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DESCRIPTION:SPECTRUM HIVE: ARTIFICIAL INTELLIGENCE RADIO SPECTRUM SENSING A
 LGORITHMS FOR CONTESTED NETWORKS\n\nSpeaker\n\nAlexandru Lavric\, PhD\n\ni
 s an Associate Professor at the Faculty of Electrical Engineering and Comp
 uter Science at Ștefan cel Mare University of Suceava\, Romania\, where h
 e leads research on intelligent radio spectrum management and coordinates 
 the Wireless Lab – Complex Wireless Communications Research Group.\n\nHi
 s work is at the intersection of machine learning\, AI\, and wireless comm
 unications. Current research directions include deep learning models for r
 eal-time spectrum sensing\, signal presence detection\, radio transmission
  classification\, bandwidth and SNR directly from IQ samples\, as well as 
 multi-protocol detection across LoRa\, Sigfox\, LTE and emerging 5G to 6G 
 standards\, jamming and interference detection with adaptive fallback stra
 tegies\, and AI-native orchestration for dense IoT deployments.\n\nMore re
 cent work addresses 6G enablers: numerology inference from IQ samples\, dy
 namic OFDM/OTFS waveform switching for high mobility scenarios\, and integ
 rated sensing and communications. This line of research started with funda
 mental spectrum coexistence studies in 2016 and has evolved into an adapti
 ve AI-based spectrum sensing orchestrator.\n\nHe has authored more than 12
 5 peer-reviewed journal and conference publications\, across wireless sens
 or networks\, LPWAN and IoT architectures\, spectrum coexistence\, and app
 lied machine learning for advanced wireless communications.\n\nAlso\, he i
 s the founder of Spectrum Hive (spectrumhive.com)\, a startup translating 
 this research into deployable products for advanced radio spectrum analyti
 cs and drone RF detection. The company serves telecommunications operators
  (e.g.\, Orange)\, IoT network operators\, smart-city programmers\, and de
 fense applications\, providing real-time spectrum monitoring\, interferenc
 e mitigation\, and resilience against jamming.\n\nSpectrum Hive treats dro
 ne detection as a dual-use security problem in a radio environment that is
  not just congested but actively contested. The designed system fuses thre
 e layers: RF spectrum analysis\, computer vision\, and acoustic sensing wi
 th AI correlating the streams to detect\, classify\, and validate aerial t
 argets in real time\, improving both accuracy and range.\n----------------
 -----------------------------------------------\n\nEvent Details\n\nDate:\
 nMonday\, August 3\, 2026\n\nTime:\n9:30 AM\n\nLocation:\nEC 3930\n\nJoin 
 Online via Zoom:\nhttps://fiu.zoom.us/j/3053485350\n\nMeeting ID:\n305 348
  5350\n---------------------------------------------------------------\n\n
 Organizers\n\nHosted by:\n\n- IEEE Miami Section\n- IEEE Women in Engineer
 ing (WIE)\n- IEEE Life Members\n- IEEE Antennas and Propagation Society (A
 P-S)\n\nOrganized by:\nIEEE Miami Section PES Chapter\n\nIn collaboration 
 with:\nIEEE WIE\, IEEE Life Members &amp; IEEE AP-S\n\nRoom: EC 3930\, Bldg: E
 C\, 10555 west flagler st\, Miami\, Florida\, United States\, 33174
LOCATION:Room: EC 3930\, Bldg: EC\, 10555 west flagler st\, Miami\, Florida
 \, United States\, 33174
ORGANIZER:jlnu@fiu.edu
SEQUENCE:7
SUMMARY:SPECTRUM HIVE: ARTIFICIAL INTELLIGENCE RADIO SPECTRUM SENSING ALGOR
 ITHMS FOR CONTESTED NETWORKS
URL;VALUE=URI:https://events.vtools.ieee.org/m/570554
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 art=&quot;212&quot; data-end=&quot;502&quot;&gt;is an Associate Professor at the Faculty of Elect
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 d=&quot;353&quot;&gt;Ștefan cel Mare University of Suceava\, Romania&lt;/strong&gt;\, where 
 he leads research on intelligent radio spectrum management and coordinates
  the Wireless Lab &amp;ndash\; Complex Wireless Communications Research Group.
 &lt;/p&gt;\n&lt;p data-start=&quot;504&quot; data-end=&quot;1003&quot;&gt;His work is at the intersection 
 of machine learning\, AI\, and wireless communications. Current research d
 irections include deep learning models for real-time spectrum sensing\, si
 gnal presence detection\, radio transmission classification\, bandwidth an
 d SNR directly from IQ samples\, as well as multi-protocol detection acros
 s LoRa\, Sigfox\, LTE and emerging 5G to 6G standards\, jamming and interf
 erence detection with adaptive fallback strategies\, and AI-native orchest
 ration for dense IoT deployments.&lt;/p&gt;\n&lt;p data-start=&quot;1005&quot; data-end=&quot;1348
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 ples\, dynamic OFDM/OTFS waveform switching for high mobility scenarios\, 
 and integrated sensing and communications. This line of research started w
 ith fundamental spectrum coexistence studies in 2016 and has evolved into 
 an adaptive AI-based spectrum sensing orchestrator.&lt;/p&gt;\n&lt;p data-start=&quot;13
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 g&gt;\, across wireless sensor networks\, LPWAN and IoT architectures\, spect
 rum coexistence\, and applied machine learning for advanced wireless commu
 nications.&lt;/p&gt;\n&lt;p data-start=&quot;1587&quot; data-end=&quot;2009&quot;&gt;Also\, he is the foun
 der of &lt;strong data-start=&quot;1614&quot; data-end=&quot;1650&quot;&gt;Spectrum Hive (spectrumhi
 ve.com)&lt;/strong&gt;\, a startup translating this research into deployable pro
 ducts for advanced radio spectrum analytics and drone RF detection. The co
 mpany serves telecommunications operators (e.g.\, Orange)\, IoT network op
 erators\, smart-city programmers\, and defense applications\, providing re
 al-time spectrum monitoring\, interference mitigation\, and resilience aga
 inst jamming.&lt;/p&gt;\n&lt;p data-start=&quot;2011&quot; data-end=&quot;2382&quot;&gt;Spectrum Hive trea
 ts drone detection as a dual-use security problem in a radio environment t
 hat is not just congested but actively contested. The designed system fuse
 s three layers: RF spectrum analysis\, computer vision\, and acoustic sens
 ing with AI correlating the streams to detect\, classify\, and validate ae
 rial targets in real time\, improving both accuracy and range.&lt;/p&gt;\n&lt;hr da
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