Energy-efficient video streaming over Bluetooth using rateless coding

Energy-efficient error control for IEEE 802.15.1 (Bluetooth) video communication is proposed. The scheme is based on block-oriented incremental redundancy provided by rateless coding and receiver feedback. Results are presented for time-sensitive video streaming applications under a Markovian channel model. When the proposed algorithm is compared to variations of Bluetooth forward error control, there are improvements of around 3 dB in received video quality and of over 10% in energy efficiency.     

An incentive scheduling mechanism for peer-to-peer video streaming

P2P video streaming networks are found as a scalable solution and an alternative for traditional client–server based video streaming over the Internet. One of the significant issues affecting the success of any P2P streaming network is cooperation between peers. Practical observations have proved the prevalence of free riders in P2P networks that degrade their performance. To solve this problem, using incentive mechanisms, which encourage peers to contribute more in the network, is necessary. In this paper, we designed and proposed a distributed and scalable incentive mechanism for mesh based P2P video streaming networks. In the proposed approach the contribution of the peers is measured and maintained in a distributed fashion. Furthermore, we proposed an incentive sending side scheduler in which peers are served based on their contribution in the network. Our simulation evaluations show the efficiency of the proposed approach in improving the overall perceived video quality by the non-free rider nodes and consequently in the whole network.     

On the new solutions for a fully fuzzy linear system

In this paper, we propose a new method to present a fuzzy trapezoidal solution, namely “suitable solution”, for a fully fuzzy linear system (FFLS) based on solving two fully interval linear systems (FILSs) that are 1-cut and 0-cut of the related FILS. After some manipulations, two FILSs are transformed to 2n crisp linear equations and 4n crisp linear nonequations and n crisp nonlinear equations. Then, we propose a nonlinear programming problem (NLP) to computing simultaneous (synchronic) equations and nonequations. Moreover, we define two other new solutions namely, “fuzzy surrounding solution” and “fuzzy peripheral solution” for an FFLS. It is shown that the fuzzy surrounding solution is placed in a tolerable fuzzy solution set and the fuzzy peripheral solution is placed in a controllable fuzzy solution set. Finally, some numerical examples are given to illustrate the ability of the proposed methods.     

Layered image coding using the DCT pyramid

A block-based subband image coder that exploits the ability to perform decimation in the discrete cosine transform (DCT) domain to effect a pyramidal data structure is described. The proposed "DCT pyramid" has a distinct feature of improved image rendition properties without the associated blocking artifacts at low bit-rates.     

Aqueous solution of Oxone as new oxidizing agent for multi-walled carbon nanotube functionalization

In this work, multi-wall carbon nanotube (MWCNT) was successfully modified using aqueous solution of Oxone as a new oxidant. The effect of oxidation temperature on various characteristics of the treated MWCNTs was also investigated. FTIR and titration analysis proved the formation of carboxyl, carbonyl and epoxide groups at the surface of MWCNTs. The concentration of the functional groups increased as the modification temperature increased. The presence of such oxygen containing groups at the surface of MWCNTs justified the long time stability of the treated MWCNTs suspensions in water and methanol. The modified MWCNTs showed higher entanglement compared to row MWCNT due to the cross-links adjacent effect of pendant functional groups. Finally, it was concluded that Oxone oxidation process destroys the structure of the MWCNTs, but not severe enough to unzip the MWCNTs.     

Solving bus terminal location problems using evolutionary algorithms

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential objective function (POF) for the nodes and design a new mutation operator based on POF. To make the memetic algorithm faster, we estimate the variation of the objective function based on POF in the local search as part of an operator in memetic algorithms. Finally, we explore numerically the performance of nine proposed algorithms on over a thousand randomly generated problems and select the best two algorithms for further testing. The comparative studies show that our new hybrid algorithm composing the evolutionary algorithm with the GLS outperforms the multistart simulated annealing algorithm.     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

He’s homotopy perturbation method: A strongly promising method for solving non-linear systems of the mixed Volterra–Fredholm integral equations

In this paper, He’s homotopy perturbation method is applied to solve non-linear systems of mixed Volterra–Fredholm integral equations. Two examples are presented to illustrate the ability of the method. Also comparisons are made between the Adomian decomposition method and the homotopy perturbation method. The results reveal that He’s homotopy perturbation method is very effective and simple and in these examples leads to the exact solutions.     

An efficient classifier to diagnose of schizophrenia based on the EEG signals

In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.