Temporal Decorrelation source SEParation (TDSEP) is a blind separation scheme that utilizes the time structure of the source signals, typically, their periodicities. The advantage of TDSEP over non-Gaussianity based methods is that it can separate Gaussian signals as long as they are periodic. However, its shortcoming is that separation performance (SEP) heavily depends upon the values of the time shift parameters (TSPs). This paper proposes a method to automatically and blindly estimate a set of TSPs that achieves optimal SEP against periodic Gaussian signals. It is also shown that, selecting the same number of TSPs as that of the source signals, is sufficient to obtain optimal SEP, and adding more TSPs does not improve SEP, but only increases the computational complexity. The simulation example showed that the SEP is higher by approximately 20dB, compared with the ordinary method. It is also shown that the proposed method successfully selects just the same number of TSPs as that of incoming signals.

This letter presents a scheme to improve the running time of exemplar-based image inpainting, first proposed by Criminisi et al. In the exemplar-based image inpainting, a patch that contains unknown pixels is compared to all the patches in the known region in order to find the best match. This is very time-consuming and hinders the practicality of Criminisi's method to be used in real time. We show that a simple bounding algorithm can significantly reduce number of distance calculations, and thus the running time. Performance of the bounding algorithm is affected by the order of patches that are compared, as well as the order of pixels in a patch. We present pixel and patch ordering schemes that improve the performance of bounding algorithms. Experiments with well-known images used in inpainting literature show that the proposed reordering scheme can reduce running time of the bounding algorithm up to 50%.

In the Cloud computing era, users could have their data outsourced to cloud service provider (CSP) to enjoy on-demand high quality service. On the behalf of the user, a third party auditor (TPA) which could verify the real data possession on CSP is critically important. The central challenge is to build efficient and provably secure data verification scheme while ensuring that no users' privacy is leaked to any unauthorized party, including TPA. In this paper, we propose the first identity-based public verification scheme, based on the identity-based aggregate signature (IBAS). In particular, by minimizing information that verification messages carry and TPA obtains or stores, we could simplify key management and greatly reduce the overheads of communication and computation. Unlike the existing works based on certificates, in our scheme, only a private key generator (PKG) has a traditional public key while the user just keeps its identity without binding with certificate. Meanwhile, we utilize privacy-preserving technology to keep users' private data off TPA. We also extend our scheme with the support of batch verification task to enable TPA to perform public audits among different users simultaneously. Our scheme is provably secure in the random oracle model under the hardness of computational Diffie-Hellman assumption over pairing-friendly groups and Discrete Logarithm assumption.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

Nowadays, with spreads of inexpensive small communication devices, a number of wireless local area networks (WLANs) have been deployed even in the same building for the Internet access services. Their wireless access-points (APs) are often independently installed and managed by different groups such as departments or laboratories in a university or a company. Then, a user host can access to multiple WLANs by detecting signals from their APs, which increases the energy consumption and the operational cost. It may also degrade the communication performance by increasing interferences. In this paper, we present an AP aggregation approach to solve these problems in multiple WLAN environments by aggregating deployed APs of different groups into limited ones using virtual APs. First, we formulate the AP aggregation problem as a combinatorial optimization problem and prove the NP-completeness of its decision problem. Then, we propose its heuristic algorithm composed of five phases. We verify the effectiveness through extensive simulations using the WIMNET simulator.

In this study, the influence of Gd composition on current-induced domain wall motion in a Gd-Co ferrimagnetic nanowire was theoretically investigated with taking into account of composition dependence of magnetic properties. As a result, the intrinsic critical density to move domain wall significantly reduces near the compensation composition, which is achieved to be less than 105A/cm2. Moreover, the intrinsic critical current density also significantly reduces near a certain Gd composition where the domain wall energies of Bloch and Néel walls are almost the same.

This paper presents a real-time and memory-efficient arrhythmia detection system with binary classification that uses antidictionary coding for the analysis and classification of electrocardiograms (ECGs). The measured ECG signals are encoded using a lossless antidictionary encoder, and the system subsequently uses the compression rate to distinguish between normal beats and arrhythmia. An automated training data procedure is used to construct the automatons, which are probabilistic models used to compress the ECG signals, and to determine the threshold value for detecting the arrhythmia. Real-time computer simulations with samples from the MIT-BIH arrhythmia database show that the averages of sensitivity and specificity of the proposed system are 97.8% and 96.4% for premature ventricular contraction detection, respectively. The automatons are constructed using training data and comprise only 11 kilobytes on average. The low complexity and low memory requirements make the system particularly suitable for implementation in portable ECG monitors.

In spite of continuous improvement of computational power of multi/many-core processors, the memory access performance of the processors has not been improved sufficiently, and thus the overall performance of recent processors is often restricted by the delay of off-chip memory accesses. Low-delay data compression for last level cache (LLC) would be effective to improve the processor performance because the compression increases the effective size of LLC, and thus reduces the number of off-chip memory accesses. This paper proposes a novel data compression method suitable for high-speed parallel decoding in the LLC. Since cache line data often have periodicity of certain lengths, such as 32- or 64-bit instructions, 32-bit integers, and 64-bit floating point numbers, an information word is encoded as a base pattern and a differential pattern between the original word and the base pattern. Evaluation using a GPU simulator shows that the compression ratio of the proposed coding is comparable to LZSS coding and X-Match Pro and superior to other conventional compression algorithms for cache memories. Also this paper presents an experimental decoder designed for ASIC, and the synthesized result shows that the decoder can decompress cache line data of length 32bytes in four clock cycles. Evaluation of the IPC on the GPU simulator shows that, for several benchmark programs, the IPC achieved by the proposed coding is higher than that by the conventional BΔI coding, where the maximum improvement of the IPC is 20%.

This paper proposes a novel design method involving the stages from analog circuit design to layout synthesis in hope of suppressing the process-induced variations with a design style called transistor array. We manage to decompose the transistors into unified sub-transistors, and arrange the sub-transistors on a uniform placement grid so that a better post-CMP profile is expected to be achieved, and that the STI-stress is evened up to alleviate the process variations. However, since lack of direct theoretical support to the transistor decomposition, we analyze and evaluate the errors arising from the decomposition in both large and small signal analysis. A test chip with decomposed transistors on it confirmed our analysis and suggested that the errors are negligibly small and the design with transistor array is applicable. Based on this conclusion, a design flow with transistor array covering from circuit design to layout synthesis is proposed, and several design cases, including three common-source amplifiers, three two-stage OPAMPS and a nano-watt current reference, are implemented on a test chip with the proposed method, to demonstrate the feasibility of our idea. The measurement results from the chip confirmed that the designs with transistor array are successful, and the proposed method is applicable.

In this letter, parallel orthogonal matching pursuit (POMP) is proposed to supplement orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. Empirical simulations show that POMP outperforms the existing sparse signal recovery algorithms including OMP, compressive sampling matching pursuit (CoSaMP), and linear programming (LP) in terms of the exact recovery ratio (ERR) for the sparse pattern and the mean-squared error (MSE) between the estimated signal and the original signal.

To analyze the structure of a set of perfect sequences over a composition algebra of the real number field, transforms of a set of sequences similar to the discrete Fourier transform (DFT) are introduced. The discrete cosine transform, discrete sine transform, and generalized discrete Fourier transform (GDFT) of the sequences are defined and the fundamental properties of these transforms are proved. We show that GDFT is bijective and that there exists a relationship between these transforms and a convolution of sequences. Applying these properties to the set of perfect sequences, a parameterization theorem of such sequences is obtained.

The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.

Channel modeling, which is quite important for wireless communications system design, is difficult to be statistically generated from experimental results due to the expense and time constraints. However, with the computational electromagnetics method, the Electro-Magnetic (EM) field can be emulated and the corresponding EM wave propagation scenario can be analyzed. In this letter, the Finite Integration Technique (FIT) method is utilized to calculate the EM wave propagation of the onboard mobile communications in the cabin of an aircraft. With the simulation results, the channel model is established. Compared with Finite-Difference Time-Domain (FDTD), the proposed scheme is more accurate, which is promising to be used in the cabin channel modeling for onboard mobile system design.

As an extension of H.264/AVC, Scalable Video Coding (SVC) provides the ability to adapt to heterogeneous networks and user-end requirements, which offers great scalability in multi-point applications such as videoconferencing. However, transcoding between SVC and AVC becomes necessary due to the existence of legacy AVC-based systems. The straightforward full re-encoding method requires great computational cost, and the fast SVC-to-AVC spatial transcoding techniques have not been thoroughly investigated yet. This paper proposes a low-complexity hybrid-domain SVC-to-AVC spatial transcoder with drift compensation, which provides even better coding efficiency than the full re-encoding method. The macroblocks (MBs) of input SVC bitstream are divided into two types, and each type is suitable for pixel- or transform-domain processing respectively. In the pixel-domain transcoding, a fast re-encoding method is proposed based on mode mapping and motion vector (MV) refinement. In the transform-domain transcoding, the quantized transform coefficients together with other motion data are reused directly to avoid re-quantization loss. The drift problem caused by proposed transcoder is solved by compensation techniques for I frame and P frame respectively. Simulation results show that proposed transcoder achieves averagely 96.4% time reduction compared with the full re-encoding method, and outperforms the reference methods in coding efficiency.

In this paper, we consider the optimization of measurement matrix in Compressed Sensing (CS) framework. Based on the boundary constraint, we propose a novel algorithm to make the “mutual coherence” approach a lower bound. This algorithm is implemented by using an iterative strategy. In each iteration, a neighborhood interval of the maximal off-diagonal entry in the Gram matrix is scaled down with the same shrinkage factor, and then a lower mutual coherence between the measurement matrix and sparsifying matrix is obtained. After many iterations, the magnitudes of most of off-diagonal entries approach the lower bound. The proposed optimization algorithm demonstrates better performance compared with other typical optimization methods, such as t-averaged mutual coherence. In addition, the effectiveness of CS can be used for the compression of complex synthetic aperture radar (SAR) image is verified, and experimental results using simulated data and real field data corroborate this claim.

In this paper, we propose a chromatic adaptation model based on the adapting degree according to the level of adapting luminance and chromaticity in various surround illuminants. In the proposed model, first maximum adapted cone responses are calculated through the estimation of adapting degree for viewing conditions then corresponding colors are reproduced from original colors using the ratio of maximum adapted cone responses between different viewing conditions. The purpose of this study is to produce chromatic adaptation transform applied to environment-adaptive color display system. As a result, our proposed model can give better estimation performance than prior models and be embodied easily as a linear model in display systems. So it is confirmed that the implemented system can predict corresponding-color data very well under a variety of viewing conditions.

Cloud computing is broadly recognized as as the prevalent trend in IT. However, in cloud computing mode, customers lose the direct control of their data and applications hosted by the cloud providers, which leads to the trustworthiness issue of the cloud providers, hindering the widespread use of cloud computing. This paper proposes a trustworthiness verification and audit mechanism on cloud providers called T-YUN. It introduces a trusted third party to cyclically attest the remote clouds, which are instrumented with the trusted chain covering the whole architecture stack. According to the main operations of the clouds, remote verification protocols are also proposed in T-YUN, with a dedicated key management scheme. This paper also implements a proof-of-concept emulator to validate the effectiveness and performance overhead of T-YUN. The experimental results show that T-YUN is effective and the extra overhead incurred by it is acceptable.

Avoiding runway incursions is a significant challenge and a top priority in aviation. Due to all causes of runway incursions belong to human factors, runway incursion prevention systems should remove human from the system operation loop as much as possible. Although current runway incursion prevention systems have made big progress on how to obtain accurate and sufficient information of aircraft/vehicles, they cannot predict and detect runway incursions as early as experienced air traffic controllers by using the same surveillance information, and cannot give explicit instructions and/or suggestions to prevent runway incursions like real air traffic controllers either. In one word, human still plays an important position in current runway incursion prevention systems. In order to remove human factors from the system operation loop as much as possible, this paper proposes a new type of runway incursion prevention system based on logic-based reasoning. The system predicts and detects runway incursions, then gives explicit instructions and/or suggestions to pilots/drivers to avoid runway incursions/collisions. The features of the system include long-range prediction of incidents, explicit instructions and/or suggestions, and flexible model for different policies and airports. To evaluate our system, we built a simulation system, and evaluated our system using both real historical scenarios and conventional fictional scenarios. The evaluation showed that our system is effective at providing earlier prediction of incidents than current systems, giving explicit instructions and/or suggestions for handling the incidents effectively, and customizing for specific policies and airports using flexible model.

The iCAM06 has been used as an image appearance model for HDR image rendering. iCAM06 goes through the color space conversions of the several steps to present HDR images. The dynamic range of a HDR image needs to be mapped onto the range of output devices, which is called the tone mapping. However, tone compression process of iCAM06 causes white point shift and color distortion because of color-clipping and cross-stimulus. Therefore, we proposed a modified white-balanced method in low-chromatic region and a color adjustment method in IPT space to compensate the color distortion during in tone compression process. Through the experimental results, we confirmed the proposed compatible color adjustment method had better performance than iCAM06 and enhanced models.