This paper presents a fully automated system for detecting, classifying, microscopic imaging, and individually identifying multiple parts without ID-marking or tagging. The system is beneficial for product assemblers, who handle multiple types of parts simultaneously. They can ensure traceability quite easily by only placing the parts freely on the system platform. The system captures microscopic images of parts as their "fingerprints," which are matched with pre-registered images in a database to identify an individual part's information such as its serial number. We demonstrate a working prototype and interaction scenario.

This paper presents a new identification system for tiny parts that have no space for applying conventional ID marking or tagging. The system marks the parts with a single dot using ink containing shiny particles. The particles in a single dot naturally form a unique pattern. The parts are then identified by matching microscopic images of this pattern with a database containing images of these dots. In this paper, we develop an automated system to conduct dotting and image capturing for mass-produced parts. Experimental results show that our "Tip-on-a-chip" system can uniquely identify more than ten thousand chip capacitors.

We propose a new smart lock system that uses user's own item as a smart key to the lock system. The system marks the item with an ink dot, named mIDoT-key (pronounced "my-dot-key"), and the assigned lock is opened by identifying the microscopic image of the dot. Unlike conventional smart keys, new users do not have to get an IC card or to install new software for using our system. Thus, the registration process is much easier for new users. This instant registration is beneficial to access control of many rooms. New visitors of these rooms can be supplied with a mIDoT registration key for easy access. We demonstrate a miniature door lock that can be opened with various items marked with the mIDoT-keys.

We present Intelli-Wrench, a smart tool for providing just-in-time information for mechanical assembly and maintenance workers, which releases the workers from miss-operation and annoying manual documents. The Intelli-Wrench captures the image of a bolt head as its "fingerprint" by using FIBAR (Fingerprint Imaging by Binary Angular Reflection) imaging method. The fingerprint image is then uploaded to a cloud server and matched with parts databases in order to provide the specific information associated with the bolt. Receiving the information, the Intelli-Wrench informs the workers about the designated location and torque requirements of the bolt. This direct integration of information retrieval into a tangible tool provides immediate access to relevant information otherwise found in manual documents. Furthermore, the Intelli-Wrench automatically logs the interaction and eliminates the annoying pointing-and-calling procedure which is the traditional way of secure servicing. We demonstrate a working prototype and interaction scenario.