Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured during the last decade. This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools. R. I. Davis ( ) . A. Burns

A simple lattice-gas model for the electrocatalytic carbon monoxide oxidation on a platinum electrode is studied by dynamic Monte Carlo simulations. The CO oxidation takes place through a Langmuir-Hinshelwood reaction between adsorbed CO and an adsorbed OH radical resulting from the dissociative adsorption of water. The model enables the investigation of the role of CO surface mobility on the macroscopic electrochemical response such as linear sweep voltammetry and potential step chronoamperometry. Our results show that the mean-field approximation, the traditional but often tacitly made assumption in electrochemistry, breaks down severely in the limit of vanishing CO surface mobility. Comparison of the simulated and experimental voltammetry suggests that on platinum CO oxidation is the intrinsically fastest reaction on the surface and that CO has a high surface mobility. However, under the same conditions, the model predicts some interesting deviations from the potential step current transients derived from the classical nucleation and growth theories. Such deviations have not been reported experimentally. Furthermore, it is shown that our simple model predicts different Tafel slopes at low and high potential, the qualitative features of which are not strongly influenced by the CO mobility. The comparison of our simulation results to the experimental literature is discussed in some detail.

Monte Carlo methods for the simulation of the dynamic behavior of surface reactions are developed, based on the chemical master equation. The methods are stated in a general framework which makes them applicable to a variety of models. Three methods are developed. A comparative analysis of the performance of the three methods, both theoretically and empirically, is included. ͓S1063-651X͑98͒08207-5͔

Deep learning methods are successfully used in applications pertaining to ubiquitous computing, pervasive intelligence, health, and well-being. Speci cally, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations directly from raw input. However, in order to extract generalizable features massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues and annotation costs. erefore, unsupervised representation learning (i.e., learning without manually labeling the instances) is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels, i.e., activity classes. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classi cation result in a strong supervisory signal for extracting useful features for the down-stream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised and transfer learning se ings. Our method achieves performance levels superior to or comparable with fully-supervised networks trained directly with activity labels, and it performs signi cantly be er than unsupervised learning through autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by a aining a kappa score between 0.7 − 0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a di erent data source. Self-supervision drastically reduces the requirement of labeled activity data, e ectively narrowing the gap between supervised and unsupervised techniques for learning meaningful representations. While this paper focuses on HAR as the application domain, the proposed approach is general and could be applied to a wide variety of problems in other areas. 000:2 • A. Saeed et al.activity recognition (HAR), 1D convolutional and recurrent neural networks trained on raw labeled signals signi cantly improve the detection rate over traditional methods [20,44,68,72,73]. Despite the recent advances in the eld of HAR, learning representations from a massive amount of unlabeled data still presents a signi cant challenge. Obtaining large, well-curated activity recognition datasets is problematic due to a number of issues. First, smartphone data are privacy sensitive, which makes it hard to collect su cient amounts of user-activity instances in a real-life se ing. Second, the annotation cost and the time it takes to generate a large volume of labeled instances are prohibitive. Finally, the diversity of devices, types of embedd...

We propose a lattice gas model for the carbon monoxide oxidation on platinum−ruthenium electrode surfaces. The kinetic model includes the main mechanistic “bifunctional” features as they are generally agreed upon in the literature. The CO stripping voltammetry is solved by dynamic Monte Carlo simulations. For a randomly dispersed alloy of Ru and Pt, the model gives a satisfactory semiquantitative agreement with the experimental CO stripping results of Gasteiger et al. [J. Phys. Chem. 1 994, 98, 617]. It is shown how the bifunctional mechanism cannot operate if CO is not mobile on the surface, and a simple Tafel-type experiment with a low concentration of active Pt−Ru sites is suggested to check quantitatively the CO mobility rate. On a surface with large Ru islands, the overpotential for CO oxidation increases, and two CO stripping peaks may appear if the CO mobility is sufficiently low. A mean-field model of the system reproduces the DMC results for high CO mobility but breaks down for a system with large Ru islands and a comparatively low CO surface diffusion constant.