Google dl

Dixit A, Sharma P (2014) A comparative study of wavelet thresholding for image denoising. Int J Image Graph Signal Process 6(12):39 Google Scholar Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627MathSciNet MATH Google Scholar Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455MathSciNet MATH Google Scholar Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224MathSciNet MATH Google Scholar Dtissibe FY, Ari AAA, Titouna C, Thiare O, Gueroui AM (2020) Flood forecasting based on an artificial neural network scheme. Nat Hazards 104 (2):1211–1237 Google Scholar Eslami R, Radha H (2006) Translation-invariant contourlet transform and its application to image denoising. IEEE Trans Image Process 15 (11):3362–3374 Google Scholar Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Vis Comput Ind Biomed Art 2(1):7 Google Scholar Feng X-C, Li X-H, Wang W-W, Jia X-X (2017) Improvement of bm3d algorithm based on wavelet and directed diffusion. In: 2017 international conference on machine vision and information technology (CMVIT). IEEE, pp 28–33Gao H-Y (1998) Wavelet shrinkage denoising using the non-negative garrote. J Comput Graph Stat 7(4):469–488MathSciNet Google Scholar Gopi VP, Pavithran M, Nishanth T, Balaji S, Rajavelu V, Palanisamy P (2013) Image denoising based on undecimated double density dual tree wavelet transform and modified firm shrinkage. In: 2013 2nd international conference on advanced computing, networking and security, pp 68–73. A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. IEEE, New York, pp 2366–2369. Google Scholar Isogawa K, Ida T, Shiodera T, Takeguchi T (2018) Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Process Lett 25(2):224–228 Google Scholar Kudo T, Fujisawa T, Ikehara M (2018) Random valued impulse noise removal using improved directional weighted median and bm3d. IEICE Tech Rep 118(84):179–183 Google

AbstractChildren present unique ophthalmologic considerations and require an examination approach that corresponds to his or her level of visual and psychosocial development. Employment of age-related, patient-specific strategies may be utilized to maximize the information obtained from the clinical examination as well as make the examination both enjoyable and rewarding for the patient, family, and practitioner. This chapter will discuss the approach to the ophthalmologic examination in pediatric patients including visual assessment, motility, strabismus, and motor fusion. Similar content being viewed by others Strabismus Chapter © 2019 ReferencesKushner BJ, Lucchese NJ, Morton GV. Grating visual acuity with Teller cards compared with Snellen visual acuity in literate patients. Arch Ophthalmol. 1995;113(4):485–93.Article CAS PubMed Google Scholar Clifford CE, Haynes BM, Dobson V. Are norms based on the original Teller Acuity Cards appropriate for use with the new Teller Acuity Cards II? J AAPOS. 2005;9(5):475–9.Article PubMed Google Scholar Mayer DL, Beiser AS, Warner AF, Pratt EM, Raye KN, Lang JM. Monocular acuity norms for the Teller Acuity Cards between ages one month and four years. Invest Ophthalmol Vis Sci. 1995;36(3):671–85.CAS PubMed Google Scholar Adoh TO, Woodhouse JM. The Cardiff acuity test used for measuring visual acuity development in toddlers. Vision Res. 1994;34(4):555–60.Article CAS PubMed Google Scholar Mackie RT, Saunders KJ, Day RE, Dutton GN, McCulloch DL. Visual acuity assessment of children with neurological impairment using grating and vanishing optotype acuity cards. Acta Ophthalmol Scand. 1996;74(5):483–7.Article CAS PubMed Google Scholar Sturm V, Cassel D, Eizenman M. Objective estimation of visual acuity with preferential looking. Invest Ophthalmol Vis Sci. 2011;52(2):708–13.Article PubMed Google Scholar Wenner Y, Heinrich SP, Beisse C, Fuchs A, Bach M. Visual evoked potential-based acuity assessment: overestimation in amblyopia. Doc Ophthalmol. 2014;128(3):191–200.Article PubMed Google Scholar Jeon J, Oh S, Kyung S. Assessment of visual disability using visual evoked potentials. BMC Ophthalmol. 2012;12:36.Article PubMed PubMed Central Google Scholar Procianoy L, Procianoy E. The accuracy of binocular fixation preference for the diagnosis of strabismic amblyopia. J AAPOS. 2010;14(3):205–10.Article PubMed Google Scholar Laws D, Noonan CP, Ward A, Chandna A. Binocular fixation pattern and visual acuity in children with strabismic amblyopia. J Pediatr Ophthalmol Strabismus. 2000;37(1):24–8.CAS PubMed Google Scholar Hakim OM. Association between fixation preference testing and strabismic pseudoamblyopia. J Pediatr Ophthalmol Strabismus. 2007;44(3):174–7.PubMed Google Scholar Rodier DW, Mayer DL, Fulton AB. Assessment of young amblyopes: array vs. single picture acuities. Ophthalmology. 1985;92(9):1197–202.Article CAS PubMed Google Scholar von Noorden GK. Burian-von Noorden’s Binocular vision and ocular motility. Theory and management of strabismus. 3rd ed. St. Louis: C.V. Mosby; 1985. p. 217–20. Google Scholar Thompson JT, Guyton DL. Ophthalmic prisms: measurement errors and how to minimize them. Ophthalmology. 1983;90(3):204–10.Article CAS PubMed Google Scholar von Noorden GK. Burian-von Noorden’s Binocular vision and ocular motility. Theory and management of strabismus. 3rd ed. St. Louis: C.V. Mosby; 1985. p. 189. Google Scholar Fray KJ. Fusional amplitudes: exploring where fusion falters. Am Orthopt J. 2013;63:41–54.Article PubMed Google Scholar Abraham NG, Srinivasan K, Thomas J. Normative data for near point of convergence, accommodation, and phoria. Oman J Ophthalmol. 2015;8(1):14–8. Google Scholar Kraft SP,

By activation and overexpression of adenylyl cyclase. Proc Natl Acad Sci U S A 102(2):437–442. Article CAS PubMed Google Scholar Wang CY, Wang ZY, Xie JW, Wang T, Wang X, Xu Y, Cai JH (2016b) Dl-3-n-butylphthalide-induced upregulation of antioxidant defense is involved in the enhancement of cross talk between CREB and Nrf2 in an Alzheimer’s disease mouse model. Neurobiol Aging 38:32–46. Article CAS PubMed Google Scholar Yang W, Li L, Huang R, Pei Z, Liao S, Zeng J (2012) Hypoxia inducible factor-1alpha mediates protection of DL-3-n-butylphthalide in brain microvascular endothelial cells against oxygen glucose deprivation-induced injury. Neural Regen Res 7(12):948–954. CAS PubMed PubMed Central Google Scholar Yeh YH, Hsu LA, Chen YH, Kuo CT, Chang GJ, Chen WJ (2016) Protective role of heme oxygenase-1 in atrial remodeling. Basic Res Cardiol 111(5):58. Article PubMed Google Scholar Zhao Y, Lee JH, Chen D, et al (2017) DL-3-n-butylphthalide induced neuroprotection, regenerative repair, functional recovery and psychological benefits following traumatic brain injury in mice. Neurochem Int 111:82–92Download referencesAcknowledgmentsSincere thanks to Professor Matt Springer (Cardiovascular Research Institute, University of California, San Francisco; Division of Cardiology, University of California, San Francisco; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco), Dr. Xiaoyin Wang (Cardiovascular Research Institute, University of California, San Francisco) for continued advice, and De-Chun He (Animal Experimental Center, Guangdong Provincial Academy of Chinese Medicine) for careful care of animals and technical assistance.FundingThe study was supported by joint research project of the Guangdong Provincial Department of Science and Technology and the Guangdong Provincial Academy of Chinese Medicine (No. 2014A020221045), and science and technology research project of the Guangdong Provincial Hospital of Chinese Medicine (No. YN2016MJ04).Author informationAuthors and AffiliationsSecond Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, ChinaHuiliang Qiu, Huanlin Wu & Wencong QiuDepartment of Cardiology,

Hands-on labs, quizzes, and projects. It assumes a basic knowledge of Python, but the first week includes a crash course in Python for data science. The workload is 4-6 hours per week over 6 weeks, with flexible deadlines.By the end of the course, you‘ll have a solid understanding of the ML process and be able to build and evaluate ML models using Python. You‘ll also gain practical experience through projects like predicting housing prices and recommending products to users. The course can be audited for free, with paid upgrades available for graded assignments, feedback, and a certificate of completion.2. Intro to TensorFlow for Deep Learning (Udacity)Offered by: GoogleInstructors: Laurence Moroney and Andrew InnesDeep learning (DL) is a subfield of ML that uses artificial neural networks to automatically learn patterns and representations from data. It powers many of today‘s AI breakthroughs, from computer vision to natural language processing.This free course from Google and Udacity provides a hands-on introduction to DL using TensorFlow, one of the most popular open-source DL libraries. Designed for students with some programming experience, it teaches you how to build and train neural networks for a variety of DL tasks.Key topics include:Introduction to DL and neural networksBuilding and training models with TensorFlowConvolutional neural networks (CNNs) for image classification Recurrent neural networks (RNNs) for time series and sequence data Transfer learning and fine-tuning pre-trained modelsDeploying DL models in real-world applicationsThe course consists of short video lessons, interactive quizzes, and coding exercises in Python notebooks. It emphasizes hands-on learning and provides numerous real-world examples and datasets to practice with.By the end of the course, you‘ll be able to build and deploy your own DL models using TensorFlow, and have a solid foundation for further study and application of DL techniques. The course is self-paced and typically takes about 2 months