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https://sites.temple.edu/vocaldevlab/files/2021/12/HellerMurray_CPP_selfarchived-2.pdf
Prominence in Praat Elizabeth S. Heller Murraya,*, Andie Chaoa, Lauren Collettia
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https://cis.temple.edu/~pwang/demos.html
The file contains three tables showing the relations among uncertainty measurements, the truth-value functions of the one-premise rules, and the truth-value functions of the two-premise rules, respectively. In each table, the input values can be modified, and the outputs of the functions change accordingly. The system parameters used in the functions can also be adjusted.
https://sites.temple.edu/silage/text-books/
Digital Communication Systems Using SystemVue Da Vinci Engineering Press, Thomson Delmar Publishing 2006 SystemVue models of baseband and bandpass modulation and demodulation systems, time, frequency and code division multiplexing, synchronization and equalization, channel models, frequency hopping and discrete sequence spread spectrum, orthogonal frequency division multiplexing and baseband ...
https://cis.temple.edu/~hughes/cs3223c24.html
Comparative analysis of algorithms and data structures. Data structures include heaps, priority queues, binary and general trees, binary search trees, and graphs. Algorithms include heapsort, topological sort, breadth-first search, depth-first search, shortest path algorithms, and Greedy algorithms. NOTE: For Computer Science Majors. COURSE CONTENT (Syllabus) See CANVAS page ATTENDANCE
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https://cis.temple.edu/tagit/
We are the Temple AGI Team. We do research and experiments in the field of Artificial General Intelligence. Team meetings occur each Wednesday on Zoom. We welcome outside collaborators to get involved in various ways. Feel free to explore our various links & resources and contact us.
https://cis.temple.edu/~wu/teaching/Spring2020_CIS5644_Distributed_Computing/distributed-computing-2020-notes-1.pdf
A distributed system is collection of independent computers that appear to the users of the system as single computer.
https://cis-linux1.temple.edu/~jiewu/research/publications/Publication_files/ICPADS2024.pdf
Abstract—Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative ...