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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Intelligent detection and removing of straight periodic streaks on digital images
Published in Journal of Physics, 2017
Straight periodic steaks are common negative errors on images, an intelligent method is proposed to test and remove the periodic streaks on digital images. Because of many artificial errors or negative experiment factors, digital images are always contaminated by straight periodic streaks, which will correspondently incur error information on them. By Fast Two Dimensional Fourier Transform, the space spectra from these periodic streaks are discrete and can be test intelligently. After eliminate these frequency ingredient, the periodic streaks disappear after inverse Fourier Transform. The numerical simulation experiment shows that this method is robust in detesting and removing the periodic streaks on digital figures.
Recommended citation: Zehao Zhao, "Intelligent detection and removing of straight periodic streaks on digital images" et al 2017 J. Phys.: Conf. Ser. 887 012078DOI 10.1088/1742-6596/887/1/012078. https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012078
Performance Optimization on Practical Quantum Random Number Generators: Modification on Min-entropy Evaluation and Acceleration on Post Processing.
Published in Journal, 2020
In this paper, we consider the generators based on laser phase noise and propose a method to modify the estimation of min-entropy, which can guarantee no information leakage to the eavesdropper. We also accelerate post processing based on Toeplitz matrix with Fast Fourier Transformation, reducing its time complexity to O(nlogn). Furthermore, we discuss the influence on post processing speed by block length and find a proper block length to process a fixed-length raw sequence.
Recommended citation: Zhao, Zehao, Xiongfeng Ma, and Hongyi Zhou. "Performance Optimization on Practical Quantum Random Number Generators: Modification on Min-entropy Evaluation and Acceleration on Post Processing." arXiv preprint arXiv:2011.04130 [2020]. https://arxiv.org/abs/2011.04130
Automatic shoreline extraction and change detection: A study on the southeast coast of Bangladesh
Published in Marine Geology, 2021
This paper highlights the shoreline of Bangladesh’s southeast coast from 1980 to 2020 with a 10-year interval to automatically extract the shoreline and the changes in coastline position due to accretion and erosion. Threshold, Sobel, Prewitt, Canny and Robert generic edge detection algorithms are used for automatic shoreline extraction, including canny’s performance in accurately detecting the coastline. The Digital Shoreline Analysis System (DSAS) is carried out using Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR) to statistically measure shoreline changes. The findings suggest that in terms of accretion and erosion, the shoreline is dynamic, but overall ac- cretion is dominated rather than erosion.
Recommended citation: Hossain, Md Sakaouth, Muhammad Yasir, Ping Wang, Saleem Ullah, Maftuha Jahan, Sheng Hui, and Zehao Zhao. "Automatic shoreline extraction and change detection: A study on the southeast coast of Bangladesh." Marine Geology 441 [2021]: 106628. https://www.sciencedirect.com/science/article/abs/pii/S0025322721002103
On-Device Augmented Image Training and Inference utilizing the TinyEngine Library on a STM32F746G-DISCO Microcontroller
Published in Journal, 2023
This paper presents Tiny Engine, an optimization-driven inference library tai- lored for microcontrollers, which facilitates on-device training and inference in resource-constrained environments. We demonstrate the effectiveness of Tiny Engine through a series of benchmarks, highlighting significant improvements in computational efficiency and cost-effectiveness compared to traditional cloud- based and mobile AI platforms by training and fine-tuning an augmented image data set live on a microcontroller with a camera. Our approach leverages a combi- nation of in-place depth-wise convolution, patch-based inference, and advanced data augmentation techniques, which collectively enable the execution of sophis- ticated neural network models on edge devices. We validate our model using a dataset augmented through transformations, showcasing the potential for broader application in real-world scenarios.
Recommended citation: Zehao Zhao, Javi Ocampo.(2023). On-Device Augmented Image Training and Inference utilizing the TinyEngine Library on a STM32F746G-DISCO Microcontroller. DOI: 10.13140/RG.2.2.34074.21447. http://zehao-zhao.github.io/me/files/TinyML_Writeup.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
CS 61a: Structure and Interpretation of Computer Programs
Spring 2019, UC Berkeley, EECS, 2019
Overview The CS 61 series is an introduction to computer science, with particular emphasis on software and on machines from a programmer’s point of view.
Math 115: Number Theory
Fall 2019, UC Berkeley, Math, 2019
Course Description In this course, we will study properties of the integers using the language of divisibility and congruences. We will explore results about writing integers as sums of squares and quadratic reciprocity, among other results. Then, we will study arithmetic functions and prove certain analytic results about the distribution of primes. Finally, time permitted, we will discuss one of a few possible advanced topics such as elliptic curves, p-adic numbers, quadratic number fields, or partitions.
Math 10b: Discrete Math
Spring 2020, UC Berkeley, Math, 2020
Course Format: Three hours of lecture and three hours of discussion per week.
Math 32: Pre-Calculus
Summer 2020, UC Berkeley, Math, 2020
Course Format: Three hours of lecture, two hours of discussion per week.
Stat 198: Probability
Spring 2022, UC Berkeley, Statistics, 2022
This course is intended for students exploring the actuarial career and is designed to prepare students for the Actuarial Exam 1/P (Probability), jointly sponsored and administered by the Society of Actuaries and CAS Casualty Actuarial Society. We will cover the topics listed in the exam syllabus provided by the SOA, including general probability, univariate and multivariate probability distributions, moment generating functions, and more.