With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

Because dielectrics between active layers have low thermal conductivities, there is a demand to reduce the temperature increase in three-dimensional integrated circuits (3D ICs). This paper demonstrates that, in the design of 3D ICs, different layer assignments often lead to different temperature increases. Based on this observation, we are motivated to perform temperature-aware layer assignment. Our work includes two parts. Firstly, an integer linear programming (ILP) approach that guarantees a minimum temperature increase is proposed. Secondly, a polynomial-time heuristic algorithm that reduces the temperature increase is proposed. Compared with the previous work, which does not take the temperature increase into account, the experimental results show that both our ILP approach and our heuristic algorithm produce a significant reduction in the temperature increase with a very small area overhead.

Time synchronization is important for frequency hopping, power management, scheduling, and basic operations of multi-hop ad-hoc networks. The main problem of existing time synchronization methods is that they depend on a particular node that has the fastest time information among neighbor nodes. The Cucker-Smale flocking model describes that global emergent behavior can be obtained by locally averaging the velocity of each bird. Inspired by this flocking model, we propose a time synchronization method not depending on a particular node. In the proposed method, each node revises its time information via the local-averaging procedure in a distributed manner. A self-correcting procedure is added to the proposed method in order to preserve the effect of time adjustment executed by the local-averaging procedure. The simulation results show that the proposed time synchronization method reduces the time difference among nodes, and enhances the performance of time synchronization in the context of IEEE 802.11-based ad-hoc networks.

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.

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.

We present the adaptation of the acoustic models of hidden Markov models (HMMs) to the target speaker and noise environment using bilinear models. Acoustic models trained from various speakers and noise conditions are decomposed to build the bases that capture the interaction between the two factors. The model for the target speaker and noise is represented as a product of bases and two weight vectors. In experiments using the AURORA4 corpus, the bilinear model outperforms the linear model.

We discuss a new high performance computing service (HPCS) platform that has been developed to provide domain-neutral computing service under the governmental support from “EDucation-research Integration through Simulation On the Net” (EDISON) project. With a first focus on technical features, we not only present in-depth explanations of the implementation details, but also describe the strengths of the EDISON platform against the successful nanoHUB.org gateway. To validate the performance and utility of the platform, we provide benchmarking results for the resource virtualization framework, and prove the stability and promptness of the EDISON platform in processing simulation requests by analyzing several statistical datasets obtained from a three-month trial service in the initiative area of computational nanoelectronics. We firmly believe that this work provides a good opportunity for understanding the science gateway project ongoing for the first time in Republic of Korea, and that the technical details presented here can be served as an useful guideline for any potential designs of HPCS platforms.

This letter considers the weighted sum-rate maximization (WSRMax) problem in downlink multicell multiuser orthogonal frequency-division multiplexing system. The WSRMax problem under per base station transmit power constraint is known to be NP-hard, and the optimal solution is computationally very expensive. We propose two less-complex suboptimal convex approximated solutions which are based on sequential parametric convex approximation approach. We derive provably faster convergent iterative convex approximation techniques that locally optimize the weighted sum-rate function. Both the iterative solutions are found to converge to the local optimal solution within a few iterations compared to other well-known techniques. The numerical results demonstrate the effectiveness and superiority of the proposed approaches.

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

This paper investigates power allocation and outage performance for the MIMO full duplex relaying (MFDR) based on orthogonal space-time block Codes (OSTBC) in cognitive radio systems. OSTBC transmission is used as a simple way to obtain multi-antenna diversity gain. Cognitive MFDR systems offer the advantage not only of increasing spectral efficiency by spectrum sharing but also of extending the coverage through the use of relays. In cognitive MFDR systems, the primary user experiences interference from the secondary source and relay simultaneously due to the full duplexing. What is therefore needed is a way to optimize the transmission powers at the secondary source and relay. Therefore, we propose an optimal power allocation (OPA) scheme based on minimizing the outage probability in cognitive MFDR systems. We then analyze the outage probability of the secondary user in the noise-limited and interference-limited environments under Nakagami-m fading channels. Simulation results show that the proposed schemes achieve performance improvement in terms of outage probability.

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.

This paper provides an overview of power line communication (PLC) applications, challenges and possible evolution. Emphasis is put on two relevant aspects: a) channel characterization and modeling, b) filter bank modulation for spectral efficient transmission. The main characteristics of both the indoor channel (in-home, in-ship, in-car) and the outdoor low voltage and medium voltage channels are reported and compared. A simple approach to statistically model the channel frequency response (CFR) is described and it is based on the generation of a vector of correlated random variables. To overcome the channel distortions, it is shown that filter bank modulation can provide robust performance. In particular, it is shown that the sub-channel spectral containment of filtered multitone modulation (FMT) can provide high notching capability and spectral efficiency. Reduced complexity can be obtained with a cyclic filter bank modulation approach that we refer to as cyclic block FMT modulation (CB-FMT) which still provides higher spectral flexibility/efficiency than OFDM.

Dubé and Beaudoin proposed a lossless data compression called compression via substring enumeration (CSE) in 2010. We evaluate an upper bound of the number of bits used by the CSE technique to encode any binary string from an unknown member of a known class of k-th order Markov processes. We compare the worst case maximum redundancy obtained by the CSE technique for any binary string with the least possible value of the worst case maximum redundancy obtained by the best fixed-to-variable length code that satisfies the Kraft inequality.

Complex-valued Hopfield associative memory (CHAM) is one of the most promising neural network models to deal with multilevel information. CHAM has an inherent property of rotational invariance. Rotational invariance is a factor that reduces a network's robustness to noise, which is a critical problem. Here, we proposed complex-valued bipartite auto-associative memory (CBAAM) to solve this reduction in noise robustness. CBAAM consists of two layers, a visible complex-valued layer and an invisible real-valued layer. The invisible real-valued layer prevents rotational invariance and the resulting reduction in noise robustness. In addition, CBAAM has high parallelism, unlike CHAM. By computer simulations, we show that CBAAM is superior to CHAM in noise robustness. The noise robustness of CHAM decreased as the resolution factor increased. On the other hand, CBAAM provided high noise robustness independent of the resolution factor.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

This paper assesses the main challenges associated with the propagation and channel modeling of broadband radio systems in a complex environment of high speed and metropolitan railways. These challenges comprise practical simulation, modeling interferences, radio planning, test trials and performance evaluation in different railway scenarios using Long Term Evolution (LTE) as test case. This approach requires several steps; the first is the use of a radio propagation simulator based on ray-tracing techniques to accurately predict propagation. Besides the radio propagation simulator, a complete test bed has been constructed to assess LTE performance, channel propagation conditions and interference with other systems in real-world environments by means of standard-compliant LTE transmissions. Such measurement results allowed us to evaluate the propagation and performance of broadband signals and to test the suitability of LTE radio technology for complex railway scenarios.

Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.

This paper proposes a novel multimodal interactive surgical simulator that incorporates haptic, olfactory, as well as traditional vision feedback. A scent diffuser was developed to produce odors when errors occur. Haptic device was used to provide the sense of touch to the user. The preliminary results show that adding smell as an aid to the simulation enhanced the memory retention that lead to better performance.

The rendering mechanism plays an indispensable role in browser-based Web application. It generates active webpages dynamically and provides human-readable layout through template engines, which are used as a standard programming model to separate the business logic and data computations from the webpage presentation. The client-side rendering mechanism, owing to the advances of rich application technologies, has been widely adopted. The adoption of client side rendering brings not only various merits but also new problems. In this paper, we propose and construct “pagelet”, a segment-based template engine for developing flexible and extensible Web applications. By presenting principles, practice and usage experience of pagelet, we conduct a comprehensive analysis of possible advantages and disadvantages brought by client-side rendering mechanism from the viewpoints of both developers and end-users.

A smoothing method for minimum phone error linear regression (MPELR) is proposed in this paper. We show that the objective function for minimum phone error (MPE) can be combined with a prior mean distribution. When the prior mean distribution is based on maximum likelihood (ML) estimates, the proposed method is the same as the previous smoothing technique for MPELR. Instead of ML estimates, maximum a posteriori (MAP) parameter estimate is used to define the mode of prior mean distribution to improve the performance of MPELR. Experiments on a large vocabulary speech recognition task show that the proposed method can obtain 8.4% relative reduction in word error rate when the amount of data is limited, while retaining the same asymptotic performance as conventional MPELR. When compared with discriminative maximum a posteriori linear regression (DMAPLR), the proposed method shows improvement except for the case of limited adaptation data for supervised adaptation.