Search results for "TELECOMMUNICATIONS"
showing 10 items of 1639 documents
A new Adaptive and Progressive Image Transmission Approach using Function Superpositions
2010
International audience; We present a novel approach to adaptive and progressive image transmission, based on the decomposition of an image into compositions and superpositions of monovariate functions. The monovariate functions are iteratively constructed and transmitted, one after the other, to progressively reconstruct the original image: the progressive transmission is performed directly in the 1D space of the monovariate functions and independently of any statistical properties of the image. Each monovariate function contains only a fraction of the pixels of the image. Each new transmitted monovariate function adds data to the previously transmitted monovariate functions. After each tra…
A Comparison of Special Bonding Techniques for Transmission and Distribution Cables
2020
In this paper, a review of the existing special bonding techniques for medium voltage (MV) and high-voltage (HV) cables is presented. Special bonding techniques have the purpose of reducing sheath currents, thereby limiting copper losses and the reduction of the ampacity of cables. The authors present a literature review to identify various bonding techniques and how these have evolved over the years thanks to new technologies. Simulations of the most used techniques have been performed in Matlab/Simulink, to compare their strengths and drawbacks.
Adaptive Population Importance Samplers: A General Perspective
2016
Importance sampling (IS) is a well-known Monte Carlo method, widely used to approximate a distribution of interest using a random measure composed of a set of weighted samples generated from another proposal density. Since the performance of the algorithm depends on the mismatch between the target and the proposal densities, a set of proposals is often iteratively adapted in order to reduce the variance of the resulting estimator. In this paper, we review several well-known adaptive population importance samplers, providing a unified common framework and classifying them according to the nature of their estimation and adaptive procedures. Furthermore, we interpret the underlying motivation …
Fault Injection into VHDL Models: Experimental Validation of a Fault-Tolerant Microcomputer System
1999
This work presents a campaign of fault injection to validate the dependability of a fault tolerant microcomputer system. The system is duplex with cold stand-by sparing, parity detection and a watchdog timer. The faults have been injected on a chip-level VHDL model, using an injection tool designed with this purpose. We have carried out a set of injection experiments (with 3000 injections each), injecting transient and permanent faults of types stuck-at, open-line and indetermination on both the signals and variables of the system, running a workload. We have analysed the pathology of the propagated errors, measured their latency, and calculated both detection and recovery coverage. We have…
Distributed Particle Metropolis-Hastings Schemes
2018
We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called Distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Recycling Gibbs sampling
2017
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…
Multi-agent Systems for Estimating Missing Information in Smart Cities
2019
International audience; Smart cities aim at improving the quality of life of citizens. To do this, numerous ad-hoc sensors need to be deployed in a smart city to monitor the environmental state. Even if nowadays sensors are becoming more and more cheap their installation and maintenance costs increase rapidly with their number. This paper makes an inventory of the dimensions required for designing an intelligent system to support smart city initiatives. Then we propose a multi-agent based solution that uses a limited number of sensors to estimate at runtime missing information in smart cities using a limited number of sensors.
Impact of Spreading Factor Imperfect Orthogonality in LoRa Communications
2017
In this paper we study the impact of imperfect-orthogonality in LoRa spreading factors (SFs) in simulation and real-world experiments. First, we analyze LoRa modulation numerically and show that collisions between packets of different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings using commercial devices, confirming our numerical results. Third, we modified and extended LoRaSim, an open-source LoRa simulator, to measure the impact of inter-SF collisions and fading (which was not taken into account previously in the simulator). Our results show that non-orthogonality of the SFs can deteriorate significantly the perform…
SREP: An Energy Efficient Relay Protocol for Wireless Sensor Networks
2018
While wireless sensor networks continue to break new grounds in applications, favored by technological innovations, energy efficiency continues to stagnate. Duty cycling remains the most popular and effective technique used to improve energy efficiency and thus lifetime of the network. Nevertheless, duty cycling imposes temporary unavailability on the network leading to deterioration of quality of service. To take care of this rather contradicting reality, this paper proposes Sleep Relay Protocol (SREP). Network nodes are divided into sets according to their location and the sets sleep in relay within a duty cycle period. Two set formation algorithms are proposed at initiation of our propos…