With the proliferation of built-in cameras barcode scanning on smartphones has become widespread in both consumer and enterprise domains. To avoid making the user precisely align the barcode at a dedicated position and angle in the camera image, barcode localization algorithms are necessary that quickly scan the image for possible barcode locations and pass those to the actual barcode decoder. In this paper, we present a barcode localization approach that is orientation, scale, and symbology (1D and 2D) invariant and shows better blur invariance than existing approaches while it operates in real time on a smartphone. Previous approaches focused on selected aspects such as orientation invariance and speed for 1D codes or scale invariance for 2D codes. Our combined method relies on the structure matrix and the saturation from the HSV color system. The comparison with three other real-time barcode localization algorithms shows that our approach outperforms the state of the art with respect to symbology and blur invariance at the expense of a reduced speed.

Smartphones and tablets are increasingly used to scan visual codes that act as physical hyperlinks to digital information. Compared with the outstanding performance of enterprise laser scanners, smartphone cameras suffer from defocus and motion blur. In this project, we propose to turn every smartphone into an enterprise-grade barcode scanner by adapting the latest research results in photograph restoration to the very specific properties of barcode images.

Radio-frequency identification (RFID) has been well established as an effective technology for track and trace applications. In this paper, we go beyond the ID in RFID, and discuss the potential for RFID tags to be used as low-cost sensors by mapping a change in some physical parameter of interest to a controlled change in RFID tag antenna electrical properties. We will also show that it is possible to design the tag antenna to suffer a permanent change in case of violation of a critical threshold in the parameter of interest thereby creating a low-cost threshold sensing mechanism. This can be achieved by inducing controlled changes to the tag antenna geometry parameters or to the antenna boundary conditions, in effect creating a nonelectric memory to monitor state. After identifying the application space for which this class of sensing is well suited, we present details into the design and testing of three different kinds of sensors based on this sensing paradigm. We demonstrate how we use this concept to sense displacements, temperature thresholds, and fluid levels. We will show that RFID-tag-antenna-based sensing has the potential to revolutionize application domains in which there is a need for low-cost, long-lasting, ubiquitous sensors.

Radio-frequency identification (RFID) poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. A number of new approaches to address these issues have been proposed recently, but due to the highly integrated nature of passive RFID tags, it is difficult to evaluate them in real-world scenarios. In this paper, we present an RFID simulation engine, RFIDSim, which implements the ISO 18000-6C communication protocol and supports pathloss, fading, backscatter, capture, and tag mobility models. This paper also shows that our implementation of RFIDSim that relies on a discrete event simulator can be used to simulate large populations featuring thousands of RFID tags. RFIDSim also simulates the deep fades that lead to frequent power losses of the battery-less RFID tags by modeling the multipath effects statistically.

Passive RFID technology and unobtrusive Bluetooth-enabled active tags are means to augment products and everyday objects with information technology invisible to human users. This paper analyzes general interaction patterns in such pervasive computing settings where information about the user's context is derived by a combination of active and passive tags present in the user's environment. The concept of invisible preselection of interaction partners based on the user's context is introduced It enables unobtrusive interaction with smart objects in that it combines different forms of association, e.g. implicit and user initiated association, by transferring interaction stubs to mobile devices based on the user's current situation. Invisible preselection can also be used for remote interaction. By assigning phone numbers to smart objects, we propose making this remote user interaction with everyday items as easy as making a phone call. We evaluate the suitability of the proposed concepts on the basis of three concrete examples: a product monitoring system, a smart medicine cabinet, and a remote interaction application.