In this paper, we show that motion sharpening phenomenon can be explained as a form of visual masking for a special case where a video sequence is composed of alternate frames with different level of sharpness. A frame of higher sharpness behaves to mask the ambiguity of a subsequent frame of lower sharpness and hence preserves the perceptive quality of the whole sequence. Borrowing the mechanism for visual masking, we formulated a quantitative model for deriving the minimum spatial frequency conditions which preserves the subjective quality of the frames being masked. The quantitative model takes into account three fundamental properties of the video signals, namely the size of motion, average luminance and the power of each frequency components. The psychophysical responses towards the changes of these properties are obtained through subjective assessment tests using video sequences of simple geometrical patterns. Subjective experiments on natural video sequences show that more than 75% of viewers could make no distinction between the original sequence and the one processed using the quantitative model.

A new spread spectrum clock generator (SSCG) using two-point delta-sigma modulation is presented in this paper. Not only the divider is varied, but also the voltage controlled oscillator is modulated. This technique can enhance the modulation bandwidth so that the effect of EMI suppression is improved with lower order ΣΔ modulator and can simultaneously optimize the jitter and the modulation profile. In addition, the method of two-path is applied to the loop filter to reduce the capacitance value such that the total integration can be achieved. The proposed SSCG has been fabricated in a 0.35 µm CMOS process. The clock of 400 MHz with center spread ratios of 1.25% and 2.5% are verified. The peak EMI reduction is 19.73 dB for the case of 2.5%. The size of chip area is 0.900.89 mm2.

To improve the computation efficiency of the application specific instruction-set processor (ASIP), a strategy of hardware/software collaborative design is usually utilized. In this process, the auto-customization of specific instruction set has always been a key part to support the automated design of ASIP. The key issue of this problem is how to effectively reduce the huge exponential exploration space in the instruction identification process. To address this issue, we first formulate it as a feasible sub-graph enumeration problem under multiple constraints, and then propose a fast instruction identification algorithm based on a new model called basic convex pattern (BCP). The kernel technique in this algorithm is the transformation from the graph exploration to the formula-based computations. The experimental results have indicated that the proposed algorithm has a distinct reduction in the execution time.

In this paper, we propose a two-microphone noise reduction method to deal with non-stationary interfering noises in multiple-noise-source environments in which the traditional two-microphone algorithms cannot function well. In the proposed algorithm, multiple interfering noise sources are regarded as one virtually integrated noise source in each subband, and the spectrum of the integrated noise is then estimated using its virtual direction of arrival. To do this, we suggest a direction finder for the integrated noise using only two microphones that performs well even in speech active periods. The noise spectrum estimate is further improved by integrating a single-channel noise estimation approach and then subtracted from that of the noisy signal, finally enhancing the desired target signal. The performance of the proposed algorithm is evaluated and compared with the traditional algorithms in various conditions. Experimental results demonstrate that the proposed algorithm outperforms the traditional algorithms in various conditions in terms of objective and subjective speech quality measures.

This paper proposes an accurate modeling method of the copper interconnect cross-section in which the width and thickness dependence on layout patterns and density caused by processes (CMP, etching, sputtering, lithography, and so on) are fully incorporated and universally expressed. In addition, we have developed specific test patterns for the model parameters extraction, and an efficient extraction flow. We have extracted the model parameters for 0.15 µm CMOS using this method and confirmed that 10% τpd error normally observed with conventional LPE (Layout Parameters Extraction) was completely dissolved. Moreover, it is verified that the model can be applied to more advanced technologies (90 nm, 65 nm and 55 nm CMOS). Since the interconnect delay variations due to the processes constitute a significant part of what have conventionally been treated as random variations, use of the proposed model could enable one to greatly narrow the guardbands required to guarantee a desired yield, thereby facilitating design closure.

We present IND-ID-CPA secure identity-based encryption (IBE) schemes with tight reductions to the bilinear Diffie-Hellman (BDH) problem. Since the methods for obtaining IND-ID-CCA secure schemes from IND-ID-CPA secure schemes with tight reductions are already known, we can consequently obtain IND-ID-CCA secure schemes with tight reductions to the BDH problem. Our constructions are based on IBE schemes with tight reductions to the list bilinear Diffie-Hellman (LBDH) problem, and the schemes are converted to those with tight reductions to the BDH problem. Interestingly, it can be shown that there exists a black box construction, in which the former IBE schemes are given as black boxes. Our constructions are very simple and reasonably efficient.

Due to the computational complexity of the optimum maximum likelihood detector (OMD) growing exponentially with the number of users, suboptimum techniques have received significant attention. We have proposed the particle swarm optimization (PSO) for the multiuser detection (MUD) in asynchronous multicarrier code division multiple access (MC-CDMA) system. The performance of PSO based MUD is near optimum, while its computational complexity is far less than OMD. Performance of PSO-MUD has also been shown to be better than that of genetic algorithm based MUD (GA-MUD) at practical SNR.

We propose a new type of direction-of-arrival estimation method for robot audition that is free from strict head related transfer function estimation. The proposed method is based on statistical pattern recognition that employs a ratio of power spectrum amplitudes occurring for a microphone pair as a feature vector. It does not require any phase information explicitly, which is frequently used in conventional techniques, because the phase information is unreliable for the case in which strong reflections and diffractions occur around the microphones. The feature vectors we adopted can treat these influences naturally. The effectiveness of the proposed method was shown from direction-of-arrival estimation tests for 19 kinds of directions: 92.4% of errors were reduced compared with the conventional phase-based method.

The accumulator was introduced as a decentralized alternative to digital signatures. While most of accumulators are based on number theoretic assumptions and require time-consuming modulo exponentiations, Nyberg's combinatoric accumulator dose not depend on any computational assumption and requires only bit operations and hash function evaluations. In this article, we present a generalization of Nyberg's combinatoric accumulator, which allows a lower false positive rate with the same output length. Our generalization also shows that the Bloom filter can be used as a cryptographic accumulator and moreover excels the Nyberg's accumulator.

This study describes the dependence of the surface electric field to the junction depth of source/drain-extension, and the suppression of gate induced drain leakage (GIDL) in fully depleted shallow junction gate-overlapped source/drain-extension (SDE). The GIDL can be reduced by reducing shallow junction depth of drain-extension. Total space charges are a function of junction depth in fully depleted shallow junction drain-extension, and the surface potential is proportional to these charges. Because the GIDL is proportional to surface potential, GIDL is the function of junction depth in fully depleted shallow junction drain-extension. Therefore, the GIDL is suppressed in a fully depleted shallow junction drain-extension by reducing surface potential. Negative substrate bias and halo doping could suppress the GIDL, too. The GIDL characteristic under negative substrate bias is contrary to other GIDL models.

Facial skin detection is an important step in facial surgical planning like as many other applications. There are many problems in facial skin detection. One of them is that the image features can be severely corrupted due to illumination, noise, and occlusion, where, shadows can cause numerous strong edges. Hence, in this paper, we present an automatic Expectation-Maximization (EM) algorithm for facial skin color segmentation that uses knowledge of chromatic space and varying illumination conditions to correct and segment frontal and lateral facial color images, simultaneously. The proposed EM algorithm leads to a method that allows for more robust and accurate segmentation of facial images. The initialization of the model parameters is very important in convergence of algorithm. For this purpose, we use a method for robust parameter estimation of Gaussian mixture components. Also, we use an additional class, which includes all pixels not modeled explicitly by Gaussian with small variance, by a uniform probability density, and amending the EM algorithm appropriately, in order to obtain significantly better results. Experimental results on facial color images show that accurate estimates of the Gaussian mixture parameters are computed. Also, other results on images presenting a wide range of variations in lighting conditions, demonstrate the efficiency of the proposed color skin segmentation algorithm compared to conventional EM algorithm.

In this paper, we propose a reduced-complexity radial basis function (RBF)-assisted decision-feedback equalizer (DFE)-based turbo equalization (TEQ) scheme using a novel extended fuzzy c-means (FCM) algorithm, which not only is comparable in performance to the Jacobian RBF DFE-based TEQ but also is low-complexity. Previous TEQ research has shown that the Jacobian RBF DFE TEQ considerably reduces the computational complexity with similar performance, when compared to the logarithmic maximum a posteriori (Log-MAP) TEQ. In this study, the proposed reduced-complexity RBF DFE TEQ further greatly reduces the computational complexity and is capable of attaining a similar performance in contrast to the Jacobian RBF DFE TEQ in the context of both binary phase-shift keying (BPSK) modulation and 4 quadrature amplitude modulation (QAM). With this proposal, the materialization of the RBF-assisted TEQ scheme becomes more feasible.

The computation involved in multiuser detection (MUD) for multicarrier CDMA (MC-CDMA) based on maximum likelihood (ML) principle grows exponentially with the number of users. Particle swarm optimization (PSO) with soft decisions has been proposed to mitigate this problem. The computational complexity of PSO, is comparable with genetic algorithm (GA), but is much less than the optimal ML detector and yet its performance is much better than GA.

We present a method for the objective quality evaluation of noise-reduced speech in wideband speech communication services, which utilize speech with a wider bandwidth (e.g., 7 kHz) than the usual telephone bandwidth. Experiments indicate that the amount of residual noise and the distortion of speech and noise, which are quality factors, influence the perceived quality degradation of noise-reduced speech. From the results, we observe the principal relationships between these quality factors and perceived speech quality. On the basis of these relationships, we propose a method that quantifies each quality factor in noise-reduced speech by analyzing signals that can be measured and assesses the overall perceived quality of noise-reduced speech using values of these quality factors. To verify the validity of the method, we perform a subjective listening test and compare subjective quality of noise-reduced speech with its estimation. In the test, we use various types of background noise and noise-reduction algorithms. The verification results indicate that the correlation between subjective quality and its objective estimation is sufficiently high regardless of the type of background noise and noise-reduction algorithm.

Today, users themselves are becoming subjects of content creation. The fact that blog, wiki, and UCC have become very popular shows that users want to participate to create and modify digital content. Users who participate in composing content also want to have their copyrights on their modification parts. Thus, a copyright protection system for the content which can be modified by multiple users is required. However, the conventional DRM (Digital Rights Management) systems like OMA DRM are not suitable for the modifiable content because they do not support the content created and modified by different users. Therefore in this paper, we propose a new copyright protection system which allows each modifier of the content created and modified by multiple users to have one's own copyright. We propose data formats and protocols, and analyze the proposed system in terms of the correctness and security. Performance evaluation in the view of response time shows that the proposed system is 2 to 18 times shorter than other comparative schemes.

As pervasive computing technologies develop fast, the privacy protection becomes a crucial issue and needs to be coped with very carefully. Typically, it is difficult to efficiently identify and manage plenty of the low-cost pervasive devices like Radio Frequency Identification Devices (RFID), without leaking any privacy information. In particular, the attacker may not only eavesdrop the communication in a passive way, but also mount an active attack to ask queries adaptively, which is obviously more dangerous. Towards settling this problem, in this paper, we propose two lightweight authentication protocols which are privacy-preserving against active attack, in an asymmetric way. That asymmetric style with privacy-oriented simplification succeeds to reduce the load of low-cost devices and drastically decrease the computation cost for the management of server. This is because that, unlike the usual management of the identities, our approach does not require any synchronization nor exhaustive search in the database, which enjoys great convenience in case of a large-scale system. The protocols are based on a fast asymmetric encryption with specialized simplification and only one cryptographic hash function, which consequently assigns an easy work to pervasive devices. Besides, our results do not require the strong assumption of the random oracle.

This paper presents a new quarter-wavelength microstrip coupler compensated with a periodic sequence of floating metallic strips in the slots on the inner edges. After describing the characteristics of the coupled-line, as an example, a 15-dB coupler is designed and a high directivity of 30 dB or more in theory is obtained over a full band of a single-section coupler. Next, couplers with various coupling factors are designed, and the usefulness for very loose coupling is demonstrated. Furthermore, a three-section coupler is designed to show the effectiveness in a wide frequency range. The validity of the design concept and procedure is confirmed by electromagnetic simulations and experiments.

We propose an algorithm for finding heavy hitters in terms of cardinality (the number of distinct items in a set) in massive traffic data using a small amount of memory. Examples of such cardinality heavy-hitters are hosts that send large numbers of flows, or hosts that communicate with large numbers of other hosts. Finding these hosts is crucial to the provision of good communication quality because they significantly affect the communications of other hosts via either malicious activities such as worm scans, spam distribution, or botnet control or normal activities such as being a member of a flash crowd or performing peer-to-peer (P2P) communication. To precisely determine the cardinality of a host we need tables of previously seen items for each host (e.g., flow tables for every host) and this may infeasible for a high-speed environment with a massive amount of traffic. In this paper, we use a cardinality estimation algorithm that does not require these tables but needs only a little information called the cardinality summary. This is made possible by relaxing the goal from exact counting to estimation of cardinality. In addition, we propose an algorithm that does not need to maintain the cardinality summary for each host, but only for partitioned addresses of a host. As a result, the required number of tables can be significantly decreased. We evaluated our algorithm using actual backbone traffic data to find the heavy-hitters in the number of flows and estimate the number of these flows. We found that while the accuracy degraded when estimating for hosts with few flows, the algorithm could accurately find the top-100 hosts in terms of the number of flows using a limited-sized memory. In addition, we found that the number of tables required to achieve a pre-defined accuracy increased logarithmically with respect to the total number of hosts, which indicates that our method is applicable for large traffic data for a very large number of hosts. We also introduce an application of our algorithm to anomaly detection. With actual traffic data, our method could successfully detect a sudden network scan.

Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion detection employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched to the signatures, and their methods consume huge amounts of cost and time to acquire the signatures. In order to cope with the problems, many researchers have proposed various kinds of methods that are based on unsupervised learning techniques. Although they enable one to construct intrusion detection model with low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we present a new clustering method to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that superiority of our approach to other existing algorithms reported in the literature.

Spoken language understanding (SLU) aims to map a user's speech into a semantic frame. Since most of the previous works use the semantic structures for SLU, we verify that the structure is useful even for noisy input. We apply a structured prediction method to SLU problem and compare it to an unstructured one. In addition, we present a combined method to embed long-distance dependency between entities in a cascaded manner. On air travel data, we show that our approach improves performance over baseline models.