Due to outsourcing of numerous stages of the IC manufacturing process to different foundries, the security risk, such as hardware Trojan becomes a potential threat. In this paper, we present a layout aware localized hardware Trojan detection method that magnifies the detection sensitivity for small Trojan in power-based side-channel analysis. A scan segmentation approach with a modified launch-on-capture (LoC) transition delay fault test pattern application technique is proposed so as to maximize the dynamic power consumption of any target region. The new architecture allows activating any target region and keeping others quiet, which reduces total circuit toggling activity. We evaluate our approach on ISCAS89 benchmark and two practical circuits to demonstrate its effectiveness in side-channel analysis.

We previously proposed an unsupervised model using the inclusion-exclusion principle to compute sentence information content. Though it can achieve desirable experimental results in sentence semantic similarity, the computational complexity is more than O(2n). In this paper, we propose an efficient method to calculate sentence information content, which employs the thinking of the difference set in hierarchical network. Impressively, experimental results show that the computational complexity decreases to O(n). We prove the algorithm in the form of theorems. Performance analysis and experiments are also provided.

In these days, we can see digital signages in many places, for example, inside stations or trains with the distribution of attractive promotional video clips. Users can easily get additional information related to such video clips via mobile devices such as smartphone by using some websites for retrieval. However, such retrieval is time-consuming and sometimes leads users to incorrect information. Therefore, it is desirable that the additional information can be directly obtained from the video clips. We implement a suitable digital watermarking method on smartphone to extract watermarks from video clips on signages in real-time. The experimental results show that the proposed method correctly extracts watermarks in a second on smartphone.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, suitable for measurement during motor learning. However, effects of contamination by systemic artifacts derived from the scalp layer on learning-related fNIRS signals remain unclear. Here we used fNIRS to measure activity of sensorimotor regions while participants performed a visuomotor task. The comparison of results using a general linear model with and without systemic artifact removal shows that systemic artifact removal can improve detection of learning-related activity in sensorimotor regions, suggesting the importance of removal of systemic artifacts on learning-related cerebral activity.

Quick Response (QR) code is a two dimensional barcode widely used in many applications. A standard QR code consists of black and white square modules, and it appears randomized patterns. By modifying the modules using certain rule, it is possible to display a logo image on the QR code. Such a QR code is called an aesthetic QR code. In this paper, we change the encoding method of the Reed-Solomon (RS) code to produce an aesthetic QR code without sacrificing its error correcting capability. The proposed method randomly produces candidates of RS blocks and finds the best one during encoding. Considering an image to be displayed, we also introduce a weighting function during random selection that classifies the visually important regions in the image. We further investigate the shape of modules which represents the image and consider the trade-off between the visual quality and its readability. As a result, we can produce a beautiful aesthetic QR code, which still can be decoded by standard QR code reader.

This letter proposes a Light Space Partitioned Shadow Maps (LSPSMs) algorithm which implements shadow rendering based on a novel partitioning scheme in light space. In stead of splitting the view frustum like traditional Z-partitioning methods, we split partitions from the projection of refined view frustum in light space. The partitioning scheme is performed dual-directionally while limiting the wasted space. Partitions are created in dynamic number corresponding to the light and view directions. Experiments demonstrate that high quality shadows can be rendered in high efficiency with our algorithm.

A multi-phase crystal-less clock generator (MPCLCG) with a process-voltage-temperature (PVT) calibration circuit is proposed. It operates at 192 MHz with 8 phases outputs, and is implemented as a 0.18µm CMOS process for digital power management systems. A temperature calibrated circuit is proposed to align operational frequency under process and supply voltage variations. It occupies an area of 65µm × 75µm and consumes 1.1mW with the power supply of 1.8V. Temperature coefficient (TC) is 69.5ppm/°C from 0 to 100°C, and 2-point calibration is applied to calibrate PVT variation. The measured period jitter is a 4.58-ps RMS jitter and a 34.55-ps peak-to-peak jitter (P2P jitter) at 192MHz within 12.67k-hits. At 192MHz, it shows a 1-MHz-offset phase noise of -102dBc/Hz. Phase to phase errors and duty cycle errors are less than 5.5% and 4.3%, respectively.

Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into different categories. However, ACA needs to be complemented with other algorithms for the classification process. In this paper, we present a fuzzy anomaly detection system that works in two phases. In the first phase, the training phase, we propose ACA to determine clusters. In the second phase, the classification phase, we exploit a fuzzy approach by the combination of two distance-based methods to detect anomalies in new monitored data. We validate our hybrid approach using the KDD Cup'99 dataset. The results indicate that, compared to several traditional and new techniques, the proposed hybrid approach achieves higher detection rate and lower false positive rate.

Many researchers studied computationally-secure (verifiable) secret sharing schemes which distribute multiple secrets with a bulletin board. However, the security definition is ambiguous in many of the past articles. In this paper, we first review existing schemes based on formal definitions of indistinguishability of secrets, verifiability of consistency, and cheater-detectability. And then, we propose a new secret sharing scheme which is the first scheme with indistinguishability of secrets, verifiability, and cheater-detectability, and allows to share secrets with arbitrary access structures. Further, our scheme is provably secure under well known computational assumptions.

Optical label processing is expected to reduce power consumption in label switching network nodes. Previously, we proposed passive waveguide circuits for the recognition of BPSK labels with a theoretically infinite contrast ratio. The recognizable label number was limited to four and eight for 4-bit and 8-bit BPSK labels, respectively. In this paper, we propose methods to increase the recognizable label number. The proposed circuits can recognize eight and sixteen labels of 4-bit BPSK codes with a contrast ratio of 4.00 and 2.78, respectively. As 8-bit BSPK codes, 64, 128, and 256 labels can be recognized with a contrast ratio of 4.00, 2.78, and 1.65, respectively. In recognition of all encoded labels, that is, 16 and 256 labels for 4-bit and 8-bit BPSK labels, a reference signal is employed to identify the sign of the optical output signals. The effect of phase deviation and loss along the optical waveguides of the devices is also discussed.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

Many hemodialysis patients undergo plasitc surgery to form the arterio-venous fistula (AVF) in their forearm to improve the vascular access by shunting blood flows. The issue of AVF is the stenosis caused by the disturbance of blood flows; therefore the auscultation system to assist the stenosis diagnosis has been developed. Although the system is intended to be used as a steady monitoring for stenosis assessment, its efficiency was not always high because it cannot estimate where the stenosis locates. In this study, for extracting and estimating the stenosis signal, the shunt murmurs captured by many microphones were decomposed by the principal component analysis (PCA). Furthermore, applying the hierarchical categorization of the recursive subdivision self-organizing map (rs-SOM), the modelling of the stenosis signal was proposed to realise the effective stenosis assessment. The false-positive rate of the stenosis assessment was significantly reduced by using the improved auscultation system.

This paper presents a novel non-native speech synthesis technique that preserves the individuality of a non-native speaker. Cross-lingual speech synthesis based on voice conversion or Hidden Markov Model (HMM)-based speech synthesis is a technique to synthesize foreign language speech using a target speaker's natural speech uttered in his/her mother tongue. Although the technique holds promise to improve a wide variety of applications, it tends to cause degradation of target speaker's individuality in synthetic speech compared to intra-lingual speech synthesis. This paper proposes a new approach to speech synthesis that preserves speaker individuality by using non-native speech spoken by the target speaker. Although the use of non-native speech makes it possible to preserve the speaker individuality in the synthesized target speech, naturalness is significantly degraded as the synthesized speech waveform is directly affected by unnatural prosody and pronunciation often caused by differences in the linguistic systems of the source and target languages. To improve naturalness while preserving speaker individuality, we propose (1) a prosody correction method based on model adaptation, and (2) a phonetic correction method based on spectrum replacement for unvoiced consonants. The experimental results using English speech uttered by native Japanese speakers demonstrate that (1) the proposed methods are capable of significantly improving naturalness while preserving the speaker individuality in synthetic speech, and (2) the proposed methods also improve intelligibility as confirmed by a dictation test.

In this study, we consider techniques to search for high-rate punctured convolutional code (PCC) encoders using dual code encoders. A low-rate R=1/n convolutional code (CC) has a dual code that is identical to a PCC with rate R=(n-1)/n. This implies that a rate R=1/n convolutional code encoder can assist in searches for high-rate PCC encoders. On the other hand, we can derive a rate R=1/n CC encoder from good PCC encoders with rate R=(n-1)/n using dual code encoders. This paper proposes a method to obtain improved high-rate PCC encoders, using exhaustive search results of PCC encoders with rate R=1/3 original encoders, and dual code encoders. We also show some PCC encoders obtained by searches that utilized our method.

A generalization of compression via substring enumeration (CSE) for k-th order Markov sources with a finite alphabet is proposed, and an upper bound of the codeword length of the proposed method is presented. We analyze the worst case maximum redundancy of CSE for k-th order Markov sources with a finite alphabet. The compression ratio of the proposed method asymptotically converges to the optimal one for k-th order Markov sources with a finite alphabet if the length n of a source string tends to infinity.

SUMMARY This paper proposes a power-supply-noise-aware timing analysis and test pattern regeneration framework suitable for testing 3D IC. The proposed framework analyzes timing with reasonable accuracy at much faster speed than existing tools. This technique is very scalable because it is based on analytical functions, instead of solving nonlinear equations. The experimental results show, for small circuits, the error is less than 2% compared with SPICE. For large circuits, we achieved 272 times speed up compared with a commercial tool. For a large benchmark circuit (638K gates), we identified 88 risky patterns out of 31K test patterns. We propose a test pattern regeneration flow to replace those risky patterns with very little (or even no) penalty in fault coverage. Our test sets are shorter than commercial power-aware ATPG while the fault coverage is almost the same as power-unaware ATPG.

Peer-to-peer (P2P) networks have attracted increasing attention in the distribution of large-volume and frequently accessed content. In this paper, we mainly consider the problem of key leakage in secure P2P content distribution. In secure content distribution, content is encrypted so that only legitimate users can access the content. Usually, users (peers) cannot be fully trusted in a P2P network because malicious ones might leak their decryption keys. If the redistribution of decryption keys occurs, copyright holders may incur great losses caused by free riders who access content without purchasing it. To decrease the damage caused by the key leakage, the individualization of encrypted content is necessary. The individualization means that a different (set of) decryption key(s) is required for each user to access content. In this paper, we propose a P2P content distribution scheme resilient to the key leakage that achieves the individualization of encrypted content. We show the feasibility of our scheme by conducting a large-scale P2P experiment in a real network.

This paper proposes a Bayesian approach to image recognition based on separable lattice hidden Markov models (SL-HMMs). The geometric variations of the object to be recognized, e.g., size, location, and rotation, are an essential problem in image recognition. SL-HMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. This makes it possible to model not only invariances to the size and location of the object but also nonlinear warping in both dimensions. The maximum likelihood (ML) method has been used in training SL-HMMs. However, in some image recognition tasks, it is difficult to acquire sufficient training data, and the ML method suffers from the over-fitting problem when there is insufficient training data. This study aims to accurately estimate SL-HMMs using the maximum a posteriori (MAP) and variational Bayesian (VB) methods. The MAP and VB methods can utilize prior distributions representing useful prior information, and the VB method is expected to obtain high generalization ability by marginalization of model parameters. Furthermore, to overcome the local maximum problem in the MAP and VB methods, the deterministic annealing expectation maximization algorithm is applied for training SL-HMMs. Face recognition experiments performed on the XM2VTS database indicated that the proposed method offers significantly improved image recognition performance. Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks.

State-of-the-art parallel computers, which are growing in parallelism, require a lot of things in their interconnection networks. Although wide spectrum of efforts in research and development for effective and practical interconnection networks are reported, the problem is still open. One of the largest issues is congestion control that intends to maximize the network performance in terms of throughput and latency. Throttling, or injection limitation, is one of the center ideas of congestion control. We have proposed a new class of throttling method, Entropy Throttling, whose foundation is entropy concept of packets. The throttling method is successful in part, however, its potentials are not sufficiently discussed. This paper aims at exploiting capabilities of the Entropy Throttling method via comprehensive evaluation. Major contributions of this paper are to introduce two ideas of hysteresis function and guard time and also to clarify wide performance characteristics in steady and unsteady communication situations. By introducing the new ideas, we extend the Entropy throttling method. The extended methods improve communication performance at most 3.17 times in the best case and 1.47 times in average compared with non-throttling cases in collective communication, while the method can sustain steady communication performance.

This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.