Browsing by Author "Liu, Chia-Lin"

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A 446.6K-Gates 0.55-1.2V H.265/HEVC Decoder for Next Generation Video Applications
(2013)
Tsai, Chang-Hung
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Wang, Hsiuan-Ting
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Liu, Chia-Lin
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Li, Yao
;
Lee, Chen-Yi
An architecture of H. 265/HEVC video decoder for next generation video applications is presented. By exploiting near-lossless data compression and Sharing Above Line Buffer (SALB) schemes, both memory bandwidth and on-chip storage can be reduced. Moreover, cross-stage scheduling is applied to the 4-stage decoding pipeline to minimize idle computations. Fabricated in 90nm 1P9M CMOS process, the test chip of the proposed H. 265/HEVC video decoder occupies an area of 1.60x1.98mm(2) to achieve 1080p@30fps and 720p@30fps real-time decoding with power consumption of 36.90 and 9.57mW.
A Memory-Efficient Architecture for Intra Predictor and De-Blocking Filter in Video Coding System
(2012)
Liu, Chia-Lin
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Tsai, Chang-Hung
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Wang, Hsiuan-Ting
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Li, Yao
;
Lee, Chen-Yi
In the hardware architecture of the H. 264/AVC video coding systems, the storage size of the intra predictor and deblocking filter occupies a great portion of the internal memory size in the video coding. However, the higher resolution video costs huge internal memory size to store pixels to predict block data and eliminate the blocking effect, especially for the next-generation video applications which target resolution is Ultra-HD (8Kx4K). In this article, a memory-efficient architecture for intra predictor and de-blocking filter has been proposed which can roughly reduce up to 19% internal memory usage to efficiently reduce the decoder size and power consumption, and the sequential-interleaving memory architecture has also been adopted in the proposed architecture to solve the memory access conflict during video decoding. A test module is designed for the proposal and operates at 200 MHz for real-time processing with 85.1 K gates and 8.4 KB SRAM in 90nm CMOS technology.
An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer
(2020-05-01)
Chuang, Yi-Hsuan
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Lee, Chia-Hwa
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Lin, Chun-Yu
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Liu, Chia-Lin
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Huang, Sing-Han
Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55-5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.
Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data
(2006-07-01)
Tsou, Ann-Ping
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Sun, Yi-Ming
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Liu, Chia-Lin
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Huang, Hsien-Da
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Horng, Jorng-Tzong
Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.
Development of an Anti-Influenza Drug Screening Assay Targeting Nucleoproteins with Tryptophan Fluorescence Quenching
(1970-01-01)
Hung, Hui-Chen
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Liu, Chia-Lin
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Hsu, John T. -A.
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Horng, Jim-Tong
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Fang, Ming-Yu
"Recent studies have shown that NP (nucleoprotein), which possesses multiple functions in the viral life cycle, is a new potential anti-influenza drug target. NP inhibitors reliably induce conformational changes in NPs, and these changes may confer inhibition of the influenza virus. The six conserved tryptophan residues in NP can be used as an intrinsic probe to monitor the change in fluorescence of the tryptophan residues in the protein upon binding to an NP inhibitor. In the present study, we found that the fluorescence of recombinant NP proteins was quenched following the binding of available NP inhibitors (such as nucleozin) in a concentration- and time-dependent manner, which suggests that the inhibitor induced conformational changes in the NPs. The minimal fluorescence-quenching effect and weak binding constant of nucleozin to the swine-origin influenza virus H1N1pdm09 (SOIV) NP revealed that the SOIV is resistant to nucleozin. We have used the fluorescence-quenching property of tryptophans in NPs that were bound to ligands in a 96-well-plate-based drug screen to assess the ability of promising small molecules to interact with NPs and have identified one new anti-influenza drug, CSV0C001018, with a high SI value. This convenient method for drug screening may facilitate the development of antiviral drugs that target viruses other than the influenza virus, such as HIV and HBV."
DRESSING FOR ATTENTION: OUTFIT BASED FASHION POPULARITY PREDICTION
(2019-01-01)
Lo, Ling
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Liu, Chia-Lin
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Lin, Rong-An
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Wu, Bo
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Shuai, Hong-Han
Accurate analysis of fashion trends is crucial. However, existing predictive algorithms of fashion popularity are restricted to be feasible on the coarse style level but not a finer item level. That is, they are only predictive in the future popularity of a given type of fashion styles (e.g., Rocker), but cannot be precisely down to a particular outfit look chosen by individuals. This paper thus proposes the first solution directly aimed at predicting the fine-grained fashion popularity of an outfit look by taking social media as the learning source. Particularly, a deep temporal sequence learning framework is developed and the proposed framework is evaluated on a real dataset of 380,000 street fashion images collected from the fashion website lookbook.nu. The experimental results show that our proposed framework outperforms the state-of-the-art approaches, with a relative increase of 11.51% to 27.62% (MSE metric) and 7.02% to 32.61% (CSE metric) in the prediction accuracy.
The Optimal RGB LED Driving Scheme for Color Sequential LCD
(2010)
Lin, Tzu-Pin
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Hu, Kai-Ting
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Liu, Chia-Lin
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Tien, Chung-Hao
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顯示科技研究所
This paper demonstrated a 15-inch color sequential display by using RGB LED-based backlit embedded with a color sensor feedback system. The driving sequence of RGB LEDs is essential for eventual image qualities. And a color sensor feedback system was employed to regulate LED brightness and any customized white point.