Three-dimensional (3D) field-programmable gate arrays (FPGAs) are expected to offer higher logic density as well as improved delay and power performance by utilizing 3D integrated circuit technology. However, because through-silicon-vias (TSVs) for conventional 3D FPGA interlayer connections have a large area overhead, there is an inherent tradeoff between connectivity and small size. To find a balance between cost and performance, and to explore 3D FPGAs with realistic 3D integration processes, we propose two types of 3D FPGA and construct design tool sets for architecture exploration. In previous research, we created a TSV-free 3D FPGA with a face-down integration method; however, this was limited to two layers. In this paper, we discuss the face-up stacking of several face-down stacked FPGAs. To minimize the number of TSVs, we placed TSVs peripheral to the FPGAs for 3D-FPGA with 4 layers. According to our results, a 2-layer 3D FPGA has reasonable performance when limiting the design to two layers, but a 4-layer 3D FPGA is a better choice when area is emphasized.

As an effective method, exponential discriminant analysis (EDA) has been proposed and widely used to solve the so-called small-sample-size (SSS) problem. In this paper, a simple and effective generalization of EDA is presented and named as GEDA. In GEDA, a general exponential function, where the base of exponential function is larger than the Euler number, is used. Due to the property of general exponential function, the distance between samples belonging to different classes is larger than that of EDA, and then the discrimination property is largely emphasized. The experiment results on the Extended Yale and CMU-PIE face databases show that, GEDA gets more advantageous recognition performance compared to EDA.

Feature visualization is of great significances in volume visualization, and feature extraction has been becoming extremely popular in feature visualization. While precise definition of features is usually absent which makes the extraction difficult. This paper employs probability density function (PDF) as statistical property, and proposes a statistical property guided approach to extract features for volume data. Basing on feature matching, it combines simple liner iterative cluster (SLIC) with Gaussian mixture model (GMM), and could do extraction without accurate feature definition. Further, GMM is paired with a normality test to reduce time cost and storage requirement. We demonstrate its applicability and superiority by successfully applying it on homogeneous and non-homogeneous features.

In this paper, we use the MCA (Multi-Cache Approximation) algorithm to numerically determine cache hit probability in a multi-cache network. We then analytically obtain performance metrics for Content-Centric networking (CCN). Our analytical model contains multiple routers, multiple repositories (e.g., storage servers), and multiple entities (e.g., clients). We obtain three performance metrics: content delivery delay (i.e., the average time required for an entity to retrieve a content through a neighboring router), throughput (i.e., number of contents delivered from an entity per unit of time), and availability (i.e., probability that an entity can successfully retrieve a content from a network). Through several numerical examples, we investigate how network topology affects the performance of CCN. A notable finding is that content caching becomes more beneficial in terms of content delivery time and availability (resp., throughput) as distance between the entity and the requesting repository narrows (resp., widens).

Many concurrency control protocols for B-trees use latch-coupling because its execution is efficient on a single machine. Some studies have indicated that latch-coupling may involve a performance bottleneck when using multicore processors in a shared-everything environment, but no studies have considered the possible performance bottleneck caused by sending messages between processing elements (PEs) in shared-nothing environments. We propose two new concurrency control protocols, “LCFB” and “LCFB-link”, which require no latch-coupling in optimistic processes. The LCFB-link also innovates B-link approach within each PE to reduce the cost of modifications in the PE, as a solution to the difficulty of consistency management for the side pointers in a parallel B-tree. The B-link algorithm is well known as a protocol without latch-coupling, but B-link has the difficulty of guaranteeing the consistency of the side pointers in a parallel B-tree. Experimental results in various environments indicated that the system throughput of the proposed protocols was always superior to those of the conventional protocols, particularly in large-scale configurations, and using LCFB-link was effective for higher update ratios. In addition, to mitigate access skew, data should migrate between PEs. We have demonstrated that our protocols always improve the system throughput and are effective as concurrency controls for data migration.

This paper presents a pivot-set generation algorithm for accelerating exact similarity search in a large-scale data set. To deal with the large-scale data set, it is important to efficiently construct a search index offline as well as to perform fast exact similarity search online. Our proposed algorithm efficiently generates competent pivots with two novel techniques: hierarchical data partitioning and fast pivot optimization techniques. To make effective use of a small number of pivots, the former recursively partitions a data set into two subsets with the same size depending on the rank order from each of two assigned pivots, resulting in a complete binary tree. The latter calculates a defined objective function for pivot optimization with a low computational cost by skillfully operating data objects mapped into a pivot space. Since the generated pivots provide the tight lower bounds on distances between a query object and the data objects, an exact similarity search algorithm effectively avoids unnecessary distance calculations. We demonstrate that the search algorithm using the pivots generated by the proposed algorithm reduces distance calculations with an extremely high rate regarding a range query problem for real large-scale image data sets.

This letter investigates the performance of a legitimate surveillance system, where a wireless powered legitimate monitor aims to eavesdrop a suspicious communication link. Power splitting technique is adopted at the monitor for simultaneous information eavesdropping and energy harvesting. In order to maximize the successful eavesdropping probability, the power splitting ratio is optimized under the minimum harvested energy constraint. Assuming that perfect channel state information (CSI) or only the channel distribution information (CDI) is available, the closed-form maximum successful eavesdropping probability is obtained in Rayleigh fading channels. It is shown that the minimum harvested energy constraint has no impact on the eavesdropping performance if the minimum harvested energy constraint is loose. It is also shown that the eavesdropping performance loss due to partial knowledge of CSI is negligible when the eavesdropping link channel condition is much better than that of the suspicious communication link channel.

This paper addresses the task of assigning labels of fine-grained named entity (NE) types to Wikipedia articles. Information of NE types are useful when extracting knowledge of NEs from natural language text. It is common to apply an approach based on supervised machine learning to named entity classification. However, in a setting of classifying into fine-grained types, one big challenge is how to alleviate the data sparseness problem since one may obtain far fewer instances for each fine-grained types. To address this problem, we propose two methods. First, we introduce a multi-task learning framework, in which NE type classifiers are all jointly trained with a neural network. The neural network has a hidden layer, where we expect that effective combinations of input features are learned across different NE types. Second, we propose to extend the input feature set by exploiting the hyperlink structure of Wikipedia. While most of previous studies are focusing on engineering features from the articles' contents, we observe that the information of the contexts the article is mentioned can also be a useful clue for NE type classification. Concretely, we propose to learn article vectors (i.e. entity embeddings) from Wikipedia's hyperlink structure using a Skip-gram model. Then we incorporate the learned article vectors into the input feature set for NE type classification. To conduct large-scale practical experiments, we created a new dataset containing over 22,000 manually labeled articles. With the dataset, we empirically show that both of our ideas gained their own statistically significant improvement separately in classification accuracy. Moreover, we show that our proposed methods are particularly effective in labeling infrequent NE types. We've made the learned article vectors publicly available. The labeled dataset is available if one contacts the authors.

Transient scattering by a metal cylinder covered with inhomogeneous lossy material is analyzed for application of radar systems to nondestructive testing of reinforced concrete structures. First, inhomogeneous lossy material that is a model of corrosion by rust is approximated by a cylindrical multilayered medium, and analytic solution of a scattered field in frequency domain is derived. Next, time domain scattering response is calculated from the frequency domain data by using the inverse Fourier transform. Numerical results of pulse responses indicate that corrosion rate of the concrete can be evaluated by checking the waveform distortion of the pulse responses.

Surveillance with multiple cameras and microphones is promising to trace activities of suspicious persons for security purposes. When these sensors are connected to the Internet, they might also jeopardize innocent people's privacy because, as a result of human error, signals from sensors might allow eavesdropping by malicious persons. This paper presents a proposal for exploiting super-resolution to address this problem. Super-resolution is a signal processing technique by which a high-resolution version of a signal can be reproduced from a low-resolution version of the same signal source. Because of this property, an intelligible speech signal is reconstructed from multiple sensor signals, each of which is completely unintelligible because of its sufficiently low sampling rate. A method based on Bayesian linear regression is proposed in comparison with one based on maximum likelihood. Computer simulations using a simple sinusoidal input demonstrate that the methods restore the original signal from those which are actually measured. Moreover, results show that the method based on Bayesian linear regression is more robust than maximum likelihood under various microphone configurations in noisy environments and that this advantage is remarkable when the number of microphones enrolled in the process is as small as the minimum required. Finally, listening tests using speech signals confirmed that mean opinion score (MOS) of the reconstructed signal reach 3, while those of the original signal captured at each single microphone are almost 1.

To support the rapid increase of mobile traffic, the LTE-based air interface is expected to be employed in the unlicensed spectrum known as “Licensed-Assisted Access (LAA).” The LAA terminal, which employs an LTE-based air interface, suffers from interference from WiFi access points as well as the LAA base station. The interference rejection combining (IRC) receiver, which employs a linear minimum mean square error (MMSE) filter, can suppress this interference from WiFi access points in addition to that of the LAA base station. The IRC receiver is effective, since it requires no knowledge of the interference, which is generally difficult to obtain for different systems. In this paper, we use a link-level simulation to evaluate the performance of the IRC receiver in suppressing the interference from WiFi access points, and show that the IRC receiver can effectively cancel the interference from WiFi systems as well as LTE systems, although we observed a slight performance degradation due to the covariance matrix estimation error caused by the WiFi interference fluctuation in the frequency-domain.

To aim to achieve a high-performance computation for microwave simulations with low cost, small size machine and low energy consumption, a method of the FDTD dedicated computer has been investigated. It was shown by VHDL logical circuit simulations that the FDTD dedicated computer with a dataflow architecture has much higher performance than that of high-end PC and GPU. Then the remaining task of this work is large scale computations by the dedicated computer, since microwave simulations for only 18×18×Z grid space (Z is the number of girds for z direction) can be executed in a single FPGA at most. To treat much larger numerical model size for practical applications, this paper considers an implementation of a domain decomposition method operation of the FDTD dedicated computer in a single FPGA.

Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs:comment and rdfs:label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results.

SIMON is a lightweight block cipher designed by NSA in 2013. NSA presented the specification and the implementation efficiency, but they did not provide detailed security analysis nor the design rationale. The original SIMON has rotation constants of (1,8,2), and Kölbl et al. regarded the constants as a parameter (a,b,c), and analyzed the security of SIMON block cipher variants against differential and linear attacks for all the choices of (a,b,c). This paper complements the result of Kölbl et al. by considering integral and impossible differential attacks. First, we search the number of rounds of integral distinguishers by using a supercomputer. Our search algorithm follows the previous approach by Wang et al., however, we introduce a new choice of the set of plaintexts satisfying the integral property. We show that the new choice indeed extends the number of rounds for several parameters. We also search the number of rounds of impossible differential characteristics based on the miss-in-the-middle approach. Finally, we make a comparison of all parameters from our results and the observations by Kölbl et al. Interesting observations are obtained, for instance we find that the optimal parameters with respect to the resistance against differential attacks are not stronger than the original parameter with respect to integral and impossible differential attacks. Furthermore, we consider the security against differential attacks by considering differentials. From the result, we obtain a parameter that is potential to be better than the original parameter with respect to security against these four attacks.

This paper discusses a mode for pseudorandom functions (PRFs) based on the hashing mode of Lesamnta-LW and the domain extension called Merkle-Damgård with permutation (MDP). The hashing mode of Lesamnta-LW is a plain Merkle-Damgård iteration of a block cipher with its key size half of its block size. First, a PRF mode is presented which produces multiple independent PRFs with multiple permutations and initialization vectors if the underlying block cipher is a PRP. Then, two applications of the PRF mode are presented. One is a PRF with minimum padding. Here, padding is said to be minimum if the produced message blocks do not include message blocks only with the padded sequence for any non-empty input message. The other is a vector-input PRF using the PRFs with minimum padding.

Physically Unclonable Function (PUF) is a cryptographic primitive that is based on physical property of each entity or Integrated Circuit (IC) chip. It is expected that PUF be used in security applications such as ID generation and authentication. Some responses from PUF are unreliable, and they are usually discarded. In this paper, we propose a new PUF-based authentication system that exploits information of unreliable responses. In the proposed method, each response is categorized into multiple classes by its unreliability evaluated by feeding the same challenges several times. This authentication system is named Q-class authentication, where Q is the number of classes. We perform experiments assuming a challenge-response authentication system with a certain threshold of errors. Considering 4-class separation for 4-1 Double Arbiter PUF, it is figured out that the advantage of a legitimate prover against a clone is improved form 24% to 36% in terms of success rate. In other words, it is possible to improve the tolerance of machine-learning attack by using unreliable information that was previously regarded disadvantageous to authentication systems.

Image sensor communication (ISC), derived from visible light communication (VLC) is an attractive solution for outdoor mobile environments, particularly for intelligent transport systems (ITS). In ITS-ISC, tracking a transmitter in the image plane is critical issue since vehicle vibrations make it difficult to selsct the correct pixels for data reception. Our goal in this study is to develop a precise tracking method. To accomplish this, vehicle vibration modeling and its parameters estimation, i.e., represetative frequencies and their amplitudes for inherent vehicle vibration, and the variance of the Gaussian random process represnting road surface irregularity, are required. In this paper, we measured actual vehicle vibration in a driving situation and determined parameters based on the frequency characteristics. Then, we demonstrate that vehicle vibration that induces transmitter displacement in an image plane can be modeled by only Gaussian random processes that represent road surface irregularity when a high frame rate (e.g., 1000fps) image sensor is used as an ISC receiver. The simplified vehicle vibration model and its parameters are evaluated by numerical analysis and experimental measurement and obtained result shows that the proposed model can reproduce the characteristics of the transmitter displacement sufficiently.

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.

This paper describes the design and measurements of two-gain-mode MMIC power amplifier modules (PAMs) for Band 1 and Band 5 WCDMA data communications. The PAMs are based on the two-stage single-chain amplifier topology with an L-shaped FET step attenuator (ATT) placed at the interstage, featuring not only high-efficiency operation but also both a small phase discontinuity and a small input return loss variation between the two gain modes: a high-gain mode (0-dB thru state for the ATT) and a low-gain mode (14-dB attenuation state for the ATT). The PAMs are assembled on a 3 mm × 3 mm FR-4 laminate together with several surface mount devices, and a high-directivity, 20-dB bilayer-type directional coupler is integrated on the laminate for accurate forward-power monitoring even under a 2.5:1-VSWR load mismatching condition. To validate the design and analysis for the PAMs using the L-shaped ATT, two PAM products — a Band 1 PAM and a Band 5 PAM — were fabricated using our in-house GaAs-BiFET process. The main RF measurements under the condition of a WCDMA (R99) modulated signal and a 3.4-V supply voltage are as follows. The Band 1 PAM can deliver a power-added efficiency (PAE) as high as 46% at an output power (Pout) of 28.25 dBm while maintaining a ±5-MHz-offset adjacent channel power ratio (ACLR1) of approximately -40 dBc or less and a small phase discontinuity of less than 5°. The Band 5 PAM can also deliver a high PAE of 46% at the same Pout and ACLR1 levels with small phase discontinuity of less than 4°. This small discontinuity is due to the phase-shift compensation capacitance embedded in the ATT. The measured input return loss is well maintained at better than 10 dB at the two modes. In addition, careful coupler design achieves a small detection error of less than 0.5 dB even under a 2.5:1-VSWR load mismatching condition.

This paper addresses recommendation diversification. Existing diversification methods have difficulty in dealing with the tradeoff between accuracy and diversity. We point out the root of the problem in diversification methods and propose a novel method that can avoid the problem. Our method aims to find an optimal solution of the objective function that is carefully designed to consider user preference and the diversity among recommended items simultaneously. In addition, we propose an item clustering and a greedy approximation to achieve efficiency in recommendation.