A 3.4μW CMOS Image Sensor with Embedded Feature-Extraction Algorithm for Motion-Triggered Object-of-Interest Imaging

📋 论文概要

该论文提出了一种功耗仅为3.4μW的CMOS图像传感器，集成了运动触发的特征提取算法，旨在解决无线传感器网络中图像传输功耗过高的问题。通过仅在检测到运动事件时输出感兴趣目标的信息，大幅降低了数据传输带宽和系统功耗。

💡 主要创新点

• 提出了一种超低功耗（3.4μW）的CMOS图像传感器架构，适用于能量受限的分布式传感器节点。
• 片上集成了运动触发的特征提取算法，实现事件驱动的成像，有效减少冗余数据传输。
• 通过模拟域处理实现低功耗运动检测和感兴趣目标提取，避免了传统数字处理的功耗开销。

🏷 关键词

📄 原文摘要

Distributed sensor nodes typically operate under the constraint of limited energy source, and power consumption is an important factor to extend the lifetime of sensor systems. Several low-power imagers have been reported for application to wireless sensor networks [1,2]. However, the biggest power consumption comes from wireless signal transmission due to the large bandwidth of image signals [3]. One way to reduce the bandwidth is to generate signals only when an event happens, by monitoring temporal changes [3-5]. However, this event-based imaging has extraneous redundancy because the sensor may also respond to environmental conditions, such as change of illumination or background movement in addition to actual target objects. In this paper, we report motion-triggered object-of-interest (OOI) imaging to suppress the redundancy in imaging as well as transmission of signals. Most of the time the sensor is in sleep mode, where it remains until it is triggered by

👥 作者与机构

Jaehyuk Choi, Seokjun Park, Jihyun Cho, Euisik Yoon

University of Michigan, Ann Arbor, MI