加州大学伯克利分校电子工程与计算机硕博论文及技术报告全文获取

加州大学伯克利分校(人称UC Berkeley或UCB)是世界殿堂级名校之一。UCB的计算机专业算得上最大的系之一,隶属于工程学院的电气工程与计算机科学系Department of Electrical Engineering & Computer Sciences,缩写:EECS),加州大学伯克利的EECS项目在世界范围内居于顶级,其计算机科学、电气与电子工程均位列全美第一(US news)。

下面列出了加州大学伯克利分校的电子与计算机博士最近研究主要集中在哪些方面:

Advances in Machine Learning: Nearest Neighbour Search, Learning to Optimize and Generative Modelling

机器学习进展:最近邻搜索、优化学习和生成模型

Approximate counting, phase transitions and geometry of polynomials

多项式的近似计数、相变和几何

Approximation and Hardness: Beyond P and NP

Chip-Scale Fluorescence Microscope

芯片级荧光显微镜

Circuits and Systems for Decentralized Power Conversion

分散功率转换电路和系统

Coded Illumination for Multidimensional Quantitative Phase Imaging

多维定量相位成像的编码照明

Collaborative Tools and Strategies for Data-driven Development Engineering

数据驱动开发工程的协作工具和策略

Compact Modeling of Advanced CMOS and Emerging Devices for Circuit Simulation

用于电路仿真的先进CMOS和新兴器件的紧凑建模

Complex-valued Deep Learning with Applications to Magnetic Resonance Image Synthesis

复值深度学习及其在磁共振图像合成中的应用

Computational fluorescence and phase super-resolution microscopy

计算荧光和相位超分辨率显微术

Constructive Formal Control Synthesis through Abstraction and Decomposition

通过抽象和分解的构造形式控制合成

Context and Interaction in the Internet of Things

物联网中的语境交互

Crystal-free wireless communication with relaxation oscillators and its applications

张弛振荡器无晶体无线通信及其应用

Decentralized Authorization with Private Delegation

私人授权的分散授权

Design and Physics of VCSELs for Emerging Applications

新兴应用VCSELs的设计与物理

Design Techniques for Energy-Efficient, Low Latency High Speed Wireline Links

高能效、低延迟高速有线链路的设计技术

Directional Wireless Mesh Network Design

定向无线网状网络设计

Efficient Deep Neural Networks

高效的深层神经网络

Evaluation of Methods for Data-Driven Tools that Empower Mental Health Professionals

增强心理健康专家能力的数据驱动工具方法评估

Expert-Level Detection of Acute Intracranial Hemorrhage on Head Computed Tomography using Deep Learning

基于深度学习的头部计算机断层扫描急性颅内出血专家级检测

Faster Algorithms and Graph Structure via Gaussian Elimination 基于高斯消去的快速算法和图形结构

FPGA-Accelerated Evaluation and Verification of RTL Designs

可编程门阵列——RTL设计的加速评估和验证

Hybrid Aesthetics: Bridging Material Practices and Digital Fabrication through Computational Crafting Proxies

混合美学:通过计算工艺代理连接材料实践和数字制造

Learning to Generalize via Self-Supervised Prediction

通过自监督预测学习泛化

Learning to Predict Human Behavior from Video

从视频中学习预测人类行为

Local and Adaptive Image-to-Image Learning and Inference

局部自适应图像间学习和推理

Low Power, Crystal-Free Design for Monolithic Receivers

单片接收机的低功耗、无晶体设计

Machine Learning: Why Do Simple Algorithms Work So Well?

机器学习:为什么简单算法工作得这么好?

Measuring Generalization and Overfitting in Machine Learning

机器学习中的测量泛化和过拟合

Monolayer Transition Metal Dichalcogenide NanoLEDs: Towards High Speed and High Efficiency

单层过渡金属二硫代纳米发光二极管:走向高速高效

Navigating Video Using Structured Text

使用结构化文本定位视频

On Systems and Algorithms for Distributed Machine Learning

分布式机器学习系统和算法

Optimizing for Robot Transparency

机器人透明度的优化

Optoelectronics for refrigeration and analog circuits for combinatorial optimization

制冷用光电子学和组合优化用模拟电路

Performance Guarantees in Learning and Robust Control

学习和鲁棒控制中的性能保证

Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

Ray:机器学习生态系统的分布式执行引擎

Real-World Robotic Perception and Control Using Synthetic Data 使用合成数据的真实世界机器人感知和控制

Realization of Integrated Coherent LiDAR

集成相干激光雷达的实现

Sample Complexity Bounds for the Linear Quadratic Regulator

线性二次调节器的样本复杂度界限

Stochastic Local Search and the Lovasz Local Lemma

随机局部搜索和洛瓦兹局部引理

System Design for Software Packet Processing

软件包处理系统设计

Towards Automatic Machine Learning Pipeline Design

走向自动机器学习流水线设计

Towards Practical Serverless Analytics

走向实用的无服务器分析

Towards Scalable Community Networks

走向可扩展的社区网络

Towards Secure Computation with Optimal Complexity

走向最优复杂度的安全计算

Two-Dimensional Semiconductor Optoelectronics

二维半导体光电子学

Untethered Microrobots of the Rolling, Jumping & Flying kinds 滚动、跳跃和飞行类型的无束缚微型机器人

Visual Dynamics Models for Robotic Planning and Control

机器人规划和控制的视觉动力学模型

Visual Understanding through Natural Language

通过自然语言的视觉理解

Algorithmic Improvisation

基于算法即兴创作

以上大部分研究项目都可以免费获取到全文,你也可以追溯到1935年以前的研究。当然,除了博士论文,一些技术报告和硕士论文也可以免费获取。相关专业的研究人员均可下载借鉴学习。附下载链接:

博士论文:https://www2.eecs.berkeley.edu/Pubs/Dissertations/

硕士论文:https://www2.eecs.berkeley.edu/Pubs/Theses/

技术报告:https://www2.eecs.berkeley.edu/Pubs/TechRpts/

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