Performance analysis of linear precoding based on field trials results of MIMO-OFDM system

We use field trial results obtained from a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system with two transmitter and three receiver antennas (2/spl times/3), to first validate the properties of the transmit correlation matrix in a macro-cellular environment. We find that approximately 20% of the locations have well-defined transmit correlation matrices. Furthermore, the eigenvectors of the transmit correlation matrix vary slowly over distance with 60% of the locations having eigenvector variation of less than 1 dB over a distance of 20 m. Next, we quantify the performance of the optimal statistical linear precoding (OSLP) , and statistical one-dimensional (1-D) eigenbeamforming (SEB) based on transmit correlation matrices, and the 1-D eigenbeamforming (EB)-based on perfect channel knowledge at the transmitter. We find that the OSLP and SEB schemes obtain array gain over the Alamouti scheme at lower signal-to-noise ratio (SNR) with a median gain of 2.0 (1.5) dB at the 1.0-(3.5) km cell-radii. However, the SEB scheme (unlike the OSLP scheme) looses diversity order at higher SNR that leads to a performance loss. The EB scheme provides the best performance over the Alamouti scheme, at the expense of increased feedback requirements.     

Analysis and optimization of fixture under dynamic machining condition with chip removal effect

Dynamic interactions in the tool–workpiece and workpiece–fixture systems significantly impinge on the quality of finished workpieces. The presented simulation system integrates the effects of workpiece fixture dynamics with the other factors contributing to the machining process dynamics. It provides more accurate prediction of the process output which helps in the design of the optimum fixture configuration prior to the production stage. Modelling of the frictional contact behaviour between the fixture element and the workpiece helps to improve the prediction accuracy of the simulation system which accelerates the convergence to the optimum fixture configuration design and consequently improves the machined part dimensional accuracy and geometric integrity. The developed simulation is capable of modelling complicated part geometries by interfacing with commercial ANSYS.V10^® packages. This research work minimizes the deformation of workpiece using integrated optimization tool of Genetic algorithm (GA) and ANSYS Parametric Design Language (APDL) of finite element analysis. The same layouts given by the above optimization tool are used in the experimental setup and it is found that the improved geometric tolerance of squarness and flatness of the given workpiece. The chip removal effect and frictional contact between the workpiece and the fixture elements are taken into account based on element death technique and nonlinear finite-element analysis. A Case study of an open slot milling process illustrates the application of the proposed improved geometric tolerance approach.     

PSOM<Superscript>2</Superscript>—partitioning-based scalable ontology matching using MapReduce

The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity. Ontology matching algorithms generally do not scale well due to the massive number of complex computations required to achieve matching. One of the methods used to address this problem is the use of partition-based systems to reduce the matching space. In this paper, we propose a new partitioning-based scalable ontology matching system called PSOM². We have designed a new neighbour-based intra-similarity measure to increase the quality of the cluster set formation for the partition-based ontology matching process. These sets of clusters or sub-ontologies are matched across the input ontologies to identify matchable cluster pairs, based on anchors that are efficiently discovered through a new light-weight linguistic matcher (EI-sub). However, in order to further increase the efficiency of the time-consuming anchor discovery process we have designed a MapReduce-based EI-sub process where anchors are discovered in distributed and parallel fashion. Experiments on benchmark OAEI (Ontology Alignment Evaluation Initiative) large scale ontologies demonstrate that the new PSOM² system achieves, on an average, 31% decrease in entropy of the clusters and 54.5% reduction in overall run time. Based on the experimental results, it is evident that the new PSOM² achieves better quality clusters and a major reduction in execution time, leading to an effective and scalable ontology matching system.     

An optimal weighted HEVC coding for video compression

Multimedia Tools and Applications - In recent years, the applications of multimedia are rising in greedy mode and hence the amount of video transactions are also increasing exponentially. It has...     

Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

In this paper, parameter optimization of FSW of cryorolled AA2219 alloy was carried out to obtain defect free weld joint with maximum weld strength. To achieve this, artificial neural network (ANN) was used to model the relationship between the input parameters and the mechanical and corrosion properties (output) of the weld joints. The optimal FSW parameters were determined by genetic algorithm (GA). The feasible solution of the GA was tool rotational speed of 1005 rpm, tool travel speed of 20 mm/min and tool tilt angle of 3°. The feasible parameter was used to weld and check the ability of the parameter to produce better weld joint than the L₉ orthogonal array parameters. The weld, subjected to the confirmation test, was investigated by means of metallurgical, mechanical, and corrosion testing. This process reduces the costs associated with trial runs to obtain optimal parameters and also the production cost of the cryorolled (CR) plate which is high.     

Asymptotic behaviors of support vector machines with Gaussian kernel

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.     

Finite-SNR diversity-multiplexing tradeoffs in fading relay channels

We analyze the diversity-multiplexing tradeoff in a fading relay channel at finite signal-to-noise ratios (SNRs). In this framework, the rate adaptation policy is such that the target system data rate is a multiple of the capacity of an additive white Gaussian noise (AWGN) channel. The proportionality constant determines how aggressively the system scales the data rate and can be interpreted as a finite-SNR multiplexing gain. The diversity gain is given by the negative slope of the outage probability with respect to the SNR. Finite-SNR diversity performance is estimated using a constrained max-flow min-cut upper bound on the relay channel capacity. Moreover, the finite-SNR diversity-multiplexing tradeoff is characterized for three practical decode and forward half-duplex cooperative protocols with different amounts of broadcasting and simultaneous reception. For each configuration, system performance is computed as a function of SNR under a system-wide power constraint on the source and relay transmissions. Our analysis yields the following findings; (i) improved multiplexing performance can be achieved at any SNR by allowing the source to transmit constantly, (ii) both broadcasting and simultaneous reception are desirable in half-duplex relay cooperation for superior diversity-multiplexing performance, and (iii) the diversity-multiplexing tradeoff at finite-SNR is impacted by the power partitioning between the source and the relay terminals. Finally, we verify our analytical results by numerical simulations     

Space-Frequency Precoding with Space–Tap Correlation Information at the Transmitter

In closed-loop methods for obtaining exact channel state information at the transmitter (CSI-Tx), the overhead associated with the feedback can be excessive for fast mobiles. Channel statistics-based CSI-Tx requires a much smaller overhead and is, therefore, attractive for use with fast mobiles. We study ways to exploit correlation-based CSI-Tx in a multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) system. We focus on a channel environment in which spatial and tap correlations are present. We propose a channel model for the case that spatial and tap correlations can be separated and show that in this case channel correlation decreases the ergodic capacity of an MIMO-OFDM system when no CSI-Tx is available. However, this decrease can be mitigated when correlation-based CSI-Tx is exploited. We introduce an optimal precoding approach to maximize capacity with spatial and tap correlation-based CSI-Tx. We also propose a statistical waterfilling scheme, which leads to almost optimal capacity performance without requiring computationally intensive numerical optimization. Based on these approaches, the impact of spatial and tap correlations is investigated.     

Feasibility of neural network approach in spectral mixture analysis of reflectance spectra

In the present work, we perform spectral mixture analysis using Chi‐square minimization (χ² minimization) procedure and test the feasibility of applying an inverse technique, neural network (NN) approach, for the spectral unmixing. The training of NN is carried out using the Levenberg–Marquardt algorithm (LM) with the initial weights for training being chosen randomly. The experiments are performed in the laboratory by mixing young, matured and dead leaves of a sequoia tree in various proportions and reflectance spectra of these mixtures are recorded. The proportions are chosen to model a few near‐real situations like different kinds of vegetation in a forest (by mixing young leaves and matured leaves) and trees damaged in a forest fire or affected by certain virus (by mixing matured and dead leaves) and a combination of all these (by mixing young, matured and dead leaves). The spectral mixture analysis employing χ² minimization and the inverse procedure utilizing NN with two hidden layers yielded consistent results in accordance with the proportion of each kind of leaf.