerical determination of response time densities in GSPN models. The technique places no structural restrictions on the models that can be analysed, and allows for the high-level specification of multiple source and destination markings, including any combination of tangible and vanishing markings. The technique is implemented using a scalable parallel Laplace transform inverter that employs a modified Laguerre inversion technique. We present numerical results, including a study of the full distribution of end-to-end response time in a GSPN model of the Courier communication protocol software. The numerical results are validated against simulation. 1
Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine
This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined
Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine
This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined
Performance queries on Semi-Markov Stochastic Petri nets with an extended continuous Stochastic logic
Semi-Markov Stochastic Petri Nets (SM-SPNs) are a highlevel formalism for defining semi-Markov processes. We present an extended Continuous Stochastic Logic (eCSL) which provides an expressive way to articulate performance queries at the SM-SPN model level. eCSL supports queries involving steady-state, transient and passage time measures. We demonstrate this by formulating and answering eCSL queries on an SM-SPN model of a distributed voting system with up to ¢¤£¦ ¥ states.
Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex
The precise timing of spikes of cortical neurons relative to stimulus onset carries substantial sensory information. To access this information the sensory systems would need to maintain an internal temporal reference that reflects the precise stimulus timing. Whether and how sensory systems implement such reference frames to decode time-dependent responses, however, remains debated. Studying the encoding of naturalistic sounds in primate (Macaca mulatta) auditory cortex we here investigate potential intrinsic references for decoding temporally precise information. Within the population of recorded neurons, we found one subset responding with stereotyped fast latencies that varied little across trials or stimuli, while the remaining neurons had stimulus-modulated responses with longer and variable latencies. Computational analysis demonstrated that the neurons with stereotyped short latencies constitute an effective temporal reference for relative coding. Using the response onset of a simultaneously recorded stereotyped neuron allowed decoding most of the stimulus information carried by onset latencies and the full spike train of stimulus-modulated neurons. Computational modeling showed that few tens of such stereotyped reference neurons suffice to recover nearly all information that would be available when decoding the same responses relative to the actual stimulus onset. These findings reveal an explicit neural signature of an intrinsic reference for decoding temporal response patterns in the auditory cortex of alert animals. Furthermore, they highlight a role for apparently unselective neurons as an early saliency signal that provides a temporal reference for extracting stimulus information from other neurons
Classically entangled optical beams for high-speed kinematic sensing
Tracking the kinematics of fast-moving objects is an important diagnostic
tool for science and engineering. Existing optical methods include high-speed
CCD/CMOS imaging, streak cameras, lidar, serial time-encoded imaging and
sequentially timed all-optical mapping. Here, we demonstrate an entirely new
approach to positional and directional sensing based on the concept of
classical entanglement in vector beams of light. The measurement principle
relies on the intrinsic correlations existing in such beams between transverse
spatial modes and polarization. The latter can be determined from intensity
measurements with only a few fast photodiodes, greatly outperforming the
bandwidth of current CCD/CMOS devices. In this way, our setup enables
two-dimensional real-time sensing with temporal resolution in the GHz range. We
expect the concept to open up new directions in photonics-based metrology and
sensing.Comment: v2 includes the real-time measurement from the published version.
Reference [29] added. Minor experimental details added on page
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