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pro vyhledávání: '"Chieh Fang"'
Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited memory bandwidth for transmitting large intermediated data during infe
Externí odkaz:
http://arxiv.org/abs/2207.07931
Autor:
Shih-Chieh Fang PhD, Chen-Wei Yang PhD
Publikováno v:
Inquiry: The Journal of Health Care Organization, Provision, and Financing, Vol 61 (2024)
This study proposes a multi-level model of institutional innovation in the healthcare sector—in other words, field-level institutional change pressures that start as network-level institutional innovation by hospitals and government for their organ
Externí odkaz:
https://doaj.org/article/a826a6c60f1542388096e9602554ace4
Convolutional neural networks (CNNs) achieve remarkable performance in a wide range of fields. However, intensive memory access of activations introduces considerable energy consumption, impeding deployment of CNNs on resourceconstrained edge devices
Externí odkaz:
http://arxiv.org/abs/2110.08828
Autor:
Cheng-Hung Huang, Kuan-Chieh Fang
Publikováno v:
Mathematics, Vol 12, Iss 10, p 1584 (2024)
In this study, an inverse conjugate heat transfer problem is examined to estimate temporally and spatially the dependent unknown surface heat flux using thermography techniques with a thermal camera in a three-dimensional domain. Thermography techniq
Externí odkaz:
https://doaj.org/article/4b7814748d0c482893c7f75e5e65c011
Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural
Externí odkaz:
http://arxiv.org/abs/2006.01125
Autor:
Teng, Chieh-Fang, Chen, Yen-Liang
Recently, the syndrome loss has been proposed to achieve "unsupervised learning" for neural network-based BCH/LDPC decoders. However, the design approach cannot be applied to polar codes directly and has not been evaluated under varying channels. In
Externí odkaz:
http://arxiv.org/abs/2001.01426
In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the
Externí odkaz:
http://arxiv.org/abs/2001.01395
Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Although the cyclic redundancy check (CRC)- assisted successive cancellatio
Externí odkaz:
http://arxiv.org/abs/1912.05158
Autor:
Teng, Chieh-Fang, Wu, An-Yeu
With the rapid growth of deep learning in many fields, machine learning-assisted communication systems had attracted lots of researches with many eye-catching initial results. At the present stage, most of the methods still have great demand of massi
Externí odkaz:
http://arxiv.org/abs/1911.01710
Known for their capacity-achieving abilities, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy the needs of high throughput and low latency, belief propagation (BP) is chosen as the decoding algori
Externí odkaz:
http://arxiv.org/abs/1911.01704