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Detecting, Localizing, and Tracking an Unknown Number of Moving Targets ...

https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf

Abstract Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter ...

Geospatial Data Science PSM - Temple University

https://www.temple.edu/academics/degree-programs/geospatial-data-science-psm-la-gsds-psm

Program Format & Curriculum All classes for the Geospatial Data Science PSM program are held after 4:30 p.m. on Temple’s Main Campus. The curriculum is designed so that full-time students can complete the program in one academic year and quickly reenter the workforce. Working professionals may complete the degree program on a part-time basis over two to three academic years. Advanced ...

How Michael Jordan revolutionized the sneaker industry—and our ...

https://news.temple.edu/news/2023-04-03/how-michael-jordan-revolutionized-sneaker-industry-and-our-relationship-shoes

Temple University Professor Thilo Kunkel talks Michael Jordan’s influence on sports marketing, athlete endorsement deals, fashion and sneakerhead culture.

Automating Conflict Detection and Mitigation in Large-Scale IoT Syste

https://cis.temple.edu/~apal/ccgrid_iot.pdf

Abstract—In this paper we examine the problem of conflict detection and mitigation across multiple independently designed IoT subsystems deployed in a shared environment. The desired behavior of the system is codified in terms of predefined “safety properties”. We allow both the operational rules and safety properties to include time and temporal logic operations and detect their ...

Targeted Recommendations and Spillover Effects

https://www.fox.temple.edu/sites/fox/files/targetingPPT.GBM_-1.pdf

Targeted Promotions on an E-Book Platform : Crowding Out, Heterogeneity, and Opportunity Costs Nathan Fong, Yuchi Zhang, Xueming Luo, and Xiaoyi Wang

Risky Substance Use Environments and Addiction:

https://scholarshare.temple.edu/server/api/core/bitstreams/2ff3ac1b-e78f-4c21-a0b8-e24e8ba5ecf3/content

Abstract: Substance use disorders are widely recognized as one of the most pressing global public health problems, and recent research indicates that environmental factors, including access and exposure to substances of abuse, neighborhood disadvantage and disorder, and environmental barriers to treatment, influence substance use behaviors. Racial and socioeconomic inequities in the factors ...

Network Architectures 3329 Spring 2018 03/30/2018 Name: Homework 4 Due ...

https://cis.temple.edu/~tug29203/18spring-3329/reading/hw4a.pdf

c. Suppose that different VC numbers are permitted in each link along a VC’s path. During connection setup, after an end-to-end path is determined, describe how the links can choose their VC numbers and configure their for-warding tables in a decentralized manner, without reliance on a central node. P3. A bare-bones forwarding table in a VC network has four columns. What is the meaning of ...

Alison M. Reynolds - Boyer College of Music and Dance

https://boyer.temple.edu/directory/alison-m-reynolds

Biography PhD, music education, Temple University MM, music education, Temple University BME, Texas Christian University Dr. Alison Reynolds focuses her research and teaching interests on expressive and creative music development, early childhood and general music teacher preparation, and developing curriculum materials for children 12 years old and younger. Reynolds has been published in ...

Greedy Algorithms

https://cis.temple.edu/~wu/teaching/Spring2022/Chapter4.pdf

Greedy approaches Seek to maximize the overall utility of some process by making the

Building Classification Models: ID3 and C4.5 - Temple University

https://cis.temple.edu/~giorgio/cis587/readings/id3-c45.html

Introduction ID3 and C4.5 are algorithms introduced by Quinlan for inducing Classification Models, also called Decision Trees, from data.