In this paper, we propose a fully automatic method for extracting carotid artery contours from ultrasound images based on an active contour approach. Several contour extraction techniques have been proposed to measure carotid artery walls for early detection of atherosclerotic disease. However, the majority of these techniques require a certain degree of user interaction that demands time and effort. Our proposal automatically detects the position of the carotid artery by identifying blood flow information related to the carotid artery, and an active contour model is employed that uses initial contours placed in the detected position. Our method also applies a global energy minimization scheme to the active contour model. Experiments on clinical cases show that the proposed method automatically extracts the carotid artery contours at an accuracy close to that achieved by manual extraction.

Let T1,T2,...,Tk be spanning trees in a graph G. If, for any two vertices u,v of G, the paths joining u and v on the k trees are mutually vertex-disjoint, then T1,T2,...,Tk are called completely independent spanning trees (CISTs for short) of G. The construction of CISTs can be applied in fault-tolerant broadcasting and secure message distribution on interconnection networks. Hasunuma (2001) first introduced the concept of CISTs and conjectured that there are k CISTs in any 2k-connected graph. Unfortunately, this conjecture was disproved by Péterfalvi recently. In this note, we give a necessary condition for k-connected k-regular graphs with ⌊k/2⌋ CISTs. Based on this condition, we provide more counterexamples for Hasunuma's conjecture. By contrast, we show that there are two CISTs in 4-regular chordal rings CR(N,d) with N=k(d-1)+j under the condition that k ≥ 4 is even and 0 ≤ j ≤ 4. In particular, the diameter of each constructed CIST is derived.

This paper presents an experimental study that analyzes how conversational agents activate human communication in thought-evoking multi-party dialogues between multi-users and multi-agents. A thought-evoking dialogue is a kind of interaction in which agents act to provoke user thinking, and it has the potential to activate multi-party interactions. This paper focuses on quiz-style multi-party dialogues between two users and two agents as an example of thought-evoking multi-party dialogues. The experimental results revealed that the presence of a peer agent significantly improved user satisfaction and increased the number of user utterances in quiz-style multi-party dialogues. We also found that agents' empathic expressions significantly improved user satisfaction, improved user ratings of the peer agent, and increased the number of user utterances. Our findings should be useful for activating multi-party communications in various applications such as pedagogical agents and community facilitators.

The second edition of the international standard of IEC 61508, functional safety of electrical/electronic/programmable electronic safety-related system (SRS), was published in 2010. This international standard adopts a risk-based approach by which safety integrity requirements can be determined. It presents a formula to estimate the hazardous event rate taking account of non-perfect proof-tests. But it is not clear how to derive the formula. In the present paper, firstly, taking account of non-perfect proof-tests, the relationship between the dangerous undetected failure of SRS, the demand on the SRS and hazardous event is modeled by a fault tree and state-transition diagrams. Next, the hazardous event rate is formulated by use of the state-transition diagrams for the determination of the safety integrity requirements. Then, a comparison is made between the formulas obtained by this paper and given in the standard, and it is found that the latter does not always present rational formulation.

The last decade has witnessed an explosion of interest in research on human emotion modeling for generating intelligent virtual agents. This paper proposes a novel personality model based on the Revised NEO Personality Inventory (NEO PI-R). Compared to the popular Big-Five-Personality Factors (Big5) model, our proposed model is more capable than Big5 on describing a variety of personalities. Combining with emotion models it helps to produce more reasonable emotional reactions to external stimuli. A novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Emotional reactions towards multiple stimuli are also effectively simulated with the proposed personality model.

Due to the recent development of underlying hardware technology and improvement in installing environments, public display has been becoming more common and attracting more attention as a new type of signage. Any signage is required to make its content more attractive to its viewers by evaluating the current attractiveness on the fly, in order to deliver the message from the sender more effectively. However, most previous methods for public display require time to reflect the viewers' evaluations. In this paper, we present a novel system, called Mood-Learning Public Display, which automatically adapts its content design. This system utilizes viewers' involuntary behaviors as a sign of evaluation to make the content design more adapted to local viewers' tastes evolutionarily on site. The system removes the current gap between viewers' expectations and the content actually displayed on the display, and makes efficient mutual transmission of information between the cyberworld and the reality.

Braille fonts allow us to easily make braille labels on capsule paper. For legibility, fonts should be printed at optimum sizes. To find the optimum sizes for Japanese braille fonts, we conducted an experiment in which a Japanese braille font was printed at various sizes on capsule paper and read and rated by young braille users. The results show that braille printed at 17 and 18 point sizes were read faster and evaluated higher than those printed at smaller or bigger sizes.

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

Mobile video services are becoming a dominant traffic category in emerging fourth generation (4G) cellular networks such as the 3GPP Long-Term Evolution (LTE) and LTE-Advanced (LTE-A). In particular, mobile video unicasting services based on 3GPP Dynamic Adaptive Streaming over HTTP (DASH) and multicasting/broadcasting services based on 3GPP evolved Multimedia Multicast/Broadcast Service (eMBMS) will require considerable resources for high-quality service delivery with high coverage probability. Faced with the challenge of energy efficient multimedia service provisioning over LTE/LTE-A, in this paper, we present simple analytical tools for evaluation of average service data rates, bandwidth and energy-consumption requirements applicable for different multimedia delivery services and LTE/LTE-A radio access network (RAN) configurations. Moreover, we introduce and evaluate novel energy and bandwidth performance measures defined per unit of service. As a result, we are able to compare the efficiency of different multimedia service delivery configurations over LTE/LTE-A. In particular, in this paper, as a running example we focus on eMBMS and compare the Energy of Service (EoS) of the two macro-cellular LTE/LTE-A configurations recently proposed in 3GPP: i) a single frequency network eMBMS (SFN-eMBMS), and ii) a single-cell eMBMS (SC-eMBMS). Furthermore, we extend this analysis to eMBMS provisioning over Heterogeneous Networks (HetNets) environment. However, the methodology presented is general and targets light-weight system design and comparison of bandwidth/energy costs of different LTE/LTE-A multimedia service delivery configurations.

When deploying a new mobile technology such as LTE, it is crucial to identify the factors that affect the radio network in terms of capacity and quality of service. In this context, network coverage is arguably the single most influential factor. This work presents a metaheuristic-optimization approach to automatically adapt the signal losses due to clutter, based on a set of field measurements. The optimization procedure is performed regionally, enabling the calculation of accurate radio-propagation predictions. The evaluation of the proposed approach is carried out on three different regions in Slovenia, where Telekom Slovenije, d.d., provides LTE coverage. The results show radio-propagation predictions of improved quality and the benefits of the presented approach over manual methods, both in terms of problem size and solution accuracy.

Non-Volatile Memories (NVMs) are considered as promising memory technologies for Last-Level Cache (LLC) due to their low leakage and high density. However, NVMs have some drawbacks such as high dynamic energy in modifying NVM cells, long latency for write operation, and limited write endurance. A number of approaches have been proposed to overcome these drawbacks. But very little attention is paid to consider the cache coherency issue. In this letter, we suggest a new cache coherence protocol to reduce the write operations of the LLC. In our protocol, the block data of the LLC is updated only if the cache block is written-back from a private cache, which leads to avoiding useless write operations in the LLC. The simulation results show that our protocol provides 27.1% energy savings and 26.3% lifetime improvements in STT-RAM at maximum.

We propose a speaker adaptation method based on the probabilistic principal component analysis (PPCA) of acoustic models. We define a training matrix which is represented in a two-way array and decompose the training models by PPCA to construct bases. In the two-way array representation, each training model is represented as a matrix and the columns of each training matrix are treated as training vectors. We formulate the adaptation equation in the maximum a posteriori (MAP) framework using the bases and the prior.

Open-access femtocell networks assure the cellular user of getting a better and stronger signal. However, due to the small range of femto base stations (FBSs), any motion of the user may trigger handover. In a dense environment, the possibility of such handover is very frequent. To avoid frequent communication disruptions due to phenomena such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Existing selection methods commonly uses a measured channel/cell quality metric such as the channel capacity (between the user and the target cell). However, the throughput experienced by the user is time-varying because of the channel condition, i.e., owing to the propagation effects or receiver location. In this context, the conventional approach does not reflect the future performance. To ensure the efficiency of cell selection, user's decision needs to depend not only on the current state of the network, but also on the future possible states (horizon). To this end, we implement a learning algorithm that can predict, based on the past experience, the best performing cell in the future. We present in this paper a reinforcement learning (RL) framework as a generic solution for the cell selection problem in a non-stationary femtocell network that selects, without prior knowledge about the environment, a target cell by exploring past cells' behavior and predicting their potential future states based on Q-learning algorithm. Then, we extend this proposal by referring to a fuzzy inference system (FIS) to tune Q-learning parameters during the learning process to adapt to environment changes. Our solution aims at minimizing the frequency of handovers without affecting the user experience in terms of channel capacity. Simulation results demonstrate that· our solution comes very close to the performance of the opportunistic method in terms of capacity, while fewer handovers are required on average.· the use of fuzzy rules achieves better performance in terms of received reward (capacity) and number of handovers than fixing the values of Q-learning parameters.

High-speed wireless access technologies have evolved over the last years setting new challenges for TCP. That is, to effectively utilize the available network resources and to minimize the effects of wireless channel errors on TCP performance. This paper introduces a new TCP variant, called TCP-BIAD aiming at enhancing TCP performance in broadband wireless access networks. We provide analytical expressions for evaluating the stability, throughput, fairness and friendliness properties of our proposal, and we validate our results by means of computer simulations. Initial results presented in this paper show that this approach achieves high network utilization levels in a wide range of network conditions, while maintaining an adequately fair and friendly behavior with respect to coexisting TCP flows.

Digital map systems can be categorized, based on the support they provide, into map navigation systems and map touring systems. Map navigation systems put more focus on helping travelers finding routes or directions instantly. By contrast, map touring systems such as Google Maps running on desktop computers are built to support users in developing their routes and survey knowledge before they go for travel. In this paper, traveler conceptual models are proposed as an interaction paradigm to enhance user immersion and interaction experience on map touring systems. A map touring system, MapXplorer, is also introduced as a proof of concept with its system design and implementation explained in detail. Twenty participants were invited to join the user study that investigates users' performance and preferences on navigation and exploration tasks. The results of experiments show that the proposed system surpasses traditional map touring systems on both navigation and exploration tasks for about 50 percent on average, and provides better user experience.

A new stock index trend analysis approach is proposed in this paper, which is based on a newly developed signal decomposition approach - adaptive Fourier decomposition (AFD). AFD can effectively extract the signal's primary trend, which specifically suits the Dow Theory based technique analysis. The proposed approach integrates two different kinds of forecasting approaches, including the Dow theory the RBF neural network. Effectiveness of the proposed approach is assessed through comparison with the direct RBF neural network approach. The result is proved to be promising.

Shadow mapping is an efficient method to generate shadows in real time computer graphics and has broad variations from hard to soft shadow synthesis. Soft shadowing based on shadow mapping is a blurring technique on a shadow map or on screen space. Blurring on screen space has an advantage for efficient sampling on a shadow map, since the blurred target array has exactly the same coordinates as the screen. However, a previous blurring method on screen space has a drawback: the generated shadow is not correct when a view direction has a large angle to the normal of the shadowed plane. In this paper, we introduce a new screen space based method for soft shadowing that is fast and generates soft shadows more accurately than the previous screen space soft shadow mapping method. The resultant images show shadows produced by our method just stand in the same place, while shadows by the previous method change in terms of penumbra while the view moves. Surprisingly, although our method is more complex than the previous method, the measurement results of the calculation time show our method is almost the same performance. This is because it controls the blurring area more accurately and thus successfully reduces multiplications for blurring.

Wireless Local Area Networks (WLANs) are widely deployed for internet access. Multiple interfering Access Points (APs) lead to a significant increase in collisions, that reduces throughput and affects media traffic. Thus, interference mitigation among different APs becomes a crucial issue in Multi-Cell WLAN systems. One solution to this issue is to assign a different frequency channel to each AP so as to prevent neighboring cells from operating on the same channel. However, most of the existing WLANs today operate in the unlicensed 2.4GHz Industrial, Scientific and Medical (ISM) band, which suffers from lack of the available channels. Therefore, effective channel assignment to minimize the interference in Multi-Cell WLANs is necessary. In this article, we formulate the channel assignment problem as a mixed integer linear programming (MILP) problem that minimizes the worst case total interference power. The main advantage of this algorithm is that it provides a global solution and at the same time guarantees a non-overlapping channel assignment. We also propose a Lagrangian relaxation approach to transform the MILP into a low complexity linear program which can be solved efficiently in real time, even for large sized networks. Simulation results reveal that both the MILP algorithm and the Lagrangian relaxation approach provide a total interference reduction below the default setting of having all APs assigned the same channel. In addition, simulation results on cumulative density function (CDF) of the SINR at the user level prove the validity of the proposed algorithms.

A novel auto-tuning digital DC--DC converter is presented. In order to reduce the recovery time and undershoot, the auto-tuning control combines LnL, conventional PID and a predictive PID with a configurable predictive coefficient. A switch module is used to select an algorithm from the three control algorithms, according to the difference between the error signal and the two initially predefined thresholds. The detection and control logic is designed for both window delay line ADC and $Sigma Delta$ DPWM to correct the delay deviation. When the output of the converter exceeds the quantization range, the digital output of ADC is set at 0 or 1, and the delay line stops working to reduce power consumption. Theoretical analysis and simulations in the CSMC CMOS 0.5,$mu$m process are carried out to verify the proposed DC--DC converter. It is found that the converter achieves a power efficiency of more than 90% at heavy load, and reduces the recovery time and undershoot.

This paper studies an underlay-based cognitive two-way relay network which consists of a primary network (PN) and a secondary network (SN). Two secondary users (SUs) exchange information with the aid of multiple single-antenna amplify-and-forward relays while a primary transmitter communicates with a primary receiver in the same spectrum. Unlike the existing contributions, the transmit powers of the SUs and the distributed beamforming weights of the relays are jointly optimized to minimize the sum interference power from the SN to the PN under the quality-of-service (QoS) constraints of the SUs determined by their output signal-to-interference-plus-noise ratio (SINR) and the transmit power constraints of the SUs and relays. This approach leads to a non-convex optimization problem which is computationally intractable in general. We first investigate two necessary conditions that optimal solutions should satisfy. Then, the non-convex minimization problem is solved analytically based on the obtained conditions for single-relay scenarios. For multi-relay scenarios, an iterative numerical algorithm is proposed to find suboptimal solutions with low computational complexity. It is shown that starting with an arbitrarily initial feasible point, the limit point of the solution sequence derived from the iterative algorithm satisfies the two necessary conditions. To apply this algorithm, two approaches are developed to find an initial feasible point. Finally, simulation results show that on average, the proposed low-complexity solution considerably outperforms the scheme without source power control and performs close to the optimal solution obtained by a grid search technique which has prohibitively high computational complexity.