PROGRAM NSF ASU-ITESM INDUSTRY MEETING Presentations

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2 PROGRAM NSF ASU-ITESM INDUSTRY MEETING Presentations Panel on Sensor & Machine Learning Applications Networking Primer on Machine Learning - Student Research Posters DETAILS ON PROGRAM IN NEXT PAGE

3 SENS MACH 2017 PROGRAM - Hayes Mansion - Morgan Hill Room Continental Breakfast, Registration 8:15 am, Ruby Sayed. Mike Stanley, Morning Session Chair 8:40 am Opening Remarks 8:45 am Making always on vision a reality, Evgeni Gousev, Senior Director Qualcomm (25min) 9:10am Implementation of Efficient, Low Power Deep Neural Nets on Next-Generation Intel Client Platforms, Mike Deisher, Principal Eng., Intel (25min) 9:35 am What s in store for optical sensing? Ian Chen, Maxim Integrated, Industrial, IoT Sensors Business Manager (25min) 10:00 am Coffee Break 10:10 am Blockchain of Food and sensing requirements, Raja Ramachandran, CEO of Ripe (25min) 10:35 amtrending Always-On Sensor Use Cases, Vinu Godavarti, Intel Software Architect (25min) 11 am PANEL: Advancing Sensor Solutions with Machine Learning, Panel Session headed by Steve Whalley, Consultant on Sensors (50 min) 11:50 am - 1 minute Elevator Pitch from Poster Presenters followed by Poster Session (SenSIP Graduate Students) 12 PM Lunch / Posters Cesar Vargas Afternoon Session Chair 1:15 pm Why Should I Trust your Deep Learning Machine, Jayaraman Thiagarajan, Lawrence Livermore National Labs (15 min) 1:30 pm SenSIP Center Sensor and Machine Learning Industry Projects and, Andreas Spanias (15 min) 1:45 pm Anomaly Detection as a Subset of Machine Learning, Mike Stanley, NXP (15 min) 2:00 pm Machine Learning in Healthcare Analytics Systems, Deepta Rajan, Technical Staff, IBM (15 min) 2:15 pm Optimizing Massive MIMO for 5G, Cesar Vargas, Professor ITESM (15 min) 2:30 pm Parametric Position Location using Doppler, Rafaela Villalpando Hernandez, Professor, ITESM (15 min) 2:45 PM Coffee Break 3:00 PM Short Course: Primer in Sensors and Machine Learning, A. Spanias, M. Stanley, U. Shankar (90 min) ASU SenSIP Research Posters Concept Drift Analysis for Integrating Machine Learning Algorithms in Embedded Sensor Systems & IoT Applications, U. Shankar Location based distributed spectral clustering for wireless sensor networks, G. Muniraju Max consensus in Wireless Sensor Networks, G. Muniraju Cross Platform Sensor System Monitoring for Solar Array Analytics, D. Ramirez Musical Query-By-Humming: State of the Art Analysis and Implementation, D. Ramirez Musical Query-By-Humming: Melody Similarity Matching, D. Ramirez Aim of Fault detection using Research Facility containing 104, 18kw Solar Array Panels, S. Rao Early Diagnosis of Neurological Disorders by Detecting Irregularities in Speech, A. Dixit Human Activity Understanding Beyond the visual spectrum, S. Katoch Human Activity Recognition with Smartphone Sensors, H. Song Feature Fusion in Machine Learning Problems, H. Song A Signal Recovery Method for Array Processing, J. Fan A Cyber-Physical System for PV Monitoring and Control (Cloud Movement and Shading Prediction), S. Katoch Evaluation of a Reconstruction Free Compressive Video Tracker, H. Braun Direct Classification from Compressively Sensed Images via Deep Boltzmann Machine, H. Braun Nanopore Sensors and Signal Processing, M. Bowers, REU Student Mobile Applications for Health Monitoring, C. Snyder, REU Student Photoplethysmogram Sensor Array, C. Jenkins, REU Student Development of a CO2 Analyzer for Health Monitoring, R. Ramirez, REU Student Fluorescent-based point of care detection of cervical cancer biomarkers, M. Zhu, REU student Managing Respiratory Disease with Wearable Devices, N. Sharma, REU Student Physiological Monitoring for Childhood Asthma, S. Martinez, REU Student Crowd Sourced Environmental Monitoring, B. Ausby, REU Student Exercise Routine Optimization Via Sensor Fusion, F. Khondoker, REU Student 5:00 pm Adjourn

4 Short Course: A Primer on Machine Learning for Engineers and Managers Description of Course: This tutorial provides an introduction to the principles and applications of machine learning algorithms, software and applications. The tutorial begins with an introduction to the basics of pattern matching, feature extraction, and supervised and unsupervised learning. The lecture then covers basic methods such as the k-means, support vector machines, neural nets and deep learning. The coverage is at at high level for beginners featuring functional block diagrams, qualitative descriptions, and software examples. The course connects algorithms with sensor applications including health monitoring, IoT, and security applications. Topics: Qualitative Overview, What is machine learning?, Use in Sensors and Big Data, Algorithms and Software, Beginings from Vector Quantization and Cell Phones, Feature Extraction, K-means, Adaptive Neural Nets, Support Vector Machines, Bayesian Methods, Deep Learning, Embedding machine learning on sensor boards, Applications; IoT, health monitoring, security; smart campus, smart cities; social implications, software tools Who Should Attend: The tutorial is designed for students, engineers and managers who need to understand the basics of machine learning and their utility in various sensor applications. The tutorial should be of particular interest to engineers and managers who need to prepare for projects that involve learning algorithms for sensors. Sensors Machine Learning IoT

5 VENUE DOLCE HAYES MANSION - HOTEL & RESORT 200 Edenvale Ave,San Jose, CA Meeting Room Amenities

6 Organizations Participating in SENSMACH 2017 San Jose Prior SenSIP Events NSF REU Group Hosted by SenSIP Poster Session at SenSIP Industry Event

7 Organizing Committee Stephen Whalley, Strategic World Ventures Andreas Spanias, ASU SenSIP Cesar Vargas Rosales, ITESM Mike Stanley, NXP Jayaraman Thiagarajan, Lawrence Livermore Labs Local Arrangements Meeting Coordinator Robina Sayed Volunteers SenSIP Center Students Sam Katoch, Gowtham Muniraju Uday Shankar, Abhi Dixit Huan Song, Sunil Rao Jie Fan, David Ramirez Technical Co-Sponsors SenSIP, IEEE SPCOM Chapter, NSF I/UCRC & International Programs Main Organizing Center: ASU SenSIP I/UCRC:

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