LMS Based Adaptive Channel Estimation for LTE Uplink

In this paper, a variable step size based least mean squares (LMS) channel estimation (CE) algorithm is presented for a single carrier frequency division multiple access(SC-FDMA) system under the umbrella of the long term evolution (LTE). This unbiased CE method can automatically adapts the weighting coefficients on the channel condition. Therefore, it does not require knowledge of channel,and noise statistics. Furthermore, it uses a phase weighting scheme to eliminate the signal fluctuations due to noise and decision errors. Such approaches can guarantee the convergence towards the true channel coefficient. The mean and mean square behaviors of the proposed CE algorithm are also analyzed. With the help of theoretical analysis and simulation results, we prove that the proposed algorithm outperforms the existing algorithms in terms of mean square error (MSE) and bit error rate (BER) by more than around 2.5dB

Interoperability Benefits and Challenges in Smart City Services: Blockchain as a Solution

The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network

Cortical Network for Gaze Control in Humans Revealed Using Multimodal MRI

Functional magnetic resonance imaging (fMRI) techniques allow definition of cortical nodes that are presumed to be components of large-scale distributed brain networks involved in cognitive processes. However, very few investigations examine whether such functionally defined areas are in fact structurally connected. Here, we used combined fMRI and diffusion MRI-based tractography to define the cortical network involved in saccadic eye movement control in humans. The results of this multimodal imaging approach demonstrate white matter pathways connecting the frontal eye fields and supplementary eye fields, consistent with the known connectivity of these regions in macaque monkeys. Importantly, however, these connections appeared to be more prominent in the right hemisphere of humans. In addition, there was evidence of a dorsal frontoparietal pathway connecting the frontal eye field and the inferior parietal lobe, also right hemisphere dominant, consistent with specialization of the right hemisphere for directed attention in humans. These findings demonstrate the utility and potential of using multimodal imaging techniques to define large-scale distributed brain networks, including those that demonstrate known hemispheric asymmetries in human

Statistics of non-linear stochastic dynamical systems under L\'evy noises by a convolution quadrature approach

This paper describes a novel numerical approach to find the statistics of the non-stationary response of scalar non-linear systems excited by L\'evy white noises. The proposed numerical procedure relies on the introduction of an integral transform of Wiener-Hopf type into the equation governing the characteristic function. Once this equation is rewritten as partial integro-differential equation, it is then solved by applying the method of convolution quadrature originally proposed by Lubich, here extended to deal with this particular integral transform. The proposed approach is relevant for two reasons: 1) Statistics of systems with several different drift terms can be handled in an efficient way, independently from the kind of white noise; 2) The particular form of Wiener-Hopf integral transform and its numerical evaluation, both introduced in this study, are generalizations of fractional integro-differential operators of potential type and Gr\"unwald-Letnikov fractional derivatives, respectively.Comment: 20 pages, 5 figure