Shrinking feature sizes and higher levels of integration in semiconductor device manufacturing technologies are increasingly causing the gap between defect levels estimated in the design stage and reported ones for fabricated devices. In this paper, we propose a unified weighted fault coverage approach that includes both bridge and open faults, considering the critical area as the incident rate of each fault. We then propose a test pattern reordering scheme that incorporates our weighted fault coverage with an aim to reduce test costs. Here we apply a greedy algorithm to reorder test patterns generated by the bridge and stuck-at automatic test pattern generator (ATPG), evaluating the relationship between the number of patterns and the weighted fault coverage. Experimental results show that by applying this reordering scheme, the number of test patterns was reduced, on average, by approximately 50%. Our results also indicate that relaxing coverage constraints can drastically reduce test pattern set sizes to a level comparable to traditional 100% coverage stuck-at pattern sets, while targeting the majority of bridge faults and keeping the defect level to no more than 10 defective parts per milion (DPPM) with a 99% manufacturing yield.

In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.

In accordance with Moore's law, recent design issues include shortening of time-to-market and detection of delay faults. Several studies with respect to formal techniques have examined the first issue. Using the equivalence checking, it is possible to identify whether large circuits are equivalent or not in a practical time frame. With respect to the latter issue, it is difficult to achieve 100% fault efficiency even for transition faults in full scan designs. This study involved proposing a redundant transition fault identification method using equivalence checking. The main concept of the proposed algorithm involved combining the following two known techniques, 1. modeling of a transition fault as a stuck-at fault with temporal expansion and 2. detection of a stuck-at fault by using equivalence checking tools. The experimental results indicated that the proposed redundant identification method using a formal approach achieved 100% fault efficiency for all benchmark circuits in a practical time even if a commercial ATPG tool was unable to achieve 100% fault efficiency for several circuits.

This letter presents a novel memory-based architecture for radix-2 fast Walsh-Hadamard-Fourier transform (FWFT) based on the constant geometry FWFT algorithm. It is composed of a multi-function Processing Engine, a conflict-free memory addressing scheme and an efficient twiddle factor generator. The address for memory access and the control signals for stride permutation are formulated in detail and the methods can be applied to other memory-based FFT-like architectures.

It inevitably comes out information overload problem with the increasing available data on e-commence websites. Most existing approaches have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction. However, the existing approaches are unable to perform user prediction and item prediction, since they just treat the ratings as real numbers and learn nothing about the ratings' embeddings in the training process. In this paper, motivated by relation prediction in multi-relational graph, we propose a novel embedding model, namely RPEM, to solve the problem including the tasks of rating prediction, user prediction and item prediction simultaneously for recommendation systems, by learning the latent semantic representation of the users, items and ratings. In addition, we apply the proposed model to cross-domain recommendation, which is able to realize recommendation generation in multiple domains. Empirical comparison on several real datasets validates the effectiveness of the proposed model. The data is available at https://github.com/yuzhaour/da.

Numerous methods have been developed to handle lighting variations in the preprocessing step of face recognition. However, most of them only use the high-frequency information (edges, lines, corner, etc.) for recognition, as pixels lied in these areas have higher local variance values, and thus insensitive to illumination variations. In this case, information of low-frequency may be discarded and some of the features which are helpful for recognition may be ignored. In this paper, we present a new and efficient method for illumination normalization using an energy minimization framework. The proposed method aims to remove the illumination field of the observed face images while simultaneously preserving the intrinsic facial features. The normalized face image and illumination field could be achieved by a reciprocal iteration scheme. Experiments on CMU-PIE and the Extended Yale B databases show that the proposed method can preserve a very good visual quality even on the images illuminated with deep shadow and high brightness regions, and obtain promising illumination normalization results for better face recognition performance.

We evaluated a technique for protecting the copyright of digital data for 3-D printing. To embed copyright information, the inside of a 3-D printed object is constructed from fine domains that have different physical characteristics from those of the object's main body surrounding them, and to read out the embedded information, these fine domains inside the objects are detected using nondestructive inspections such as X-ray photography or thermography. In the evaluation, copyright information embedded inside the 3-D printed object was expressed using the depth of fine cavities inside the object, and X-ray photography were used for reading them out from the object. The test sample was a cuboid 46mm wide, 42mm long, and 20mm deep. The cavities were 2mm wide and 2mm long. The difference in the depths of the cavities appeared as a difference in the luminance in the X-ray photographs, and 21 levels of depth could be detected on the basis of the difference in luminance. These results indicate that under the conditions of the experiment, each cavity expressed 4 to 5bits of information with its depth. We demonstrated that the proposed technique had the possibility of embedding a sufficient volume of information for expressing copyright information by using the depths of cavities.

The massive multiple input multiple output (MIMO) system with large-scale antenna array at base station (BS) simultaneously communicates with many mobile stations (MSs) ensuring high reliability using the pre-coding. But, in mobile communication, the performance of the pre-coding is degraded by fast fading. For improving the performance of the pre-coding, this letter proposes the power control scheme of the new approach that has the variable step size using the statistical characteristic of fast fading and Doppler frequency. From the simulation results, it is shown that the proposed scheme improves signal-to-interference-plus-noise ratio (SINR) performance.

Existing noise inference algorithms neglected the smooth characteristics of noise data, which results in executing slowly of noise inference. In order to address this problem, we present a noise inference algorithm based on fast context-aware tensor decomposition (F-CATD). F-CATD improves the noise inference algorithm based on context-aware tensor decomposition algorithm. It combines the smoothness constraint with context-aware tensor decomposition to speed up the process of decomposition. Experiments with New York City 311 noise data show that the proposed method accelerates the noise inference. Compared with the existing method, F-CATD reduces 4-5 times in terms of time consumption while keeping the effectiveness of the results.

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity.

Key fabrication technology for the Pt/TiO2-x/TiO2/TiO2+x/Pt nano-film memristor is investigated, including preparing platinum (Pt) electrodes and TiO2-x/TiO2/TiO2+x nano-films. The effect of oxygen flow rate and deposition rate during fabrication on O:Ti ratio of thin films is demonstrated. The fabricated nano-films with different oxygen concentration are validated by the analyzed results from X-ray photoelectron spectroscopy (XPS). The obtained memristor device shows the typical resistive switching behavior and nonvolatile memory effects. An analytical device model for Pt/TiO2-x/TiO2/TiO2+x/Pt nano-film memristor is developed based on the fundamental linear relationships between drift-diffusion velocity and the electric field, and boundary conditions are also incorporated in this model. This model is able to predict the relation between variables in the form of explicit formula, which is very critical in memristor-based circuit designs. The measurement results from real devices validate the proposed analytical device model. Some deviations of the model from the measured data are also analyzed and discussed.

In this paper, a miniaturized absorptive/transmissive radome with switchable passband and wide absorbing band is designed. Pin diodes are loaded on the radome in order to obtain switchable passband and miniaturized unit cells, while the resistor loaded double square loops are used to absorb the incident wave. The total thickness of the radome is only 4.5mm. Its transmission and absorbing properties are verified by both synthetic experiments and measurements in the anechoic chamber. Furthermore, the switchable passband of the radome is also evaluated using a waveguide simulator.

In this paper, the performance of orthogonal space-time block codes (OSTBC) for distributed multiple-input multiple-output (MIMO) systems employing adaptive M-QAM transmission is investigated over independent but not necessarily identically distributed (i.n.i.d.) generalized-K fading channels with arbitrary positive integer-valued k(inversely reflects the shadowing severity) and m (inversely reflects the fading severity). Before this, i.n.i.d. generalized-K fading channel has never been considered for distributed OSTBC-MIMO systems. Especially, the effects of the shape parameter k on the distributed OSTBC-MIMO system performance are unknown. Thus, we investigate mainly the significance of the shape parameter k on the distributed OSTBC-MIMO system performance, in terms of the average symbol error probability (SEP), outage probability, and spectral efficiency (SE). By establishing the system model, the approximated probability density function (PDF) of the equivalent signal to noise ratio (SNR) is derived and thereafter the approximated closed-form expressions of the above performance metrics are obtained successively. Finally, the derived expressions are validated via a set of Monte-Carlo simulations and the implications of the shape parameter k on the overall performance are highlighted.

High availability is crucial for industrial Ethernet networks and Ethernet-based control systems, such as automation networks and substation automation systems. Because the standard Ethernet does not support fault tolerance capability, the high availability of Ethernet networks can be increased by using redundancy protocols. Various redundancy protocols for Ethernet networks have been developed and standardized, such as rapid spanning tree protocol (RSTP), media redundancy protocol (MRP), parallel redundancy protocol (PRP), high-availability seamless redundancy (HSR), and others. RSTP and MRP provide redundancy in the network, whereas PRP and HSR provide redundancy in the end nodes. RSTP and MRP have a disadvantage in switchover delay. PRP and HSR provide zero recovery time, but PRP requires a duplicate network infrastructure, and HSR is mainly used in ring-based topologies. Additionally, PRP and HSR provide seamless redundancy in the end nodes and are applied in dedicated HSR networks with dedicated HSR nodes. In this paper, we present a novel seamless redundancy protocol for Ethernet networks, which is called the Redundancy Protocol for Ethernet (RPE). The RPE provides seamless redundancy in the network. This protocol not only provides seamless communications with zero switchover time in case of failure but also supports all topologies. The RPE is transparent and compatible with standard Ethernet nodes. These features make the RPE very useful in time-critical and mission-critical systems, such as substation automation systems, automation networks, and other industrial Ethernet networks.

In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.

A dual-hop amplify-and-forward (AF) relaying system with beamforming is analyzed over η-µ fading channels that includes Nakagami-m, Nakagami-q (Hoyt), and Rayleigh fading channels as special cases. New and exact expressions for the outage probability (OP) and average capacity are derived. Moreover, a new asymptotic analysis is also conducted for the OP and average capacity in terms of basic elementary functions which make it easy to understand the system behavior and the impact of channel parameters. The viability of the analysis is verified by Monte Carlo simulations.

Adaptive and flexible network control technology is considered essential for efficient network resource utilization. Moreover, such technology is becoming a key to cost-effectively meet diverse service requirements and accommodate heavier traffic with limited network resources; demands that conventional static operation cannot satisfy. To address this issue, we previously studied dynamic network control technology for large-capacity network services including on-demand broad bandwidth provisioning services and layer-one VPN. Our previous study introduced a simple weighting function for achieving fairness in terms of path length and proposed two dynamic Make Before Break Routing algorithms for reducing blocking probability. These algorithms enhance network utilization by rerouting existing paths to alternative routes while completely avoiding disruption for highly reliable services. However, the impact of this avoidance of service disruption on blocking probability has not been clarified. In this paper, we propose modified versions of the algorithms that enhance network utilization while slightly increasing disruption by rerouting, which enable us to elucidate the effectiveness of hitless rerouting. We also provide extensive evaluations including a comparison of original and modified algorithms. Numerical examples demonstrate that they achieve not only a high degree of fairness but also low service blocking probability. Hitless rerouting is achieved with a small increase in blocking probability.

This paper proposes a walking route recommender system aiming at continuously supporting a user to take a walk as means for health promotion. In recent years, taking a walk becomes popular with not only the elderly, but also those from all ages as one of the easiest ways for health promotion. From the viewpoint of health promotion, it is desirable to take a walk as daily exercise. However, walking is very simple activity, which makes it difficult for people to maintain their motivation. Although using an activity monitor is expected to improve the motivation for taking a walk as daily exercise, it forces users to manage their activities by themselves. The proposed system solves such a problem by recommending a walking route that can consume target calories. When a system is to be used for long period of time for supporting user's daily exercise, it should consider the case when a user will not follow the recommended route. It would cause a gap between consumed and target calories. We think this problem becomes serious when a user gradually gets bored with taking a walk during a long period of time. In order to solve the problem, the proposed method implicitly manages calories on monthly basis and recommends walking routes that could keep a user from getting bored. The effectiveness of the recommendation algorithm is evaluated with agent simulation. As another important factor for walking support, this paper also proposes a navigation interface that presents direction to the next visiting point without using a map. As users do not have to continuously focus on the interface, it is not only useful for their safety, but also gives them room to enjoy the landscape. The interface is evaluated by an experiment with test participants.

In tutoring systems, students are more likely to utilize hints to assist their decisions about difficult or confusing problems. In the meanwhile, students with weaker knowledge mastery tend to choose more hints than others with stronger knowledge mastery. Hints are important assistances to help students deal with questions. Students can learn from hints and enhance their knowledge about questions. In this paper we firstly use hints alone to build a model named Hints-Model to predict student performance. In addition, matrix factorization (MF) has been prevalent in educational fields to predict student performance, which is derived from their success in collaborative filtering (CF) for recommender systems (RS). While there is another factorization method named non-negative matrix factorization (NMF) which has been developed over one decade, and has additional non-negative constrains on the factorization matrices. Considering the sparseness of the original matrix and the efficiency, we can utilize an element-based matrix factorization called regularized single-element-based NMF (RSNMF). We compared the results of different factorization methods to their combination with Hints-Model. From the experiment results on two datasets, we can find the combination of RSNMF with Hints-Model has achieved significant improvement and obtains the best result. We have also compared the Hints-Model with the pioneer approach performance factor analysis (PFA), and the outcomes show that the former method exceeds the later one.

After exploring the classic Lambertian reflectance model, we proposed an effective illumination estimation model to extract illumination invariants for face recognition under complex illumination conditions in this paper. The estimated illumination by our method not only meets the actual lighting conditions of facial images, but also conforms to the imaging principle. Experimental results on the combined Yale B database show that the proposed method can extract more robust illumination invariants, which improves face recognition rate.