This paper introduces a partially parallel inter-chip link architecture for asynchronous multi-chip Network-on-Chips (NoCs). The multi-chip NoCs that operate as a large NoC have been recently proposed for very large systems, such as automotive applications. Inter-chip links are key elements to realize high-performance multi-chip NoCs using a limited number of I/Os. The proposed asynchronous link based on level-encoded dual-rail (LEDR) encoding transmits several bits in parallel that are received by detecting the phase information of the LEDR signals at each serial link. It employs a burst-mode data transmission that eliminates a per-bit handshake for a high-speed operation, but the elimination may cause data-transmission errors due to cross-talk and power-supply noises. For triggering data retransmission, errors are detected from the embedded phase information; error-detection codes are not used. The throughput is theoretically modelled and is optimized by considering the bit-error rate (BER) of the link. Using delay parameters estimated for a 0.13 µm CMOS technology, the throughput of 8.82 Gbps is achieved by using 10 I/Os, which is 90.5% higher than that of a link using 9 I/Os without an error-detection method operating under negligible low BER (<10-20).

Synthetic aperture radar (SAR) is an indispensable tool for low visibility ground surface measurement owing to its robustness against optically harsh environments such as adverse weather or darkness. As a leading-edge approach for SAR image processing, the coherent change detection (CCD) technique has been recently established; it detects a temporal change in the same region according to the phase interferometry of two complex SAR images. However, in the case of general damage assessment following an earthquake or mudslide, the technique requires not only the detection of surface change but also an assessment for height change quantity, such as occurs with a building collapse or road subsidence. While the interferometric SAR (InSAR) approach is suitable for height assessment, it is basically unable to detect change if only a single observation is made. To address this issue, we previously proposed a method of estimating height change according to phase interferometry of the coherence function obtained by dual band-divided SAR images. However, the accuracy of this method significantly degrades in noisy situations owing to the use of the phase difference. To resolve this problem, this paper proposes a novel height estimation method by exploiting the frequency characteristic of coherence phases obtained by each SAR image multiply band-divided. The results obtained from numerical simulations and experimental data demonstrate that our proposed method offers accurate height change estimation while avoiding degradation in the spatial resolution.

This paper proposes an ultra-low-power 5.5-GHz PLL which employs the new divide-by-4 injection-locked frequency divider (ILFD) and a class-C VCO with linearity-compensated varactor for low supply voltage operation. A forward-body-biasing (FBB) technique can decrease threshold voltage of MOS transistors, which can improve operation frequency and can widen the lock range of the ILFD. The FBB is also employed for linear-frequency-tuning of VCO under low supply voltage of 0.5V. The double-switch injection technique is also proposed to widen the lock range of the ILFD. The digital calibration circuit is introduced to control the lock-range of ILFD automatically. The proposed PLL was fabricated in a 65nm CMOS process. With a 34.3-MHz reference, it shows a 1-MHz-offset phase noise of -106dBc/Hz at 5.5GHz output. The supply voltage is 0.54V for divider and 0.5V for other components. Total power consumption is 0.95mW.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

Consider a simple undirected graph G = (V,E) with vertex set V and edge set E. Let G-u be a subgraph induced by the vertex set V-{u}. The distance δG(x,y) is defined as the length of the shortest path between vertices x and y in G. The vertex u ∈ V is a hinge vertex if there exist two vertices x,y ∈ V-{u} such that δG-u(x,y)>δG(x,y). Let U be a set consisting of all hinge vertices of G. The neighborhood of u is the set of all vertices adjacent to u and is denoted by N(u). We define d(u) = max{δG-u(x,y) | δG-u(x,y)>δG(x,y),x,y ∈ N(u)} for u ∈ U as detour degree of u. A maximum detour hinge vertex problem is to find a hinge vertex u with maximum d(u) in G. In this paper, we proposed an algorithm to find the maximum detour hinge vertex on an interval graph that runs in O(n2) time, where n is the number of vertices in the graph.

The research reported in this paper is an attempt to elucidate the predictors of pause duration in read-aloud discourse. Through simple linear regression analysis and stepwise multiple linear regression, we examined how different factors (namely, syntactic structure, discourse hierarchy, topic structure, preboundary length, and postboundary length) influenced pause duration both separately and jointly. Results from simple regression analysis showed that discourse hierarchy, syntactic structure, topic structure, and postboundary length had significant impacts on boundary pause duration. However, when these factors were tested in a stepwise regression analysis, only discourse hierarchy, syntactic structure, and postboundary length were found to have significant impacts on boundary pause duration. The regression model that best predicted boundary pause duration in discourse context was the one that first included syntactic structure, and then included discourse hierarchy and postboundary length. This model could account for about 80% of the variance of pause duration. Tests of mediation models showed that the effects of topic structure and discourse hierarchy were significantly mediated by syntactic structure, which was most closely correlated with pause duration. These results support an integrated model combining the influence of several factors and can be applied to text-to-speech systems.

This paper presents an electrolaryngeal (EL) speech enhancement method capable of significantly improving naturalness of EL speech while causing no degradation in its intelligibility. An electrolarynx is an external device that artificially generates excitation sounds to enable laryngectomees to produce EL speech. Although proficient laryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding to EL speech as noise. To address these issues, there are mainly two conventional approached to EL speech enhancement through either noise reduction or statistical voice conversion (VC). The former approach usually causes no degradation in intelligibility but yields only small improvements in naturalness as the mechanical excitation sounds remain essentially unchanged. On the other hand, the latter approach significantly improves naturalness of EL speech using spectral and excitation parameters of natural voices converted from acoustic parameters of EL speech, but it usually causes degradation in intelligibility owing to errors in conversion. We propose a hybrid approach using a noise reduction method for enhancing spectral parameters and statistical voice conversion method for predicting excitation parameters. Moreover, we further modify the prediction process of the excitation parameters to improve its prediction accuracy and reduce adverse effects caused by unvoiced/voiced prediction errors. The experimental results demonstrate the proposed method yields significant improvements in naturalness compared with EL speech while keeping intelligibility high enough.

We prove NP-completeness of deciding whether a given loop of colored right isosceles triangles, hinged together at edges, can be folded into a specified rectangular three-color pattern. By contrast, the same problem becomes polynomially solvable with one color or when the target shape is a tree-shaped polyomino.

Artificial blurring is a typical operation in image forging. Most existing image forgery detection methods consider only one single feature of artificial blurring operation. In this manuscript, we propose to adopt feature fusion, with multifeatures for artificial blurring operation in image tampering, to improve the accuracy of forgery detection. First, three feature vectors that address the singular values of the gray image matrix, correlation coefficients for double blurring operation, and image quality metrics (IQM) are extracted and fused using principal component analysis (PCA), and then a support vector machine (SVM) classifier is trained using the fused feature extracted from training images or image patches containing artificial blurring operations. Finally, the same procedures of feature extraction and feature fusion are carried out on the suspected image or suspected image patch which is then classified, using the trained SVM, into forged or non-forged classes. Experimental results show the feasibility of the proposed method for image tampering feature fusion and forgery detection.

In this paper, we provide several methods to construct nonlinear resilient functions with multiple good cryptographic properties, including high nonlinearity, high algebraic degree, and non-existence of linear structures. Firstly, we present an improvement on a known construction of resilient S-boxes such that the nonlinearity and the algebraic degree will become higher in some cases. Then a construction of highly nonlinear t-resilient Boolean functions without linear structures is given, whose algebraic degree achieves n-t-1, which is optimal for n-variable t-resilient Boolean functions. Furthermore, we construct a class of resilient S-boxes without linear structures, which possesses the highest nonlinearity and algebraic degree among all currently known constructions.

The present study aims to investigate the effect of voluntary movements on human temporal perception in multisensory integration. We therefore performed temporal order judgment (TOJ) tasks in audio-tactile integration under three conditions: no movement, involuntary movement, and voluntary movement. It is known that the point of subjective simultaneity (PSS) under the no movement condition, that is, normal TOJ tasks, appears when a tactile stimulus is presented before an auditory stimulus. Our experiment showed that involuntary and voluntary movements shift the PSS to a value that reduces the interval between the presentations of auditory and tactile stimuli. Here, the shift of the PSS under the voluntary movement condition was greater than that under the involuntary movement condition. Remarkably, the PSS under the voluntary movement condition appears when an auditory stimulus slightly precedes a tactile stimulus. In addition, a just noticeable difference (JND) under the voluntary movement condition was smaller than those under the other two conditions. These results reveal that voluntary movements alternate the temporal integration of audio-tactile stimuli. In particular, our results suggest that voluntary movements reverse the temporal perception order of auditory and tactile stimuli and improve the temporal resolution of temporal perception. We discuss the functional mechanism of shifting the PSS under the no movement condition with voluntary movements in audio-tactile integration.

We consider the problem of learning a classifier using only positive and unlabeled samples. In this setting, it is known that a classifier can be successfully learned if the class prior is available. However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class-conditional density of the positive class to the input density. By performing this partial matching in terms of the Pearson divergence, which we estimate directly without density estimation via lower-bound maximization, we can obtain an analytical estimator of the class prior. We further show that an existing class prior estimation method can also be interpreted as performing partial matching under the Pearson divergence, but in an indirect manner. The superiority of our direct class prior estimation method is illustrated on several benchmark datasets.

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.

In this paper, a one-class Naïve Bayesian classifier (One-NB) for detecting toll frauds in a VoIP service is proposed. Since toll frauds occur irregularly and their patterns are too diverse to be generalized as one class, conventional binary-class classification is not effective for toll fraud detection. In addition, conventional novelty detection algorithms have struggled with optimizing their parameters to achieve a stable detection performance. In order to resolve the above limitations, the original Naïve Bayesian classifier is modified to handle the novelty detection problem. In addition, a genetic algorithm (GA) is employed to increase efficiency by selecting significant variables. In order to verify the performance of One-NB, comparative experiments using five well-known novelty detectors and three binary classifiers are conducted over real call data records (CDRs) provided by a Korean VoIP service company. The experimental results show that One-NB detects toll frauds more accurately than other novelty detectors and binary classifiers when the toll frauds rates are relatively low. In addition, The performance of One-NB is found to be more stable than the benchmark methods since no parameter optimization is required for One-NB.

In this letter, we exploit a new framework for detecting the non-specific object via combing the top-down and bottom-up cues. Specifically, a novel supervised discriminative dictionaries learning method is proposed to learn the coupled dictionaries for the object and non-object feature spaces in terms of the top-down cue. Different from previous dictionary learning methods, the new data reconstruction residual terms of coupled feature spaces, the sparsity penalty measures on the representations and an inconsistent regularizer for the learned dictionaries are all incorporated in a unitized objective function. Then we derive an iterative algorithm to alternatively optimize all the variables efficiently. Considering the bottom-up cue, the proposed discriminative dictionaries learning is then integrated with an unsupervised dictionary learning to capture the objectness windows in an image. Experimental results show that the non-specific object detection problem can be effectively solved by the proposed dictionary leaning framework and outperforms some established methods.

Although many companies have developed robots that assist humans in the activities of daily living, safety requirements and test methods for such robots have not been established. Given the risk associated with a robot malfunctioning in the human living space, from the viewpoints of safety and EMC, it is necessary that the robot does not create a hazardous situation even when exposed to possibly severe electromagnetic disturbances in the operating environment. Thus, in immunity tests for personal care robots, the safety functions should be more rigorously tested than the other functions, and be repeatedly activated in order to ascertain that the safety functions are not lost in the presence of electromagnetic disturbances. In this paper, immunity test procedures for personal care robots are proposed that take into account functional safety requirements. A variety of test apparatuses are presented, which were built for activating the safety functions of robots, and detecting whether they were in a safe state. The practicality of the developed immunity test system is demonstrated using actual robots.

Let p be an odd prime and m be any positive integer. Assume that n=2m and e is a positive divisor of m with m/e being odd. For the decimation factor $d=rac{(p^{m}+1)^2}{2(p^e+1)}$, the cross-correlation between the p-ary m-sequence ${tr_1^n(alpha^i)}$ and its decimated sequence ${tr_1^n(alpha^{di})}$ is investigated. The value distribution of the correlation function is completely determined. The results in this paper generalize the previous results given by Choi, Luo and Sun et al., where they considered some special cases of the decimation factor d with a restriction that m is odd. Note that the integer m in this paper can be even or odd. Thus, the decimation factor d here is more flexible than the previous ones. Moreover, our method for determining the value distribution of the correlation function is different from those adopted by Luo and Sun et al. in that we do not need to calculate the third power sum of the correlation function, which is usually difficult to handle.

To better support data-intensive workflows which are typically built out of various independently developed executables, this paper proposes extensions to parallel database systems called User-Defined eXecutables (UDX) and collective queries. UDX facilitates the description of workflows by enabling seamless integrations of external executables into SQL statements without any efforts to write programs confirming to strict specifications of databases. A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary UDX. It provides efficient parallelization of executables through the data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and computational loads into account. We implement this concept in a system called ParaLite, a parallel database system based on a popular lightweight database SQLite. Our experiments show that ParaLite has several times higher performance over Hive for typical SQL tasks and has 10x speedup compared to a commercial DBMS for executables. In addition, this paper studies a real-world text processing workflow and builds it on top of ParaLite, Hadoop, Hive and general files. Our experiences indicate that ParaLite outperforms other systems in both productivity and performance for the workflow.

This paper proposes a multipacket-per-slot reservation-based random access protocol with multiuser detection (MD) and automatic repeat request (ARQ), called MPRMD, and analyzes its performance by computer simulations. In MPRMD, before data packet (DP) transmission, a user terminal (UT) transmits a small access request packet (AP) that is composed of an orthogonal preamble sequence and a UT identifier (UT-ID) in a randomly selected minislot during a short dedicated period. Even when several APs collide, a base station (BS) distinguishes them by matched filtering against the preamble part and then extracts the UT-IDs after separating each AP by MD. If the APs are not successfully detected, a small number of minislots are additionally arranged to retransmit them. Thus, by using MD under AP crowded conditions, BS can maximally detect the access requests in a short period, which results in reducing the overhead. Furthermore, in the assignment of a slot, BS intentionally assigns one slot to multiple UTs in order to enhance the efficiency and separates UT's DPs by MD. Since MPRMD can detect a multitude of access requests by utilizing MD in the short period and efficiently assign the slot to separable DPs by MD, it can enhance the system throughput. Computer simulations are conducted to demonstrate the effectiveness of MPRMD. It is shown that the maximum throughputs of MPRMD with the average SNR of 30dB reach 1.4 and 1.7 packets/slot when a data packet is 10 times and 50 times as long as a control packet, respectively.

Numerous studies have been focusing on the improvement of bag of features (BOF), histogram of oriented gradient (HOG) and scale invariant feature transform (SIFT). However, few works have attempted to learn the connection between them even though the latter two are widely used as local feature descriptor for the former one. Motivated by the resemblance between BOF and HOG/SIFT in the descriptor construction, we improve the performance of HOG/SIFT by a) interpreting HOG/SIFT as a variant of BOF in descriptor construction, and then b) introducing recently proposed approaches of BOF such as locality preservation, data-driven vocabulary, and spatial information preservation into the descriptor construction of HOG/SIFT, which yields the BOF-driven HOG/SIFT. Experimental results show that the BOF-driven HOG/SIFT outperform the original ones in pedestrian detection (for HOG), scene matching and image classification (for SIFT). Our proposed BOF-driven HOG/SIFT can be easily applied as replacements of the original HOG/SIFT in current systems since they are generalized versions of the original ones.