text:
@inproceedings{brizuela_improving_2013,
	address = {Cancun, Mexico},
	title = {Improving an evolutionary multi-objective algorithm for the biclustering of gene expression data},
	isbn = {978-1-4799-0454-9 978-1-4799-0453-2 978-1-4799-0451-8 978-1-4799-0452-5},
	url = {http://ieeexplore.ieee.org/document/6557574/},
	doi = {10.1109/CEC.2013.6557574},
	urldate = {2023-05-06},
	booktitle = {2013 {IEEE} {Congress} on {Evolutionary} {Computation}},
	publisher = {IEEE},
	author = {Brizuela, Carlos A. and Luna-Taylor, Jorge E. and Martinez-Perez, Israel and Guillen, Hugo A. and Rodriguez, David O. and Beltran-Verdugo, Armando},
	month = jun,
	year = {2013},
	pages = {221--228},
}

@inproceedings{guillen-ramirez_accurate_2017,
	address = {Anchorage, AK, USA},
	title = {Accurate classification of immunomodulatory {RNA} sequences},
	isbn = {978-1-5090-6182-2},
	url = {http://ieeexplore.ieee.org/document/7965860/},
	doi = {10.1109/IJCNN.2017.7965860},
	urldate = {2023-05-06},
	booktitle = {2017 {International} {Joint} {Conference} on {Neural} {Networks} ({IJCNN})},
	publisher = {IEEE},
	author = {Guillen-Ramirez, Hugo A. and Colbes, Jose and Brizuela, Carlos A. and Martinez-Perez, Israel M.},
	month = may,
	year = {2017},
	pages = {236--241},
}

@article{guillen-ramirez_classification_2018,
	title = {Classification of riboswitch sequences using k-mer frequencies},
	volume = {174},
	issn = {03032647},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0303264718302077},
	doi = {10.1016/j.biosystems.2018.09.001},
	language = {en},
	urldate = {2023-05-06},
	journal = {Biosystems},
	author = {Guillén-Ramírez, Hugo A. and Martínez-Pérez, Israel M.},
	month = dec,
	year = {2018},
	pages = {63--76},
}

@inproceedings{ibarra-hernandez_benchmark_2018,
	address = {Mazatlán, Mexico},
	title = {A benchmark of heart sound classification systems based on sparse decompositions},
	isbn = {978-1-5106-2605-8 978-1-5106-2606-5},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10975/2506758/A-benchmark-of-heart-sound-classification-systems-based-on-sparse/10.1117/12.2506758.full},
	doi = {10.1117/12.2506758},
	urldate = {2023-05-06},
	booktitle = {14th {International} {Symposium} on {Medical} {Information} {Processing} and {Analysis}},
	publisher = {SPIE},
	author = {Ibarra-Hernández, Roilhi Frajo and Bertin, Nancy and Alonso-Arévalo, Miguel Angel and Guillén-Ramírez, Hugo Armando},
	editor = {Romero, Eduardo and Lepore, Natasha and Brieva, Jorge},
	month = dec,
	year = {2018},
	pages = {14},
	file = {Ibarra-Hernández et al_2018_A benchmark of heart sound classification systems based on sparse decompositions.pdf:C\:\\Users\\hugoa\\OneDrive\\POSTDOC GOLDLAB\\Papers\\Ibarra-Hernández et al_2018_A benchmark of heart sound classification systems based on sparse decompositions.pdf:application/pdf},
}

@article{aguilera-mendoza_graph-based_2019,
	title = {Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis},
	volume = {35},
	issn = {1367-4803, 1367-4811},
	shorttitle = {Graph-based data integration from bioactive peptide databases of pharmaceutical interest},
	url = {https://academic.oup.com/bioinformatics/article/35/22/4739/5474901},
	doi = {10.1093/bioinformatics/btz260},
	abstract = {Abstract

              Motivation
              Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites.

              Results
              After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options.

              Availability and implementation
              Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/.

              Supplementary information
              Supplementary data are available at Bioinformatics online.},
	language = {en},
	number = {22},
	urldate = {2023-05-06},
	journal = {Bioinformatics},
	author = {Aguilera-Mendoza, Longendri and Marrero-Ponce, Yovani and Beltran, Jesus A and Tellez Ibarra, Roberto and Guillen-Ramirez, Hugo A and Brizuela, Carlos A},
	editor = {Wren, Jonathan},
	month = nov,
	year = {2019},
	pages = {4739--4747},
}

@article{calvillo-vazquez_particle_2019,
	title = {Particle size distribution from extinction and absorption data of metallic nanoparticles},
	volume = {58},
	issn = {1559-128X, 2155-3165},
	url = {https://opg.optica.org/abstract.cfm?URI=ao-58-36-9955},
	doi = {10.1364/AO.58.009955},
	language = {en},
	number = {36},
	urldate = {2023-05-06},
	journal = {Applied Optics},
	author = {Calvillo-Vázquez, J. Gabriela and Guillén-Ramírez, Hugo A. and DiazDuarte-Rodríguez, Melissa and Licea-Claverie, Angel and Méndez, Eugenio R.},
	month = dec,
	year = {2019},
	pages = {9955},
}

@article{esposito_multi-hallmark_2022,
	title = {Multi-hallmark long noncoding {RNA} maps reveal non-small cell lung cancer vulnerabilities},
	volume = {2},
	issn = {2666979X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2666979X22001136},
	doi = {10.1016/j.xgen.2022.100171},
	language = {en},
	number = {9},
	urldate = {2023-05-06},
	journal = {Cell Genomics},
	author = {Esposito, Roberta and Polidori, Taisia and Meise, Dominik F. and Pulido-Quetglas, Carlos and Chouvardas, Panagiotis and Forster, Stefan and Schaerer, Paulina and Kobel, Andrea and Schlatter, Juliette and Kerkhof, Erik and Roemmele, Michaela and Rice, Emily S. and Zhu, Lina and Lanzós, Andrés and Guillen-Ramirez, Hugo A. and Basile, Giulia and Carrozzo, Irene and Vancura, Adrienne and Ullrich, Sebastian and Andrades, Alvaro and Harvey, Dylan and Medina, Pedro P. and Ma, Patrick C. and Haefliger, Simon and Wang, Xin and Martinez, Ivan and Ochsenbein, Adrian F. and Riether, Carsten and Johnson, Rory},
	month = sep,
	year = {2022},
	pages = {100171},
	file = {Esposito et al_2022_Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer.pdf:C\:\\Users\\hugoa\\OneDrive\\POSTDOC GOLDLAB\\Papers\\Esposito et al_2022_Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer.pdf:application/pdf},
}

@article{bosch-guiteras_enhancing_2021,
	title = {Enhancing {CRISPR} deletion via pharmacological delay of {DNA}-{PKcs}},
	volume = {31},
	issn = {1088-9051, 1549-5469},
	url = {http://genome.cshlp.org/lookup/doi/10.1101/gr.265736.120},
	doi = {10.1101/gr.265736.120},
	abstract = {CRISPR-Cas9 deletion (CRISPR-del) is the leading approach for eliminating DNA from mammalian cells and underpins a variety of genome-editing applications. Target DNA, defined by a pair of double-strand breaks (DSBs), is removed during nonhomologous end-joining (NHEJ). However, the low efficiency of CRISPR-del results in laborious experiments and false-negative results. By using an endogenous reporter system, we show that repression of the DNA-dependent protein kinase catalytic subunit (DNA-PKcs)—an early step in NHEJ—yields substantial increases in DNA deletion. This is observed across diverse cell lines, gene delivery methods, commercial inhibitors, and guide RNAs, including those that otherwise display negligible activity. We further show that DNA-PKcs inhibition can be used to boost the sensitivity of pooled functional screens and detect true-positive hits that would otherwise be overlooked. Thus, delaying the kinetics of NHEJ relative to DSB formation is a simple and effective means of enhancing CRISPR-deletion.},
	language = {en},
	number = {3},
	urldate = {2023-05-06},
	journal = {Genome Research},
	author = {Bosch-Guiteras, Núria and Uroda, Tina and Guillen-Ramirez, Hugo A. and Riedo, Rahel and Gazdhar, Amiq and Esposito, Roberta and Pulido-Quetglas, Carlos and Zimmer, Yitzhak and Medová, Michaela and Johnson, Rory},
	month = mar,
	year = {2021},
	pages = {461--471},
	file = {Bosch-Guiteras et al_2021_Enhancing CRISPR deletion via pharmacological delay of DNA-PKcs.pdf:C\:\\Users\\hugoa\\OneDrive\\POSTDOC GOLDLAB\\Papers\\Bosch-Guiteras et al_2021_Enhancing CRISPR deletion via pharmacological delay of DNA-PKcs.pdf:application/pdf},
}

@article{aguilera-mendoza_automatic_2020,
	title = {Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach},
	volume = {10},
	issn = {2045-2322},
	shorttitle = {Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides},
	url = {https://www.nature.com/articles/s41598-020-75029-1},
	doi = {10.1038/s41598-020-75029-1},
	abstract = {Abstract

              The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the “ocean” of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool (
              http://mobiosd-hub.com/starpep/
              ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.},
	language = {en},
	number = {1},
	urldate = {2023-05-06},
	journal = {Scientific Reports},
	author = {Aguilera-Mendoza, Longendri and Marrero-Ponce, Yovani and García-Jacas, César R. and Chavez, Edgar and Beltran, Jesus A. and Guillen-Ramirez, Hugo A. and Brizuela, Carlos A.},
	month = oct,
	year = {2020},
	pages = {18074},
	file = {Aguilera-Mendoza et al_2020_Automatic construction of molecular similarity networks for visual graph mining.pdf:C\:\\Users\\hugoa\\OneDrive\\POSTDOC GOLDLAB\\Papers\\Aguilera-Mendoza et al_2020_Automatic construction of molecular similarity networks for visual graph mining.pdf:application/pdf},
}


text:
LICENSE = "GPLv2"
DESCRIPTION = "To make access to the Linux kernel cpufreq subsystem easier for users and cpufreq userspace tools, a cpufrequtils package was created"

PR = "r1"

SRC_URI = "http://www.kernel.org/pub/linux/utils/kernel/cpufreq/cpufrequtils-${PV}.tar.bz2 \
           file://fix-proc-stat-reading.patch \
"

export CROSS = "${TARGET_PREFIX}"
export LIBTOOL = "${TARGET_PREFIX}libtool --tag cc"

TARGET_CC_ARCH += "${LDFLAGS}"

do_compile() {
	oe_runmake -e
}

do_install() {
	oe_runmake -e install DESTDIR=${D}
}

SRC_URI[md5sum] = "124b0de8f3a4d672539a85ce13eed869"
SRC_URI[sha256sum] = "b8e77854a1400b0cc73295f3ee5d0a0c3650438e677526a199e08f6680c15aa1"

license: MIT

repository: overo/overo-oe-natty

license_type: permissive

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Christopher Latina at 2015-10-05 13:05:56 -0400

%% Saved with string encoding Unicode (UTF-8)

@article{Cook2001,
	Abstract = {This paper will present observations on the design, artistic, and human factors of creating digital music controllers. Specific projects will be presented, and a set of design principles will be supported from those examples. Keywords},
	Author = {Cook, Perry},
	Date-Added = {2015-10-05 17:05:55 +0000},
	Date-Modified = {2015-10-05 17:05:55 +0000},
	Doi = {10.1101/gr.075622.107},
	File = {:Users/chrislatina/Documents/GeorgiaTech/F15/7100/Papers/prc\_chi2001.pdf:pdf},
	Issn = {10889051},
	Journal = {Proceedings of the 2001 conference on New interfaces for musical expression},
	Keywords = {artistic interfaces,musical control},
	Pages = {1--4},
	Pmid = {18417725},
	Title = {{'Principles for Designing Computer Music Controllers'}},
	Year = {2001},
	Bdsk-Url-1 = {http://dx.doi.org/10.1101/gr.075622.107}}

@article{Settel1994,
	Abstract = {The fast Fourier transform (FFT) is a powerful general-purpose algorithm widely used in signal analysis. FFTs are useful when the spectral information of a signal is needed, such as in pitch tracking or vocoding algorithms. The FFT can be combined with the inverse fast Fourier transform (IFFT) in order to re-synthesize signals based on its analysis. This application of the FFT/IFFT is of great interest in electroacoustic music because it allows for a high degree of control of a given signal's spectral information, an important aspect of timbre, allowing for flexible and efficient implementation of signal processing algorithms. Real-time implementations of the FFT and IFFT are of particular interest since they may be used to provide musicians with highly responsive and straightforward means for generating and controlling sound in live performance situations. This paper presents musical applications using the IRCAM Signal Processing Workstation (ISPW) that make use of FFT/IFFT-based re-synthesis for timbral transformation in real time. An intuitive and straightforward user interface, intended for use by musicians, has been developed by the authors in the Max programming environment. Techniques for filtering, cross-synthesis, noise reduction, dynamic spectral shaping, and resynthesis are presented along with control structures that allow for fine timbral modification and control of complex sound transformations using few parameters. Emphasis is also placed on developing control structures derived from real-time analysis---time and frequency domain---of a musician's input. The ideas and musical applications discussed in this paper offer composers an intuitive approach to timbral transformation in electroacoustic music and new possibilities in the domain of live signal processing that promise to be of general interest to musicians.},
	Author = {{Settel (Author)}, Zack and {Lippe (Author)}, Cort},
	Date-Added = {2015-09-28 17:28:16 +0000},
	Date-Modified = {2015-09-28 17:28:41 +0000},
	File = {:Users/chrislatina/Documents/GeorgiaTech/F15/7100/Papers/FFT\_NEWLONDON95.pdf:pdf},
	Journal = {Collected Work: The human touch. Pages: 338-343. (AN: 1994-17738).},
	Keywords = {68: Theory, analysis, and composition -- Computer ,electronic music and computer music -- composition,electronic sound generation -- signal analysis -- ,performance practice--by topic -- electronic music},
	Title = {{Real-time musical applications using FFT-based resynthesis}},
	Url = {http://search.ebscohost.com/login.aspx?direct=true\&db=rih\&AN=1994-17822\&site=ehost-live$\backslash$nhttp://quod.lib.umich.edu/cgi/p/pod/dod-idx/realtime-musical-applications-using-fft-based-resynthesis.pdf?c=icmc$\backslash$nidno=bbp2372.1994.087},
	Year = {1994},
	Bdsk-Url-1 = {http://search.ebscohost.com/login.aspx?direct=true%5C&db=rih%5C&AN=1994-17822%5C&site=ehost-live$%5Cbackslash$nhttp://quod.lib.umich.edu/cgi/p/pod/dod-idx/realtime-musical-applications-using-fft-based-resynthesis.pdf?c=icmc$%5Cbackslash$nidno=bbp2372.1994.087}}

@article{Nystrom2011,
	Abstract = {The concepts introduced by Smalley in the context of space-form (2007) have firmly put acousmatic music on a discourse of spatial exploration, holding much potential for the developing of aesthetics in new directions. This article approaches space from the low level of musical structure, with a multi-dimensional attitude to space-form, exploring spatial texture, a concept introduced by Smalley to describe the temporal formations of space in spectromorphology (1997). Spatial articulation is investigated in the context of granular-oriented textures, proposing a micro-spatial, perceptual morphology ‚\"{A}\`{\i} the texton ‚\"{A}\`{\i} as an aesthetic approach to acousmatic music. This follows Albert Bregman's speculation regarding equivalents to visual perception in texture, where the theory of textons was first developed by the neuroscientist B√{\copyright}la Julesz.The article discusses acousmatic textons, in terms of intrinsic properties, the way they propagate in time, and how they organise in distributions to form spatial textures. The emergent macroscopic qualities of textonal formations are also reflected upon in the introduction of a group of textural states, where source-bonded spaces and abstract musical thinking coalesce.},
	Author = {Nystr\"{o}m, Erik},
	Date-Added = {2015-09-28 17:28:11 +0000},
	Date-Modified = {2015-09-28 17:28:11 +0000},
	Doi = {10.1017/S1355771810000397},
	File = {:Users/chrislatina/Documents/GeorgiaTech/F15/7100/Papers/Textons\_and\_the\_Propagation\_of\_Space.pdf:pdf},
	Isbn = {1355-7718},
	Issn = {1355-7718},
	Journal = {Organised Sound},
	Number = {01},
	Pages = {14--26},
	Title = {{Textons and the Propagation of Space in Acousmatic Music}},
	Volume = {16},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1017/S1355771810000397}}

@article{brent2011,
	Author = {Brent, William},
	Date-Added = {2015-09-28 17:28:08 +0000},
	Date-Modified = {2015-09-28 17:30:34 +0000},
	File = {:Users/chrislatina/Library/Application Support/Mendeley Desktop/Downloaded/Dc - 2011 - a Perceptually Based Onset Detector for Real-Time and Offline Audio Parsing.pdf:pdf},
	Number = {August},
	Pages = {284--287},
	Title = {{A Perceptually Based Onset Detector for Real-Time and Offline Audio Parsing}},
	Year = {2011}}

@article{brent2009,
	Abstract = {A set of low level and cepstral feature extraction externals for pure data (Pd) are introduced and evaluated in a percussion instrument classi cation problem. Because this set of tools is intended for immediate real-time timbre identi - cation using an accompanying classi cation external, a constraint of reporting results within 30 ms after a detected onset is applied. While Bark-frequency cepstrum is the most e ective single feature, it is shown that combining several simple measures into a feature vector produces useful results as well. Temporally evolving feature data collected by taking a series of successive analysis snapshots rather than a single snapshot immediately after onset detection is also discussed.},
	Author = {Brent, William},
	Date-Added = {2015-09-28 17:28:08 +0000},
	Date-Modified = {2015-09-28 17:30:39 +0000},
	File = {:Users/chrislatina/Library/Application Support/Mendeley Desktop/Downloaded/Brent - 2009 - Cepstral analysis tools for percussive timbre identification.pdf:pdf},
	Journal = {Proceedings of the 3rd International Pure \ldots},
	Keywords = {cepstrum mfcc barks timbre},
	Title = {{Cepstral Analysis Tools for Percussive Timbre Identification}},
	Url = {http://www.williambrent.conflations.com/papers/features.pdf},
	Year = {2009},
	Bdsk-Url-1 = {http://www.williambrent.conflations.com/papers/features.pdf}}

@webpage{mutable,
	Author = {Oliver Gillet},
	Date-Added = {2015-09-28 17:21:07 +0000},
	Date-Modified = {2015-09-28 17:21:37 +0000},
	Lastchecked = {September 28, 2015},
	Title = {Mutable Instruments: Clouds},
	Url = {http://mutable-instruments.net/modules/clouds/specifications},
	Bdsk-Url-1 = {http://mutable-instruments.net/modules/clouds/specifications}}

@url{terminaltedium,
	Author = {mxmxmx},
	Date-Added = {2015-09-28 17:16:35 +0000},
	Date-Modified = {2015-09-28 17:37:42 +0000},
	Lastchecked = {September 28, 2015},
	Title = {{Github: terminal tedium}},
	Url = {https://github.com/mxmxmx/terminal_tedium},
	Bdsk-Url-1 = {https://github.com/mxmxmx/terminal_tedium}}

@url{headoff,
	Author = {Mark Fell and EVOL},
	Date-Added = {2015-09-28 17:01:44 +0000},
	Date-Modified = {2015-09-28 17:05:15 +0000},
	Keywords = {rave, machine listening, computer music, cassette},
	Lastchecked = {September 28, 2015},
	Title = {{Head Office Transformation}},
	Url = {http://vivapunani.org/pmwiki/pmwiki.php/Main/HeadOfficeTransformation},
	Year = {2010},
	Bdsk-Url-1 = {http://vivapunani.org/pmwiki/pmwiki.php/Main/HeadOfficeTransformation}}

@webpage{csounds,
	Author = {Andrew Ikenberry and Jason Lim},
	Date-Added = {2015-09-28 15:21:03 +0000},
	Date-Modified = {2015-09-28 16:56:55 +0000},
	Lastchecked = {Monday September 28},
	Title = {{CSound Journal Issue 18: Csound Eurorack Module}},
	Url = {http://www.csounds.com/journal/issue18/eurorack.html},
	Bdsk-Url-1 = {http://www.csounds.com/journal/issue18/eurorack.html}}

@webpage{nickcollins,
	Author = {Nick Collins},
	Date-Added = {2015-09-28 15:20:00 +0000},
	Date-Modified = {2015-09-28 16:56:50 +0000},
	Lastchecked = {September 28, 2015},
	Title = {{SuperCollider Classes: Onsets}},
	Url = {http://doc.sccode.org/Classes/Onsets.html},
	Bdsk-Url-1 = {http://doc.sccode.org/Classes/Onsets.html}}

@url{idreamof,
	Author = {Robert Fantinatto},
	Date-Added = {2015-09-28 14:48:41 +0000},
	Date-Modified = {2015-09-28 16:58:03 +0000},
	Lastchecked = {Monday September 28, 2015},
	Title = {{I Dream of Wires}},
	Url = {http://www.idreamofwires.org/},
	Year = {2014},
	Bdsk-Url-1 = {http://www.idreamofwires.org/}}

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repo_name: chrislatina/MachineListening

branch_name: refs/heads/master

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text:
@inproceedings{botection2020,
  title={BOTection: Bot Detection by Building Markov Chain Models of Bots Network Behavior},
  author={ Alahmadi, Bushra and  Mariconti, Enrico and Spolaor, Riccardo and Stringhini, Gianluca and Martinovic, Ivan},
  booktitle={Proceeding of the 15th ACM ASIA Conference on Computer and Communications Security},
  year={2020},
  organization={ACM}
}

gha_license_id: MIT


text:
@Article{SC21,
Author = {Rahmani Thomas},
Title = {La vie d'un Homme},
Year = {2014},
Month = {11},
Journal = {Good morning journal},
Publisher = {publi},
Volume = {2},
Number = {5},
Pages = {521},
Editor = {editeur},
Series = {2},
Address = {localite},
Note = {
In his tractibus navigerum nusquam visitur flumen sed in locis plurimis aquae suapte natura calentes emergunt ad usus aptae multiplicium medelarum. verum has quoque regiones pari sorte Pompeius Iudaeis domitis et Hierosolymis captis in provinciae speciem delata iuris dictione formavit. Accedebant enim eius asperitati, ubi inminuta vel laesa amplitudo imperii dicebatur, et iracundae suspicionum quantitati proximorum cruentae blanditiae exaggerantium incidentia et dolere inpendio simulantium, si principis periclitetur vita, a cuius salute velut filo pendere statum orbis terrarum fictis vocibus exclamabant. Hac ita persuasione reducti intra moenia bellatores obseratis undique portarum aditibus, propugnaculis insistebant et pinnis, congesta undique saxa telaque habentes in promptu, ut si quis se proripuisset interius, multitudine missilium sterneretur et lapidum.},
Abstract = {Omnium cum fui ego factorum quis esse atque semper amicus.},
Keywords = {Vanille},
,}

gha_language: JavaScript


text:
@book{William1992,
doi = {10.5555/148286},
author = {Press, William H. and Teukolsky, Saul A. and Vetterling, William T. and Flannery, Brian P.},
title = {Numerical Recipes in C (2nd Ed.): The Art of Scientific Computing},
year = {1992},
isbn = {0521431085},
publisher = {Cambridge University Press},
address = {USA}
}

repository: NCAR/musica-core

repo_name: NCAR/musica-core

branch_name: refs/heads/main

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Automatically generated by Mendeley Desktop 1.17.11
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Options -> BibTeX in Mendeley Desktop

@misc{TensorFlow,
title = {{TensorFlow}},
howpublished = "\url{https://www.tensorflow.org/}",
note = {Accessed 2017-11-26},
urldate = {2017-11-26}
}
@misc{Med_diag,
author = {Med{\_}diag},
title = {{7 Applications of Machine Learning in Pharma and Medicine}},
howpublished = "\url{https://www.techemergence.com/machine-learning-in-pharma-medicine/}",
note = {Accessed 2017-11-22},
url = {https://www.techemergence.com/machine-learning-in-pharma-medicine/},
urldate = {2017-11-22}
}
@misc{Qt,
title = {{Qt | Cross-platform software development for embedded {\&} desktop}},
howpublished = "\url{https://www.qt.io/}",
note = {Accessed 2017-11-24},
url = {https://www.qt.io/},
urldate = {2017-11-24}
}
@article{Kolodzy2005,
author = {Kolodzy, Paul J},
journal = {Building},
number = {April},
title = {{Cognitive Radio Fundamentals}},
year = {2005}
}
@article{Zeiler2012a,
abstract = {We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment.},
archivePrefix = {arXiv},
arxivId = {1212.5701},
author = {Zeiler, Matthew D.},
eprint = {1212.5701},
month = {dec},
title = {{ADADELTA: An Adaptive Learning Rate Method}},
url = {http://arxiv.org/abs/1212.5701},
year = {2012}
}
@article{OShea2017a,
abstract = {We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation.},
archivePrefix = {arXiv},
arxivId = {1702.00832},
author = {O'Shea, Timothy J. and Hoydis, Jakob},
doi = {10.1109/TCCN.2017.2758370},
eprint = {1702.00832},
isbn = {2332-7731},
issn = {2332-7731},
pages = {1--13},
title = {{An Introduction to Deep Learning for the Physical Layer}},
url = {http://arxiv.org/abs/1702.00832},
year = {2017}
}
@article{Dechter,
abstract = {The popular use of backtracking as a control strategy for theorem proving in PROLOG and in Truth-Maintenance-Systems (TMS) led to increased interest in various schemes for enhancing the efficiency of backtrack search. Researchers have referred to these enhancement schemes by the names ' 'Intelligent Backtracking' ' (in PROLOG), ' 'Dependency-directed-backtracking " (in TMS) and others. Those improve-ments center on the issue of " jumping-back " to the source of the problem in front of dead-end situations. This paper examines another issue (much less explored) which arises in dead-ends. Specifically, we concen-trate on the idea of constraint recording, namely, analyzing and storing the reasons for the dead-ends, and using them to guide future decisions, so that the same conflicts will not arise again. We view constraint recording as a process of learning, and examine several possible learning schemes studying the tradeoffs between the amount of learning and the improve-ment in search efficiency.},
author = {Dechter, Rina},
journal = {Proceedings of the Fifth National Conference on Artificial Intelligence},
keywords = {Deep Learning,Introduction},
pages = {178--183},
title = {{Learning while searching in constraint-satisfaction problems}},
year = {1986}
}
@misc{Ettus,
author = {Ettus},
title = {{Ettus Research - The leader in Software Defined Radio (SDR)}},
howpublished = "\url{https://www.ettus.com/}",
note = {Accessed 2017-11-30},
url = {https://www.ettus.com/},
urldate = {2017-11-30}
}
@misc{OSX,
title = {{Building and Installing the USRP Open-Source Toolchain (UHD and GNU Radio) on OS X - Ettus Knowledge Base}},
howpublished = "\url{https://kb.ettus.com/Building\_and\_Installing\_the\_USRP\_Open-Source\_Toolchain\_(UHD\_and\_GNU\_Radio)\_on\_OS\_X}",
note = {Accessed 2017-11-26},
url = {https://kb.ettus.com/Building{\_}and{\_}Installing{\_}the{\_}USRP{\_}Open-Source{\_}Toolchain{\_}(UHD{\_}and{\_}GNU{\_}Radio){\_}on{\_}OS{\_}X},
urldate = {2017-11-26}
}
@misc{Jupyter,
title = {{Project Jupyter | Home}},
howpublished = "\url{http://jupyter.org/}",
note = {Accessed 2017-11-25},
url = {http://jupyter.org/},
urldate = {2017-11-25}
}
@article{Bose2001,
abstract = {The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category. {\textcopyright} 2001 Elsevier Science B.V. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {1102.5735},
author = {Bose, Indranil and Mahapatra, Radha K.},
doi = {10.1016/S0378-7206(01)00091-X},
eprint = {1102.5735},
isbn = {0378-7206},
issn = {03787206},
journal = {Information and Management},
keywords = {Business applications,Data mining,Machine learning},
month = {dec},
number = {3},
pages = {211--225},
publisher = {North-Holland},
title = {{Business data mining - A machine learning perspective}},
url = {http://www.sciencedirect.com/science/article/pii/S037872060100091X},
volume = {39},
year = {2001}
}
@misc{Windows,
title = {{Building and Installing the USRP Open Source Toolchain (UHD and GNU Radio) on Windows - Ettus Knowledge Base}},
howpublished =" \url{https://kb.ettus.com/Building\_and\_Installing\_the\_USRP\_Open\_Source\_Toolchain\_(UHD\_and\_GNU\_Radio)\_on\_Windows}",
note = {Accessed 2017-11-26},
url = {https://kb.ettus.com/Building{\_}and{\_}Installing{\_}the{\_}USRP{\_}Open{\_}Source{\_}Toolchain{\_}(UHD{\_}and{\_}GNU{\_}Radio){\_}on{\_}Windows},
urldate = {2017-11-26}
}
@article{Fu2017,
abstract = {In March of 2016, Google DeepMind's AlphaGo, a computer Go-playing program, defeated the reigning human world champion Go player, 4-1, a feat far more impressive than previous victories by computer programs in chess (IBM's Deep Blue) and Jeopardy (IBM's Watson). The main engine behind the program combines machine learning approaches with a technique called Monte Carlo tree search. Current versions of Monte Carlo tree search used in Go-playing algorithms are based on a version developed for games that traces its roots back to the adaptive multi-stage sampling simulation optimization algorithm for estimating value functions in finite-horizon Markov decision processes (MDPs) introduced by Chang et al. (2005), which was the first use of Upper Confidence Bounds (UCBs) for Monte Carlo simulation-based solution of MDPs. We review the main ideas in UCB-based Monte Carlo tree search by connecting it to simulation optimization through the use of two simple examples: decision trees and tic-Tac-Toe. {\textcopyright} 2016 IEEE.},
author = {Fu, Michael C.},
doi = {10.1109/WSC.2016.7822130},
isbn = {9781509044863},
issn = {08917736},
journal = {Proceedings - Winter Simulation Conference},
pages = {659--670},
title = {{Alphago and Monte Carlo tree search: The simulation optimization perspective}},
year = {2017}
}
@article{Hawkins,
author = {Hawkins, Jeff},
title = {{What Intelligente Machines Need to Learn From the Neocortex}}
}
@article{OShea2017,
abstract = {We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding transmitter and receivers to the multi-antenna case. We introduce a widely used domain appropriate wireless channel impairment model (Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. We considered both spatial diversity and spatial multiplexing techniques in our implementation. Our deep learning-based approach demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems. We discuss how the proposed scheme can be easily adapted for open-loop and closed-loop operation in spatial diversity and multiplexing modes and extended use with only compact binary channel state information (CSI) as feedback.},
archivePrefix = {arXiv},
arxivId = {1707.07980},
author = {O'Shea, Timothy J. and Erpek, Tugba and Clancy, T. Charles},
eprint = {1707.07980},
pages = {1--9},
title = {{Deep Learning Based MIMO Communications}},
url = {http://arxiv.org/abs/1707.07980},
year = {2017}
}
@article{Jondral2005,
abstract = {We provide a brief overview over the development of software-defined or reconfigurable radio systems. The need for software-defined radios is underlined and the most important notions used for such reconfigurable transceivers are thoroughly defined. The role of standards in radio development is emphasized and the usage of transmission mode parameters in the construction of software-defined radios is described. The software communications architecture is introduced as an example for a framework that allows an object-oriented development of software-defined radios. Cognitive radios are introduced as the next step in radio systems' evolution. The need for cognitive radios is exemplified by a comparison of present and advanced spectrum management strategies.},
author = {Jondral, Friedrich K},
journal = {EURASIP Journal on Wireless Communications and Networking},
keywords = {advanced spectrum management.,cognitive radio,mobile communication standards,reconfigurable transceiver,software defined radio},
pages = {275--283},
title = {{Software-Defined Radio—Basics and Evolution to Cognitive Radio}},
url = {https://link.springer.com/content/pdf/10.1155{\%}2FWCN.2005.275.pdf},
volume = {3},
year = {2005}
}
@misc{UBX,
title = {{UBX 10 MHz - 6 GHz Rx/Tx, 40 MHz BW - Ettus Research}},
howpublished = "\url{https://www.ettus.com/product/details/UBX40}",
note = {Accessed 2017-11-25},
url = {https://www.ettus.com/product/details/UBX40},
urldate = {2017-11-25}
}
@misc{IPython,
title = {{Jupyter and the future of IPython — IPython}},
howpublished ="\url{https://ipython.org/}",
note = {Accessed 2017-11-25},
url = {https://ipython.org/},
urldate = {2017-11-25}
}
@misc{CNTK,
title = {{The Microsoft Cognitive Toolkit | Microsoft Docs}},
howpublished = "\url{https://docs.microsoft.com/en-us/cognitive-toolkit/}",
note = {Accessed 2017-11-26},
url = {https://docs.microsoft.com/en-us/cognitive-toolkit/},
urldate = {2017-11-26}
}
@misc{GNURadio2016,
abstract = {GNU Radio is a free {\&} open-source software development toolkit that provides signal processing blocks to implement software radios. It can be used with readily-available low-cost external RF hardware to create software-defined radios, or without hardware in a simulation-like environment. It is widely used in hobbyist, academic and commercial environments to support both wireless communications research and real-world radio systems.$\backslash$r$\backslash$n$\backslash$r$\backslash$nGNU Radio is licensed under the GNU General Public License (GPL) version 3 or later. All of the code is copyright of the Free Software Foundation.},
author = {GNURadio},
booktitle = {Webpage},
pages = {1},
title = {{GNU Radio}},
url = {http://gnuradio.org/},
urldate = {2017-11-24},
year = {2016}
}
@misc{N210,
title = {{USRP N210 Software Defined Radio (SDR) - Ettus Research}},
howpublished = "\url{https://www.ettus.com/product/details/UN210-KIT}",
note = {Accessed 2017-11-25},
url = {https://www.ettus.com/product/details/UN210-KIT},
urldate = {2017-11-25}
}
@article{Hsu1999,
abstract = {The IBM Deep Blue supercomputer that defeated World Chess Champion$\backslash$nGarry Kasparov in 1997 employed 480 custom chess chips. This article$\backslash$ndescribes the design philosophy, general architecture, and performance$\backslash$nof the chess chips, which provided most of Deep Blue's computational$\backslash$npower},
author = {Hsu, Feng Hsiung},
doi = {10.1109/40.755469},
issn = {02721732},
journal = {IEEE Micro},
number = {2},
pages = {70--81},
title = {{IBM's Deep Blue chess grandmaster chips}},
volume = {19},
year = {1999}
}
@inproceedings{Wu2007,
author = {Wu, Zhiqiang and Natarajan, Bala},
booktitle = {2007 4th Annual IEEE Consumer Communications and Networking Conference, CCNC 2007},
doi = {10.1109/CCNC.2007.207},
isbn = {1424406668},
month = {jan},
pages = {1027--1031},
publisher = {IEEE},
title = {{Interference tolerant agile cognitive radio maximize channel capacity of cognitive radio}},
url = {http://ieeexplore.ieee.org/document/4199295/},
year = {2007}
}
@inproceedings{Papadakis2017,
author = {Papadakis, Stefanos and Surligas, Manolis and Triantafyllakis, Kostis and Vardakis, George and Gkiolias, Athanasios and Karamolegos, Nikolaos and Makrogiannakis, Antonis},
booktitle = {2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
doi = {10.1109/DySPAN.2017.7920783},
isbn = {978-1-5090-2830-6},
month = {mar},
pages = {1--2},
publisher = {IEEE},
title = {{An agile OFDM cognitive radio engine}},
url = {http://ieeexplore.ieee.org/document/7920783/},
year = {2017}
}
@article{Kingma2014,
abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
archivePrefix = {arXiv},
arxivId = {1412.6980},
author = {Kingma, Diederik P. and Ba, Jimmy},
eprint = {1412.6980},
month = {dec},
title = {{Adam: A Method for Stochastic Optimization}},
url = {http://arxiv.org/abs/1412.6980},
year = {2014}
}
@article{repo:cognitive_radio_ml,
abstract = {Using Machine Learning for Spectrum Awareness},
author = {Cuervo, Nicolas},
keywords = {Machine learning,cognitive radio,deep learning},
title = {{Github, Cognitive Radio ML}},
howpublished = "\url{https://github.com/primercuervo/cognitive{\_}radio{\_}ml}",
note = {Accessed 2017-11-25},
url = {https://github.com/primercuervo/cognitive{\_}radio{\_}ml},
year = {2017}
}
@misc{Oxford,
author = {Oxford},
title = {{learn | Definition of learn in English by Oxford Dictionaries}},
howpublished = "\url{https://en.oxforddictionaries.com/definition/learn}",
note = {Accessed 2017-11-22},
url = {https://en.oxforddictionaries.com/definition/learn},
urldate = {2017-11-22}
}
@misc{PyBOMBS,
title = {{PyBOMBS – The What, the How and the Why - GNU Radio - GNU Radio}},
howpublished = "\url{https://www.gnuradio.org/blog/pybombs-the-what-the-how-and-the-why/}",
note = {Accessed 2017-11-25},
url = {https://www.gnuradio.org/blog/pybombs-the-what-the-how-and-the-why/},
urldate = {2017-11-25}
}
@inproceedings{Lackpour2017,
author = {Lackpour, Alex and Mason, Sean and Hamilton, Chase and Tigreros, David and Giovannucci, Matthew and Marcou, Matthew and Liu, Yuqiao and Jacovic, Marko and Dandekar, Kapil R.},
booktitle = {2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
doi = {10.1109/DySPAN.2017.7920785},
isbn = {978-1-5090-2830-6},
month = {mar},
pages = {1--2},
publisher = {IEEE},
title = {{Design and implementation of the Secondary User-Enhanced Spectrum Sharing (SUESS) radio}},
url = {http://ieeexplore.ieee.org/document/7920785/},
year = {2017}
}
@misc{VOLK,
title = {{Vector Optimized Library of Kernels}},
howpublished = "\url{http://libvolk.org/}",
note = {Accessed 2017-11-30},
url = {http://libvolk.org/},
urldate = {2017-11-30}
}
@misc{IEEEDySPAN2015,
author = {{IEEE DySPAN}},
title = {{IEEE DySPAN Standards Committee (DySPAN-SC)}},
howpublished = "\url{http://grouper.ieee.org/groups/dyspan/}",
note = {Accessed 2017-11-21},
url = {http://grouper.ieee.org/groups/dyspan/},
urldate = {2017-11-21},
year = {2015}
}
@book{Andreas,
abstract = {applicability for this approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Mueller, Andreas and Guido, Sarah},
booktitle = {Journal of Chemical Information and Modeling},
doi = {10.1017/CBO9781107415324.004},
eprint = {arXiv:1011.1669v3},
isbn = {9788578110796},
issn = {1098-6596},
keywords = {icle},
number = {9},
pages = {1689--1699},
pmid = {25246403},
title = {{Introduction to Machine Learning with Python}},
volume = {53},
year = {2016}
}
@misc{B210,
title = {{USRP B210 USB Software Defined Radio (SDR) - Ettus Research}},
howpublished = "\url{https://www.ettus.com/product/details/UB210-KIT}",
note = {Accessed 2017-11-25},
url = {https://www.ettus.com/product/details/UB210-KIT},
urldate = {2017-11-25}
}
@article{Goldsmith,
abstract = {See, stats, and : https : / / www . researchgate . net / publication / 224408389 Breaking Radios : An Article DOI : 10 . 1109 / JPROC . 2009 . 2015717 : IEEE CITATIONS 1 , 500 READS 254 4 , including : Ivana Ericsson , San , CA , United 54 , 774 SEE All . The . Abstract—Cognitive radios hold tremendous promise for in - creasing spectral efficiency in wireless systems . This paper sur - veys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology . These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization . This side informa - tion typically comprises knowledge about the activity , channels , codebooks and / or messages of other nodes with which the cognitive node shares the spectrum . Based on the nature of the available side information as well as a priori rules about spectrum usage , cognitive radio systems seek to underlay , overlay or interweave the cognitive radios ' signals with the transmissions of noncognitive nodes . We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches . The increase in system degrees of freedom obtained through cognitive radios is also illuminated . This information theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios , as well as practical design ideas to mitigate the coexistence challenges in today ' s crowded spectrum .},
author = {Goldsmith, Andrea and {Ali Jafar}, Syed and Mari, Ivana and Srinivasa, Sudhir},
doi = {10.1109/JPROC.2009.2015717},
title = {{Breaking Spectrum Gridlock with Cognitive Radios : An Information Theoretic Perspective}},
url = {https://www.researchgate.net/profile/Ivana{\_}Maric2/publication/224408389{\_}Breaking{\_}Spectrum{\_}Gridlock{\_}With{\_}Cognitive{\_}Radios{\_}An{\_}Information{\_}Theoretic{\_}Perspective/links/54d92cce0cf2970e4e7ae685.pdf}
}
@article{Wunsch2017a,
author = {Wunsch, Felix and Koslowski, Sebastian and Muller, Sebastian and Cuervo, Nicolas and Jondral, Friedrich K.},
doi = {10.1109/DySPAN.2017.7920782},
isbn = {9781509028306},
journal = {2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017},
pages = {6--7},
title = {{A cognitive overlay system based on FBMC}},
year = {2017}
}
@misc{Linux,
title = {{Building and Installing the USRP Open-Source Toolchain (UHD and GNU Radio) on Linux - Ettus Knowledge Base}},
howpublished = "\url{https://kb.ettus.com/Building\_and\_Installing\_the\_USRP\_Open-Source\_Toolchain\_(UHD\_and\_GNU\_Radio)\_on\_Linux}",
note = {Accessed 2017-11-26},
url = {https://kb.ettus.com/Building{\_}and{\_}Installing{\_}the{\_}USRP{\_}Open-Source{\_}Toolchain{\_}(UHD{\_}and{\_}GNU{\_}Radio){\_}on{\_}Linux},
urldate = {2017-11-26}
}
@article{Wunsch2017,
author = {Wunsch, Felix and Paisana, Francisco and Rajendran, Sreeraj and Selim, Ahmed and Alvarez, Pedro and Muller, Sebastian and Koslowski, Sebastian and {Van den Bergh}, Bertold and Pollin, Sofie},
doi = {10.1109/TCCN.2017.2745682},
issn = {2332-7731},
journal = {IEEE Transactions on Cognitive Communications and Networking},
keywords = {cognitive radio,deep learning,spectrum challenge},
pages = {1--1},
title = {{DySPAN Spectrum Challenge: situational awareness and opportunistic spectrum access benchmarked}},
url = {http://ieeexplore.ieee.org/document/8017499/},
year = {2017}
}
@article{Kononenko2001,
abstract = {The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment. Copyright {\textcopyright} 2001 Elsevier Science B.V.},
author = {Kononenko, Igor},
doi = {10.1016/S0933-3657(01)00077-X},
isbn = {0933-3657},
issn = {09333657},
journal = {Artificial Intelligence in Medicine},
keywords = {Kirlian camera,Machine learning,Medical diagnosis,Reliability of prediction},
month = {aug},
number = {1},
pages = {89--109},
pmid = {11470218},
publisher = {Elsevier},
title = {{Machine learning for medical diagnosis: History, state of the art and perspective}},
url = {http://www.sciencedirect.com/science/article/pii/S093336570100077X},
volume = {23},
year = {2001}
}
@book{Segaran2007,
abstract = {Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general-all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features-crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving-how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." - Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." - Tim Wolters, CTO, Collective Intellect},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Segaran, Toby},
booktitle = {Book},
doi = {10.1016/j.jconhyd.2010.08.009},
eprint = {arXiv:1011.1669v3},
isbn = {0596529325},
issn = {13613200},
pages = {334},
pmid = {20926156},
title = {{Programming Collective Intelligence: Building Smart Web 2.0 Applications}},
url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20{\&}path=ASIN/0596529325},
year = {2007}
}
@article{Mitola2000,
abstract = {This dissertation addresses the intersection of personal wireless technology and computational intelligence. The primary research issue addressed is the organization of radio domain knowledge into data structures processable in real-time that integrate machine learning and natural language processing technology into software radio. The thesis defines and develops the cognitive radio architecture. The features needed in the architecture are derived from cognitive radio use cases. These include inferring user communications context, shaping accessnetwork demand, and realizing a protocol for real-time radio spectrum rental. Mathematical foundations for the knowledge-representation architecture are derived by applying point-set topology to the requirements of the use cases. This results in the set-theoretic ontology of radio knowledge defined in the Radio Knowledge Representation Language (RKRL). The mathematical analysis also demonstrates that isochronous radio software is not Turingcomputable. Instead, it is constrained to a bounded-recursive subset of the total functions. A rapid-prototype cognitive radio, CR1, was developed to apply these mathematical foundations in a simulated environment. CR1 demonstrated the principles of cognitive radio and focused the research issues. This led to an important contribution of this dissertation, the cognitive radio architecture. This is an open architecture framework for integrating agent-based control, natural language processing, and machine learning technology into software-defined radio platforms.},
author = {Mitola, Joseph},
doi = {10.1109/SURV.2013.082713.00141},
isbn = {9780471742449},
issn = {14035286},
journal = {Doctor of Technology, Royal Inst. Technol.(KTH), {\ldots}},
keywords = {Joseph Mitola III},
pages = {271--350.},
pmid = {16502613},
title = {{Cognitive radio: An integrated agent architecture for software defined radio}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Cognitive+Radio+An+Integrated+Agent+Architecture+for+Software+Defined+Radio+Dissertation{\#}8{\%}5Cnhttp://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Cognitive+radio:+An+integrated+agent+},
year = {2000}
}
@inproceedings{Coates2011,
abstract = {Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both. In this paper, we apply methods recently developed in machine learning -- specifically, large-scale algorithms for learning the features automatically from unlabeled data -- and show that they allow us to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.},
archivePrefix = {arXiv},
arxivId = {fa},
author = {Coates, Adam and Carpenter, Blake and Case, Carl and Satheesh, Sanjeev and Suresh, Bipin and Wang, Tao and Wu, David J. and Ng, Andrew Y.},
booktitle = {Proceedings of the International Conference on Document Analysis and Recognition, ICDAR},
doi = {10.1109/ICDAR.2011.95},
eprint = {fa},
isbn = {9780769545202},
issn = {15205363},
keywords = {Robust reading,character recognition,feature learning,photo OCR},
month = {sep},
pages = {440--445},
pmid = {43479959},
publisher = {IEEE},
title = {{Text detection and character recognition in scene images with unsupervised feature learning}},
url = {http://ieeexplore.ieee.org/document/6065350/},
year = {2011}
}
@misc{Dyspanchalle,
author = {Challenge, Dyspan},
title = {{IEEE DySPAN | IEEE DySPAN}},
howpublished = "\url{http://dyspan2015.ieee-dyspan.org/}",
note = {Accessed 2017-11-21},
url = {http://dyspan2015.ieee-dyspan.org/},
urldate = {2017-11-21}
}
@article{Nilsson2005,
abstract = {These notes are in the process of becoming a textbook. The process is quite unfinished, and the author solicits corrections, criticisms, and suggestions from students and other readers. Although I have tried to eliminate errors, some un- doubtedly remain—caveat lector. Many typographical infelicities will no doubt me have your suggestions about topics that are too important to be left out. I hope that future versions will cover Hopfield nets, Elman nets and other re- current nets, radial basis functions, grammar and automata learning, genetic algorithms, and Bayes networks . . .. I am also collecting exercises and project suggestions which will appear in future versions. My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that would be of practical importance in applications; the book is not a handbook of machine learning practice. Instead, my goal is to give the reader sufficient preparation to make the extensive literature on machine learning accessible. Students in my Stanford courses on machine learning have already made several useful suggestions, as have my colleague, Pat Langley, and my teaching assistants, Ron Kohavi, Karl Pfleger, Robert Allen, and Lise Getoor. persist until the final version. More material has yet to be added. Please let Some of my plans for additions and other reminders are mentioned in marginal notes.},
archivePrefix = {arXiv},
arxivId = {0904.3664v1},
author = {Nilsson, Nils J.},
doi = {10.1016/j.neuroimage.2010.11.004},
eprint = {0904.3664v1},
isbn = {9780262012430},
issn = {10959572},
pages = {1--188},
pmid = {21172442},
title = {{Introduction To Machine Learning an Early Draft of a Proposed Text}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:INTRODUCTION+TO+MACHINE+LEARNING+AN+EARLY+DRAFT+OF+A+PROPOSED+TEXTBOOK+Department+of+Computer+Science{\#}0},
year = {2005}
}
@misc{KERAS,
title = {{Keras Documentation}},
howpublished = "\url{https://keras.io/}",
note = {Accessed 2017-11-25},
url = {https://keras.io/},
urldate = {2017-11-25}
}
@misc{X300,
title = {{USRP X300 High Performance Software Defined Radio (SDR) - Ettus Research}},
howpublished = "\url{https://www.ettus.com/product/details/X300-KIT}",
note = {Accessed 2017-11-25},
url = {https://www.ettus.com/product/details/X300-KIT},
urldate = {2017-11-25}
}
@misc{Oxforda,
author = {Oxford},
title = {{intelligence | Definition of intelligence in English by Oxford Dictionaries}},
howpublished = "\url{https://en.oxforddictionaries.com/definition/intelligence}",
note = {Accessed 2017-11-22},
url = {https://en.oxforddictionaries.com/definition/intelligence},
urldate = {2017-11-22}
}
@inproceedings{Rykaczewski2007,
author = {Rykaczewski, P and Jondral, F},
booktitle = {Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on},
doi = {10.1109/ISCAS.2007.378174},
isbn = {1-4244-0920-9},
issn = {02714310},
keywords = {Monte Carlo methods,Monte Carlo simulations,blind I/Q imbalance compensation,blind source separation,fading channels,image frequency band,multipath channels,multipath fading,quadrature receivers,receivers,signal-to-noise-and-distortion ratio},
month = {may},
pages = {29--32},
publisher = {IEEE},
title = {{Blind I/Q Imbalance Compensation in Multipath Environments}},
url = {http://ieeexplore.ieee.org/document/4252563/},
year = {2007}
}
@inproceedings{Dahl2013,
abstract = {Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech recognition benchmarks. Deep neural nets have also achieved excellent results on various computer vision tasks using a random “dropout” procedure that drastically improves generalization error by randomly omitting a fraction of the hidden units in all layers. Since dropout helps avoid overﬁtting, it has also been successful on a small-scale phone recognition task using larger neural nets. However, training deep neural net acoustic models for large vocabulary speech recognition takes a very long time and dropout is likely to only increase training time. Neural networks with rectiﬁed linear unit (ReLU) non-linearities have been highly successful for computer vision tasks and proved faster to train than standard sigmoid units, sometimes also improving discriminative performance. In this work, we show on a 50-hour English Broadcast News task that modiﬁed deep neural networks using ReLUs trained with dropout during frame level training provide an 4.2{\%} relative improvement over a DNN trained with sigmoid units, and a 14.4{\%} relative improvement over a strong GMM/HMM system. We were able to obtain our results with minimal human hyper-parameter tuning using publicly available Bayesian optimization code},
archivePrefix = {arXiv},
arxivId = {arXiv:1301.3605v3},
author = {Dahl, George E. and Sainath, Tara N. and Hinton, Geoffrey E.},
booktitle = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings},
doi = {10.1109/ICASSP.2013.6639346},
eprint = {arXiv:1301.3605v3},
isbn = {9781479903566},
issn = {15206149},
keywords = {Bayesian optimization,LVCSR,acoustic modeling,broadcast news,deep learning,dropout,neural networks,rectified linear units},
month = {may},
pages = {8609--8613},
publisher = {IEEE},
title = {{Improving deep neural networks for LVCSR using rectified linear units and dropout}},
url = {http://ieeexplore.ieee.org/document/6639346/},
year = {2013}
}
@article{OShea2016d,
abstract = {We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative met- rics for quality of encoding using domain relevant performance metrics.},
archivePrefix = {arXiv},
arxivId = {1604.07078},
author = {O'Shea, Timothy J. and Corgan, Johnathan and Clancy, T. Charles},
eprint = {1604.07078},
month = {apr},
title = {{Unsupervised Representation Learning of Structured Radio Communication Signals}},
url = {http://arxiv.org/abs/1604.07078},
year = {2016}
}
@misc{SKLEARN,
title = {{scikit-learn: machine learning in Python — scikit-learn 0.19.1 documentation}},
howpublished = "\url{http://scikit-learn.org/stable/index.html}",
note = {Accessed 2017-11-25},
url = {http://scikit-learn.org/stable/index.html},
urldate = {2017-11-25}
}
@article{Kaushik2015,
author = {Kaushik, Ankit and Wunsch, Felix and Sagainov, Andrej and Cuervo, Nicolas and Demel, Johannes and Koslowski, Sebastian and Holger, J and Jondral, Friedrich},
isbn = {9781479974528},
number = {1},
pages = {6--7},
title = {{Spectrum Sharing for 5G Wireless Systems}},
year = {2015}
}
@misc{Theano,
title = {{Welcome — Theano 1.0.0 documentation}},
howpublished = "\url{http://www.deeplearning.net/software/theano/}",
note = {Accessed 2017-11-26},
url = {http://www.deeplearning.net/software/theano/},
urldate = {2017-11-26}
}
@article{Paisana2017,
author = {Paisana, Francisco and Selim, Ahmed and Kist, Maicon and Alvarez, Pedro and Tallon, Justin and Bluemm, Christian and Puschmann, Andre and Dasilva, Luiz},
isbn = {9781509028306},
journal = {IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), Spectrum Challenge},
keywords = {cognitive,deep learning,radio,software-defined radio,spectrum sensing,spectrum sharing},
pages = {1--2},
title = {{Context-Aware Cognitive Radio Using Deep Learning}},
year = {2017}
}
@article{OShea2016,
abstract = {We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.},
archivePrefix = {arXiv},
arxivId = {1602.04105},
author = {O'Shea, Timothy J. and Corgan, Johnathan and Clancy, T. Charles},
doi = {10.1007/978-3-319-44188-7_16},
eprint = {1602.04105},
isbn = {9783319441870},
issn = {18650929},
journal = {Communications in Computer and Information Science},
keywords = {Cognitive radio,Convolutional networks,Deep learning,Dynamic spectrum access,Machine learning,Modulation recognition,Radio,Software radio},
pages = {213--226},
title = {{Convolutional radio modulation recognition networks}},
volume = {629},
year = {2016}
}
@article{Nakamura2002,
abstract = {Feature interactions (FIs, in short) occur when features of different communication services interfere with each other. The FI filtering is a pre-processing before the FI detection, which roughly identifies FI-prone service combinations based on simple indications of the FIs. We have previously proposed an FI filtering method at requirements stage using Use Case Maps (UCMs). This method identifies FI-prone service combinations by focusing on changes in user's scenarios before/after the service composition, but does not tell which scenarios in the compound services have a potential of FIs.In this paper, as an extension of the previous method, we propose a new method to derive FI-prone scenarios from the FI-prone combinations obtained by the previous method. From many practical FIs, we first make the following two observations: (a) FI tends to occur in scenarios where both services are activated, and (b) FI tends to occur in scenarios where a service bypasses a feature of the other service. Then, based on the observations, we propose heuristics on the UCM scenario paths to derive FI-prone scenarios. An experimental evaluation demonstrates that the derived scenarios successfully cover all scenarios that lead to actual FIs.},
author = {Nakamura, M. and Leelaprute, P. and Kikuno, T.},
doi = {10.1109/WORDS.2002.1000058},
isbn = {0769515762},
issn = {15301443},
journal = {Proceedings - International Workshop on Object-Oriented Real-Time Dependable Systems, WORDS},
keywords = {Computer aided software engineering,Costs,Explosions,Filtering,Floods,IP networks,Informatics,Intelligent networks,Mobile communication,Telecommunication services},
number = {Words},
pages = {237--244},
title = {{Deriving interaction-prone scenarios in feature interaction filtering with use case maps}},
volume = {2002-Janua},
year = {2002}
}
@article{Hoeffding1963,
abstract = {Upper bounds are derived for the probability that the sum S of n independent random variables exceeds its mean ES by a positive num- ber nt. It is assumed that the range of each summand of S is bounded or bounded above. The bounds for Pr {\{} S -ES {\textgreater}nt {\}} depend only on the endpoints of the ranges of the summands and the mean, or the mean and the variance of S. These results are then used to obtain analogous inequalities for certain sums of dependent random variables such as U statistics and the sum of a random sample without replace- ment from a finite population.},
archivePrefix = {arXiv},
arxivId = {math/0603030},
author = {Hoeffding, Wassily},
doi = {10.1080/01621459.1963.10500830},
eprint = {0603030},
isbn = {0162-1459},
issn = {1537274X},
journal = {Journal of the American Statistical Association},
keywords = {Binomials,Chebyshevs inequality,Integers,Mathematical inequalities,Probabilities,Random variables,Sample mean,Statistical theories,Statistical variance,Statistics},
month = {mar},
number = {301},
pages = {13--30},
pmid = {17163781},
primaryClass = {math},
publisher = {Taylor {\&} Francis, Ltd.American Statistical Association},
title = {{Probability Inequalities for Sums of Bounded Random Variables}},
url = {http://www.jstor.org/stable/2282952?origin=crossref},
volume = {58},
year = {1963}
}
@article{Srinivasa2009,
author = {Srinivasa, Sudhir},
journal = {Proceedings of the IEEE},
keywords = {increasing spectral efficiency in,limits and associated trans-,mission techniques for different,surveys the fundamental capacity,this paper,wireless network design,wireless systems},
number = {5},
title = {{With Cognitive Radios : An Information Theoretic Perspective}},
volume = {97},
year = {2009}
}
@book{HawkinsJeff2004,
abstract = {1st Owl Books ed. "An Owl book." "Originally published in hardcover in 2004 by Times Books"--Title page verso. The developer of the PalmPilot and creator of the Redwood Neuroscience Institute examines the real future of artificial intelligence, explaining why the way we build computers today won't result in intelligent machines. He shows, using accessible examples, that the brain's neocortex is a memory-driven system that uses our senses and our perception of time, space, and consciousness to construct a predictive model of the world in a way that's totally unlike even the most complex computer software. 1. Artificial intelligence -- 2. Neural networks -- 3. The human brain -- 4. Memory -- 5. A new framework of intelligence -- 6. How the cortex works -- 7. Consciousness and creativity -- 8. The future of intelligence.},
author = {{Hawkins Jeff} and Blakeslee, Sandra},
isbn = {0805078533},
pages = {261},
publisher = {Henry Holt and Co},
title = {{On Intelligence}},
year = {2004}
}
@inproceedings{Zappone2016,
abstract = {{\textcopyright} 2016 IEEE.This paper studies the problem of resource allocation in overlay systems. A multiple-input single-output (MISO) primary link shares the spectrum with a multiple-input multiple-output (MIMO) secondary link, which in return acts as an amplify-And-forward (AF) relay, forwarding the primary message. The considered problem is the maximization of the secondary energy efficiency (EE) subject to a primary rate requirement. The resulting optimization problem is a fractional program which can not be tackled by traditional fractional optimization methods. Two algorithms are proposed, based on an interplay between fractional programming and sequential optimization theory, which trade-off performance and complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity.},
author = {Zappone, Alessio and Matthiesen, Bho and Jorswieck, Eduard A.},
booktitle = {2016 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2016},
doi = {10.1109/WCNCW.2016.7552685},
isbn = {9781467386661},
issn = {15253511},
month = {apr},
pages = {115--120},
publisher = {IEEE},
title = {{Energy-efficient MIMO overlay communications for device-To-device and cognitive radio systems}},
url = {http://ieeexplore.ieee.org/document/7564656/},
year = {2016}
}
@article{Brooks1991,
abstract = {Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporate-everything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. ?? 1991.},
author = {Brooks, Rodney A.},
doi = {10.1016/0004-3702(91)90053-M},
isbn = {0004-3702},
issn = {00043702},
journal = {Artificial Intelligence},
number = {1-3},
pages = {139--159},
pmid = {14599324},
title = {{Intelligence without representation}},
volume = {47},
year = {1991}
}
@article{Zeiler2012,
abstract = {We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment.},
archivePrefix = {arXiv},
arxivId = {1212.5701},
author = {Zeiler, Matthew D.},
eprint = {1212.5701},
month = {dec},
title = {{ADADELTA: An Adaptive Learning Rate Method}},
url = {http://arxiv.org/abs/1212.5701},
year = {2012}
}
@article{Iii2006,
author = {Iii, Joseph Mitola},
title = {{SDR ( Software-Defined Radio )}},
year = {2006}
}
@article{Afrasiabi-Gorgani2017,
abstract = {The Filter Bank MultiCarrier (FBMC) modulation with Offset QAM (OQAM), also know as OFDM-OQAM, has been very well studied in, roughly speaking, the last 15 years. A rich literature exists on the principle itself, discrete-time version and implementation, equalization, extension to MIMO, etc. This text, written in a tutorial style, explains the idea of Offset QAM and why it works from a Communications Engineer point of view. The language is very simple and the math elementary. Students and researchers in universities or practitioners who are interested in gaining an intuition into the logic behind OFDM-OQAM will hopefully enjoy reading this text. On the other hand, those interested in a quick understanding of the idea, just enough to start using the waveform, can find far better material.},
archivePrefix = {arXiv},
arxivId = {1707.08934},
author = {Afrasiabi-Gorgani, Saeed},
eprint = {1707.08934},
number = {July},
title = {{An Introduction to OFDM-OQAM}},
url = {http://arxiv.org/abs/1707.08934},
year = {2017}
}
@misc{CGRAN,
title = {{The Comprehensive GNU Radio Archive Network}},
howpublished = "\url{http://cgran.org/}",
note = {Accessed 2017-11-25},
url = {http://cgran.org/},
urldate = {2017-11-25}
}
@article{Staple2004,
abstract = {Radio spectrum may be one of the most tightly regulated resources of all time. Every wireless device depends on access to the radio frequency wireless spectrum. New technologies that use spectrum more efficiently and more cooperatively, unleashed by regulatory reforms, may soon overcome the spectrum shortage. To understand the impact new radio technologies are having on spectrum availability, it is helpful first to address a common misconception: that spectrum is a concrete and finite resource. Not so. Radio waves do not pass through some ethereal medium called "spectrum"; they are the medium. What is licensed by the governments is not a piece of a finite pie, but simply the right to deploy transmitters and receivers that operate in particular ways.},
author = {Staple, G. and Werbach, K.},
doi = {10.1109/MSPEC.2004.1270548},
isbn = {00189235},
issn = {00189235},
journal = {IEEE Spectrum},
month = {mar},
number = {3},
pages = {48--52},
pmid = {22215942},
title = {{The end of spectrum scarcity}},
url = {http://ieeexplore.ieee.org/document/1270548/},
volume = {41},
year = {2014}
}
@book{Yaser,
author = {Abu-Mostafa, Yaser S. and Magdon-Ismail, Malik and Lin, Hsuan-Tien},
booktitle = {Learning from Data},
isbn = {10: 1 60049 006 9},
number = {4},
pages = {479--481},
publisher = {AMLbook.com},
title = {{Learning From Data}},
volume = {21},
year = {2010}
}
@article{Zhai2013,
abstract = {A spectrum sharing scheme is proposed for overlaid wireless networks based on the decode-and-forward two-path successive relaying technique. In this scheme, two secondary transmitters are used to transmit secondary information alternately to their respective receivers while relaying the primary signal at the same time. The transmission of the primary system is continuous, and the secondary system can opportunistically help the primary transmission in exchange for the spectrum sharing. Superposition coding is used at the secondary transmitters, where the primary signal and secondary signal are linearly combined. Successive interference decoding and cancelation is performed by secondary users to extract their desired signals. For the primary system, joint decoding is performed by treating the secondary signal as noise at the receiver side. The optimal power allocation is determined to maximize the success probability of the secondary system without violating the outage performance of the primary system. Numerical and simulation results show that the proposed scheme can conservatively protect the primary performance while realizing the secondary transmission requirement.},
author = {Zhai, Chao and Zhang, Wei and Ching, P. C.},
doi = {10.1109/TCOMM.2013.041113.120768},
isbn = {9781467300469},
issn = {00906778},
journal = {IEEE Transactions on Communications},
keywords = {Cognitive radio,Cooperative communication,Spectrum sharing,Two-path successive relaying},
title = {{Cooperative spectrum sharing based on two-path successive relaying}},
year = {2013}
}
@article{Kaushik2013,
author = {Kaushik, Ankit and Mueller, Marcus and Jondral, FK},
isbn = {9783800735297},
issn = {21540225},
journal = {{\ldots} Systems (ISWCS 2013), {\ldots}},
pages = {680--681},
title = {{Cognitive Relay: Detecting Spectrum Holes in a Dynamic Scenario}},
url = {http://ieeexplore.ieee.org/xpls/abs{\_}all.jsp?arnumber=6629824},
volume = {9},
year = {2013}
}
@misc{Andrews,
author = {Andrews, Martin},
title = {{Nvidia (8.0) installation for TensorFlow on Fedora 25}},
howpublished = "\url{http://blog.mdda.net/oss/2016/11/25/nvidia-on-fedora-25}",
note = {Accessed 2017-11-26},
url = {http://blog.mdda.net/oss/2016/11/25/nvidia-on-fedora-25},
urldate = {2017-11-26}
}
@article{Reed2007,
abstract = {Machines might physically interact with humans more smoothly if we better understood the subtlety of human- human physical interaction. We recently reported that two people working cooperatively on a physical task will quickly negotiate an emergent strategy: typically subjects formed a temporal specialization such that one member commands the early parts of motion and the other the late parts [1]. In our current study, we replaced one of the humans with a robot programmed to perform one of the typical human specialized roles. We expected the remaining human to adopt the complementary specialized role. Subjects did believe that they were interacting with another human but did not adopt a specialized behavior as subjects would when physically working with another human; our negative result suggests a very subtle negotiation takes place in human-human physical interaction.},
author = {Reed, Kyle B. and Patton, James and Peshkin, Michael},
doi = {10.1109/ROBOT.2007.364032},
isbn = {1424406021},
issn = {10504729},
journal = {Proceedings - IEEE International Conference on Robotics and Automation},
number = {April},
pages = {3615--3620},
title = {{Replicating human-human physical interaction}},
url = {http://ieeexplore.ieee.org/document/4209650/},
year = {2007}
}
@misc{GoogleInc.2017,
author = {{Google Inc.}},
title = {{Machine Learning -- Explore -- Google Trends}},
howpublished = "\url{https://trends.google.com/trends/explore?q=Machine\%5C Learning}",
note = {Accessed 2017-11-24},
url = {https://trends.google.com/trends/explore?q=Machine{\%}5C Learning},
urldate = {2017-11-24},
year = {2017}
}
@misc{Yadak,
author = {Yadak, Vivek},
title = {{Deep learning setup for Ubuntu 16.04: Tensorflow 1.2, keras, opencv3, python3, cuda8 and cudnn5.1}},
howpublished = "\url{https://medium.com/@vivek.yadav/deep-learning-setup-for-ubuntu-16-04-tensorflow-1-2-keras-opencv3-python3-cuda8-and-cudnn5-1-324438dd46f0}",
note = {Accessed 2017-11-26},
url = {https://medium.com/@vivek.yadav/deep-learning-setup-for-ubuntu-16-04-tensorflow-1-2-keras-opencv3-python3-cuda8-and-cudnn5-1-324438dd46f0},
urldate = {2017-11-26}
}
@article{Mitola1999,
abstract = {Software radios are emerging as platforms for multiband multimode$\backslash$npersonal communications systems. Radio etiquette is the set of RF bands,$\backslash$nair interfaces, protocols, and spatial and temporal patterns that$\backslash$nmoderate the use of the radio spectrum. Cognitive radio extends the$\backslash$nsoftware radio with radio-domain model-based reasoning about such$\backslash$netiquettes. Cognitive radio enhances the flexibility of personal$\backslash$nservices through a radio knowledge representation language. This$\backslash$nlanguage represents knowledge of radio etiquette, devices, software$\backslash$nmodules, propagation, networks, user needs, and application scenarios in$\backslash$na way that supports automated reasoning about the needs of the user.$\backslash$nThis empowers software radios to conduct expressive negotiations among$\backslash$npeers about the use of radio spectrum across fluents of space, time, and$\backslash$nuser context. With RKRL, cognitive radio agents may actively manipulate$\backslash$nthe protocol stack to adapt known etiquettes to better satisfy the$\backslash$nuser's needs. This transforms radio nodes from blind executors of$\backslash$npredefined protocols to radio-domain-aware intelligent agents that$\backslash$nsearch out ways to deliver the services the user wants even if that user$\backslash$ndoes not know how to obtain them. Software radio provides an ideal$\backslash$nplatform for the realization of cognitive radio},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Mitola, Joseph and Maguire, Gerald Q.},
doi = {10.1109/98.788210},
eprint = {arXiv:1011.1669v3},
isbn = {1070-9916},
issn = {10709916},
journal = {IEEE Personal Communications},
number = {4},
pages = {13--18},
pmid = {25246403},
title = {{Cognitive radio: making software radios more personal}},
volume = {6},
year = {1999}
}
@article{Haykin2005,
author = {Haykin, Simon},
number = {2},
pages = {201--220},
title = {{Cognitive Radio : Brain-Empowered}},
volume = {23},
year = {2005}
}
@misc{gnuradiohw,
abstract = {Free and open software radio ecosystem},
keywords = {Gnuradio},
title = {{GNU Radio - Supported Hardware}},
howpublished = "\url{https://wiki.gnuradio.org/index.php/Hardware http://gnuradio.org/redmine/projects/gnuradio/wiki}",
note = {Accessed 2017-11-24},
url = {https://wiki.gnuradio.org/index.php/Hardware http://gnuradio.org/redmine/projects/gnuradio/wiki},
urldate = {2017-11-24}
}
@article{Duchi2011,
abstract = {We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows us to find needles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal function that can be chosen in hindsight. We give several efficient algorithms for empirical risk minimization problems with common and important regularization functions and domain constraints. We experimentally study our theoretical analysis and show that adaptive subgradient methods outperform state-of-the-art, yet non-adaptive, subgradient algorithms.},
archivePrefix = {arXiv},
arxivId = {arXiv:1103.4296v1},
author = {Duchi, John and Hazan, Elad and Singer, Yoram},
doi = {10.1109/CDC.2012.6426698},
eprint = {arXiv:1103.4296v1},
isbn = {9780982252925},
issn = {15324435},
journal = {Journal of Machine Learning Research},
keywords = {adaptivity,online learning,stochastic convex optimization,subgradient methods},
number = {Jul},
pages = {2121--2159},
pmid = {2868127},
title = {{Adaptive Subgradient Methods for Online Learning and Stochastic Optimization}},
url = {http://jmlr.org/papers/v12/duchi11a.html},
volume = {12},
year = {2011}
}
@article{Bennett2007,
abstract = {In October, 2006 Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch. We briefly describe the challenge itself, review related work and efforts, and summarize visible progress to date. Other potential uses of the data are outlined, including its application to the KDD Cup 2007.},
author = {Bennett, James and Lanning, Stan},
doi = {10.1145/1562764.1562769},
isbn = {9781595938343},
issn = {1554351X},
journal = {KDD Cup and Workshop},
keywords = {machine learning,netflix prize,rmse},
pages = {3--6},
title = {{The Netflix Prize}},
url = {http://brettb.net/project/papers/2007 The Netflix Prize.pdf},
year = {2007}
}
@misc{Comsoc,
author = {Comsoc},
booktitle = {DySpan Symposium History},
title = {{ComSoc Conference Search | IEEE Communications Society | Historic of DySpan Spectrum Challenges}},
howpublished = "\url{https://www.comsoc.org/conferences/conferencesearch?conf\_search=IEEE DySPAN\&field\_date\_value\%5Bmin\%5D\%5Bdate\%5D=\&field\_date\_value\%5Bmax\%5D\%5Bdate\%5D=\&field\_confs\_city\_value=\&field\_confs\_country\_value=\&field\_confs\_cfp\_value\%5Bmin\%5D\%5Bdate\%5D=\&field\_conf}",
note = {Accessed 2017-11-21},
url = {https://www.comsoc.org/conferences/conferencesearch?conf{\_}search=IEEE DySPAN{\&}field{\_}date{\_}value{\%}5Bmin{\%}5D{\%}5Bdate{\%}5D={\&}field{\_}date{\_}value{\%}5Bmax{\%}5D{\%}5Bdate{\%}5D={\&}field{\_}confs{\_}city{\_}value={\&}field{\_}confs{\_}country{\_}value={\&}field{\_}confs{\_}cfp{\_}value{\%}5Bmin{\%}5D{\%}5Bdate{\%}5D={\&}field{\_}conf},
urldate = {2017-11-21}
}
@article{Haykin2005a,
abstract = {Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: {\textperiodcentered} highly reliable communication whenever and wherever needed; {\textperiodcentered} efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.},
author = {Haykin, Simon},
doi = {10.1109/JSAC.2004.839380},
isbn = {0733-8716 VO - 23},
issn = {07338716},
journal = {IEEE Journal on Selected Areas in Communications},
keywords = {Awareness,Channel-state estimation and predictive modeling,Cognition,Competition and cooperation,Emergent behavior,Interference temperature,Machine learning,Radio-scene analysis,Rate feedback,Spectrum analysis,Spectrum holes,Spectrum management},
month = {feb},
number = {2},
pages = {201--220},
pmid = {1391031},
title = {{Cognitive radio: Brain-empowered wireless communications}},
url = {http://ieeexplore.ieee.org/document/1391031/},
volume = {23},
year = {2005}
}
@article{OShea2016b,
abstract = {This paper surveys emerging applications of Machine Learning (ML) to the Radio Signal Processing domain. Provides some brief background on enabling methods and discusses some of the potential advancements for the field. It discusses the critical importance of good datasets for model learning, testing, and evaluation and introduces several public open source synthetic datasets for various radio machine learning tasks. These are intended to provide a robust common baselines for those working in the field and to provide a benchmark measure against which many techniques can be rapidly evaluated and compared.},
author = {O'Shea, Timothy J and West, Nathan},
journal = {Proceedings of the GNU Radio Conference},
title = {{Radio Machine Learning Dataset Generation with GNU Radio}},
url = {https://scholar.google.com/citations?view{\_}op=view{\_}citation{\&}hl=en{\&}user=4S4GyXYAAAAJ{\&}citation{\_}for{\_}view=4S4GyXYAAAAJ:9Nmd{\_}mFXekcC},
year = {2016}
}


text:
@InProceedings{sakaguchi:icra:2010,
  author    = {Sakaguchi, Takeshi and Tsunoo, Shinichi and Kubo, Eiji and Yokoi, Kazuhito and Wada, Kazuyoshi},
  title     = {Intelligent Ambience That Can Lead Robot\textquotesingle s Actions--System Design Concept and Experimental Evaluation of Intelligent Ambience},
  booktitle = {IEEE International Conference on Robotics and Automation},
  year      = {2010},
  pages     = {737--744},
  address   = {Anchorage (AK), USA},
  month     = {May 3-May 8},
  url       = {https://www.researchgate.net/profile/Takeshi-Sakaguchi/publication/221068204\_Intelligent\_Ambience\_That\_Can\_Lead\_Robot\textquotesingle s\_Actions-System\_Design\_Concept\_and\_Experimental\_Evaluation\_of\_Intelligent\_Ambience-/links/02e7e52a0080b0df42000000/Intelligent-Ambience-That-Can-Lead-Robots-Actions-System-Design-Concept-and-Experimental-Evaluation-of-Intelligent-Ambience.pdf},
  keywords  = {Intelligent robots, Robot sensing systems, Orbital robotics, Intelligent sensors, Humans, Space technology, Ambient intelligence, RFID tags, Robotics and automation, Mobile robots},
  doi       = {10.1109/ROBOT.2010.5509619},
  abstract  = {In order to execute multiple tasks in offices and houses, robots require a large amount of information. If each item that is part of the ambience is endowed with some intelligence, robots can simply change the state of the item as requested by the item itself. We refer to such an ambience as \textquotedblleft intelligent ambience (IA).\textquotedblright  This paper describes a constitution method for designing an IA. We constructed two IA models - one comprised a drawer and a mobile robot and the other comprised two types of doors and a drawer, a device developed specially for this IA, and a humanoid robot. We also verified the effectiveness of the IA experimentally.}
}

gha_language: TeX


text:
SUMMARY = "ShellHub Configuration"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/COPYING.MIT;md5=3da9cfbcb788c80a0384361b4de20420"
INHIBIT_DEFAULT_DEPS = "1"

do_patch[noexec] = "1"
do_configure[noexec] = "1"

SHELLHUB_SERVER_ADDRESS ??= "https://cloud.shellhub.io"
SHELLHUB_PRIVATE_KEY    ??= "${sysconfdir}/shellhub-agent.key"
SHELLHUB_TENANT_ID      ??= "undefined"

python () {
    if d.getVar("SHELLHUB_TENANT_ID", False) == "undefined":
        raise bb.parse.SkipRecipe("To enable ShellHub support, the 'SHELLHUB_TENANT_ID' variable must be set.")
}

do_compile () {
    cat <<- EOF > shellhub-agent.default
	SERVER_ADDRESS="${SHELLHUB_SERVER_ADDRESS}"
	PRIVATE_KEY="${SHELLHUB_PRIVATE_KEY}"
	TENANT_ID="${SHELLHUB_TENANT_ID}"
	EOF
}

do_install () {
    # Install the global configuration
    install -Dm 0644 shellhub-agent.default ${D}${sysconfdir}/default/shellhub-agent
}

PACKAGE_ARCH = "${MACHINE_ARCH}"

repository: shellhub-io/meta-shellhub

repo_name: shellhub-io/meta-shellhub


text:
SUMMARY = "SDDM is a modern display manager for X11"
LICENSE = "GPLv2 & CC-BY-3.0 & CC-BY-NC-SA-3.0"
LIC_FILES_CHKSUM = " \
    file://COPYING;md5=b234ee4d69f5fce4486a80fdaf4a4263 \
    file://COPYING-CC-BY-3.0;md5=3f91257948001a369d427b9028de84b2 \
    file://COPYING-CC-BY-SA-3.0;md5=c0e44077d8998dd1cd1324f7bc4d7b67 \
"

REQUIRED_DISTRO_FEATURES = "x11"

inherit cmake_qt5 cmake-extra-sanity pkgconfig systemd useradd distro_features_check

DEPENDS += "qtbase qtdeclarative qttools-native libxcb"
# REVISIT optionals
DEPENDS += "libpam"

SRC_URI = " \
    git://github.com/sddm/${BPN}.git;protocol=git;branch=master \
    file://0001-fix-qml-install-dir.patch \
    file://sddm.pam \
    file://sddm-autologin.pam \
    file://sddm.conf \
"
SRCREV = "f3c810407e4e79597dcdc76508f08c33152fe3cd"
PV = "0.13.0"

S = "${WORKDIR}/git"

EXTRA_OECMAKE += "-DQML_INSTALL_DIR=${OE_QMAKE_PATH_QML}"

do_configure_append() {
    # fix sysroot path
    sed -i 's:${STAGING_DIR_NATIVE}.*${libdir}:${libdir}:g' ${B}/src/common/Constants.h
}

do_install_append() {
    install -d ${D}/${sysconfdir}
    install -m 644 ${WORKDIR}/sddm.conf ${D}/${sysconfdir}

    install -d ${D}${sysconfdir}/pam.d
    install -m 644 ${WORKDIR}/sddm.pam ${D}${sysconfdir}/pam.d/sddm
    install -m 644 ${WORKDIR}/sddm-autologin.pam ${D}${sysconfdir}/pam.d/sddm-autologin

    install -d ${D}${localstatedir}/lib/sddm
    chown -R sddm:sddm ${D}${localstatedir}/lib/sddm
    chmod 0750 ${D}${localstatedir}/lib/sddm
}

FILES_${PN} += "${OE_QMAKE_PATH_QML}"

SYSTEMD_SERVICE_${PN} = "${BPN}.service"

USERADD_PACKAGES = "${PN}"
USERADD_PARAM_${PN} = "--system --home ${localstatedir}/lib/sddm --shell /bin/false --user-group --groups video sddm"

RDEPENDS_${PN} += " \
    xauth \
    qtbase-plugins \
    qtbase-fonts \
    qtdeclarative-plugins \
    qtdeclarative-qmlplugins \
    pam-plugin-tally \
"


text:
@misc{abc,
    author = {Berkeley Logic Synthesis and Verification Group},
    title = {{ABC}: A System for Sequential Synthesis and Verification},
    howpublished = {\url{http://www.eecs.berkeley.edu/~alanmi/abc/}}
}

@misc{cryptol,
  author = {{Galois, Inc.}},
  title = {Cryptol},
  howpublished =
    {\\\url{http://www.galois.com/technology/communications\_security/cryptol}}
}

@misc{erkok10cryptol,
  author = {Levent Erk\"{o}k},
  title = {Programming in Cryptol},
  month = oct,
  year = {2010},
  organization = {Galois, Inc.}
}

@misc{erkok08eqcheck,
  author = {Levent Erk\"{o}k},
  title = {Equivalence and Safety Checking in Cryptol},
  month = aug,
  year = {2008},
  organization = {Galois, Inc.}
}

repository: GaloisInc/llvm-verifier

repo_name: GaloisInc/llvm-verifier

gha_license_id: NOASSERTION

gha_language: Haskell


text:
set(alltreel 0 1 2 3 )    # --treel Option for local tree
set(alltreeh 0 1 2 3 4 )  # --treeh Option for distributed tree
set(allP 3 5 7 8)         # --qr_p  Fix the size of the distributed tree
set(allA 1 2 4 7)         # --qr_a  Fix the size of the domain killed by TS
#set(allM 1 2 3 4 5 10 13 17 25 26 58 128)
#set(allN 1 2 3 4 5 10 13 17 25 26 58 128)
set(allM 1 3 4 10 17 25 128)
set(allN 1 2 5 13 26 58)

foreach( treel ${alltreel} )
  foreach( a ${allA} )
    foreach( m ${allM} )
      foreach( n ${allN} )
        add_test(pivgen_l${treel}_a${a}_m${m}_n${n} ./testing_dpivgen -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=1 )
        add_test(qrparam_l${treel}_a${a}_m${m}_n${n} ./testing_dgeqrf_hqr -t 1 -i 1 -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=1 )
        if(${a} GREATER 1)
          add_test(pivgen_l${treel}_a${a}_m${m}_n${n}_rr ./testing_dpivgen -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=1 --tsrr=1)
          add_test(qrparam_l${treel}_a${a}_m${m}_n${n}_rr ./testing_dgeqrf_hqr -t 1 -i 1 -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=1 --tsrr=1)
        endif()
      endforeach()
    endforeach()
  endforeach()

  foreach( domino 0 1 )
    foreach( treeh ${alltreeh} )
      foreach( p ${allP} )
        foreach( a ${allA} )
          foreach( m ${allM} )
            foreach( n ${allN} )
              add_test(pivgen_h${treeh}_p${p}_d${domino}_l${treel}_a${a}_m${m}_n${n} ./testing_dpivgen -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=${p} --treeh=${treeh} --domino=${domino})
              add_test(qrparam_h${treeh}_p${p}_d${domino}_l${treel}_a${a}_m${m}_n${n} ./testing_dgeqrf_hqr -t 1 -i 1 -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=${p} --treeh=${treeh} --domino=${domino})
              if(${a} GREATER 1)
                add_test(pivgen_h${treeh}_p${p}_d${domino}_l${treel}_a${a}_m${m}_n${n}_rr ./testing_dpivgen -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=${p} --treeh=${treeh} --domino=${domino} --tsrr=1)
                add_test(qrparam_h${treeh}_p${p}_d${domino}_l${treel}_a${a}_m${m}_n${n}_rr ./testing_dgeqrf_hqr -t 1 -i 1 -M ${m} -N ${n} --treel=${treel} --qr_a=${a} --qr_p=${p} --treeh=${treeh} --domino=${domino} --tsrr=1)
              endif()
            endforeach()
          endforeach()
        endforeach()
      endforeach()
    endforeach()
  endforeach()
endforeach()

set(allP 1 4 9 16 81)         # --qr_p  Fix the size of the distributed tree
set(allQ 2 4 9 16 81)         # --qr_p  Fix the size of the distributed tree
#set(allM 1 2 3 4 5 10 13 17 25 26 58 128)
#set(allN 1 2 3 4 5 10 13 17 25 26 58 128)
set(allM 3 4 10 17 25 128) #1 3 4 10 17 25 128)
set(allN 1 2 5 13 26 58)

#foreach( m ${allM} )
#  foreach( n ${allN} )
#    add_test(sys_pivgen_m${m}_n${n} ./testing_dpivgen -M ${m} -N ${n} --treel=0 --qr_a=1 --qr_p=1 )
#    add_test(sys_qrparam_m${m}_n${n} ./testing_dgeqrf_param -t 1 -i 1 -M ${m} -N ${n} --treel=0 --qr_a=1 --qr_p=1 )
#  endforeach()
#endforeach()

foreach( p ${allP} )
  foreach( q ${allQ} )
    foreach( m ${allM} )
      foreach( n ${allN} )
        add_test(sys_pivgen_p${p}_q${q}_m${m}_n${n}  ./testing_dpivgen                   -M ${m} -N ${n} --qr_a=${q} --qr_p=${p})
        add_test(sys_qrparam_p${p}_q${q}_m${m}_n${n} ./testing_dgeqrf_systolic -t 1 -i 1 -M ${m} -N ${n} --qr_a=${q} --qr_p=${p})
      endforeach()
    endforeach()
  endforeach()
endforeach()

repository: NLAFET/ABFT

repo_name: NLAFET/ABFT

extension: cmake