For the further scaling and lower voltage applications of nonvolatile ferroelectric memory, the effect of Kr/O2 sputtering for SrBi2Ta2O9 (SBT) thin film formation was investigated utilizing a SrBi2Ta2O9 target. The 80-nm-thick SBT films were deposited by radio-frequency (RF) magnetron sputtering on Pt/Ti/SiO2/Si(100). Compared with Ar/O2 sputtering, the ferroelectric properties such as larger remnant polarization (Pr) of 3.2 μC/cm2 were observed with decrease of leakage current in case of Kr/O2 sputtering. X-ray diffraction (XRD) patterns indicated that improvement of the crystallinity with suppressing pyrochlore phases and enhancing ferroelectric phases was realized by Kr/O2 sputtering.

A novel image enhancement method for vein recognition is introduced. Inspired by observation that the intensity of the vein vessel changes rapidly during the smoothing process compared to that of background (i.e., skin tissue) due to its thin and long shape, we propose to exploit the smoothing speed as a restoration weight for the vein image enhancement. Experimental results based on the CASIA multispectral palm vein database demonstrate that the proposed method is effective to improve the performance of vein recognition.

In the library, recognizing the activity of the reader can better uncover the reading habit of the reader and make book management more convenient. In this study, we present the design and implementation of a reading activity recognition approach based on passive RFID tags. By collecting and analyzing the phase profiling distribution feature, our approach can trace the reader's trajectory, recognize which book is picked up, and detect the book misplacement. We give a detailed analysis of the factors that can affect phase profiling in theory and combine these factors with relevant activities. The proposed approach recognizes the activities based on the amplitude of the variation of phase profiling, so that the activities can be inferred in real time through the phase monitoring of tags. We then implement our approach with off-the-shelf RFID equipment, and the experiments show that our approach can achieve high accuracy and efficiency in activity recognition in a real-world situation. We conclude our work and further discuss the necessity of a personalized book recommendation system in future libraries.

Data generated from the Internet of Things (IoT) devices in smart spaces are utilized in a variety of fields such as context recognition, service recommendation, and anomaly detection. However, the missing values in the data streams of the IoT devices remain a challenging problem owing to various missing patterns and heterogeneous data types from many different data streams. In this regard, while we were analyzing the dataset collected from a smart space with multiple IoT devices, we found a continuous missing pattern that is quite different from the existing missing-value patterns. The pattern has blocks of consecutive missing values over a few seconds and up to a few hours. Therefore, the pattern is a vital factor to the availability and reliability of IoT applications; yet, it cannot be solved by the existing missing-value imputation methods. Therefore, a novel approach for missing-value imputation of the continuous missing pattern is required. We deliberate that even if the missing values of the continuous missing pattern occur in one data stream, missing-values imputation is possible through learning other data streams correlated with this data stream. To solve the missing values of the continuous missing pattern problem, we analyzed multiple IoT data streams in a smart space and figured out the correlations between them that are the interdependencies among the data streams of the IoT devices in a smart space. To impute missing values of the continuous missing pattern, we propose a deep learning-based missing-value imputation model exploiting correlation information, namely, the deep imputation network (DeepIN), in a smart space. The DeepIN uses that multiple long short-term memories are constructed according to the correlation information of each IoT data stream. We evaluated the DeepIN on a real dataset from our campus IoT testbed, and the experimental results show that our proposed approach improves the imputation performance by 57.36% over the state-of-the-art missing-value imputation algorithm. Thus, our approach can be a promising methodology that enables IoT applications and services with a reasonable missing-value imputation accuracy (80∼85%) on average, even if a long-term block of values is missing in IoT environments.

Organic electronics devices can be applicable to implant sensors. The noises in the acquired data can be removed by smoothing using sliding windows. We developed a new criterion for window-size decision based on smoothness and similarity (SSC). The smoothed curve fits the raw data well and is sufficiently smooth.

Photophysical properties of water-soluble porphyrin were studied in aqueous solutions with/without DNA and in DNA solid films. Ultrathin films were prepared from aqueous DNA solutions by a spin-coating method on glass or on gold nanoparticles (AuNPs). Remarkable enhancement of phosphorescence was observed for porphyrin immobilized in DNA films spin-coated on AuNPs, which was attributed to the electric field enhancement and the increased radiative rate by localized surface plasmon resonance of AuNPs.

The thin film organic photovoltaic cells (OPVs) using organic semiconductors are inferior to oxgen-resistance and water-resistance, and the OPVs have a drawback that the photoelectric conversion efficiency (η) is low. For high efficiency of the OPVs, control of bulk heterojunction (BHJ) structure in the active layer is demanded. Therefore, it is thought that we can control the BHJ structure easily if we can bring a change in the aggregated structure and the crystallinity of the BHJ structure by introducing the third component that is different from the organic semiconductor into the activity layer. In this study, we introduced peptide consisting of phenylalanine of 2 molecules into the active layer prepared by poor solvent addition effect for the organic thin film solar cells and intended to try high efficiency of the organic thin film solar cells and examined the electrochemistry characteristic of the cells.

Depth image based rendering (DIBR), which is utilized to render virtual views with a color image and the corresponding depth map, is one of the key procedures in the 2D to 3D conversion process. However, some troubling problems, such as depth edge misalignment, disocclusion occurrences and cracks at resampling, still exist in current DIBR systems. To solve these problems, in this paper, we present a robust depth image based rendering scheme for stereoscopic view synthesis. The cores of the proposed scheme are two depth map filters which share a common domain transform based filtering framework. As a first step, a filter of this framework is carried out to realize texture-depth boundary alignments and directional disocclusion reduction smoothing simultaneously. Then after depth map 3D warping, another adaptive filter is used on the warped depth maps with delivered scene gradient structures to further diminish the remaining cracks and noises. Finally, with the optimized depth map of the virtual view, backward texture warping is adopted to retrieve the final texture virtual view. The proposed scheme enables to yield visually satisfactory results for high quality 2D to 3D conversion. Experimental results demonstrate the excellent performances of the proposed approach.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

As IoT services become more popular, mobile networks will have to accommodate a wide variety of devices that have different requirements such as different bandwidth limitations and latencies. This paper describes edge distributed mobile network architectures for the IoT era based on dedicated network technology and multi-access edge computing technology, which have been discussed in 3GPP and ETSI. Furthermore, it describes two context-aware control methods that will make mobile networks on the network architecture more efficient, reliable, and real-time: autonomous and distributed mobility management and bandwidth-guaranteed transmission rate control in a networked control system.

Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications. From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement.

In historical learning, to grasp the causal relationship between historical events and to understand factors that bring about important events are significant for fostering the historical thinking. However, some students are not able to find historical events that have causal relationships. The view of observing the historical events is different among individuals, so it is not appropriate to define the historical events that have causal relationships and impose students to remember them. The students need to understand the definition of the causal relationships and find the historical events that satisfy the definition according to their viewpoints. The objective of this paper is to develop a support system for understanding the meaning of a causal relationship and creating causal relation graphs that represent the causal relationships between historical events. When historical events have a causal relationship, a state change caused by one event becomes the cause of the other event. To consider these state changes is critically important to connect historical events. This paper proposes steps for considering causal relationships between historical events by arranging the state changes of historical people along with them. It also develops the system that supports students to create the causal relation graph according to the state changes. In our system, firstly, the interface for arranging state changes of historical people according to the historical events is given. Then, the interface for drawing the causal relation graph of historical events is provided in which state changes are automatically indicated on the created links in the causal relation graph. By observing the indicated state changes on the links, students are able to check by themselves whether their causal relation graphs correctly represent the causal relationships between historical events.

Both placing responsibility of message sending on every IoT object and obfuscating the object's location from other objects are essential to realize a secure and privacy-preserved communication service. Two or more short-lived link identifiers (or pseudonyms) authorized by a trustable authority are often used in related studies, instead of a persistent or long-term use link identifier (i.e. vendor assigned MAC address). However, related studies have limitations in terms of frequently changing pseudonyms to enhance location privacy because the cryptographic algorithms used in them fixedly couple object's identifiers with its security keys. To overcome those limitations, we present a new pseudonym and key management scheme that enables dynamic coupling of pseudonym and key pairs without incurring any adverse impacts. Furthermore, we propose two lightweight pseudonym allocation protocols to effectively reduce the volume of message carrying the allocation parameters. Through qualitative analyses, we verify that the proposed scheme is more scalable than related approaches as it can efficiently allocate enough number of pseudonym/key pairs by reducing the control message overhead by more than 90%.

The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

The Smart City paradigm has become one of the most important research topics around the globe. Particularly in Europe, it is considered as a solution for the unstoppable increase of high density urban environments and the European Commission has included the Smart City research as one of the key objectives for the FP7 (Seventh Framework Program) and H2020 (Horizon 2020) research initiatives. As a result, a considerable amount of quality research, with particular emphasis on information and communication technologies, has been produced. In this paper, we review the current efforts dedicated in Europe to this research topic. Particular attention is paid in the review to the platforms and infrastructure technologies adopted to introduce the Internet of Things into the city, taking into account the constraints and harshness of urban environments. Furthermore, this paper also considers the efforts in the experimental perspective, which includes the review of existing Smart City testbeds, part of wider European initiatives such as FIRE (Future Internet Research and Experimentation) and FIWARE. Last but not least, the main efforts in providing interoperability between the different experimental facilities are also presented.

This paper describes a design of energy-efficient Standard Cell Memory (SCM) using Silicon-on-Thin-BOX (SOTB). We present automatic place and routing (P&R) methodology for optimal body-bias separation (BBS) for SCM, which enables to apply different body bias voltages to latches and to other peripheral circuits within SCM. Capability of SOTB to effectively reduce leakage by body biasing is fully exploited in BBS. Simulation results demonstrated that our approach allows us to design SCM with 40% smaller energy dissipation at the energy minimum voltage as compared to the conventional design flow. For the process and temperature variations, Adaptive Body Bias (ABB) for SCM with our BBS provided 70% smaller leakage energy than ABB for the conventional SCM, while achieving the same clock frequency.

Novel roll-to-roll (R2R) deposition and patterning of ITO on ultra-thin glass were developed with no photolithography and applied to flexible organic light emitting diodes (OLEDs). The developed deposition consists of low temperature sputtering and annealing. The developed patterning utilizes an etching paste printed by novel R2R screen printing.

Internet of Things (IoT) devices deployed in urban areas are seen as data sources for urban sensing IoT applications. Since installing cellular interfaces on a huge number of IoT devices is expensive, we propose to use a user equipment (UE) device with a local wireless interface as a mobile IoT gateway for fixed IoT devices. In this paper, we design a new mobile architecture based on cellular networks to accommodate non-cellular fixed IoT devices by UE devices working as IoT gateways. One key feature is that our architecture leverages proximity services (ProSe) to discover relay UE devices with low overhead in terms of discovery messages. Through simulation studies, we clarify the feasibility of our architecture including the relay UE discovery mechanism in urban areas.

We show that light leakage that occurs in reflective polarizers at large angles of incidence can be suppressed by using anisotropic dielectric multilayers with larger reflective indices in thickness direction and that the interference-included 2×2 Jones matrix method is useful for the investigation of the optical propagation properties of the dielectric multilayers. The thickness of the reflective polarizer can also be reduced by optimizing the distribution of the multilayers in the stack, whilst considering the visual sensitivity. These results indicate that it is possible to realize a high-quality liquid crystal display with wide viewing angles and high light utilization efficiency.