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DESCRIPTION:Low Energy Sensor Platform - Leveraging Machine Learning (ML) -
  Wednesday\, February 21st\n\nAbstract:  Applications of embedded AI\, spe
 cifically ML (Machine Learning) and object classification have been growin
 g exponentially as decision logic moves to the edge. Proven advantages of 
 low energy consumption\, low costs\, and independent target classification
  drive this implementation and are transforming smart city management\, ag
 riculture\, medical\, health care\, bridge and building structure health m
 anagement (SHM)\, and smart logistics just to name a few game-changing app
 lications.\n\nSeveral specific ML applications will be discussed including
  a vision ML application applied to logistics\, and a remote visual cargo 
 classification smart sensor (currently in production). Challenges of a sma
 ll vs. large CNN model when improving object identification and accuracy w
 ill also be discussed and why ML at the network edge is a game-changer. Fi
 nally\, we will compare and contrast the AI categories of large language m
 odels and the energy-constrained tiny ML sensor models.\n\nBio:\nThe speak
 er\, Joe Jesson\, co-founded & was CTO of a General Electric business\, As
 set Intelligence\, a GE business that designed and sold remote IoT sensors
  for the logistics and energy sectors. Machine learning and LPWAN sensor c
 ommunication became an integral part of the remote monitoring and manageme
 nt of mobile and remote assets. An ongoing research goal is to reduce the 
 smart energy costs where 100% of the power is generated by energy harvesti
 ng techniques. Joe is currently CEO of RF Sigint Group and has over 25+ ye
 ars of engineering and management experience with Motorola APD\, Oak Techn
 ology\, BP\, and General Electric. Master's degree from DePaul University 
 & working on a Ph.D. defense. No need to register\, For any questions\, em
 ail jejesson4@gmail.com\n\nCo-sponsored by:  TCNJ IEEE Student Chapter Pre
 sident Samantha Potomic - potomis1@tcnj.edu\n\nRoom: ARM102\, Bldg: Armstr
 ong Engineering Building\, The College of New Jersey\, Metzger Dr\, Ewing\
 , New Jersey\, United States\, 08618
LOCATION:Room: ARM102\, Bldg: Armstrong Engineering Building\, The College 
 of New Jersey\, Metzger Dr\, Ewing\, New Jersey\, United States\, 08618
ORGANIZER:jejesson4@gmail.com
SEQUENCE:37
SUMMARY:Low Energy Sensor Platform - Leveraging Machine Learning (ML) at th
 e Network Edge
URL;VALUE=URI:https://events.vtools.ieee.org/m/406954
X-ALT-DESC:Description: <br /><p><strong>Low Energy Sensor Platform - Lever
 aging Machine Learning (ML) - Wednesday\, February 21st &nbsp\; &nbsp\; &n
 bsp\;</strong><br /><br /><strong>Abstract:&nbsp\;</strong>&nbsp\;Applicat
 ions of embedded AI\, specifically ML (Machine Learning)&nbsp\; and object
  classification have been growing exponentially as decision logic moves to
  the edge. Proven advantages of low energy consumption\, low costs\, and i
 ndependent target classification drive this implementation and are transfo
 rming smart city management\, agriculture\, medical\, health care\, bridge
  and building structure health management (SHM)\, and smart logistics just
  to name a few game-changing applications.&nbsp\;<br /><br />Several speci
 fic ML applications will be discussed including a vision ML application ap
 plied to logistics\, and a remote visual cargo classification smart sensor
  (currently in production). Challenges of a small vs. large CNN model when
  improving object identification and accuracy will also be discussed and w
 hy ML at the network edge is a game-changer. Finally\, we will compare and
  contrast the AI categories of large language models and the energy-constr
 ained tiny ML sensor models.&nbsp\;<br /><br /><strong>Bio:</strong><br />
 The speaker\, Joe Jesson\, co-founded &amp\; was CTO of a General Electric
  business\, Asset Intelligence\, a GE business that designed and sold remo
 te IoT sensors for the logistics and energy sectors.&nbsp\; Machine learni
 ng and LPWAN sensor communication became an integral part of the remote mo
 nitoring and management of mobile and remote assets. An ongoing research g
 oal is to reduce the smart energy costs where 100% of the power is generat
 ed by energy harvesting techniques. Joe is currently CEO of RF Sigint Grou
 p and has over 25+ years of engineering and management experience with Mot
 orola APD\, Oak Technology\, BP\, and General Electric. Master's degree fr
 om DePaul University &amp\; working on a Ph.D. defense.&nbsp\; No need to 
 register\, For any questions\, email jejesson4@gmail.com&nbsp\;</p>
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