Multimodal Olfactory Scence Analysis

This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactor...

Full description

Saved in:
Bibliographic Details
Main Authors: Gelperin, Alan, Shraiman, Boris, Lee, Daniel D
Format: Report
Language:English
Subjects:
ALGORITHMS
Anatomy and Physiology
DETECTORS
LEARNING MACHINES
MATHEMATICAL MODELS
Radiofrequency Wave Propagation
SIGNAL TO NOISE RATIO
SPARSE LEAST MEAN SQUARE SOLUTION
STRATEGY
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Gelperin, Alan
Shraiman, Boris
Lee, Daniel D
description This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactory search strategies; 3. Machine learning algorithms for analyzing olfactory sensor data. The original document contains color images.
format report
fullrecord <record><control><sourceid>dtic_1RU</sourceid><recordid>TN_cdi_dtic_stinet_ADA455446</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ADA455446</sourcerecordid><originalsourceid>FETCH-dtic_stinet_ADA4554463</originalsourceid><addsrcrecordid>eNrjZFDxLc0pyczNT0nMUfDPSUtMLskvqlQITk7NS05VcMxLzKkszizmYWBNS8wpTuWF0twMMm6uIc4euiklmcnxxSWZeakl8Y4ujiampiYmZsYEpAGU4SP_</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>report</recordtype></control><display><type>report</type><title>Multimodal Olfactory Scence Analysis</title><source>DTIC Technical Reports</source><creator>Gelperin, Alan ; Shraiman, Boris ; Lee, Daniel D</creator><creatorcontrib>Gelperin, Alan ; Shraiman, Boris ; Lee, Daniel D ; PENNSYLVANIA UNIV PHILADELPHIA</creatorcontrib><description>This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactory search strategies; 3. Machine learning algorithms for analyzing olfactory sensor data. The original document contains color images.</description><language>eng</language><subject>ALGORITHMS ; Anatomy and Physiology ; DETECTORS ; LEARNING MACHINES ; MATHEMATICAL MODELS ; Radiofrequency Wave Propagation ; SIGNAL TO NOISE RATIO ; SPARSE LEAST MEAN SQUARE SOLUTION ; STRATEGY</subject><creationdate>2006</creationdate><rights>Approved for public release; distribution is unlimited.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,776,881,27546,27547</link.rule.ids><linktorsrc>$$Uhttps://apps.dtic.mil/sti/citations/ADA455446$$EView_record_in_DTIC$$FView_record_in_$$GDTIC$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Gelperin, Alan</creatorcontrib><creatorcontrib>Shraiman, Boris</creatorcontrib><creatorcontrib>Lee, Daniel D</creatorcontrib><creatorcontrib>PENNSYLVANIA UNIV PHILADELPHIA</creatorcontrib><title>Multimodal Olfactory Scence Analysis</title><description>This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactory search strategies; 3. Machine learning algorithms for analyzing olfactory sensor data. The original document contains color images.</description><subject>ALGORITHMS</subject><subject>Anatomy and Physiology</subject><subject>DETECTORS</subject><subject>LEARNING MACHINES</subject><subject>MATHEMATICAL MODELS</subject><subject>Radiofrequency Wave Propagation</subject><subject>SIGNAL TO NOISE RATIO</subject><subject>SPARSE LEAST MEAN SQUARE SOLUTION</subject><subject>STRATEGY</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2006</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZFDxLc0pyczNT0nMUfDPSUtMLskvqlQITk7NS05VcMxLzKkszizmYWBNS8wpTuWF0twMMm6uIc4euiklmcnxxSWZeakl8Y4ujiampiYmZsYEpAGU4SP_</recordid><startdate>20060725</startdate><enddate>20060725</enddate><creator>Gelperin, Alan</creator><creator>Shraiman, Boris</creator><creator>Lee, Daniel D</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>20060725</creationdate><title>Multimodal Olfactory Scence Analysis</title><author>Gelperin, Alan ; Shraiman, Boris ; Lee, Daniel D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA4554463</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2006</creationdate><topic>ALGORITHMS</topic><topic>Anatomy and Physiology</topic><topic>DETECTORS</topic><topic>LEARNING MACHINES</topic><topic>MATHEMATICAL MODELS</topic><topic>Radiofrequency Wave Propagation</topic><topic>SIGNAL TO NOISE RATIO</topic><topic>SPARSE LEAST MEAN SQUARE SOLUTION</topic><topic>STRATEGY</topic><toplevel>online_resources</toplevel><creatorcontrib>Gelperin, Alan</creatorcontrib><creatorcontrib>Shraiman, Boris</creatorcontrib><creatorcontrib>Lee, Daniel D</creatorcontrib><creatorcontrib>PENNSYLVANIA UNIV PHILADELPHIA</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gelperin, Alan</au><au>Shraiman, Boris</au><au>Lee, Daniel D</au><aucorp>PENNSYLVANIA UNIV PHILADELPHIA</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>Multimodal Olfactory Scence Analysis</btitle><date>2006-07-25</date><risdate>2006</risdate><abstract>This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactory search strategies; 3. Machine learning algorithms for analyzing olfactory sensor data. The original document contains color images.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_dtic_stinet_ADA455446
source DTIC Technical Reports
subjects ALGORITHMS
Anatomy and Physiology
DETECTORS
LEARNING MACHINES
MATHEMATICAL MODELS
Radiofrequency Wave Propagation
SIGNAL TO NOISE RATIO
SPARSE LEAST MEAN SQUARE SOLUTION
STRATEGY
title Multimodal Olfactory Scence Analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T23%3A54%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-dtic_1RU&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.btitle=Multimodal%20Olfactory%20Scence%20Analysis&rft.au=Gelperin,%20Alan&rft.aucorp=PENNSYLVANIA%20UNIV%20PHILADELPHIA&rft.date=2006-07-25&rft_id=info:doi/&rft_dat=%3Cdtic_1RU%3EADA455446%3C/dtic_1RU%3E%3Cgrp_id%3Ecdi_FETCH-dtic_stinet_ADA4554463%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true