There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.

Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.

Operations, such as text entry and zooming, are simple and frequently used on mobile touch devices. However, these operations are far from being perfectly supported. In this paper, we present our prototype, BackAssist, which takes advantage of back-of-device input to augment front-of-device touch interaction. Furthermore, we present the results of a user study to evaluate whether users can master the back-of-device control of BackAssist or not. The results show that the back-of-device control can be easily grasped and used by ordinary smart phone users. Finally, we present two BackAssist supported applications - a virtual keyboard application and a map application. Users who tried out the two applications give positive feedback to the BackAssist supported augmentation.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

In this paper, we propose an improved method of embedding and detecting data in a printed image using a camera of a mobile device. The proposed method is based on the data diffusion method. We discuss several problems in the conventional lens distortion correction method. In addition, the possibility of using multiple captured images by employing a motion-image-capturing technique is also examined. A method of selecting captured images that are expected to obtain a high detection rate is also proposed. From the experimental results, it is shown that the proposed method is effective for improving data detection.

In order to minimize packet error rate in extremely dynamic vehicular networks, a novel vehicle to vehicle (V2V) mobile content transmission scheme that jointly employs random network coding and shuffling/scattering techniques is proposed in this paper. The proposed scheme consists of 3 steps: Step 1-The original mobile content data consisting of several packets is encoded to generate encoded blocks using random network coding for efficient error recovery. Step 2-The encoded blocks are shuffled for averaging the error rate among the encoded blocks. Step 3-The shuffled blocks are scattered at different vehicle locations to overcome the estimation error of optimum transmission location. Applying the proposed scheme in vehicular networks can yield error free transmission with high efficiency. Our simulation results corroborate that the proposed scheme significantly improves the packet error rate performance in high mobility environments. Thanks to the flexibility of network coding, the proposed scheme can be designed as a separate module in the physical layer of various wireless access technologies.

NAND flash storage devices, such as eMMCs and microSD cards, are now widely used in mobile devices. In this paper, we propose a novel buffer replacement scheme for mobile NAND flash storages. It efficiently improves write performance by evicting pages flash-friendly and maintains high cache hit ratios by managing pages in order of recency. Our experimental results show that the proposed scheme outperforms the best performing scheme in the recent literature, Sp.Clock, by 48%.

This paper reports on the image compression tolerability and high implementability of a novel proposed watermarking method that uses a morphological wavelet transform based on max-plus algebra. This algorithm is suitable for embedded low-power processors in mobile devices. For objective and unified evaluation of the capability of the proposed watermarking algorithm, we focus attention on a watermarking contest presented by the IHC, which belongs to the IEICE and investigate the image quality and tolerance against JPEG compression attack. During experiments for this contest, six benchmark images processed by the proposed watermarking is done to reduce the file size of original images to 1/10, 1/20, or less, and the error rate of embedding data is reduced to 0%. Thus, the embedded data can be completely extracted. The PSNR value is up to 54.66dB in these experiments. Furthermore, when the smallest image size is attained 0.49MB and the PSNR value become about 52dB, the proposed algorithm maintains very high quality with an error rate of 0%. Additionally, the processing time of the proposed watermarking can realize about 416.4 and 4.6 times faster than that of DCT and HWT on the ARM processor, respectively. As a result, the proposed watermarking method achieves effective processing capability for mobile processors.

In information retrieval from printed images considering the use of mobile devices, the correction of geometrical deformation and lens distortion is required, posing a heavy computational burden. In this paper, we propose a method of reducing the computational burden for such corrections. This method consists of improved extraction to find a line segment of a frame, the reconsideration of the interpolation method for image correction, and the optimization of image resolution in the correction process. The proposed method can reduce the number of computations significantly. The experimental result shows the effectiveness of the proposed method.

In this paper, we propose an improved data embedding and extraction method for information retrieval considering the use of mobile devices. Although the conventional method has demonstrated good results for images captured by cellular phones, some problems remain with this method. One problem is the lack of consideration of the construction of the code grouping in the code grouping method. In this paper, a new construction method for code grouping is proposed, and it is shown that a suitable grouping of the codes can be found. Another problem is the correction method of lens distortion, which is time-consuming. Therefore, to improve the processing speed, the golden section search method is adopted to estimate the distortion coefficients. In addition, a new tuning algorithm for the gain coefficient in the embedding process is also proposed. Experimental results show an increase in the detection rate for embedding data and a reduction of the processing time.

The energy consumption is always a serious problem for mobile devices powered by battery. As the capacity and density of off-chip memory continuous to scale, its energy consumption accounts for a considerable amount of the whole system energy. There are therefore strong demands for energy efficient techniques towards memory system. Different from previous works, we explore the different power management modes of the off-chip memory by process scheduling for the multi-core mobile devices. In particular, we schedule the processes based on their memory access characteristics to maximize the number of the memory banks being in low power mode. We propose a fast approximation algorithm to solve the scheduling process problem for the dual-core mobile device. And for those equipped with more than two cores, we prove that the scheduling process problem is NP-Hard, and propose two heuristic algorithms. The proposed algorithms are evaluated through a series of experiments, for which we have encouraging results.

We propose a new speech enhancement method suitable for mobile devices used in the presence of various types of noise. In order to achieve high-performance speech recognition and auditory perception in mobile devices, various types of noise have to be removed under the constraints of a space-saving microphone arrangement and few computational resources. The proposed method can reduce both the directional noise and the diffuse noise under the abovementioned constraints for mobile devices by employing a square microphone array and conducting low-computational-cost processing that consists of multiple null beamforming, minimum power channel selection, and Wiener filtering. The effectiveness of the proposed method is experimentally verified in terms of speech recognition accuracy and speech quality when both the directional noise and the diffuse noise are observed simultaneously; this method reduces the number of word errors and improves the log-spectral distances as compared to conventional methods.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

We have been investigating a new class of ubiquitous services, called Activity Logging, which takes advantage for private and public sensors and the RFID tags on real-world objects. The purpose of Activity Logging is to digitally record users' interests with real-world objects and users' context to describe the users' activity. Such digital information acquired from a range of sensors and tags, if being accumulated, forms a great data source for users to recall their activities later or to share the activities with others. This paper explores the design space to realize Activity Logging, and proposes a simple mobile device called Activity Recorder that marries public and private sensors to provide a powerful Activity Logging service. An Activity Recorder contains a range of private sensors, and has communication capability to work with public sensors around the user.

This letter presents a new gossip-based ad hoc service discovery protocol that uses a novel decentralized, peer-to-peer mechanism to provide mobile devices with the ability to advertise and discover services in an efficient way. Our performance study shows that the proposed protocol appropriately addresses the need of proximal service discovery over a dynamic wireless medium.

Mobile devices have acceleratedly penetrated into our daily lives. Though they were originally designed as a communication tool or for personal use, and due to the rapid availability of wireless network technologies, people have begun to use mobile devices for supporting collaborative work and learning. There is, however, a serious problem in mobile devices related to their user interfaces. In this paper, we try to alleviate the problem and propose intuitive techniques for information transfer, which is one of the typical usages of mutually-connected computers. Our system, Toss-It, enables a user to send information from the user's PDA to other electronic devices with a "toss" or "swing" action, like a user would toss a ball or deal cards to others. The implementation of Toss-It consists of three principle parts - gesture recognition, location recognition, and file transfer. We especially describe the details of gesture recognition and location recognition. We then evaluate the practicability and usability of Toss-It through the experiments. We also discuss user scenarios describing how Toss-It can support users' collaborative activities.

Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.

The a-Si TFT characteristics were studied for process temperatures of as low as 100C. The a-Si TFT kept normal characteristics for process temperature of as low as 150C. The a-Si TFT bias temperature stability was evaluated and degradation of stability initiated at around 150C. The characteristics of a-Si TFT fabricated on plastic substrates were the same as those of a-Si TFT fabricated on glass substrates at low process temperature. TFT-LCD fabricated at a process temperature lower than the glass transition temperature of plastic substrates indicated good display image. These results indicate the possibility of fabricating TFT-LCD on plastic substrates, which would promote the application of a-Si TFT-LCD for mobile devices.