In Wireless LAN roaming systems such as eduroam and OpenRoaming, ANPs (Access Network Providers) and IdPs (Identity Providers) are generally separated, and there is no standard framework for attribute/consent sharing. Therefore, it is difficult for ANPs to obtain user’s consent for activity analysis or personal data usage which used to be quite important in the business model of conventional Free Wi-Fi systems. On the other hand, as the roaming systems grow, and many users come and leave, the network and the IdPs suffer from a large number of authentication requests from the devices with expired or revoked credentials. This problem would be partly solved if ANPs can receive some attributes from IdPs and suppress unnecessary packets. In this paper, we discuss some methods to share attributes in an offline manner. Some useful applications can be realized by the methods. One of them is delivering user’s preference and consent on data usage. Hash-based Message Authentication Code is employed to protect the attributes from tampering. These methods are also useful in some emerging use cases such as In-Flight Wi-Fi and the Public Wi-Fi in disaster affected areas both of which suffer from the disruption of the backhaul network.

Some WLAN (Wireless Local Area Network) roaming systems such as eduroam may experience increased authentication latency or failures, especially when authentication requests traverse multiple proxy nodes to reach their home Identity Provider (IdP). Existing systems rely on static configurations and lack adaptability to path condition changes, often leading to degraded performance.This paper presents a distributed path selection mechanism based on passive observation of EAP-TLS authentication traffic. Each proxy node locally evaluates downstream paths using metrics such as success rate and Round-Trip Time (RTT), enabling decentralized and adaptive forwarding decisions without centralized coordination.Experiments on a multi-hop simulation platform demonstrate that the proposed system improves success rates, reduces latency, and quickly recovers from failures. This work highlights the feasibility of using real authentication feedback as a lightweight yet effective basis for path optimization in federated WLAN environments.

In-Flight Wi-Fi today is suffering from various problems regarding security and usability. One of the obstacles is the disruption of the backhaul network for internet connection. Introducing a secure connection means like WPA2/WPA3 En-terprise, preferably combined with roaming capability, can solve most security problems and some usability issues. However, user authentication in the current roaming systems depends on the servers on the ground and suffers from network disruption. Since In-Flight Wi-Fi is becoming indispensable not only for internet connection but also for various in-flight services, user devices need to stay connected to the access points during the entire flight. A disruption-tolerant Public Wi-Fi system was proposed earlier to realize secure user authentication and to maintain local network use in temporary isolated areas affected by natural disasters. Based on this system, we have developed a user authentication system with roaming for continuous and secure In-Flight Wi-Fi. This paper provides an overview of prospective use cases and challenges, and proposes a certificate-based roaming system.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

While cloud services are becoming more and more widely used, their security issues are noticed by researchers. Multimedia data, especially images, which usually contain a lot of private information, sometimes are carelessly handled by users and the cloud servers as well. There for, it is necessary to protect those outsourced image data. Although data encryption is useful for storage purposes, data retrieval becomes a problem. Thanks to the techniques of searchable encryption (SE), it is becoming more efficient to perform query over encrypted data. However, most of the current SE schemes are not capable of managing image data. Another common problem for SE schemes is that it is usually inefficient for them to perform user privilege revocation and access control. This paper proposed a system framework of a SE scheme for a cloud image database based on Content-based Image Retrieval (CBIR) with flexible key revocation and access control.

A high-speed pattern matching algorithm is required for developing real-time character recognition applications especially for mobile devices with limited computational performances. Multilingual scene text recognition has recently become more important for mobile and wearable devices. Since Japanese and Chinese have thousands of characters, not only the accuracy but also the speed of classifiers are crucial. We formalized the candidate reduction technique for the Nearest Neighbor (NN) search with high-dimensional feature vectors, and proposed a tree-based clustering method to realize a fast handwritten character recognition. It works fine with ETL9B dataset consisting of Japanese handwritten characters and HCL2000 Chinese handwritten character dataset. In this paper, we propose an improved candidate reduction method based on our former one. The experimental results show that our method is 60.48% faster and more accurate than the former method.

Acceleration of Optical Character Recognition (OCR) algorithms is quite important for developing real-time applications on mobile devices with limited computational performances. Multilingual scene text recognition is becoming more important for mobile and wearable devices. Since Japanese and Chinese have thousands of characters, a fast and accurate character recognition algorithm is required. We developed and proposed a tree-based clustering technique combined with Linear Discriminant Analysis (LDA), and it worked fine with ETL9B dataset consisting of Japanese handwritten characters. However, a significant performance degradation with HCL2000 Chinese handwritten character dataset was found. In this paper, we formalize the candidate reduction technique for the Nearest Neighbor (NN) problems, and propose an improved method that works fine with both Japanese and Chinese character sets. Experimental results show that our method is faster and more accurate than the existing acceleration techniques such as Approximate Nearest Neighbor (ANN) search and Locality Sensitive Hashing (LSH).

A wireless LAN roaming system enables a user to access to a wireless network at a visiting institution with his/her account issued by his/her home institution. In the roaming system, an appropriate access privilege control is required. However, the conventional privilege authorization system gets unavailable because of some communication failures between institutions. In this work, we develop a fault-tolerant authorization system.

Disability of visual text reading has a huge impact on the quality of life for visually disabled people.One of the most anticipated devices is a wearable camera capable of finding text regions in natural scenes and translating the text into another representation such as speech or braille.In order to develop such a device,text tracking in video sequences is required as well as text detection.The device needs to group homogeneous text regions to avoid multiple and redundant speech syntheses or braille conversions.An automatic text image selection is also required for better character recognition and timely text message presentation.We have developed a prototype system equipped with a head-mounted video camera.Particle filter is employed for fast and robust text tracking.We have tested the performance of our system using 1,730 video frames of hall ways with 27 signboards.The number of text candidate regions is reduced to 1.47%.

Disability of visual text reading has a huge impact on the quality of life for visually disabled people. One of the most anticipated devices is a wearable camera capable of finding text regions in natural scenes and translating the text into another representation such as sound or braille. In order to develop such a device, text tracking in video sequences is required as well as text detection. We need to group homogeneous text regions to avoid multiple and redundant speech syntheses or braille conversions. We have developed a prototype system equipped with a head-mounted video camera. Text regions are extracted from the video frames using a revised DCT feature. Particle filtering is employed for fast and robust text tracking. We have tested the performance of our system using 1,000 video frames of a hall way with eight signboards. The number of text candidate images is reduced to 0.98%.

When an autonomous robot tries to find text in the surrounding scene using an onboard video camera, some duplicate text images appear in the video frames. To avoid recognizing the same text many times, it is necessary to decrease the number of text candidate regions for recognition. This paper presents a text capturing robot that can look around the environment using an active camera. The text candidate regions are extracted from the images using an improved DCT feature. The text regions are tracked in the video sequence so that the number of text images to be recognized is reduced. In the experiment, we tested 460 images of a corridor with fifteen signboards including text. The number of text candidate regions is reduced by 90.1% using our text tracking method.

Texts in natural scenes provide us with much useful information. In order to use such information automatically, it is necessary to make computers detect text regions in the images. Gllavata et. al. proposed a method based on unsupervised classification of high frequency wavelet coefficients for text detection in video frames [Gllavata et. al. (2004)]. Although the method is very accurate, it does not work so well with some color images, since it lacks the ability of discriminating color difference. This paper proposes an enhanced version of the method. We develop a new unsupervised clustering technique for the classification of multi-channel wavelet features to deal with color images. Experimental results show that the new method yields better results for color scene images.

Some methods have been developed for character extraction from document images and scene images with complex backgrounds. However, those methods are designed to extract rather large characters, and often fails to extract small characters. This paper proposes a new method by which character patterns can be extracted from document images with complex background. The method is based on the local multilevel thresholding and pixel labeling, and the region growing. This framework is very useful for extracting character patterns from badly illuminated document images. The performance of extracting small character patterns has also been improved by suppressing the influence of mixed-color pixels around character's edges.

Many Japanese documents contain both horizontally and vertically written text lines, and furthermore, those text lines often have local skew. In this paper, we propose a framework for detecting the local orientation of text lines and selecting text line candidates of correct orientation. The orientation detection is independent of text block extraction and has advantage of being applicable to complicated documents. The framework is intended to reduce the text line candidates of incorrect orientation and to improve the performance of document image analysis systems.