On the periodic behavior of real-time schedulers on identical multiprocessor platforms

This paper is proposing a general periodicity result concerning any deterministic and memoryless scheduling algorithm (including non-work-conserving algorithms), for any context, on identical multiprocessor platforms. By context we mean the hardware architecture (uniprocessor, multicore), as well as task constraints like critical sections, precedence constraints, self-suspension, etc. Since the result is based only on the releases and deadlines, it is independent from any other parameter. Note that we do not claim that the given interval is minimal, but it is an upper bound for any cycle of any feasible schedule provided by any deterministic and memoryless scheduler

Accelerated Steady-State Torque Computation for Induction Machines using Parallel-In-Time Algorithms

This paper focuses on efficient steady-state computations of induction machines. In particular, the periodic Parareal algorithm with initial-value coarse problem (PP-IC) is considered for acceleration of classical time-stepping simulations via non-intrusive parallelization in time domain, i.e., existing implementations can be reused. Superiority of this parallel-in-time method is in its direct applicability to time-periodic problems, compared to, e.g, the standard Parareal method, which only solves an initial-value problem, starting from a prescribed initial value. PP-IC is exploited here to obtain the steady state of several operating points of an induction motor, developed by Robert Bosch GmbH. Numerical experiments show that acceleration up to several dozens of times can be obtained, depending on availability of parallel processing units. Comparison of PP-IC with existing time-periodic explicit error correction method highlights better robustness and efficiency of the considered time-parallel approach

Spectrum Sensing of DVB-T2 Signals in Multipath Channels for Cognitive Radio Networks

© 2018 VDE VERLAG GMBHIn this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in different fading environments with energy detection (ED) is considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. By taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational complex noise power estimator is proposed. It is shown analytically that the choice of detector depends on the environment, the detector requirements, the available prior knowledge and with the noise power estimator. Simulation confirm that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in fading channels

Centralized vs distributed communication scheme on switched ethernet for embedded military applications

Current military communication network is a generation old and is no longer effective in meeting the emerging requirements imposed by the future embedded military applications. Therefore, a new interconnection system is needed to overcome these limitations. Two new communication networks based upon Full Duplex Switched Ethernet are presented herein in this aim. The first one uses a distributed communication scheme where equipments can emit their data simultaneously, which clearly improves system’s throughput and flexibility. However, migrating all existing applications into a compliant form could be an expensive step. To avoid this process, the second proposal consists in keeping the current centralized communication scheme. Our objective is to assess and compare the real time guarantees that each proposal can offer. The paper includes the functional description of each proposed communication network and a military avionic application to highlight proposals ability to support the required time constrained communications

Spectrum Sensing of DVB-T2 Signals using a Low Computational Noise Power Estimation

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the proliferation of wireless networks and mobile services. In this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in AWGN, WRAN and COST207 multipath fading environment are considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. Taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational noise power estimator is proposed. Analytical forms for the detector are derived. Simulation results show that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in multi-path fading environments. Moreover, based on this algorithm a practical sensing scheme for cognitive radio networks is proposed.Peer reviewedFinal Accepted Versio

Periodic Pattern Mining a Algorithms and Applications

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network