Deep Learning Enabled Semantic Communication Systems

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to providing a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.Comment: 13 pages, Journal, accepted by IEEE TS

Reduced order models for control of fluids using the Eigensystem Realization Algorithm

In feedback flow control, one of the challenges is to develop mathematical models that describe the fluid physics relevant to the task at hand, while neglecting irrelevant details of the flow in order to remain computationally tractable. A number of techniques are presently used to develop such reduced-order models, such as proper orthogonal decomposition (POD), and approximate snapshot-based balanced truncation, also known as balanced POD. Each method has its strengths and weaknesses: for instance, POD models can behave unpredictably and perform poorly, but they can be computed directly from experimental data; approximate balanced truncation often produces vastly superior models to POD, but requires data from adjoint simulations, and thus cannot be applied to experimental data. In this paper, we show that using the Eigensystem Realization Algorithm (ERA) \citep{JuPa-85}, one can theoretically obtain exactly the same reduced order models as by balanced POD. Moreover, the models can be obtained directly from experimental data, without the use of adjoint information. The algorithm can also substantially improve computational efficiency when forming reduced-order models from simulation data. If adjoint information is available, then balanced POD has some advantages over ERA: for instance, it produces modes that are useful for multiple purposes, and the method has been generalized to unstable systems. We also present a modified ERA procedure that produces modes without adjoint information, but for this procedure, the resulting models are not balanced, and do not perform as well in examples. We present a detailed comparison of the methods, and illustrate them on an example of the flow past an inclined flat plate at a low Reynolds number.Comment: 22 pages, 7 figure

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques

Investigation of contact deformation and wear characteristics of discrete track recording media

The even semester 2014/2015 Technical Information Engineering University of Semarang (USM) has been running the Competency Based Curriculum (CBC) in the management of learning. Conversions that occur in some subjects at an increase in scheduled meetings in the classroom or in the laboratory. Computer Networks is one of the subjects who experienced a conversion. In the curriculum in 2008, Computer Networking has a number of credits 3. From the 2 credits 3 credits are for credits 1 credits for theory and practical credits. While at the CBC in 2013, Computer Networking has 4 credits, with details of 2 credits 2 credits theory and practicum. As lecture and instructor Computer Network, researchers interested in studying the effect of applying the CBC in 2013 in the subje ct of Computer Network. Does the addition of meeting practical and theoretical material renewal in accordance with the expected competencies?. Researchers tried applying the CBC in 2013 by conducting action research. Implementation of the research was conducted during an ongoing lecture that even semester 2015/2016. The results of the study during the first half of researchers will compare with the achievements that never existed when the old curriculum still in use. The goals of this research is, subjects in the Computer Network has always been one of the subjects that the content of the material and its application in the lab was able to follow the needs of the workforc

The in-plane paraconductivity in La_{2-x}Sr_xCuO_4 thin film superconductors at high reduced-temperatures: Independence of the normal-state pseudogap

The in-plane resistivity has been measured in $La_{2-x}Sr_xCuO_4$ (LSxCO) superconducting thin films of underdoped ($x=0.10,0.12$), optimally-doped ($x=0.15$) and overdoped ($x=0.20,0.25$) compositions. These films were grown on (100)SrTiO$_3$ substrates, and have about 150 nm thickness. The in-plane conductivity induced by superconducting fluctuations above the superconducting transition (the so-called in-plane paraconductivity, $\Delta\sigma_{ab}$) was extracted from these data in the reduced-temperature range 10^{-2}\lsim\epsilon\equiv\ln(T/\Tc)\lsim1. Such a $\Delta\sigma_{ab}(\epsilon)$ was then analyzed in terms of the mean-field--like Gaussian-Ginzburg-Landau (GGL) approach extended to the high-$\epsilon$ region by means of the introduction of a total-energy cutoff, which takes into account both the kinetic energy and the quantum localization energy of each fluctuating mode. Our results strongly suggest that at all temperatures above Tc, including the high reduced-temperature region, the doping mainly affects in LSxCO thin films the normal-state properties and that its influence on the superconducting fluctuations is relatively moderate: Even in the high-$\epsilon$ region, the in-plane paraconductivity is found to be independent of the opening of a pseudogap in the normal state of the underdoped films.Comment: 35 pages including 10 figures and 1 tabl

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Performance characteristics of positive and negative delayed feedback on chaotic dynamics of directly modulated InGaAsP semiconductor lasers

The chaotic dynamics of directly modulated semiconductor lasers with delayed optoelectronic feedback is studied numerically. The effects of positive and negative delayed optoelectronic feedback in producing chaotic outputs from such lasers with nonlinear gain reduction in its optimum value range is investigated using bifurcation diagrams. The results are confirmed by calculating the Lyapunov exponents. A negative delayed optoelectronic feedback configuration is found to be more effective in inducing chaotic dynamics to such systems with nonlinear gain reduction factor in the practical value range.Comment: 18 pages, 16 figures. To appear In Pramana - journal of physic