https://cis.temple.edu/~latecki/Papers/ACM_MM2023.pdf
Figure 1: Global alignment vs. Pixel-wise alignment. (a) Il-lustration of the diference between global and pixel-wise alignment in principle. Global alignment applies a direct linear transformation (DLT) to approximate the ofset for all the pixels, while our pixel-wise alignment executes un-uniform transformations for each pixel. (b) and (c) compare two kinds of stitching results. Pixel-wise ...
https://cis.temple.edu/~yu/research/CrispBP-Mobicom21.pdf
ε ∆C( t), where ∆C( t) is the change of the volume faction of blood , ε is the absorption coeficient of the turbid tissue. The tissue scattering changes ∆μ t) ′ ( that typically accompany hemodynamic
https://scholarshare.temple.edu/server/api/core/bitstreams/6e5eb13b-2a7e-49fd-a7ba-78629e595b6f/content
3.6.2 Xilinx C Project The SDK allows for application development of C/C++ programs. For this thesis, the C programming language was used. The C program can be compiled with the SDK and an Executable and Linakable Format (ELF) file is generated. This file is used to execute on the processor.
https://medicine.temple.edu/sites/medicine/files/files/ct_analyzer.pdf
the combined density of a well-defined volume which contains a mixture of both bone and soft tissue, such as a selected volume of medullary trabecular bone in a femur or tibia, is measured as “bone mineral density”, or BMD. This parameter BMD relates to the amount of bone within a mixed bone-soft tissue region, but does not give information about the material density of the bone itself.
https://sites.temple.edu/rtwiseowls/files/2013/10/compendium-of-clinical-measures-for-community-rehabilitation.pdf
Contents of the compendium This Compendium contains a suite of outcome measures for use in community rehabilitation settings, as identified from a systematic review of the literature. This is a synthesis of 28 measures and clinical tests which have been critically appraised and then approved by an expert working group of rehabilitation clinicians.
https://cis.temple.edu/~wu/research/publications/Publication_files/ICDE2024_Xu.pdf
C. Solution and Contribution To circumvent the above challenges, we introduce the Mean-Field Game (MFG) theory [29] to reduce a one-to-many game to a one-to-one game and further propose a joint MFG framework for mobile edge Caching and Pricing, namely MFG-CP, where each EDP can optimize its own utility in a distributed manner.
https://cis.temple.edu/~jiewu/research/publications/Publications_2024.html
C. Li, Y. Mao, Q. Huang, X. Zhu, and J. Wu, " Scale-Aware Graph Convolutional Network with Part-Level Refinement for Skeleton-Based Human Action Recognition," IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 34, No. 6, 2024, 4311-4324.
https://cis.temple.edu/~giorgio/cis307/readings/futex.pdf
This exemplifies that using futexes is really tricky since they provide problems even to their inventors. This docu-ment will hopefully provide correct and detailed instruc-tions on how to use futexes. First an understanding of the kernel interface and its semantic is needed.
https://cis.temple.edu/~latecki/Papers/ICIP2019.pdf
ABSTRACT The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this prob-lem, namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic seg-mentation. More specifically, the encoder adopts a ResNet as backbone network, where two new ...
https://cis.temple.edu/~jiewu/
Recent Awards Best paper award C. Wu, M. Xiao, J. Wu, Y. Xu, J. Zhou, and H. Sun, "Towards Federated Learning on Fresh Datasets," Proc. of the 20th IEEE International Conference on Mobile Ad-Hoc and Smart Systems(MASS 2023), September 25-27, 2023.[ppt] Faculty Research Award Temple University, 2022-2023.