Graphene is attracting attention in electrical and optical research fields recently. We measured the optical absorption characteristics and polarization dependence of single-layer graphene (SLG) on sub-micrometer Si waveguide. The results for graphene lengths ranging from 2.5 to 200 $mu$ m reveal that the optical absorption by graphene is 0.09 dB/$mu$ m with the TE mode and 0.05 dB/$mu$ m with the TM mode. The absorption in the TE mode is 1.8 times higher than that in the TM mode. An optical spectrum, theoretical analysis and Raman spectrum indicate that surface-plasmon polaritons in graphene support TM mode light propagation.

Acoustic feedback is a major complaint of hearing aid users. Adaptive filters are a common method for suppressing acoustic feedback in digital hearing aids. In this letter, we propose a new variable step-size algorithm for normalized least mean square and an affine projection algorithm to combine with a variable step-size affine projection algorithm and global speech absence probability in an adaptive filter. The computer simulation used to test the proposed algorithm results in a lower misalignment error than the comparison algorithm at a similar convergence rate. Therefore, the proposed algorithm suggests an effective solution for the feedback suppression system of digital hearing aids.

Substrate noise coupling in RF receiver front-end circuitry for LTE wireless communication was examined by full-chip level simulation and on-chip measurements, with a demonstrator built in a 65nm CMOS technology. A CMOS digital noise emulator injects high-order harmonic noises in a silicon substrate and induces in-band spurious tones in an RF receiver on the same chip through substrate noise interference. A complete simulation flow of full-chip level substrate noise coupling uses a decoupled modeling approach, where substrate noise waveforms drawn with a unified package-chip model of noise source circuits are given to mixed-level simulation of RF chains as noise sensitive circuits. The distribution of substrate noise in a chip and the attenuation with distance are simulated and compared with the measurements. The interference of substrate noise at the 17th harmonics of 124.8MHz — the operating frequency of the CMOS noise emulator creates spurious tones in the communication bandwidth at 2.1GHz.

For the sake of privacy preservation, services that are offered with reference to individual user preferences should do so with a sufficient degree of anonymity. We surveyed various tools that meet requirements of such services and decided that group signature schemes with weakened anonymity (without unlinkability) are adequate. Then, we investigated a theoretical gap between unlinkability of group signature schemes and their other requirements. We show that this gap is significantly large. Specifically, we clarify that if unlinkability can be achieved from any other property of group signature schemes, it becomes possible to construct a chosen-ciphertext secure cryptosystem from any one-way function. This result implies that the efficiency of group signature schemes can be drastically improved if unlinkability is not taken into account. We also demonstrate a way to construct a scheme without unlinkability that is significantly more efficient than the best known full-fledged scheme.

In this paper, we show a connection between #P and computing the (real) value of the high order derivative at the origin. Consider, as a problem instance, an integer b and a sufficiently often differentiable function F(x) that is given as a string. Then we consider computing the value F(b)(0) of the b-th derivative of F(x) at the origin. By showing a polynomial as an example, we show that we have FP = #P if we can compute log 2F(b)(0) up to certain precision. The previous statement holds even if F(x) is limited to a function that is analytic at any x ∈ R. It implies the hardness of computing the b-th value of a number sequence from the closed form of its generating function.

This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.

This paper proposes the state observer design for feedforward nonlinear systems with delayed output. It is shown that by using the Lyapunov-Krasovskii functional, the proposed design method ensures the asymptotic stability of estimation error for an arbitrarily large output delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

Hardware Trojan (HT) has emerged as an impending security threat to hardware systems. However, conventional functional tests fail to detect HT since Trojans are triggered by rare events. Most of the existing side-channel based HT detection techniques just simply compare and analyze circuit's parameters and offer no signal calibration or error correction properties, so they suffer from the challenge and interference of large process variations (PV) and noises in modern nanotechnology which can completely mask Trojan's contribution to the circuit. This paper presents a novel HT detection method based on subspace technique which can detect tiny HT characteristics under large PV and noises. First, we formulate the HT detection problem as a weak signal detection problem, and then we model it as a feature extraction model. After that, we propose a novel subspace HT detection technique based on time domain constrained estimator. It is proved that we can distinguish the weak HT from variations and noises through particular subspace projections and reconstructed clean signal analysis. The reconstructed clean signal of the proposed algorithm can also be used for accurate parameter estimation of circuits, e.g. power estimation. The proposed technique is a general method for related HT detection schemes to eliminate noises and PV. Both simulations on benchmarks and hardware implementation validations on FPGA boards show the effectiveness and high sensitivity of the new HT detection technique.

Even with the recent development of new types of social networking services such as microblogs, Bulletin Board Systems (BBS) remains popular for local communities and vertical discussions. These BBS sites have high volume of traffic everyday with user discussions on a variety of topics. Therefore it is difficult for BBS visitors to find the posts that they are interested in from the large amount of discussion threads. We attempt to explore several main characteristics of BBS, including organizational flexibility of BBS texts, high data volume and aging characteristic of BBS topics. Based on these characteristics, we propose a novel method of Online Topic Detection (OTD) on BBS, which mainly includes a representative post selection procedure based on Markov chain model and an efficient topic clustering algorithm with candidate topic set generation based on Aging Theory. Experimental results show that our method improves the performance of OTD in BBS environment in both detection accuracy and time efficiency. In addition, analysis on the aging characteristic of discussion topics shows that the generation and aging of topics on BBS is very fast, so it is wise to introduce candidate topic set generation strategy based on Aging Theory into the topic clustering algorithm.

This letter deals with a new cell clustering problem subject to signal-to-interference-plus-noise-ratio (SINR) constraints in uplink network MIMO systems, where multiple base stations (BSs) cooperate for joint processing as forming a cluster. We first prove that the SINRs of users in a certain cluster always increase monotonically as the cluster size increases when the receiver filter that maximizes the SINR is used. Using this result, we propose an efficient clustering algorithm to minimize the maximum number of cooperative BSs in a cluster. Simulation results show that the maximum number of cooperative BSs minimized by the proposed method is close to that minimized by the exhaustive search and the proposed scheme outperforms the conventional one in terms of the outage probability.

This paper addresses the discrete abstraction problem for stochastic nonlinear systems with continuous-valued state. The proposed solution is based on a function, called the bisimulation function, which provides a sufficient condition for the existence of a discrete abstraction for a given continuous system. We first introduce the bisimulation function and show how the function solves the problem. Next, a convex optimization based method for constructing a bisimulation function is presented. Finally, the proposed framework is demonstrated by a numerical simulation.

The nth partial sums of a classical Fourier series have large oscillations near the jump discontinuities. This behaviour is the well-known Gibbs phenomenon. Recently, the inverse polynomial reconstruction method (IPRM) has been successfully implemented to reconstruct piecewise smooth functions by reducing the effects of the Gibbs phenomenon for Fourier series. This paper addresses the 2-D fractional Fourier series (FrFS) using the same approach used with the 1-D fractional Fourier series and finds that the Gibbs phenomenon will be observed in 1-D and 2-D fractional Fourier series expansions for functions at a jump discontinuity. The existing IPRM for resolution of the Gibbs phenomenon for 1-D and 2-D FrFS appears to be the same as that used for Fourier series. The proof of convergence provides theoretical basis for both 1-D and 2-D IPRM to remove Gibbs phenomenon. Several numerical examples are investigated. The results indicate that the IPRM method completely eliminates the Gibbs phenomenon and gives exact reconstruction results.

In a cognitive radio (CR) network, the channel sensing scheme used to detect the existence of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to the inefficiency of the sensing scheme. This may yield primary user interference and low system performance. In this paper, we present a learning-based scheme for channel sensing in CR networks. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. Simulation results show the effectiveness and efficiency of our proposed scheme.

A stochastic hybrid system can express complex dynamical systems such as biological systems and communication networks, but computation for analysis and control is frequently difficult. In this paper, for a class of stochastic hybrid systems, a discrete abstraction method in which a given system is transformed into a finite-state system is proposed based on the notion of bounded bisimulation. In the existing discrete abstraction method based on bisimulation, a computational procedure is not in general terminated. In the proposed method, only the behavior for the finite time interval is expressed as a finite-state system, and termination is guaranteed. Furthermore, analysis of genetic toggle switches is also discussed as an application.

This study presents efficient algorithms for performing multiply-by-3 (3N) and divide-by-3 (N/3) operations with the additions and subtractions, respectively. No multiplications and divisions are needed. Full adder (FA) and full subtractor (FS) can be implemented to realize the N3 and N/3 operations, respectively. For fast hardware implementation, this paper introduces two basic cells UCA and UCS for 3N and N/3 operations, respectively. For 3N operation, the UCA-based ripple carry adder (RCA) and carry lookahead adder (CLA) designs are proposed and their speed performances are estimated based on the delay data of standard cell library in TSMC 0.18µm CMOS process. Results show that the 16-bit UCA-based RCA is about 3 times faster than the conventional FA-based RCA and even 25% faster than the FA-based CLA. The proposed 16-bit and 64-bit UCA-based CLAs are 62% and 36% faster than the conventional FA-based CLAs, respectively. For N/3 operations, ripple borrow subtractor (RBS) is also presented. The 16-bit UCS-based RBS is about 15.5% faster than the 16-bit FS-based RBS.

We proposes a method for determining the frequency for monitoring the activities of a malware download site used for malware attacks on websites. In recent years, there has been an increase in attacks exploiting vulnerabilities in web applications for infecting websites with malware and maliciously using those websites as attack platforms. One scheme for countering such attacks is to blacklist malware download sites and filter out access to them from user websites. However, a malware download site is often constructed through the use of an ordinary website that has been maliciously manipulated by an attacker. Once the malware has been deleted from the malware download site, this scheme must be able to unblacklist that site to prevent normal user websites from being falsely detected as malware download sites. However, if a malware download site is frequently monitored for the presence of malware, the attacker may sense this monitoring and relocate that malware on a different site. This means that an attack will not be detected until the newly generated malware download site is discovered. In response to these problems, we clarify the change in attack-detection accuracy caused by attacker behavior. This is done by modeling attacker behavior, specifying a state-transition model with respect to the blacklisting of a malware download site, and analyzing these models with synthetically generated attack patterns and measured attack patterns in an operation network. From this analysis, we derive the optimal monitoring frequency that maximizes the true detection rate while minimizing the false detection rate.

In TCC2010, Lyubashevsky et al. proposed a public-key cryptosystem provably as secure as subset sum problem which will be referred to as LPS scheme. This fact gave an impact at the study of the knapsack schemes. However, this scheme seems to be very weak in practical use. In this paper, we propose an attack against LPS scheme by converting from the problem of computing the secret key into a low-density subset sum problem. Moreover, we confirm the effectiveness of the proposed attack with the computer experiment by using the conventional low-density attack proposed Coster et al. This result means that even a scheme with the provable security does not always have the practical security.

Face verification is the task of determining whether two given face images represent the same person or not. It is a very challenging task, as the face images, captured in the uncontrolled environments, may have large variations in illumination, expression, pose, background, etc. The crucial problem is how to compute the similarity of two face images. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Independent Subspace Analysis (ISA) network. Compared to the linear or kernel based metric learning methods, the proposed deep ISA network is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the Labeled Faces in the Wild dataset, and results show superior performance over some state-of-the-art methods.