196,566 research outputs found
State recovery techniques for (M,L) algorithm
No full textA breadth-first decoding algorithm, the (M, L) algorithm, is applied to the decoding of convolutional codes. Simulated performances obtained for codes with rate I/n are presented. The problem of the loss of the correct state from the set of retained states is considered, together with its influence on the decoding performance. Two recovery techniques that can avoid this problem are proposed and analysed
Simplicitas ignava: testo e intertesto di Alc. Avit. carm. 2,98-99
No full textUno studio della trama intertestuale di Alc. Avit. 2,98-99. Il fedele riecheggiamento di Prud. psych. 245-246 conferma che in 2,99 la lezione genuina è 'ignava', accantonata a favore di 'ignara' fin dalla metà del XVI secolo.
A study on the intertextual background of Alc. Avit. carm. 2,98-99. The close echoing of Prud. psych. 245-246 confirms that in 2,99 the genuine reading is 'ignava', that editors reject in favour of 'ignara' from the middle of the 16th century
Decimation Schemes for Sigma Delta A/D Converters based on Kaiser and Hamming Sharpened Filters
No full textCascaded-integrator-comb (CIC) filters are efficient anti-aliasing rate-conversion filters widely used for Sigma-Delta A/D converters. High-order structures, attempting to increase the noise rejection within the folding bands, have the drawback of inserting multiple zeros in the same positions and increasing the edge-band attenuation. A combination of sharpened and CIC filters is proposed in the paper, with the goal of increasing the rejection of the SD quantisation noise around the folding bands and reducing the pass-band drop of the designed decimation filters with respect to classic CIC structures. Design criteria, leading to optimised structures, and comparisons are given with respect to both classical and modified CIC filters
Problem of localisation in networks of randomly deployed nodes: asymptotic and finite analysis, and thresholds
No full textConsider a two-dimensional domain S # <2 containing two sets of nodes from two statistically independent uniform Poisson point processes with constant densities rL and rNL. The first point process identifies the distribution of a set of nodes having information about their positions, hereafter denoted as L-nodes (localised-nodes), whereas the other is used to model the spatial distribution of nodes that need to localise themselves, hereafter denoted as NL-nodes (not localised-nodes). For simplicity, both kinds of nodes are equipped with the same kind of transceiver, and communicate over a channel affected by shadow fading. As a first goal, the authors derive the probability that a randomly chosen NL-node over S gets localised as a function of a variety of parameters. Then, the authors derive the probability that the whole network of NLnodes over S gets localised. As with many other random graph properties, the localisation probability is a monotone graph property showing thresholds. In this work, the authors derive both finite (when the number of nodes in the bounded domain is finite and does not grow) and asymptotic thresholds for the localisation probability. In connection with the asymptotic thresholds, the authors show the presence of asymptotic thresholds on the network localisation probability in two different scenarios. The first refers to dense networks, which arise when the domain S is bounded and the densities of the two kinds of nodes tend to grow unboundedly. The second kind of thresholds manifest themselves when the considered domain increases but the number of nodes grow in such a way that the L-node density remains constant throughout the investigated domain. In this scenario, what matters is the minimum value of the maximum transmission range averaged over the fading process, denoted as dmax, above which the network of NL-nodes almost surely gets asymptotically localise
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