Packet loss recovery in audio multimedia streaming by using compressive sensing

Ciaramella, Angelo; Giunta, Giulio

doi:10.1049/iet-com.2014.0995

Cited by 18 publications

(9 citation statements)

References 26 publications

(41 reference statements)

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“…Some problems of data preprocessing have themselves become interesting research topics. Those questions are beyond the scope of this paper; however, the interested reader may refer to [13,14] for an examination of the problem of missing value estimation and to [15,16] for addressing the problem of data normalization or data compression (e.g., compressive sensing) [17,18].…”

Section: Microarray Experimental Datamentioning

confidence: 99%

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Ciaramella

Staiano

2019

Algorithms

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As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree reconstruction, protein secondary and tertiary structure prediction, protein function prediction, DNA microarray analysis, gene regulation/regulatory networks, just to mention a few, and an army of researchers, coming from several scientific backgrounds, focus their efforts on developing models to properly address these problems. In this paper, we aim to briefly review some of the huge amount of machine learning methods, developed in the last two decades, suited for the analysis of gene microarray data that have a strong impact on molecular biology. In particular, we focus on the wide-ranging list of data clustering and visualization techniques able to find homogeneous data groupings, and also provide the possibility to discover its connections in terms of structure, function and evolution.

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Section: Microarray Experimental Datamentioning

confidence: 99%

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Ciaramella

Staiano

2019

Algorithms

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“…On one hand, the integration of labelled data could improve the feature selection step. On the other hand, some specific feature extraction strategies could be adopted, indeed approaches based on the signal analysis of gene expression data (e.g., non-linear Principal Component Analysis, Compressive Sensing), could possibly further improve the performance [ 20 , 21 ]. In future, it is possible to foresee a different weight for each omic data, in order to obtain a more robust similarity, and parametric similarity measures can be adopted (e.g., uninorm) for generalizing the concept of AND and OR connections between clusters.…”

Section: Discussionmentioning

confidence: 99%

Data integration by fuzzy similarity-based hierarchical clustering

Ciaramella

Nardone²,

Staiano

2020

BMC Bioinformatics

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Background High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data. Results In this work we propose a multi-view integration methodology (named FH -Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression , Mirna Expression , Methylation ). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity between sets of observations, by using an appropriate metric. For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. Finally, a consensus matrix, that is a representative information of all dendrograms, is formed by combining multiple hierarchical agglomerations by an approach based on transitive consensus matrix construction. Several experiments and comparisons are made on real data (e.g., Glioblastoma, Prostate Cancer) to assess the proposed approach. Conclusions Fuzzy logic allows us to introduce more flexible data agglomeration techniques. From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. The results suggest that FH -Clust provides better prognostic value and clinical significance compared to the analysis of single-omic data alone and it is very competitive with respect to other techniques from literature.

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“…All these literatures are dealing with physical layer encryption only. CS also shows potential in dealing with data loss problem in wireless sensor networks [7,44,47] due to the downsampling ability of it.…”

Section: Related Workmentioning

confidence: 99%

Concurrent Encryption and Authentication for Wireless Networks using Compressed Sensing

Tang¹

2020

Preprint

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Authentication and encryption are traditionally treated as two separate processes in wireless networks, this paper integrates user authentication into the process of solving eavesdropping attacks. A compressed sensing (CS)-based framework is proposed which manipulates the measurement matrix of CS to safeguard secure computationally. The framework is also capable of continuous authentication and transmission error correction and is robust to data loss. In detail, this paper first proposes an algorithm to generate a 2D key which depends on the physical property of communication channels. The 2D key is further used to generate authentication information and signal structure as well as encrypt original data. Then an encrypted message which contains both data and authentication information is formed for anonymous transmission. The legal receiver can split authentication information and data, and performing a data loss-robust and transmission error-robust authentication and recovery strategy. The framework is evaluated quantitatively using Monte Carlo simulation with simulated sparse signal. The secure transmission performance and authentication performance as well as data loss robustness are investigated. This framework provides an integrated security solution that efficiently safeguards the confidential, privacy and robust communication in cyber-physical systems, especially in resource-limited and safety-critical wireless networks.

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Packet loss recovery in audio multimedia streaming by using compressive sensing

Cited by 18 publications

References 26 publications

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Data integration by fuzzy similarity-based hierarchical clustering

Concurrent Encryption and Authentication for Wireless Networks using Compressed Sensing

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