People are being inundated under enormous volumes of information and they often dither about making the right choices from these. Interactive user support by information service system such as concierge services will effectively assist such people. However, human-machine interaction still lacks naturalness and thoughtfulness despite the widespread utilization of intelligent systems. The system needs to estimate user's interest to improve the interaction and support the choices. We propose a novel approach to estimating the interest, which is based on the relationship between the dynamics of user's eye movements, i.e., the endogenous control mode of saccades, and machine's proactive presentations of visual contents. Under a specially-designed presentation phase to make the user express the endogenous saccades, we analyzed the timing structures between the saccades and the presentation events. We defined resistance as a novel time-delay feature representing the duration a user's gaze remains fixed on the previously presented content regardless of the next event. In experimental results obtained from 10 subjects, we confirmed that resistance is a good indicator for estimating the interest of most subjects (75% success in 28 experiments on 7 subjects). This demonstrated a higher accuracy than conventional estimates of interest based on gaze duration or frequency.

In this letter, we present a multiple-input multiple-output (MIMO) optimal combining (OC) scheme based on alternate iteration. With the channel state information (CSI) of co-channel interferers (CCIs), this algorithm can be used in flat fading and frequency selective channels to suppress CCIs. Compared with the optimal transceiver of MIMO maximal ratio combining (MRC) systems, results of simulation show that this scheme improves the uplink transmission performance significantly.

In this Letter, the problem of estimating the time-difference-of-arrival between signals received at two spatially separated sensors is addressed. By taking discrete Fourier transform of the sensor outputs, time delay estimation corresponds to finding the frequency of a noisy sinusoid with time-varying amplitude. The generalized weighted linear predictor is utilized to estimate the time delay and it is shown that its estimation accuracy attains Cramér-Rao lower bound.

This letter proposes a low-complexity scheme for estimating the frequency of a complex sinusoid in flat fading channels. The proposed estimator yields an estimation performance that is comparable to the existing autocorrelation-based frequency estimator, while retaining the same frequency range. Its implementation complexity is much lower than the conventional scheme, thus this allows for fast estimation in real time.

LCD Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. AVI systems usually report different measurements on the same defect with some variations on position or rotation mainly because we get different images. This is caused by possible variations in the image acquisition process including optical factors, non-uniform illumination, random noise, and so on. For this reason, conventional area based defect measuring method has some problems in terms of robustness and consistency. In this paper, we propose a new defect size measuring method to overcome these problems. We utilize volume information which is completely ignored in the area based conventional defect measuring method. We choose a bell shape as a defect model for experiment. The results show that our proposed method dramatically improves robustness of defect size measurement. Given proper modeling, the proposed volume based measuring method can be applied to various types of defect for better robustness and consistency.

For multiple-input multiple-output (MIMO) spatial multiplexing, signal separation/detection is one of the most important signal processing parts, so that signal separation/detection schemes are being vigorously researched. As a promising signal separation/detection scheme, frequency-domain iterative soft interference cancellation (FD-SIC) has been proposed. Although iterative FD-SIC can provide the transmission performance close to lower bound for QPSK, the accuracy of signal separation/detection significantly degrades in case of high level data modulation. Therefore, in this paper, we propose layered soft interference cancellation (LSIC). We consider single-carrier (SC)-MIMO spatial multiplexing with frequency domain equalization (FDE). The achievable frame error rate (FER) performances with LSIC are evaluated by computer simulation to show that LSIC can provide better FER performance than iterative FD-SIC.

Dual-hop wireless transmission is a technique by which the channel from the source to the destination is divided into two shorter links using relays. By using this transmission, the communications reliability is improved whereas the throughput might go down since the dual-hop transmission is performed by two times. In this letter, we propose a scheme that uses hierarchical modulation at the source and adaptive modulation based on cyclic redundancy check (CRC) code at the relays. The proposed scheme is evaluated in terms of the bit error rate (BER) and throughput performances.

Future high-resolution short-range automotive radar will have a higher false alarm probability than the conventional low-resolution radar has. In a high-resolution radar, the reception signal becomes sensitive to the difference between intended and unintended objects. However, automotive radars must distinguish targets from background objects that are the same order of size; it leads to an increase in the false alarm probability. In this paper, a CFAR circuit for obtaining the target mean power, as well as the background mean power, is proposed to reduce the false alarm probability for high-resolution radars working in automotive environments. The proposed method is analytically evaluated with use of the characteristic function method. Spatial correlation is also considered in the evaluation, because the sizes of the both target and background objects approach the dimension of several range cells. Result showed the proposed CFAR with use of two alongside range cells could reduce the ratio of 6.4 dB for an example of an automotive situation.

In this paper, a Stochastic Collocation Algorithm combined with Sparse Grid technique (SSCA) is proposed to deal with the periodic steady-state analysis for nonlinear systems with process variations. Compared to the existing approaches, SSCA has several considerable merits. Firstly, compared with the moment-matching parameterized model order reduction (PMOR) which equally treats the circuit response on process variables and frequency parameter by Taylor approximation, SSCA employs Homogeneous Chaos to capture the impact of process variations with exponential convergence rate and adopts Fourier series or Wavelet Bases to model the steady-state behavior in time domain. Secondly, contrary to Stochastic Galerkin Algorithm (SGA), which is efficient for stochastic linear system analysis, the complexity of SSCA is much smaller than that of SGA for nonlinear case. Thirdly, different from Efficient Collocation Method, the heuristic approach which may result in "Rank deficient problem" and "Runge phenomenon," Sparse Grid technique is developed to select the collocation points needed in SSCA in order to reduce the complexity while guaranteing the approximation accuracy. Furthermore, though SSCA is proposed for the stochastic nonlinear steady-state analysis, it can be applied to any other kind of nonlinear system simulation with process variations, such as transient analysis, etc.

In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naive and existing approaches through extensive experiments and clarify our approach's distinguished features.

The CSMA/CA driven MAC protocols withhold packet transmissions from exposed stations when they detect carrier signal above a certain threshold. This is to avoid collisions at other receiving stations. However, this conservative scheme often exposes many stations unnecessarily, and thus minimizes the utilization of the spatial spectral resource. In this paper, we demonstrate that remote estimation of the status at the active receivers is more effective at avoiding collisions in wireless networks than the carrier sensing. We apply a new concept of the interference range, named as n-tolerant interference range, to guarantee reliable communications in the presence of n (n≥ 0) concurrent transmissions from outside the range. We design a distributed interference preventive MAC ( IP-MAC ) using the n-tolerant interference range that enables parallel accesses from the noninterfering stations for an active communication. In IP-MAC, an exposed station goes through an Interference Potentiality Check (IPC) to resolve whether it is potentially interfering or noninterfering to the active communication. During the resolve operation, IPC takes the capture effect at an active receiver into account with interfering signals from a number of possible concurrent transmissions near that receiver. The performance enhancement offered by IP-MAC is studied via simulations in different environments. Results reveal that IP-MAC significantly improves network performance in terms of throughput and delay.

This paper presents an enhanced dual-path delta-sigma analog-to-digital converter. Compared with other architectures, the enhanced architecture increases the noise shaping order without any instability problems and displays analog complexity equivalent to the multi-stage noise shaping architecture. Our delta-sigma converter is based on this new architecture. It employs not only doubly-differential structure to reduce common-mode errors in the system-level but also delayed-feed-in structure to mitigate the timing constraint of the feedback signal. Regarding the circuit implementation, the first-order enhancement of the quantization noise shaping is achieved via the use of a switched capacitor circuit technique. The circuit is incorporated into the active adder in a low-distortion structure. The supporting clock generation circuit that provides additional phases of clocks with the enhancement block is also implemented in the CMOS logic gates. A digital dynamic element matching circuit (i.e., segmented data-weighted-average circuit) is designed to reduce mismatch errors caused by the feedback DAC of modulator. A test chip, fabricated in a 0.18-µm CMOS process, provides a signal-to-noise+distortion ratio (SNDR) of 75-dB for a 1.0-MHz signal bandwidth clocked at 40-MHz. The 2nd harmonic is -101 dB and the 3rd harmonic is -94 dB when a -4.5-dB 100-kHz input signal is applied.

Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.

RC4 is the stream cipher proposed by Rivest in 1987, which is widely used in a number of commercial products because of its simplicity and substantial security. RC4 exploits shuffle-exchange paradigm, which uses a permutation S. Many attacks have been reported so far. No study, however, has focused on correlations in the Pseudo-Random Generation (PRGA) between two permutations S and S' with some differences, nevertheless such correlations are related to an inherent weakness of shuffle-exchange-type PRGA. In this paper, we investigate the correlations between S and S' with some differences in the initial round. We show that correlations between S and S' remain before "i" is in the position where the nonzero-bit difference exists in the initial round, and that the correlations remain with non negligible probability even after "i" passed by the position. This means that the same correlations between S and S' will be observed after the 255-th round. This reveals an inherent weakness of shuffle-exchange-type PRGA.

This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.

Consider a client who intends to perform a massive computing task comprsing a number of sub-tasks, while both storage and computation are outsourced by a third-party service provider. How could the client ensure the integrity and completeness of the computation result? Meanwhile, how could the assurance mechanism incur no disincentive, e.g., excessive communication cost, for any service provider or client to participate in such a scheme? We detail this problem and present a general model of execution assurance for massive computing tasks. A series of key features distinguish our work from existing ones: a) we consider the context wherein both storage and computation are provided by untrusted third parties, and client has no data possession; b) we propose a simple yet effective assurance model based on a novel integration of the machineries of data authentication and computational private information retrieval (cPIR); c) we conduct an analytical study on the inherent trade-offs among the verification accuracy, and the computation, storage, and communication costs.

We study fast inversion of the Paillier encryption function. Especially, we focus only on key generation, and do not modify the Paillier encryption function. We propose three key generation algorithms based on the speeding-up techniques for the RSA encryption function. By using our algorithms, the size of the private CRT exponent is half of that of Paillier-CRT. The first algorithm employs the extended Euclidean algorithm. The second algorithm employs factoring algorithms, and can construct the private CRT exponent with low Hamming weight. The third algorithm is a variant of the second one, and has some advantage such as compression of the private CRT exponent and no requirement for factoring algorithms. We also propose the settings of the parameters for these algorithms and analyze the security of the Paillier encryption function by these algorithms against known attacks. Finally, we give experimental results of our algorithms.

We propose 3-hop cooperative diversity using QOSTBC (Quasi-Orthogonal Space-Time Block Code), which offers 3-hop cooperative diversity without signal separation in relay nodes. The key of our proposed scheme is encoding signal sequence in different signal unit according to relay stage. This letter explains details of the proposed scheme and shows that it offers interference reduction among streams and space diversity gain by result of simulations.

In ubiquitous sensor networks, the estimation accuracy of a node location is limited due to the presence of non-line-of-sight (NLOS) paths. To mitigate the NLOS effects, this letter proposes a simple algorithm where NLOS identification is carried out using angle-of-arrival (AOA). Simulation results show that the use of AOA improves NLOS identification rates and location estimation accuracy.

In this paper, we propose a novel active contour method for image segmentation using a local regional information on extendable search line. We call it the LRES active contour. Our active contour uses the intensity values along a set of search lines that are perpendicular to the contour front. These search lines are used to inform the contour front toward which direction to move in order to find the object's boundary. Unlike other methods, none of these search lines have a predetermined length. Instead, their length increases gradually until a boundary of the object is found. We compare the performance of our LRES active contour to other existing active contours, both edge-based and region-based. The results show that our method provides more desirable segmentation outcomes, particularly on some images where other methods may fail. Not only is our method robust to noise and able to reach into a deep concave shape, it also has a large capture range and performs well in segmenting heterogeneous textured objects.