The Generalized cross-correlation (GCC) method is most commonly used for time delay estimation (TDE). However, the GCC method can result in false peak errors (FPEs) especially at a low signal to noise ratio (SNR). These FPEs significantly degrade TDE, since the estimation error, which is the difference between a true time delay and an estimated time delay, is larger than at least one sampling period. This paper introduces an algorithm that estimates two peaks for two cross-correlation functions using three types of signals such as a reference signal, a delayed signal, and a delayed signal with an additional time delay of half a sampling period. A peak selection algorithm is also proposed in order to identify which peak is closer to the true time delay using subsample TDE methods. This paper presents simulations that compare the algorithms' performance for varying amounts of noise and delay. The proposed algorithms can be seen to display better performance, in terms of the probability of the integer TDE errors, as well as the mean and standard deviation of absolute values of the time delay estimation errors.

In this letter, we argue that user resources will be still useful in the information-centric network (ICN). From this point of view, we first examine how P2P utilizing user resources looks like in ICN. Then, we identify challenging research issues to utilize user resources in ICN.

In this letter, for offloading traffic to Wireless Local Area Network (WLAN) with transport layer mobility where WLAN service is intermittently available, we propose a novel scheme to freeze and melt the timeout handling procedure of SCTP. Simulation results show that the proposed scheme significantly improves the performance in terms of file transfer completion time.

This study presents an adaptive quantization index modulation scheme applicable on a small audio segment, which in turn allows the watermarking technique to withstand time-shifting and cropping attacks. The exploitation of auditory masking further ensures the robustness and imperceptibility of the embedded watermark. Experimental results confirmed the efficacy of this scheme against common signal processing attacks.

In this letter, we present a method for improving the front-to-back ratio (FBR) of a broadcast antenna. The digitalization of terrestrial TV demands more efficient channel usage due to the reduction in TV bands after the switch-over. Thus, we designed an antenna with an FBR improved over -45 dB as compared to the -20 to -25 dB FBR range of existing antennas. We show experimentally that this antenna satisfies the required performance.

Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

We propose an optimization method for power distribution network that explicitly deals with timing. We have found and focused on the facts that decoupling capacitance (decap) does not necessarily improve gate delay depending on the switching timing within a cycle and that power wire expansion may locally degrade the voltage. To resolve the above facts, we devised an efficient sensitivity calculation of timing to decap size and power wire width for guiding optimization. The proposed method, which is based on statistical noise modeling and timing analysis, accelerates sensitivity calculation with an approximation and adjoint sensitivity analysis. Experimental results show that decap allocation based on the sensitivity analysis efficiently minimizes the worst-case circuit delay within a given decap budget. Compared to the maximum decap placement, the delay improvement due to decap increases by 3.13% even while the total amount of decaps is reduced to 40%. The wire sizing with the proposed method also efficiently reduces required wire resource necessary to attain the same circuit delay by 11.5%.

This paper presents comprehensive comparisons of the achievable throughput between the 32-/64-ary amplitude and phase shift keying (APSK) and cross 32QAM/square 64QAM schemes based on mutual information (MI) considering the peak-to-average power ratio (PAPR) of the modulated signal. As a PAPR criterion, we use a cubic metric (CM) that directly corresponds to the transmission back-off of a power amplifier. In the analysis, we present the best ring ratio for the 32 or 64APSK scheme from the viewpoint of minimizing the required received signal-to-noise power ratio (SNR) considering the CM that achieves the peak throughput, i.e., maximum error-free transmission rate. We show that the required received SNR considering the CM at the peak throughput is minimized with the number of rings of M = 3 and 4 for 32-ary APSK and 64-asry APSK, respectively. Then, we show with the best ring ratios that the (4, 12, 16) 32APSK scheme with M = 3 achieves a lower required received SNR considering the CM compared to that for the cross 32QAM scheme. Similarly, we show that the (4, 12, 20, 28) 64APSK scheme with M = 4 achieves almost the same required received SNR considering the CM as that for the square 64QAM scheme.

SRAM-based field programmable gate arrays (FPGAs) are vulnerable to a soft-error induced by radiation. Techniques for designing dependable circuits, such as triple modular redundancy (TMR) with scrubbing, have been studied extensively. However, currently available evaluation techniques that can be used to check the dependability of these circuits are inadequate. Further, their results are restrictive because they do not represent the result in terms of general reliability indicator to decide whether the circuit is dependable. In this paper, we propose an evaluation method that provides results in terms of the realistic failure in time (FIT) by using reconfiguration-based fault-injection analysis. Current fault-injection analyses do not consider fault accumulation, and hence, they are not suitable for evaluating the dependability of a circuit such as a TMR circuit. Therefore, we configure an evaluation system that can handle fault-accumulation by using frame-based partial reconfiguration and the bootstrap method. By using the proposed method, we successfully evaluated a TMR circuit and could discuss the result in terms of realistic FIT data. Our method can evaluate the dependability of an actual system, and help with the tuning and selection in dependable system design.

This paper presents an algorithmic approach to acquiring the influencing relationships among users by discovering implicit influencing group structure from smartphone usage. The method assumes that a time series of users' application downloads and activations can be represented by individual inter-personal influence factors. To achieve better predictive performance and also to avoid over-fitting, a latent feature model is employed. The method tries to extract the latent structures by monitoring cross validating predictive performances on approximated influence matrices with reduced ranks, which are generated based on an initial influence matrix obtained from a training set. The method adopts Nonnegative Matrix Factorization (NMF) to reduce the influence matrix dimension and thus to extract the latent features. To validate and demonstrate its ability, about 160 university students voluntarily participated in a mobile application usage monitoring experiment. An empirical study on real collected data reveals that the influencing structure consisted of six influencing groups with two types of mutual influence, i.e. intra-group influence and inter-group influence. The results also highlight the importance of sparseness control on NMF for discovering latent influencing groups. The obtained influencing structure provides better predictive performance than state-of-the-art collaborative filtering methods as well as conventional methods such as user-based collaborative filtering techniques and simple popularity.

Finding rare cases with medical data is important when hospitals or research institutes want to identify rare diseases. To extract meaningful information from a large amount of sensitive medical data, privacy-preserving data mining techniques can be used. A privacy-preserving t-repetition protocol can be used to find rare cases with distributed medical data. A privacy-preserving t-repetition protocol is to find elements which exactly t parties out of n parties have in common in their datasets without revealing their private datasets. A privacy-preserving t-repetition protocol can be used to find not only common cases with a high t but also rare cases with a low t. In 2011, Chun et al. suggested the generic set operation protocol which can be used to find t-repeated elements. In the paper, we first show that the Chun et al.'s protocol becomes infeasible for calculating t-repeated elements if the number of users is getting bigger. That is, the computational and communicational complexities of the Chun et al.'s protocol in calculating t-repeated elements grow exponentially as the number of users grows. Then, we suggest a polynomial-time protocol with respect to the number of users, which calculates t-repeated elements between users.

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.

A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.

Many learning machines such as normal mixtures and layered neural networks are not regular but singular statistical models, because the map from a parameter to a probability distribution is not one-to-one. The conventional statistical asymptotic theory can not be applied to such learning machines because the likelihood function can not be approximated by any normal distribution. Recently, new statistical theory has been established based on algebraic geometry and it was clarified that the generalization and training errors are determined by two birational invariants, the real log canonical threshold and the singular fluctuation. However, their concrete values are left unknown. In the present paper, we propose a new concept, a quasi-regular case in statistical learning theory. A quasi-regular case is not a regular case but a singular case, however, it has the same property as a regular case. In fact, we prove that, in a quasi-regular case, two birational invariants are equal to each other, resulting that the symmetry of the generalization and training errors holds. Moreover, the concrete values of two birational invariants are explicitly obtained, hence the quasi-regular case is useful to study statistical learning theory.

We investigate a design strategy for subthreshold circuits focusing on energy-consumption minimization and yield maximization under process variations. The design strategy is based on the following findings related to the operation of low-power CMOS circuits: (1) The minimum operation voltage (VDDmin) of a circuit is dominated by flip-flops (FFs), and VDDmin of an FF can be improved by upsizing a few key transistors, (2) VDDmin of an FF is stochastically modeled by a log-normal distribution, (3) VDDmin of a large circuit can be efficiently estimated by using the above model, which eliminates extensive Monte Carlo simulations, and (4) improving VDDmin may substantially contribute to decreasing energy consumption. The effectiveness of the proposed design strategy has been verified through circuit simulations on various circuits, which clearly show the design tradeoff between voltage scaling and transistor sizing.

Scan-based side channel attack on hardware implementations of cryptographic algorithms has shown its great security threat. Unlike existing scan-based attacks, in our work we observed that instead of the secret-related-registers, some non-secret registers also carry the potential of being misused to help a hacker to retrieve secret keys. In this paper, we first present a scan-based side channel attack method on AES by making use of the round counter registers, which are not paid attention to in previous works, to show the potential security threat in designs with scan chains. And then we discussed the issues of secure DFT requirements and proposed a secure scan scheme to preserve all the advantages and simplicities of traditional scan test, while significantly improve the security with ignorable design overhead, for crypto hardware implementations.

Orthogonal frequency division multiplexing (OFDM) has great advantages such as high spectrum efficiency and robustness against multipath fading. In order to enhance the advantages, an Hermite-symmetric subcarrier coding for OFDM, which is used for transmission systems like the asymmetric digital subscriber line (ADSL) and multiband OFDM in ultra-wideband (UWB) communications, is very attractive. The subcarrier coding can force the imaginary part of the OFDM signal to be zero, then another data sequence can be simultaneously transmitted in the quadrature channel. In order to theoretically verify the effectiveness of the Hermite-symmetric subcarrier coding in wireless OFDM (HC-OFDM) systems, we derive closed-form equations for bit error rate (BER) and throughput over fading channels. Our analytical results can theoretically indicate that the HC-OFDM systems achieve the improvement of the performances owing to the effect of the subcarrier coding.

In this paper, we reformulate the issue related to wireless mesh networks (WMNs) from the Chief Executive Officer (CEO) problem viewpoint, and provide a practical solution to a simple case of the problem. It is well known that the CEO problem is a theoretical basis for sensor networks. The problem investigated in this paper is described as follows: an originator broadcasts its binary information sequence to several forwarding nodes (relays) over Binary Symmetric Channels (BSC); the originator's information sequence suffers from independent random binary errors; at the forwarding nodes, they just further interleave, encode the received bit sequence, and then forward it, without making heavy efforts for correcting errors that may occur in the originator-relay links, to the final destination (FD) over Additive White Gaussian Noise (AWGN) channels. Hence, this strategy reduces the complexity of the relay significantly. A joint iterative decoding technique at the FD is proposed by utilizing the knowledge of the correlation due to the errors occurring in the link between the originator and forwarding nodes (referred to as intra-link). The bit-error-rate (BER) performances show that the originator's information can be reconstructed at the FD even by using a very simple coding scheme. We provide BER performance comparison between joint decoding and separate decoding strategies. The simulation results show that excellent performance can be achieved by the proposed system. Furthermore, extrinsic information transfer (EXIT) chart analysis is performed to investigate convergence property of the proposed technique, with the aim of, in part, optimizing the code rate at the originator.

In this paper we consider the two-class classification problem with high-dimensional data. It is important to find a class of distributions such that we cannot expect good performance in classification for any classifier. In this paper, when two population variance-covariance matrices are different, we give a reasonable sufficient condition for distributions such that the misclassification rate converges to the worst value as the dimension of data tends to infinity for any classifier. Our results can give guidelines to decide whether or not an experiment is worth performing in many fields such as bioinformatics.

In large-scale collaborative computing, users and resource providers organize various Virtual Organizations (VOs) to share resources and services. A VO organizes other sub-VOs for the purpose of achieving the VO goal, which forms hierarchical VO environments. VO participants agree upon a certain policies, such as resource sharing amount or user accesses. In this letter, we provide an optimal resource sharing mechanism in hierarchical VO environments under resource sharing agreements. The proposed algorithm enhances resource utilization and reduces mean response time of each user.