In this paper, we applied cavity resonator wireless power transfer (CR WPT) to an enclosed space with scatterers and revealed that high transfer efficiency at line-of-sight (LOS) and non-line-of-sight (NLOS) position in the power transmitter can be achieved by this method. In addition, we propose a method for limiting the wireless power transfer space utilizing metal mesh and show its effectiveness by experiment. First, we confirm that the constructed experimental model is working as a cavity resonator by theoretical formula and electromagnetic field analysis. Next, we calculate the maximum power transfer efficiency using a model including a plurality of scatterers by installing a power receiver at LOS and NLOS positions in the power transmitter, and it was confirmed that transfer efficiency of 30% or more could be expected even at the NLOS position. Then, we measured the frequency characteristics of a model in which one surface of the outer wall was replaced with a metal mesh, and it was clarified that the characteristics hardly changed in the power transfer frequency band. Finally, we confirmed that simultaneous communication can be performed with driving of the battery-less sensor by CR WPT, and clarify effectiveness of the proposed method.

Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.

As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which has long been open to the public. Though designed rigorously, similar to VoIP services, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. Due to the high performance requirement, the SIP flooding attacks in VoLTE is more difficult to defend than that in traditional VoIP service. In this paper, enlightened by Counting Bloom Filter (CBF), we design a versatile CBF-like structure, PFilter, to detect the flooding anomalies. Compared with previous relevant works, our scheme gains advantages in many aspects including detection of low-rate flooding attack and stealthy flooding attack. Moreover, not only can our scheme detect the attacks with high accuracy, but also find out the attackers to ensure normal operation of VoLTE by eliminating their negative effects. Extensive experiments are performed to well evaluate the performance of the proposed scheme.

We surveyed employees who use personal devices for work. Residual analysis for cross-tabulation was carried out for three groups classified based on company rules and regulations established for mobile work. We show that the behavior of employees working with personal devices to process business data changes due to the presence or absence of the company rules and regulations.

An operating system is an essential piece of software that manages hardware and software resources. Thus, attacks on an operating system kernel using kernel rootkits pose a particularly serious threat. Detecting an attack is difficult when the operating system kernel is infected with a kernel rootkit. For this reason, handling an attack will be delayed causing an increase in the amount of damage done to a computer system. In this paper, we propose Kernel Rootkits Guard (KRGuard), which is a new method to detect kernel rootkits that monitors branch records in the kernel space. Since many kernel rootkits make branches that differ from the usual branches in the kernel space, KRGuard can detect these differences by using the hardware features of commodity processors. Our evaluation shows that KRGuard can detect kernel rootkits that involve new branches in the system call handler processing with small overhead.

This paper proposes a method for human pose estimation in still images. The proposed method achieves occlusion-aware appearance modeling. Appearance modeling with less accurate appearance data is problematic because it adversely affects the entire training process. The proposed method evaluates the effectiveness of mitigating the influence of occluded body parts in training sample images. In order to improve occlusion evaluation by a discriminatively-trained model, occlusion images are synthesized and employed with non-occlusion images for discriminative modeling. The score of this discriminative model is used for weighting each sample in the training process. Experimental results demonstrate that our approach improves the performance of human pose estimation in contrast to base models.

Human activity prediction has become a prerequisite for service recommendation and anomaly detection systems in a smart space including ambient assisted living (AAL) and activities of daily living (ADL). In this paper, we present a novel approach to predict the next-activity set in a multi-user smart space. Differing from the majority of the previous studies considering single-user activity patterns, our study considers multi-user activities that occur with a large variety of patterns. Its complexity increases exponentially according to the number of users. In the multi-user smart space, there can be inevitably multiple next-activity candidates after multi-user activities occur. To solve the next-activity problem in a multi-user situation, we propose activity set prediction rather than one activity prediction. We also propose activity sequence partitioning to reduce the complexity of the multi-user activity pattern. This divides an activity sequence into start, ongoing, and finish zones based on the features in the tendency of activity occurrences. The majority of the activities in a multi-user environment occur at the beginning or end, rather than the middle, of an activity sequence. Furthermore, the types of activities typically occurring in each zone can be sufficiently distinguishable. Exploiting these characteristics, we suggest a two-step procedure to predict the next-activity set utilizing a long short-term memory (LSTM) model. The first step identifies the zones to which current activities belong. In the next step, we construct three different LSTM models to predict the next-activity set in each zone. To evaluate the proposed approach, we experimented using a real dataset generated from our campus testbed. Our experiments confirmed the complexity reduction and high accuracy in the next-activity set prediction. Thus, it can be effectively utilized for various applications with context-awareness in a multi-user smart space.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data access control on cloud storage systems. In ABE, to revoke users' attributes, it is necessary to make them unable to decrypt ciphertexts. Some CP-ABE schemes for efficient attribute revocation have been proposed. However, they have not been given a formal security proof against a revoked user, that is, whether they satisfy forward secrecy has not been shown or they just do not achieve fine-grained access control of shared data. We propose an attribute revocable attribute-based encryption with the forward secrecy for fine-grained access control of shared data. The proposed scheme can use both “AND” and “OR” policy and is IND-CPA secure under the Decisional Parallel Bilinear Diffie-Hellman Exponent assumption in the standard model.

The present paper introduces a novel construction of structured ternary sequences having a zero-correlation zone (ZCZ) for both periodic and aperiodic correlation functions. The cross-correlation function and the side lobe of the auto-correlation function of the proposed sequence set are zero for phase shifts within the ZCZ. The proposed ZCZ sequence set can be generated from an arbitrary Hadamard matrix of order n. The sequence set of order 0 is identical to the r-th row of the Hadamard matrix. For m≥0, the sequence set of order (m+1) is constructed from the sequence set of order m by sequence concatenation and interleaving. The sequence set of order m has 2m subsets of size n. The length of the sequence is equal to n4m+2m+1(2m-1); The phase shift of the ZCZ for the whole sequence set is from -(2m-1) to (2m-1). The sequence set of order 0 is coincident with the rows of the given Hadamard sequence with no ZCZ. The subsets can be associated with a perfect binary tree of height m with 2m leaves. The r-th sequence subset consists of from the nr-th sequence to the ((n+1)r-1)-th sequence. The r-th subset is assigned to the r-th leaf of the perfect binary tree. For a longer distance between the corresponding leaves to the r-th and s-th sequences, the ZCZ of the r-th and s-th sequences is wider. This tree-structured width of ZCZ of a pair of the proposed sequences enables flexible design in applications of the proposed sequence set. The proposed sequence is suitable for a heterogeneous wireless network, which is one of the candidates for the fifth generation of radio access networks.

In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog front-ends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximation-register ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.

In this paper, invulnerability and attack strategies are discussed for the undirected unweighted urban road networks and the directed weighted taxi networks of Beijing. Firstly, five new attack strategies, i.e., Initial All Degree (IAD), Initial All Strength (IAS), Recalculated Closeness (RC), Recalculated All Degree (RAD) and Recalculated All Strength (RAS) and five traditional attack strategies, i.e., Initial Degree (ID), Initial Betweenness (IB), Initial Closeness (IC), Recalculated Degree (RD) and Recalculated Betweenness (RB) are adopted to provoke the nodes failure. Secondly, we assess the impacts of these attack strategies using two invulnerability metrics, i.e., S (the relative size of the giant component) and E (the average network efficiency) through simulation experiments by MATLAB. Furthermore, we obtain some conclusions on the basis of the simulation results. Firstly, we discover that IB is more efficient than others for the undirected unweighted 5th ring Beijing road network based on S, and IB is more efficient than others at the beginning while ID is more efficient than IB at last based on E, while IAD causes a greater damage than IAS for the directed weighted 5th ring Beijing taxi network no matter with metrics S or E. Secondly, we find that dynamic attacks are more efficient than their corresponding static attacks, and RB is more destructive than others in all attack graphs while RAD is more destructive than RAS in all attack graphs. Moreover, we propose some suggestions to advance the reliability of the networks according to the simulation results. Additionally, we notice that the damage between ID (RD) and IAD (RAD) is similar due to the large proportion of two-way roads, and we realize that global measures should be employed to estimate the best attack strategy on the basis of that we find the best attack strategy changes with the nodes failure.

By exploiting the reciprocity and randomness properties of wireless channels, physical-layer-based key generation provides a stable secrecy channel even when the main channel suffers from a bad condition. Even though the channel variation due to the mobility of nodes in wireless channels provides an improvement of key generation rate (KGR), it decreases the key consistency probability (KCP) between the node pairs. Inspired by the received signal strength(RSS)-angle of arrival(AoA)-based geolocation research, in this work, we analyze the performance of the key extraction using the RSS and AoA. We aim to identify a way to utilize the high KGR of the AoA-based method to overcome the major drawback of having a low KGR in the most common RSS-based scheme. Specifically, we derive the KCP and KGR of the RSS-AoA-based key generation scheme. Further, we propose a new performance metric called effective key generation rate (EKGR), to evaluate the designed key generation scheme in practical scenarios. Finally, we provide numerical results to verify the accuracy of the presented theoretical analysis.

We numerically investigate a PDM-QPSK multi-rate coherent burst-mode optical receiver capable of receiving 3 different line-rates, suitable for next generation optical networks such as hybrid optical circuit switching (OCS)/optical packet switching (OPS) networks, access networks and datacenter networks. The line-rate detection algorithm relies on a simple-to-generate optical header, it is based on the fast Fourier transform (FFT) which can be efficiently implemented with the Goertzel algorithm, and it is insensitive to polarization rotations and frequency offset. Numerically, we demonstrate that performance in terms of packet detection rate (PER) can be tailored by controlling the sizes of the packet header and the line-rate estimator.

A novel generalized side-information cancellation (GSIC) precoder is proposed for multiuser multi-input multi-output (MIMO) downlink systems with channel state information at the transmitter. The proposed transceiver involves the following stages. First, a minimum mean square error (MMSE) based channel inversion (MMSE-CI) technique is utilized to suppress multiuser broadcast interference. By using a GSIC technique, it can further reduce the residual multiuser interference and the noise induced by MMSE-CI preprocessing. Next, with a singular value decomposition method, the spatial stream interference of each user is suppressed by the pre-processing and post-processing eigenvector matrices. Finally, the proposed precoder can be extended to joint water filling and diagonal loading methods for performance enhancement. For the correlated MIMO channels, signal subspace and antenna selection methods, incorporating the proposed GSIC precoder, are further designed to maximize the sum rate performance. Simulation results show that the proposed GSIC precoder outperforms the conventional precoders. Besides, simulation results confirm that the proposed GSIC precoder with water filling, diagonal loading, and signal subspace techniques exhibits excellent performance.

Delay Tolerant Networks (DTN) protocols based on the store-and-carry principle offer useful functions such as forwarding, utility value, social networks, and network coding. Although many DTN protocol proposals have been offered, work continues to improve performance. In order to implement DTN functions, each protocol introduces multiple parameters; their performance is largely dependent on how the parameter values are set. In this paper, we focus on improving spray and wait (S&W) by proposing a communication protocol named a Spray and AHP-GRA-based Forwarding (S&AGF) and Spray and Fuzzy based Forwarding (S&FF) scheme for DTN. The proposed protocols include a new forwarding scheme intended to extend network lifetime as well as maintain acceptable delivery ratio by addressing a deficiency in existing schemes that do not take energy into consideration. We choose the most suitable relay node by taking the energy, mobility, measured parameters of nodes into account. The simulation-based comparison demonstrates that the proposed S&AGF and S&FF schemes show better balanced performance level in terms of both delivery ratio and network lifetime than original S&W and its variants.

Timing and frequency offsets are caused by imperfect synchronization at the receiver. These errors degrade the performance of OFDM systems by introducing inter-carrier-interference (ICI) and inter-symbol-interference (ISI). In this paper, we derive signal-to-interference ratio (SIR) analytically with timing and frequency offsets for the case that the sampling rate of analog-to-digital converter (ADC) in OFDM receiver is an integer fraction of the signal bandwidth. We find the exact form of interference power as a function of the fractional sampling rate. Our derived analysis is confirmed by simulations and can be applied to see the exact performance of OFDM systems with fractional sampling rate.

The high performance network-on-chip (NoC) router using minimal hardware resources to minimize the layout area is very essential for NoC design. In this paper, we have proposed a memory sharing method of a wormhole routed NoC architecture to alleviate the area overhead of a NoC router. In the proposed method, a memory is shared by multiple physical links by using a multi-port memory. In this paper, we have proposed a partial link-sharing method and evaluated the communication performance using the proposed method. It is revealed that the resulted communication performance by the proposed methods is higher than that of the conventional method, and the progress ratio of the 3D-torus network is higher than that of 2D-torus network. It is shown that the improvement of communication performance using partial link sharing method is achieved with slightly increase of hardware cost.

In this paper, we introduce a new method to remove random-valued impulse noise in an image. Random-valued impulse noise replaces the pixel value at a random position by a random value. Due to the randomness of the noisy pixel values, it is difficult to detect them by comparison with neighboring pixels, which is used in many conventional methods. Then we improve the recent noise detector which uses a non-local search of similar structure. Next we propose a new noise removal algorithm by sparse representation using DCT basis. Furthermore, the sparse representation can remove impulse noise by using the neighboring similar image patch. This method has much more superior noise removal performance than conventional methods at images. We confirm the effectiveness of the proposed method quantitatively and qualitatively.