Advanced augmented reality (AR) games such asPokemon Goattract millions and millions of players because they integrate various sensors to impress excellent user experiences on mobile devices. In this article, for energy saving on the mobile devices, we can use an accelerometer and a magnetometer to estimate the current user's location approaching interest of points on a game map without active GPS sensors and wireless network. In addition, we create an effective data structure with R-tree and adopt deep learning to efficiently answer the location query of Pokemons on the game location database. Our algorithm outperforms previous approaches in not only smaller running time but also in the better power consumption for the AR games on mobile devices.

In recent years, the social world of virtual reality has become more popular in education, metaverse, entertainment, design, and real-life applications with simultaneous localization and mapping. This is based on an augmented reality (AR) smartphone that supports simultaneous localization and mapping with its two cameras, RGB camera and depth camera, to sense 3D real world. In this study, the average depth data of point clouds on the real 3D objects captured by the depth camera are used to determine whether there are 3D dynamic objects for social world of virtual and real world. When the average depth data change exceeds the threshold of the average depth data, a 3D dynamic object can be detected. This threshold is also adopted to adjust 3D scanning speed and to update Virtual Reality (VR) representation. We conduct detailed experimental analysis as well as verification and completely analyze physical errors for integrating virtual and real worlds.

Advanced mobile devices with an application of Google Project Tango which can trace the motion and the orientation of the device and reconstruct the 3D scenes in real time. Since users always use such devices to scan the real 3D scenes under severe camera motion, the image blur leads to the distortion in the depth images of the scene. However, traditional image deblur methods cannot effectively address this problem since they deblur images without considering 3D information in the depth images. In this paper, we use Google Tango mobile devices equipped with 3D sensors to deblur the depth images for the high accuracy of 3D reconstruction in the real 3D scenes. These sensors can provide the precise geometry data of camera motion trajectory such that this allows us to fast recover the unknown parameters of the 3D information on the depth images without huge time-consumption estimations. Experiments show that our algorithm outperforms previous approaches in not only smaller running time but also the better quality of the restored depth image.

Privacy for mobile devices will be more and more important, while data in the devices are easily be hacked. Traditional mobile unlocking systems such as inner password unlock and graphics unlock are unsecure so that this study is based on fingerprint with touch identify and Advanced Encryption Standard (AES) to effectively secure mobile privacy data and to reduce the risk of data leaking.

Advanced digital holography attracts a lot of attentions for 3D visualization nowadays. The representation of digital holographic images suffers from computational inefficiency on the mobile devices due to the limited hardware for digital holographic processing. In this paper, we point out that the above critical issue deteriorates the digital holographic image representation on the mobile devices. To reduce computational complexity in digital holographic image reconstruction, we propose an efficient and effective algorithm to simplify Fresnel transforms for the mobile devices. Our algorithm outperforms previous approaches in not only smaller running time but also the better quality of the digital holographic image representation for the mobile devices.

People who have self-planning of itineraries are increasing. However, current travel websites only provide simple travel information and regular set itineraries and they cannot assist with users' self-planning of itineraries and increase participation intention. This study develops a travel planning platform by Google Maps and through Tra News, data sources constructed by users and users' positive system experience, it enhances users' travel experience and intention and helps users arrange itineraries.

Face recognition is a computer technique to capture the feature of the human face for user authentication. In the advanced mobile technology, mobile devices take the place of computers to become the major human-computer interaction. Related researches become more and more popular. This study discusses about the identification of users on the mobile devices. Recently, many users still use passwords for user authentication. However, such traditional password identification may not be secure since the passwords are easily intercepted. To solve this problem, this thesis adopts facial recognition for screen unlocking on the mobile devices.

Advanced 3D mobile devices attract a lot of attentions for 3D visualization nowadays. Stereoscopic images and video taken from the 3D mobile devices are uncomfortable for 3D viewing experiences due to the limited hardware for stereoscopic 3D stabilization. The existing stereoscopic 3D stabilization methods are computationally inefficient for the 3D mobile devices. In this article, we point out that this critical issue deteriorates the 3D viewing experiences on the 3D mobile devices. To improve visual comfort, we propose an efficient and effective algorithm to stabilize the stereoscopic images and video for the 3D mobile devices. To rectify the video jitter, we use the gyroscope and accelerometer embedded on the mobile devices to obtain the geometry information of the cameras. Using a different method than video-content-based motion estimation, our algorithm based on the gyroscope and acceleration data can achieve higher accuracy to effectively stabilize the video. Therefore, our approach is robust in video stabilization even under poor lighting and substantial foreground motion. Our algorithm outperforms previous approaches in not only smaller running time but also the better comfort of the stereoscopic 3D visualization for the 3D mobile devices.

Advanced 3D mobile devices attract a lot of attentions for 3D visualization nowadays. Stereoscopic images and video taken from the 3D mobile devices are uncomfortable for 3D viewing experiences due to the limited hardware for stereoscopic 3D stabilization. The existing stereoscopic 3D stabilization methods are computationally inefficient for the 3D mobile devices. In this paper, we point out that the above critical issue deteriorates the 3D viewing experiences on the 3D mobile devices. To improve visual comfort, we propose an efficient and effective algorithm to stabilize the stereoscopic images and video for the 3D mobile devices. To rectify the video jitter, we use the gyroscope and accelerometer embedded on the mobile devices to obtain the geometry information of the cameras. Different from video-content-based motion estimation, our algorithm based on the gyroscope and acceleration data can achieve higher accuracy to effectively stabilize the video. Therefore, our approach is robust in video stabilization even under poor lighting and substantial foreground motion. Our algorithm outperforms previous approaches in not only smaller running time but also the better comfort of the stereoscopic 3D visualization for the 3D mobile devices.

Context information, such as user location, time, and user profile, has been popularly applied to analyze user behavior for many societies. In this paper, we develop a restaurant recommendation system based on mobile context-aware services to provide customized information for users. We analyze the service satisfaction ratings of the users to recommend favored restaurants for them. With mobile context awareness, the proposed framework can substantially enhance the capacity to satisfy the user demands for restaurant recommendations.

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. Three dimensional (3D) contents are emerging data in the data broadcast. However, traditional data broadcast did not consider 3D data to design a data schedule in a broadcast channel. Therefore, the above drawback leads to large access delay in the 3D data broadcast. In this paper, we remedy the problem by devising an indexing technique to index the 3D data of variant geometry shapes. We propose an indexing technique using a 3D data index tree to minimize average waiting time and average tuning time for broadcasting the 3D data. Experimental results show that our approach is able to generate broadcast programs including the 3D data indices with high quality and is very efficient in the 3D data broadcast.

ABSTRACT In this paper, we propose a secure handoff scheme for data transmission in integration of 3G and 802.11 wireless local area networks (WLANs). The handoff between 802.11 WLAN and the 3G suffers from some drawbacks and has been hijacked through the middle of a communication session. We propose an architecture based on cloud computing to build our scheme to fix the problems in such heterogeneous wireless networks. Adaptive Key Exchange Protocol is proposed to protect data transmission as 3G users hand over to an 802.11 WLAN. The approach includes three phases, and all steps of each phase are protected by robust public‐key encryption. Therefore, no information can be hijacked in such environment. The security analysis shows that data transmission between 802.11 WLAN and 3G is robust and secure in various aspects. Copyright © 2012 John Wiley & Sons, Ltd.

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth.

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In the heterogeneous data broadcast, the data size is variant with time in multiple broadcast channels. However, traditional indexing schemes do not consider variant data size to design indexing techniques in the multiple channels. Therefore, the above drawback leads to large power consumption in the heterogeneous data broadcast. In this paper, we remedy the problem of devising an indexing technique to index the data of variant size via the multiple channels. In view of the characteristics of variant data size in the multiple channels, we propose an indexing technique using an index tree to minimize average waiting time and average tuning time. Experimental results show that our approach is able to generate broadcast programs including data indices with high quality and is very efficient in the heterogeneous data broadcast.

On-demand data broadcast is widely deployed to achieve high scalability in a mobile computing environment. However, traditional on-demand data broadcasting assumes that each time slot includes only one data item. Therefore, the above constraint requires the mobile users to wait until the next broadcast cycle to retrieve a data item if they miss the item in this cycle. The above constraint also limits the number of users that can be served in each time slot. In this paper, we propose a new on-demand data broadcast model with modified network coding. Our approach enables a server to encode multiple data items in each time slot, while each mobile user retrieves the data items by decoding the encoded data items with the locally stored data items. Our approach is different from the traditional network coding because each time slot encodes only a subset of data items, which are decided according to identities of the requested and stored data items of users. The simulation results show that our algorithm can reduce the access time by 66% as compared to the traditional schemes.

Data broadcast is widely deployed to realize bandwidth efficiency in a mobile computing environment. However, traditional data broadcasting assumes that mobile users can only retrieve one data item in each time slot. Therefore, the above constraint leads to access conflicts and requires the mobile users to wait until the next broadcast cycle to retrieve the requested information. In this paper, we propose a novel data broadcasting framework that adopts network coding with the stored data items in users. In our approach, a server encodes multiple data items in each time slot, while each mobile user is able to retrieve the data item by decoding the encoded data items with the locally stored data items. We propose an algorithm to generate the broadcast program to minimize the average access time. In addition, our algorithm can avoid coding redundant data in each time slot to reduce access delay. Our simulation results show that our algorithm can reduce the access time by 56% as compared to no coding scheme.

With the ubiquitous of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. However, the previous works for extracting 2D barcodes from an image do not consider the distortion resulted from camera shake. In this paper, therefore, we propose an efficient algorithm to extract 2D barcode from a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition.

With the rapid advances in mobile communication technologies, QR code in the embedded camera devices has been used as new input interfaces. However, the previous works for extracting QR code from an image do not consider a non-uniform background. In this paper, we implement the applications of QR code and propose an efficient algorithm to extract QR code from the non-uniform background. In contrast with prior works, our approach is of higher accuracy for QR-code recognition and more practical for use in a mobile information environment.

Data broadcast is a technique to realize energy saving and bandwidth utilization in a mobile computing environment. However, traditional schemes schedule data items without considering channel bandwidth. Therefore, the above drawback leads to the unfair broadcasting rate of each item of the different access frequency. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels with variant bandwidth. In view of the characteristics of variant bandwidth, we propose an algorithm using adaptive partition on bandwidth to generate broadcast program to avoid the above drawback so as to minimize the average waiting time. The empirical results show that our approach is able to produce broadcast programs of high quality and is very efficient in a data broadcasting environment with variant bandwidth.

With the ubiquitous of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. However, the previous works for extracting 2D barcodes from an image do not consider the distortion resulted from camera shake. Moreover, the previous works for extracting 2D barcodes from an image do not take a complex background into account. In this paper, therefore, we propose an efficient and effective algorithm to extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition.

In this paper, we propose a secure handoff scheme for the integration of UMTS and 802.11 WLAN networks. The handoff between 802.11 WLAN and the UMTS has some drawbacks and could be hijacked through middle of a communication session. An architecture built for a secure handoff scheme is proposed to fix that problem. The Dynamic Key Exchange Protocol (DKEP) is used to protect users during a UMTS handover to a 802.11 WLAN environment. The mobile station (MS) and access point (AP) compute their session key individually. The protocol includes three phases and all the steps of the phases are protected by public-key encryption. Therefore no information can be hijacked between MS and AP. From the security analysis, we know that the handoff between WLAN and UMTS is guaranteed in various aspects. For example, user identity and new registration can be protected, thus avoiding denial of service, key reuse, and so on.