Branding/Logomark minus Citation Combined Shape Icon/Bookmark-empty Icon/Copy Icon/Collection Icon/Close Copy 7 no author result Created with Sketch. Icon/Back Created with Sketch. Match!
Andreas Spanias
Arizona State University
464Publications
25H-index
4,512Citations
Publications 464
Newest
Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan Turaga1
Estimated H-index: 1
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Source Cite
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 3 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Source Cite
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Raksha Ramakrishna1
Estimated H-index: 1
(ASU: Arizona State University),
Anna Scaglione44
Estimated H-index: 44
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Source Cite
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Jie Fan (ASU: Arizona State University), Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Source Cite
Published on Jan 1, 2019in IEEE Access 3.56
Jaime Zuniga-Mejia (Tec: Monterrey Institute of Technology and Higher Education), Rafaela Villalpando-Hernandez1
Estimated H-index: 1
(Tec: Monterrey Institute of Technology and Higher Education)
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Reconfigurable wireless networks, such as ad hoc or wireless sensor networks, do not rely on fixed infrastructure. Nodes must cooperate in the multi-hop routing process. This dynamic and open nature make reconfigurable networks vulnerable to routing attacks that could degrade significantly network performance. Intrusion detection systems consist of a set of techniques designed to identify hostile behavior. In this paper, there are several approaches for intrusion detection in reconfigurable netw...
Source Cite
Published on Jan 1, 2019in arXiv: Learning
Vivek Sivaraman Narayanaswamy (ASU: Arizona State University), Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 2 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such as Wave-U-Net which exploit temporal context by extracting multi-scale features. However, the optimality of the feature extraction process in these architectures has not been well investigated. In this paper, we examine and recommend critical architectural chan...
Published on Jan 1, 2019in arXiv: Signal Processing
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus a...
Published on Oct 2, 2018in arXiv: Machine Learning
Uday Shankar Shanthamallu2
Estimated H-index: 2
,
Jayaraman J. Thiagarajan10
Estimated H-index: 10
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
1 Citations
Published on Oct 1, 2018
Abhinav Dixit , Uday Shankar Shanthamallu2
Estimated H-index: 2
+ 2 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature ex...
Source Cite
Published on Oct 1, 2018 in ICIP (International Conference on Image Processing)
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate pr...
1 Citations Source Cite
12345678910