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Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks

Dong Dhawa Sang, Pokhrel Yagnya Murti, Gachhadar Anand, Qamar Faizan, Amiri Iraj Sadegh, Maharjan Ram Krishna

International Journal of Electronics and Telecommunications

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2019

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Vol. 65, No. 3

463--469

EN

Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.

2

Incoherent Discriminative Dictionary Learning for Speech Enhancement

Shaheen D., Dakkak O. A., Wainakh M.

Journal of Telecommunications and Information Technology

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2018

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nr 3

42--54

EN

Speech enhancement is one of the many challenging tasks in signal processing, especially in the case of nonstationary speech-like noise. In this paper a new incoherent discriminative dictionary learning algorithm is proposed to model both speech and noise, where the cost function accounts for both “source confusion” and “source distortion” errors, with a regularization term that penalizes the coherence between speech and noise sub-dictionaries. At the enhancement stage, we use sparse coding on the learnt dictionary to ﬁnd an estimate for both clean speech and noise amplitude spectrum. In the ﬁnal phase, the Wiener ﬁlter is used to reﬁne the clean speech estimate. Experiments on the Noizeus dataset, using two objective speech enhancement measures: frequency-weighted segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) demonstrate that the proposed algorithm outperforms other speech enhancement methods tested.

3

Large-scale hyperspectral image compression via sparse representations based on online learning

Ülkü İ., Kizgut E.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 1

197--207

EN

In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.

4

Dictionary Based Intra Prediction for Image Compression

Sahoo Arabinda, Das Pranati

Annals of Computer Science and Information Systems

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2017

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Vol. 10

73--76

EN

Recently image coding has been importantand in many field it is necessary. Recently varioussparse algorithms have been developed for imagecompression. This paper presents a dictionary basedblock intra prediction for image compression withconstruction of an adaptive trained dictionary. Theadaptive trained dictionary is prepared using K-SVDalgorithm. K-SVD algorithm update the dictionarybased on the sparse algorithms and given image. Theprediction residuals selected from different image areused for dictionary training. The orthogonal matchingpursuit (OMP) algorithm have employed for selectionof dictionary elements and encoding. The proposedmethod is then integrated into 9 mode H.264 intracoding. Performance of proposed method comparedwith existing methods. Simulation result shows thatproposed scheme has improved efficiency as comparedto existing schemes.