There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Thank you for reading rfid handbook fundamentals and applications in contactless smart cards and identification. As you may know, people have search numerous times for their chosen novels like this rfid handbook fundamentals and applications in contactless smart cards and identification, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.

Rfid Handbook Fundamentals And Applications In Contactless Smart Cards And Identification

RFID is widely used in electronic identification and tracking. Both identity and location information are important in object tracking applications. Recent techniques in RFID localization generally use active tags and pay attention to RSS (received signal strength). As the signal processing of passive RFID is more complicated, RSS based localization of passive tags has a high error. Note that phase characteristics play an important role in signal propagation. This paper presents a method to locate passive RFID tags by estimating AOA (angle of arrival) using the phase difference. Modelling and experimental results show that AOA estimation by phase difference can be used to locate RFID tags accurately.

Two-dimension localization of passive RFID tags using AOA estimation

With the wide spread of the Internet of Things (IoT), wireless localization technology has provided various location-based applications and services. Radio frequency identification (RFID) has been widely used in objects’ identification and tracking. Combining with its excellent identification ability, RFID is also a promising technique for indoor localization. This paper provides the state-of-the-art of UHF RFID localization and gives an introduction of methods and applications. The major methods of UHF RFID localization have two types: 1) range-based and 2) range-free, reported in previous literature. The range-based methods obtain the distance or angle between the antenna and tag utilizing the received signal strength indicator (RSSI), time information or signal phase and estimate tag location further combining the scheme geometrical characteristics. The methods based on fingerprinting and non-fingerprinting are mainly discussed in the range-free domain. Finally, a comparative study of these surveyed works are presented and concluded with a future trend.

Review on UHF RFID Localization Methods

A broadband ultra-high frequency radio frequency identification (UHF RFID) tag patch antenna with a pair of U slots is presented. By embedding a pair of U slots on the patch, a new resonant mode adjacent to the origin mode is excited and a broadband characteristic is achieved. The measured half-power bandwidth of the proposed antenna is 133 MHz and it covers all UHF RFID frequency bands. Measurement results show that the proposed antenna can work on metallic objects of different sizes in both North America and Europe bands.

Broadband UHF RFID tag antenna with a pair of U slots mountable on metallic objects

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

A Random Forest-based ensemble method for activity recognition

An open stub feed planar patch antenna is proposed for UHF RFID tag mountable on metallic objects. Compared to conventional short stub feed patch antenna used for UHF RFID tag, the open stub feed patch antenna has planar structure which can decrease the manufacturing cost of the tags. Moreover, the open stub feed makes the impedance of the patch antenna be tuned in a large scale for conjugate impedance matching. Modeling and simulation results are presented which are in good agreement with the measurement data. Finally, differences between the open stub feed patch antenna and the short stub feed patch antenna for UHF RFID tag are discussed.

Lingfei Mo

Papers

Two-dimension localization of passive RFID tags using AOA estimation

Review on UHF RFID Localization Methods

Broadband UHF RFID tag antenna with a pair of U slots mountable on metallic objects

A Random Forest-based ensemble method for activity recognition

Planar UHF RFID Tag Antenna With Open Stub Feed for Metallic Objects