This paper presents a study of the applicability of clusters of GPUs to high-resolution 3D simulations of cardiac electrophysiology. By experimenting with representative cardiac cell models and ODE solvers, in association with solving the monodomain equation, we quantitatively analyze the obtainable computational capacity of GPU clusters. It is found that for a 501×501×101 3D mesh, which entails a 0.1mm spatial resolution, a 128-GPU cluster only needs a few minutes to carry out a 100,000-time-step cardiac excitation simulation that involves a four-variable cell model. Even higher spatial and temporal resolutions are achievable for such simplified mathematical models. On the other hand, our experiments also show that a dramatically larger cluster of GPUs is needed to handle a very detailed cardiac cell model.

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.

In this paper, we consider the problem of inferring a Boolean network (BN) from a given set of singleton attractors, where it is required that the resulting BN has the same set of singleton attractors as the given one. We show that the problem can be solved in linear time if the number of singleton attractors is at most two and each Boolean function is restricted to be a conjunction or disjunction of literals. We also show that the problem can be solved in polynomial time if more general Boolean functions can be used. In addition to the inference problem, we study two network completion problems from a given set of singleton attractors: adding the minimum number of edges to a given network, and determining Boolean functions to all nodes when only network structure of a BN is given. In particular, we show that the latter problem cannot be solved in polynomial time unless P=NP, by means of a polynomial-time Turing reduction from the complement of the another solution problem for the Boolean satisfiability problem.

Due to vehicle movement and lossy wireless channels, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks (VANETs) is a well-known challenging problem. In this paper, we propose BR-NB (broadcast with neighbor information), a fuzzy logic based multi-hop broadcast protocol for VANETs. BR-NB achieves a low overhead by using only a subset of neighbor nodes to relay data packets. For the relay node selection, BR-NB jointly considers multiple metrics of the inter-vehicle distance, vehicle mobility and link quality by employing fuzzy logic. Since the expected coverage and vehicle mobility are inferred from the two-hop neighbor information which can be acquired from the hello message exchange, BR-NB is independent of position information. BR-NB provides a practical and portable solution for broadcast services in VANETs. We use computer simulations and real-world experiments to evaluate the performance of BR-NB.

Battery life and outdoor visibility are two of the most important features for mobile applications today. It is desirable to achieve both low power consumption and excellent outdoor visibility on the display device at the same time. We have previously reported a new RGBW method to realize low power consumption and high luminance with high image quality. In this paper, the basic concept of a new RGBW calculation utilizing an “Extended HSV color space” model is described, and also its performance, such as low power consumption, color image reproducibility and outdoor visibility is presented. The new method focuses on the luminance-increase ratio by means of a White signal for the display image data, and derives the appropriate RGBW signal and backlight PWM signal for every frame period. This dynamically controlled system solves the problems of conventional RGBW systems, and realizes the same image quality as a corresponding RGB display. In order to quantify its color image reproducibility, a spectroscopic measurement has been completed using the Macbeth Color Chart. In addition, the advantages of high luminance by the new RGBW method is described. The converted tone curve with an RGBW method provides very high luminance, such as 1,000cd/m2, and improved outdoor visibility. Finally, a newly developed 4.38-inch full-HD (1,080 × 1,920) 503ppi prototype LCD utilizing this new RGBW technology is described.

A directional lapped orthogonal transform (DirLOT) is an orthonormal transform of which basis is allowed to be anisotropic with the symmetric, real-valued and compact-support property. Due to its directional property, DirLOT is superior to the existing separable transforms such as DCT and DWT in expressing diagonal edges and textures. The goal of this paper is to enhance the ability of DirLOT further. To achieve this goal, we propose a novel image restoration technique using multiple DirLOTs. This paper generalizes an image denoising technique in [1], and expands the application of multiple DirLOTs by introducing linear degradation operator P. The idea is to use multiple DirLOTs to construct a redundant dictionary. More precisely, the redundant dictionary is constructed as a union of symmetric orthonormal discrete wavelet transforms generated by DirLOTs. To select atoms fitting a target image from the dictionary, we formulate an image restoration problem as an l1-regularized least square problem, which can efficiently be solved by the iterative-shrinkage/thresholding algorithm (ISTA). The proposed technique is beneficial in expressing multiple directions of edges/textures. Simulation results show that the proposed technique significantly outperforms the non-subsampled Haar wavelet transform for deblurring, super-resolution, and inpainting.

Ocean surface current radar is a Doppler radar to observe oceanographic information using the Bragg scattering resonance mechanism. In this paper, we consider angular resolution improvement of the radar. The radar employs an antenna array with FMICW operation, then it can resolve angular distribution by Digital Beam Forming (DBF) and distance by Fourier transform of the beat signal obtained by the FMICW radar. In order to obtain sufficient angular resolution, large array length or aperture with increasing the number of elements is needed, that is often difficult to realize in the HF/VHF ocean surface current radar. In this paper we propose to apply the Khatri-Rao (KR) product array processing to the radar. To verify effectiveness of the KR product array processing in angular resolution enhancement for the ocean surface current radar, we apply the KR product array to actual experimental data set of the radar, and show that the method is available to angular resolution enhancement and Doppler spectrum improvement.

The computing of applications in embedded devices suffers tight constraints on computation and energy resources. Thus, it is important that applications running on these resource-constrained devices are aware of the energy constraint and are able to execute efficiently. The existing execution time and energy profiling tools could help developers to identify the bottlenecks of applications. However, the profiling tools need large space to store detailed profiling data at runtime, which is a hard demand upon embedded devices. In this article, a reconfigurable multi-resolution profiling (RMP) approach is proposed to handle this issue on embedded devices. It first instruments all profiling points into source code of the target application and framework. Developers can narrow down the causes of bottleneck by adjusting the profiling scope using the configuration tool step by step without recompiling the profiled targets. RMP has been implemented as an open source tool on Android systems. Experiment results show that the required log space using RMP for a web browser application is 25 times smaller than that of Android debug class, and the profiling error rate of execution time is proven 24 times lower than that of debug class. Besides, the CPU and memory overheads of RMP are only 5% and 6.53% for the browsing scenario, respectively.

This paper presents a modified orthogonal fractal super-resolution (OFSR) method to improve the visual quality of an image along sharp edges. Although the OFSR method constructs a high-quality high-resolution image from a low-resolution counterpart, there are ringing artifacts observed along sharp edges which make the visual quality relatively low with respect to the numerical quantity. These artifacts are mainly caused by unnecessarily exaggerated pixel contrast along sharp edges within a range block. We restrict each contracted pixel value in a range block to a value between the minimum and maximum of its domain block pixel values. We also extend the domain block of the contraction function and find a better domain block using the range block mean. At the final step of the iteration, we adjust each pixel in the range block so that the range block mean and the corresponding pixel value of the low-resolution image are equal. According to our experimental results, the proposed method improves the visual quality along sharp edges and shows higher levels of numerical quantity than the OFSR method.

In cognitive radio networks, the primary user (PU) can lease a fraction of its licensed spectrum to the secondary users (SUs) in exchange for their cooperative transmission if it has a minimum transmission rate requirement and is experiencing a bad channel condition. However, due to the selfish nature of the SUs, they may not cooperate to meet the PU's Quality of Service (QoS) requirement. On the other hand, the SUs may not exploit efficiently the benefit from cooperation if they compete with each other and collaborate with the PU independently. Therefore, when SUs belong to the same organization and can work as a group, how to stimulate them to cooperate with the PU and thus guarantee the PU's QoS requirement, and how to coordinate the usage of rewarded spectrum among these SUs after cooperation are critical challenges. In this paper, we propose a two-level bargaining framework to address the aforementioned problems. In the proposed framework, the interactions between the PU and the SUs are modeled as the upper level bargaining game while the lower level bargaining game is used to formulate the SUs' decision making process on spectrum sharing. We analyze the optimal actions of the users and derive the theoretic results for the one-PU one-SU scenario. To find the solutions for the one-PU multi-SU scenario, we put forward a revised numerical searching algorithm and prove its convergence. Finally, we demonstrate the effectiveness and efficiency of the proposed scheme through simulations.

In this letter, we present a fast image/video super resolution framework using edge and nonlocal constraint. The proposed method has three steps. First, we improve the initial estimation using content-adaptive bilateral filtering to strengthen edge. Second, the high resolution image is estimated by using classical back projection method. Third, we use joint content-adaptive nonlocal means filtering to get the final result, and self-similarity structures are obtained by the low resolution image. Furthermore, content-adaptive filtering and fast self-similarity search strategy can effectively reduce computation complexity. The experimental results show the proposed method has good performance with low complexity and can be used for real-time environment.

This study investigated the relationship between human use of automation and their sensitivity to changes in automation and manual performance. In the real world, automation and manual performance change dynamically with changes in the environment. However, a few studies investigated whether changes in automation or manual performance have more effect on whether users choose to use automation. We used two types of experimental tracking tasks in which the participants had to select whether to use automation or conduct manual operation while monitoring the variable performance of automation and manual operation. As a result, we found that there is a mutual relationship between human use of automation and their sensitivity to automation and manual performance changes. Also, users do not react equally to both automation and manual performance changes although they use automation adequately.

Super resolution (SR) reconstruction is the process of fusing a sequence of low-resolution images into one high-resolution image. Many researchers have introduced various SR reconstruction methods. However, these traditional methods are limited in the extent to which they allow recovery of high-frequency information. Moreover, due to the self-similarity of face images, most of the facial SR algorithms are machine learning based. In this paper, we introduce a facial SR algorithm that combines learning-based and regularized SR image reconstruction algorithms. Our conception involves two main ideas. First, we employ separated frequency components to reconstruct high-resolution images. In addition, we separate the region of the training face image. These approaches can help to recover high-frequency information. In our experiments, we demonstrate the effectiveness of these ideas.

The Internet is composed of many distinct networks, operated by independent Internet Service Providers (ISPs). The traffic and economic relationships of ISPs are mainly decided by their routing policies. However, in today's Internet, overlay routing, which changes traffic routing at the application layer, is rapidly increasing and this challenges the validity of ISPs' existing agreements. We study here the economic implications of overlay routing for ISPs, using an ISP interconnection business model based on a simple network. We then study the overlay traffic patterns in the network under various conditions. Combining the business model and traffic patterns, we study the ISPs' cost reductions with Bill-and-Keep peering and paid peering. We also discuss the ISPs' incentive to upgrade the network under each peering strategy.

High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.

We propose a compact and high-range resolution 76 GHz millimeter-wave radar system for autonomous unmanned helicopters. The purpose of the radar system is to detect and avoid obstacles that may affect the flight safety. To achieve these objectives, a high range resolution and a long detection range are required for the radar systems with small volume and weight. The radar broadband RF front-end module which employs a simple direct conversion method is proposed. The radar module enables the 6 GHz RF signal transmission as well as the output power of about 8 dBm using commercially available low-cost monolithic microwave integrated circuits. The radar system comprises the broadband RF front-end module, a Ku-band local frequency-modulated continuous wave signal synthesizer, and a very light weight carbon fiber reinforced plastic parabolic reflector antenna. The 5 cm of range resolution is experimentally obtained using the 6 GHz RF signal bandwidth. The results of the power line measurement confirm an about 23 dB signal to noise ratio, which is measured from the reflection of the high-voltage power lines about 150 m ahead. In addition, the results of the radar system on-board test using an unmanned helicopter are evaluated. The real-time radar scope, which is transferred through the wireless connection, confirms the detection of the power lines and the other surrounding objects.

Reed-Solomon (RS) code is one of the well-known and widely used error correction codes. Among the components of a hardware RS decoder, the key equation solver (KES) unit occupies a relatively large portion of the hardware. It is important to develop an efficient KES architecture to implement efficient RS decoders. In this paper, a novel polynomial division technique used in the Euclidean algorithm (EA) of the KES is presented which achieves the short critical path delay of one Galois multiplier and one Galois adder. Then a KES architecture with the EA is proposed which is efficient in the sense of the product of area and time.

Current distributions induced on a circular disk of conductor are analyzed rigorously for an electric dipole incidence, when the source dipole is polarized parallel to the disk and located at an arbitrary position, and they are evaluated numerically. As the height of the dipole increases, the current distribution of the dipole approaches that of the plane wave incidence. Using a multiple precision arithmetic, numerical data for the current distribution are obtained for larger radii of a disk than the former approach.

This paper proposes a high frequency resolution Digitally-Controlled Oscillator (DCO) using a single-period control bit switching scheme. The proposed scheme controls the tuning word of DCO in a single period for the fine frequency tuning. The LC type DCO is implemented to realize the proposed scheme, and is fabricated using a standard 65 nm CMOS technology. The measurement results show that the implemented DCO improves the frequency resolution from 560 kHz to 180 kHz without phase noise degradation with an additional area of 200 µm2.

Recently, cooperative spectrum sensing is being studied to greatly improve the sensing performance of cognitive radio networks. To develop an adaptable cooperative sensing algorithm, an important issue is how to properly induce selfish users to participate in spectrum sensing work. In this paper, a new cognitive radio spectrum sharing scheme is developed by employing the trust-based bargaining model. The proposed scheme dynamically adjusts bargaining powers and adaptively shares the available spectrum in real-time online manner. Under widely different and diversified network situations, this approach is so dynamic and flexible that it can adaptively respond to current network conditions. Simulation results demonstrate that the proposed scheme can obtain better network performance and bandwidth efficiency than existing schemes.