https://cis.temple.edu/~jiewu/teaching/Spring%202013/01-savvides-localization-wireless-sensor-networks-fine-grained.pdf
Abstract— Wireless communication systems have become increasingly common because of advances in radio and embedded system technologies. In recent years, a new class of applications that networks these wireless de-vices together is evolving. A representative of this class that has received considerable attention from the research community is the wireless sensor network. Such a sensor ...
https://klein.temple.edu/directory/tricia-s-jones-tsjones
Tricia S. Jones is a Full Professor in the Department of Communication and Social Influence and Director of the Center for Conflict Management and Media Impact in the Klein College of Media and Communication at Temple University. She is past Vice- President and Member of the Board of Directors of the Association for Conflict Management and past President of the International Association of ...
https://sites.temple.edu/trail/files/2021/11/XieXinDamesIROS2021.pdf
Zhanteng Xie, Pujie Xin, and Philip Dames Abstract—This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby ...
https://cis.temple.edu/~tug29203/21fall-4319/lectures/ch3c-2.ppt
Transport Layer 3-* Chapter 3 outline 3.1 transport-layer services 3.2 multiplexing and demultiplexing 3.3 connectionless transport: UDP 3.4 principles of reliable data transfer 3.5 connection-oriented transport: TCP segment structure reliable data transfer flow control connection management 3.6 principles of congestion control 3.7 TCP congestion control Transport Layer 3-* TCP congestion ...
https://www.fox.temple.edu/sites/fox/files/documents/CVs/david-j-wilk-cv.pdf
Real Estate Symposia on Asset Optimization & Place-Making Economic Development at University of Delaware (2012, 2016), Perdue Business School at Salisbury University (2013), Florida Atlantic University (2014), Delaware State University (2014), University of Illinois (2015, 2017), University of Washington (2016), Johns Hopkins University (2016), and Temple University (2019) https://youtu.be ...
https://teaching.temple.edu/sites/teaching/files/resource/pdf/Dweck-Perils%20%26%20Promises%20of%20Praise.pdf
Many believe that (1) praising students' intelligence builds their confidence and motivation to learn, and (2) students' inherent intelligence is the major cause of their achievement in school. Our research has shown that the first belief is false and that the second can be harmful—even for the most competent students.
https://cis.temple.edu/~lakaemper/courses/cis350_2004/etc/moeller_triangle.pdf
1 Introduction Most collision detection algorithms, such as OBBTree [Gottschalk96], sphere hier-archies [Hubbard96] and BV-trees [Klosowski97], try to minimize the number of primitive-primitive intersections that have to be computed. Still, a fast and reliable method for computing the primitive-primitive intersection is desired. Since render-ing hardware is often targeted for triangles, the ...
https://cis.temple.edu/~wu/research/publications/Publication_files/jsan-13-00044-v2.pdf
As discussed in Section 1 of this paper, in order to integrate senor networks efi-ciently with the cloud, new paradigms like edge and fog have been introduced. These new paradigms have redefined the way data are processed and managed in the realm of distributed computing, bridging the gap between the cloud and end devices.
https://www.fox.temple.edu/faculty-research/research-fox/resources
The Fox School of Business and the Office of Research have access to specialized research software and databases to support the important research work of our faculty and doctoral students. Some of these databases are accessible through WRDS, while others have a limited number of licenses for the Fox School. For access, please review the information listed for each database. For questions ...
https://cis.temple.edu/~latecki/Courses/CIS601-03/Lectures/slides_lec9.pdf
The separation between two matching objects is called the stereo disparity. Disparity is measured in pixels and can be positive or negative (conventions differ). It will vary across the image.