Named Data Networking (NDN) is a proposed future Internet architecture that shifts the fundamental abstraction of the network from host-to-host communication to request-response for named, signed data-an information dissemination focused approach. This paper describes a general design for receiver-driven, real-time streaming data (RTSD) applications over the current NDN implementation that aims to take advantage of the architecture's unique affordances. It is based on experimental development and testing of running code for real-time video conferencing, a positional tracking system for interactive multimedia, and a distributed control system for live performance. The design includes initial approaches to minimizing latency, managing buffer size and Interest retransmission, and adapting retrieval to maximize bandwidth and control congestion. Initial implementations of these approaches are evaluated for functionality and performance results, and the potential for future research in this area, and improved performance as new features of the architecture become available, is discussed.

In this research, we have investigated the deposition condition of pentacene film on nitrogen doped (N-doped) LaB6 donor layer for larger grain growth at the channel region for bottom-contact type pentacene-based organic field-effect transistors (OFETs) to improve the device characteristics. Source and drain bottom-contacts of Al were patterned and 2nm-thick N-doped LaB6 donor layer was deposited on the SiO2/Si(100) back-gate structure. The dendritic grain growth of pentacene larger than 10µm without lamellar grain growth was demonstrated when the deposition temperature and rate were 100°C and 0.5nm/min, respectively. Furthermore, it was found that the dendritic grain growth was realized at the boundary region of bottom-contact as well as channel region.

Content centric network (CCN) is conceived as a good candidate for a futuristic Internet paradigm due to its simple and robust communication mechanism. By directly applying the CCN paradigm in wireless multihop mobile ad hoc networks, we experience various kind of issues such as packet flooding, data redundancy, packet collisions, and retransmissions etc., due to the broadcast nature of the wireless channel. To cope with the problems, in this study, we propose a novel location-aware forwarding and caching scheme for CCN-based mobile ad hoc networks. Extensive simulations are performed by using simulator, named ndnSIM. Experiment results show that proposed scheme does better as compared to other schemes in terms of content retrieval time and the number of Interest retransmissions triggered in the network.

In this work, the bias polarity dependent resistive switching behaviors in Cu/Si3N4/p+ Si RRAM memory cell have been closely studied. Different switching characteristics in both unipolar and bipolar modes after the positive forming are investigated. The bipolar switching did not need a forming process and showed better characteristics including endurance cycling, uniformity of switching parameters, and on/off resistance ratio. Also, the resistive switching characteristics by both positive and negative forming switching are compared. It has been confirmed that both unipolar and bipolar modes after the negative forming exhibits inferior resistive switching performances due to high forming voltage and current.

To support the efficient gathering of diverse information about a news event, we focus on descriptions of named entities (persons, organizations, locations) in news articles. We extend the stakeholder mining proposed by Ogawa et al. and extract descriptions of named entities in articles. We propose three measures (difference in opinion, difference in details, and difference in factor coverage) to rank news articles on the basis of analyzing differences in descriptions of named entities. On the basis of these three measurements, we develop a news app on mobile devices to help users to acquire diverse reports for improving their understanding of the news. For the current article a user is reading, the proposed news app will rank and provide its related articles from different perspectives by the three ranking measurements. One of the notable features of our system is to consider the access history to provide the related news articles. In other words, we propose a context-aware re-ranking method for enhancing the diversity of news reports presented to users. We evaluate our three measurements and the re-ranking method with a crowdsourcing experiment and a user study, respectively.

The availability is an important issue of software-defined networking (SDN). In this paper, the experiments based on a SDN testbed showed that the resource utilization of the data plane and control plane changed drastically when DDoS attacks happened. This is mainly because the DDoS attacks send a large number of fake flows to network in a short time. Based on the observation and analysis, a DDoS defense mechanism based on legitimate source and destination IP address database is proposed in this paper. Firstly, each flow is abstracted as a source-destination IP address pair and a legitimate source-destination IP address pair database (LSDIAD) is established by historical normal traffic trace. Then the proportion of new source-destination IP address pair in the traffic per unit time is cumulated by non-parametric cumulative sum (CUSUM) algorithm to detect the DDoS attacks quickly and accurately. Based on the alarm from the non-parametric CUSUM, the attack flows will be filtered and redirected to a middle box network for deep analysis via south-bound API of SDN. An on-line updating policy is adopted to keep the LSDIAD timely and accurate. This mechanism is mainly implemented in the controller and the simulation results show that this mechanism can achieve a good performance in protecting SDN from DDoS attacks.

In the obnoxious facility game with a set of agents in a space, we wish to design a mechanism, a decision-making procedure that determines a location of an undesirable facility based on locations reported by the agents, where we do not know whether the location reported by an agent is where exactly the agent exists in the space. For a location of the facility, the benefit of each agent is defined to be the distance from the location of the facility to where the agent exists. Given a mechanism, all agents are informed of how the mechanism utilizes locations reported by the agents to determine a location of the facility before they report their locations. Some agent may try to manipulate the decision of the facility location by strategically misreporting her location. As a fair decision-making, mechanisms should be designed so that no particular group of agents can get a larger benefit by misreporting their locations. A mechanism is called group strategy-proof if no subset of agents can form a group such that every member of the group can increase her benefit by misreporting her location jointly with the rest of the group. For a given mechanism, a point in the space is called a candidate if it can be output as the location of the facility by the mechanism for some set of locations reported by agents. In this paper, we consider the case where a given space is a tree metric, and characterize the group strategy-proof mechanisms in terms of distribution of all candidates in the tree metric. We prove that there exists a group strategy-proof mechanism in the tree metric if and only if the tree has a point to which every candidate has the same distance.

In this study, we propose a method for measuring a photoplethysmograph using a complementary metal-oxide-semiconductor image sensor (CMOS) or smartphone camera for the adaptation of a mobile health (m-health) services. The proposed algorithm consists of six procedures. Before measuring the photoplethysmograph, the human fingertip must make contact with the smartphone camera lens and turn on the camera light. The first procedure converts the red-green-blue (RGB) to a gray image from a camera image, Then, region of interest (ROI) must be detected from the obtained image. The third procedure calculates the baseline level to reduce direct current (DC) offset effect, before extracting the photoplethysmograph from the camera image. The baseline is filtered, and the last step oversamples the resulting baseline filtered data using cubic spline interpolation. The proposed algorithm has been tested on six people using CMOS image sensors of several smartphones, which can effectively acquire a PPG signal in any situation. We believe that the proposed algorithm could easily be adapted into any m-health system that used a CMOS image sensor.

In this paper, we study the power allocation problem in a relay assisted multi-band underlay cognitive radio network. Such a network allows unlicensed users (secondary users) to access the spectrum bands under a transmission power constraint. Due to the concave increasing property of logarithm function, it is not always wise for secondary users to expend all the transmission power in one band if their aim is to maximize achievable data rate. In particular, we study a scenario where two secondary users and a half-duplexing relay exist with two available bands. The two users choose different bands for direct data transmission and use the other band for relay transmission. By properly allocating the power on two bands, each user may be able to increase its total achievable data rate while satisfying the power constraint. We formulate the power allocation problem as a non-cooperative game and investigate its Nash equilibria. We prove the power allocation game is a supermodular game and that Nash equilibria exist. We further find the best response function of users and propose a best response update algorithm to solve the corresponding dynamic game. Numerical results show the overall performance in terms of achievable rates is improved through our proposed transmission scheme and power allocation algorithm. Our proposed algorithm also shows satisfactory performance in terms of convergence speed.

In lossy image/video encoding, there is a compromise between the number of bits and the extent of distortion. Optimizing the allocation of bits to different sources, such as frames or blocks, can improve the encoding performance. In intra-frame encoding, due to the dependency among macro blocks (MBs) introduced by intra prediction, the optimization of bit allocation to the MBs usually has high complexity. So far, no practical optimal bit allocation methods for intra-frame encoding exist, and the commonly used method for intra-frame encoding is the fixed-QP method. We suggest that the QP selection inside an image/a frame can be optimized by aiming at the constant perceptual quality (CPQ). We proposed an iteration-based bit allocation scheme for H.264/AVC intra-frame encoding, in which all the local areas (which is defined by a group of MBs (GOMBs) in this paper) in the frame are encoded to have approximately the same perceptual quality. The SSIM index is used to measure the perceptual quality of the GOMBs. The experimental results show that the encoding performance on intra-frames can be improved greatly by the proposed method compared with the fixed-QP method. Furthermore, we show that the optimization on the intra-frame can bring benefits to the whole sequence encoding, since a better reference frame can improve the encoding of the subsequent frames. The proposed method has acceptable encoding complexity for offline applications.

This paper presents a practical system which allows instructors to easily introduce 3D games utilizing smartphones in a classroom. The system consists of a PC server, a big screen and smartphone clients. The server provides 3D models, so no 3D authoring is needed when using this system. For an instructor, preparing slides of quiz-questions with the correct answers is all that is required when designing 3D games. According to a quiz specified by an instructor, this system constructs a corresponding 3D game scene. The answers students provide on their smartphones will be used to play this game. Everyone in the classroom can see this 3D game in real time on a big screen. The game illustrates how every student has reacted to a quiz. This system also introduces specialized queues for mobile interactions; a queue for commands from an instructor and a queue for data from students. The command queue has higher priority than the data queue; so that an instructor can control this system by sending commands with clicks on a smartphone. Previous studies have mostly provided specially designed teaching materials to instructors, often treating them as passive consultants. However, by using slides, already familiar to instructors, this system enables instructors to combine their own teaching materials with 3D games in the classroom. Moreover, 3D games are expected to further motivate students to actively participate in classroom activities. This system is evaluated in this paper.

Detecting small infrared targets is a difficult but important task in highly cluttered coastal surveillance. The paper proposed a method called low-rank and sparse decomposition based frame difference to improve the detection performance of a surveillance system. First, the frame difference is used in adjacent frames to detect the candidate object regions which we are most interested in. Then we further exclude clutters by low-rank and sparse matrix recovery. Finally, the targets are extracted from the recovered target component by a local self-adaptive threshold. The experiment results show that, the method could effectively enhance the system's signal-to-clutter ratio gain and background suppression factor, and precisely extract target in highly cluttered coastal scene.

Vanishing point estimation is an important issue for vision based road detection, especially in unstructured roads. However, most of the existing methods suffer from the long calculating time. This paper focuses on improving the efficiency of vanishing point estimation by using a heuristic voting method based on particle swarm optimization (PSO). Experiments prove that with our proposed method, the efficiency of vanishing point estimation is significantly improved with almost no loss in accuracy. Moreover, for sequenced images, this method is further improved and can get even better performance, by making full use of inter-frame information to optimize the performance of PSO.

Autonomous driving is not only required to detect pedestrians around vehicles, but also expected to understand the behaviors of pedestrians. Pedestrian body orientation and head orientation are the relevant indicators of the pedestrian intention. This paper proposes an accurate estimation system to recognize the pedestrian body orientation and the pedestrian head orientation from on-board camera and inertial sensors. The proposed system discretizes the body orientation and the head orientation into 16 directions. In order to achieve the accurate orientation estimation, a novel training database is established, which includes strongly labeled data and weakly labeled data. Semi-Supervised Learning method is employed to annotate the weakly labeled data, and to generate the accurate classifier based on the proposed training database. In addition, the temporal constraint and the human physical model constraint are considered in orientation estimation, which are beneficial to the reasonable and stable result of orientation estimation for the pedestrian in image sequences. This estimated result is the orientation in camera space. The comprehension of the pedestrian behavior needs to be conducted in the real world space. Therefore, this paper proposes to model the motion of the host vehicle using inertial sensor, then transforms the estimated orientation from camera space to the real world space by considering the vehicle and pedestrian motion. The represented orientation indicates the behavior of the pedestrian more directly. Finally, a series of experiments demonstrate the effectiveness of the proposed pedestrian orientation system.

In this paper, a method of calculating the mean channel capacity based on S-parameter of MIMO (Multiple-Input Multiple-Output) antenna is proposed. This method exploits the correlation matrix calculated from the antenna S-parameter matrix, and offers highly accurate estimates of the mean channel capacity without dependence on SNR (Signal-to-Noise Ratio). The numerical and experimental results revealed that the proposed method can calculate the channel capacity with fair accuracy independent of the number and spacing of the antenna elements if the radiation efficiency is sufficiently high.

In the dynamic heterogeneous cellular network, spectrum allocation deeply impacts the quality of service and performance of network. In this paper, spectrum allocation is formulated as a dynamic programming problem. A two-level framework is proposed by jointly considering users' dynamic service selection and provider's spectrum allocation. In the first level, the users' service selection is modeled as an evolutionary game, and an evolutionary equilibrium is obtained. In the second level, the service provider allocates the spectral resources to macrocells and femtocells according to the users' strategies, so as to maximize its profits. By jointly considering the service selection and spectrum allocation, the equilibriums of the dynamic network are found. The stability of the equilibriums is analyzed and proven. The proposed two-level framework is validated by the numerical simulation.

The conventional bilateral motion estimation (BME) for motion-compensated frame rate up-conversion (MC-FRUC) can avoid the problem of overlapped areas and holes but usually results in lots of inaccurate motion vectors (MVs) since 1) the MV of an object between the previous and following frames is more likely to have no temporal symmetry with respect to the target block of the interpolated frame and 2) the repetitive patterns existing in video frame lead to the problem of mismatch due to the lack of the interpolated block. In this paper, a new BME algorithm with a low computational complexity is proposed to resolve the above problems. The proposed algorithm incorporates multi-resolution search into BME, since it can easily utilize the MV consistency between two adjacent pyramid levels and spatial neighboring MVs to correct the inaccurate MVs resulting from no temporal symmetry while guaranteeing low computational cost. Besides, the multi-resolution search uses the fast wavelet transform to construct the wavelet pyramid, which not only can guarantee low computational complexity but also can reserve the high-frequency components of image at each level while sub-sampling. The high-frequency components are used to regularize the traditional block matching criterion for reducing the probability of mismatch in BME. Experiments show that the proposed algorithm can significantly improve both the objective and subjective quality of the interpolated frame with low computational complexity, and provide the better performance than the existing BME algorithms.

In the literature, many cryptosystems have been proposed to be secure under the Strong Diffie-Hellman (SDH) and related problems. For example, there is a cryptosystem that is based on the SDH/related problem or allows the Diffie-Hellman oracle. If the cryptosystem employs general domain parameters, this leads to a significant security loss caused by Cheon's algorithm [14], [15]. However, all elliptic curve domain parameters explicitly recommended in the standards (e.g., ANSI X9.62/63 [1], [2], FIPS PUB 186-4 [43], SEC 2 [50], [51]) are susceptible to Cheon's algorithm [14], [15]. In this paper, we first prove that (q-1)(q+1) is always divisible by 24 for any prime order q>3. Based on this result and depending on small divisors d1,d2≤(log q)2, we classify primes q>3, such that both (q-1)/d1 and (q+1)/d2 are primes, into Perfect, Semiperfect, SEC1v2 and Acceptable. Then, we describe algorithmic procedures and show their simulation results of secure elliptic curve domain parameters over prime/character 2 finite fields resistant to Cheon's algorithm [14], [15]. Also, several examples of the secure elliptic curve domain parameters (including Perfect or Semiperfect prime q) are followed.

Recently in the mobile graphic industry, ultra-realistic visual qualities with 60fps and limited power budget for GPU have been required. For graphics-heavy applications that run at 30 fps, we easily observed very noticeable flickering artifacts. Further, the workload imposed by high resolutions at high frame rates directly decreases the battery life. Unlike the recent frame rate up sampling algorithms which remedy the flickering but cause inevitable significant overheads to reconstruct intermediate frames, we propose a dynamic rendering quality scaling (DRQS) that includes dynamic rendering based on resolution changes and quality scaling to increase the frame rate with negligible overhead using a transform matrix. Further DRQS reduces the workload up to 32% without human visual-perceptual changes for graphics-light applications.

The RO (Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes an area efficient device parameter estimation method with sensitivity-configurable ring oscillator (RO). This sensitivity-configurable RO has a number of configurations and the proposed method exploits this property for reducing sensor area and/or improving estimation accuracy. The proposed method selects multiple sets of sensitivity configurations, obtains multiple estimates and computes the average of them for accuracy improvement exploiting an averaging effect. Experimental results with a 32-nm predictive technology model show that the proposed averaging with multiple estimates can reduce the estimation error by 49% or reduce the sensor area by 75% while keeping the accuracy. Compared to previous work with iterative estimation, 23% accuracy improvement is achieved.