In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.

This paper describes the effectiveness of the geometric multi-grid method in a current density analysis using a numerical human body model. The scalar potential finite difference (SPFD) method is used as a numerical method for analyzing the current density inside a human body due to contact with charged objects in a low-frequency band, and research related to methods to solve faster large-scale simultaneous equations based on the SPFD method has been conducted. In previous research, the block incomplete Cholesky conjugate gradients (ICCG) method is proposed as an effective method to solve the simultaneous equations faster. However, even though the block ICCG method is used, many iterations are still needed. Therefore, in this study, we focus on the geometric multi-grid method as a method to solve the problem. We develop the geometric-multi-grid method and evaluate performances by comparing it with the block ICCG method in terms of computation time and the number of iterations. The results show that the number of iterations needed for the geometric multi-grid method is much less than that for the block ICCG method. In addition, the computation time is much shorter, depending on the number of threads and the number of coarse grids. Also, by using multi-color ordering, the parallel performance of the geometric multi-grid method can be greatly improved.

Data aggregation trees in wireless sensor networks (WSNs) are being used for gathering data for various purposes. Especially for the trees within buildings or civil structures, the total amount of energy consumption in a tree must be reduced to save energy. Therefore, the minimum energy-cost aggregation tree (MECAT) and MECAT with relay nodes (MECAT_RN) problems are being discussed to reduce energy consumption in data aggregation trees in WSNs. This paper proposes the tree node switching algorithm (TNSA) that improves on the previous algorithms for the MECAT and MECAT_RN problems in terms of energy efficiency. TNSA repeatedly switches nodes in a tree to reduce the number of packets sent in the tree. Packets are reduced by improving the accommodation efficiency of each packet, in which multiple sensor reports are accommodated. As a result of applying TNSA to MECATs and MECAT-RNs, energy consumption can be reduced significantly with a small burden.

We propose a nonphotorealistic rendering method for generating checkered pattern images from photographic images. The proposed method is executed by iterative calculation using a Prewitt filter with an expanded window size and can automatically generate checkered patterns according to changes in edges and shade of photographic images. To verify the effectiveness of the proposed method, an experiment was conducted using various photographic images. An additional experiment was conducted to visually confirm the checkered pattern images generated by changing the iteration number, window size, and parameter to emphasize the checkered patterns.

Magneto-optical (MO) switches operate with a dynamically applied magnetic field. The MO devices presented in this paper consist of microring resonators (MRRs) fabricated on amorphous silicon-on-garnet platform. Two types of MO switches with MRRs were developed. In the first type, the switching state is controlled by an external magnetic field component included in the device. By combination of MO and thermo-optic effects, wavelength tunable operation is possible without any additional heater, and broadband switching is achievable. The other type of switch is a self-holding optical switch integrated with an FeCoB thin-film magnet. The switching state is driven by the remanence of the integrated thin-film magnet, and the state is maintained without any power supply.

Visual question answering (VQA) is a task of answering a visual question that is a pair of question and image. Some visual questions are ambiguous and some are clear, and it may be appropriate to change the ambiguity of questions from situation to situation. However, this issue has not been addressed by any prior work. We propose a novel task, rephrasing the questions by controlling the ambiguity of the questions. The ambiguity of a visual question is defined by the use of the entropy of the answer distribution predicted by a VQA model. The proposed model rephrases a source question given with an image so that the rephrased question has the ambiguity (or entropy) specified by users. We propose two learning strategies to train the proposed model with the VQA v2 dataset, which has no ambiguity information. We demonstrate the advantage of our approach that can control the ambiguity of the rephrased questions, and an interesting observation that it is harder to increase than to reduce ambiguity.

This paper presents a large-angle imaging algorithm based on a dynamic scattering model for inverse synthetic aperture radar (ISAR). In this way, more information can be presented in an ISAR image than an ordinary RD image. The proposed model describes the scattering characteristics of ISAR target varying with different observation angles. Based on this model, feature points in each sub-image of the ISAR targets are extracted and matched using the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithms. Using these feature points, high-precision rotation angles are obtained via joint estimation, which makes it possible to achieve a large angle imaging using the back-projection algorithm. Simulation results verifies the validity of the proposed method.

A ring-resonator type of electrode (RRTE) has been proposed to detect the circulating tumor cell (CTC) for evaluation of the current cancer progression and malignancy in clinical applications. Main emphasis is placed on the identification sensitivity for the lossy materials that can be found in biomedical fields. At first, the possibility of the CTC detection was numerically considered to calculate the resonant frequency of the RRTE catching the CTC, and it was evident that the RRTE with the cell has the resonant frequency inherent in the cell featured by its complex permittivity. To confirm the numerical consideration, the BaTiO3 particle, whose size was similar to that of the CTC, was inserted in the RRTE instead of the CTC as a preliminary experiment. Next, the resonant frequencies of the RRTE with internal organs of the beef cattle such as liver, lung, and kidney were measured for evaluation of the lossy materials such as the CTC, and degraded Q curves were observed because the Q-factors inherent in the internal organs were usually low due to the poor loss tangents. To overcome such difficulty, the RRTE, the oscillator circuit consisting of the FET being added, was proposed to improve the identification sensitivity. Comparing the identification sensitivity of the conventional RRTE, it has been improved because the oscillation frequency spectrum inherent in an internal organ could be easily observed thanks to the oscillation condition with negative resistance. Thus, the validity of the proposed technique has been confirmed.

To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today's software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. To that end, this study proposes a method for artificially generating a “mimic” software project data set, whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. The proposed method uses the Box-Muller transform for generating normally distributed random numbers; and exponential transformation and number reordering for data mimicry. To evaluate the efficacy of the proposed method, effort estimation is considered as potential application domain for employing mimic data. Estimation models are built from 8 reference data sets and their concerning mimic data. Our experiments confirmed that models built from mimic data sets show similar effort estimation performance as the models built from original data sets, which indicate the capability of the proposed method in generating representative samples.

The security and reliability of Arabic text exchanged via the Internet have become a challenging area for the research community. Arabic text is very sensitive to modify by malicious attacks and easy to make changes on diacritics i.e. Fat-ha, Kasra and Damma, which are represent the syntax of Arabic language and can make the meaning is differing. In this paper, a Hybrid of Natural Language Processing and Zero-Watermarking Approach (HNLPZWA) has been proposed for the content authentication and tampering detection of Arabic text. The HNLPZWA approach embeds and detects the watermark logically without altering the original text document to embed a watermark key. Fifth level order of word mechanism based on hidden Markov model is integrated with digital zero-watermarking techniques to improve the tampering detection accuracy issues of the previous literature proposed by the researchers. Fifth-level order of Markov model is used as a natural language processing technique in order to analyze the Arabic text. Moreover, it extracts the features of interrelationship between contexts of the text and utilizes the extracted features as watermark information and validates it later with attacked Arabic text to detect any tampering occurred on it. HNLPZWA has been implemented using PHP with VS code IDE. Tampering detection accuracy of HNLPZWA is proved with experiments using four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experimental results show that HNLPZWA is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection.

Almost all existing password-based authenticated key exchange (PAKE) schemes achieve concurrent security in the standard model by relying on the common reference string (CRS) model. A drawback of the CRS model is to require a centralized trusted authority in the setup phase; thus, passwords of parties may be revealed if the authority ill-uses trapdoor information of the CRS. There are a few secure PAKE schemes in the plain model, but, these are not achievable in a constant round (i.e., containing a linear number of rounds). In this paper, we discuss how to relax the setup assumption for (constant round) PAKE schemes. We focus on the multi-string (MS) model that allows a number of authorities (including malicious one) to provide some reference strings independently. The MS model is a more relaxed setup assumption than the CRS model because we do not trust any single authority (i.e., just assuming that a majority of authorities honestly generate their reference strings). Though the MS model is slightly restrictive than the plain model, it is very reasonable assumption because it is very easy to implement. We construct a (concurrently secure) three-move PAKE scheme in the MS model (justly without random oracles) based on the Groce-Katz PAKE scheme. The main ingredient of our scheme is the multi-string simulation-extractable non-interactive zero-knowledge proof that provides both the simulation-extractability and the extraction zero-knowledge property even if minority authorities are malicious. This work can be seen as a milestone toward constant round PAKE schemes in the plain model.

This paper investigates the closure properties of multi-inkdot nondeterministic Turing machines with sublogarithmic space. We show that the class of sets accepted by the Turing machines is not closed under concatenation with regular set, Kleene closure, length-preserving homomorphism, and intersection.

Plane wave scattering from a circular conducting cylinder and a circular conducting strip has been formulated by equivalent surface currents which are postulated from the scattering geometrical optics (GO) field. Thus derived radiation far fields are found to be the same as those formulated by a conventional physical optics (PO) approximation for both E and H polarizations.

In the age of big data, recommendation systems provide users with fast access to interesting information, resulting to a significant commercial value. However, the extreme sparseness of user assessment data is one of the key factors that lead to the poor performance of recommendation algorithms. To address this problem, we propose a spectral clustering recommendation scheme with low-rank matrix completion and spectral clustering. Our scheme exploits spectral clustering to achieve the division of a similar user group. Meanwhile, the low-rank matrix completion is used to effectively predict un-rated items in the sub-matrix of the spectral clustering. With the real dataset experiment, the results show that our proposed scheme can effectively improve the prediction accuracy of un-rated items.

Analyzing aging-induced delay degradations of ring oscillators (ROs) is an effective way to detect recycled field-programmable gate arrays (FPGAs). However, it requires a large number of RO measurements for all FPGAs before shipping, which increases the measurement costs. We propose a cost-efficient recycled FPGA detection method using a statistical performance characterization technique called virtual probe (VP) based on compressed sensing. The VP technique enables the accurate prediction of the spatial process variation of RO frequencies on a die by using a very small number of sample RO measurements. Using the predicted frequency variation as a supervisor, the machine-learning model classifies target FPGAs as either recycled or fresh. Through experiments conducted using 50 commercial FPGAs, we demonstrate that the proposed method achieves 90% cost reduction for RO measurements while preserving the detection accuracy. Furthermore, a one-class support vector machine algorithm was used to classify target FPGAs with around 94% detection accuracy.

This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

We propose two algorithms for the gathering of k mobile agents in asynchronous Byzantine environments. For both algorithms, we assume that graph topology is arbitrary, each node is equipped with an authenticated whiteboard, agents have unique IDs, and at most f weakly Byzantine agents exist. Here, a weakly Byzantine agent can make arbitrary behavior except falsifying its ID. Under these assumptions, the first algorithm achieves a gathering without termination detection in O(m+fn) moves per agent (m is the number of edges and n is the number of nodes). The second algorithm achieves a gathering with termination detection in O(m+fn) moves per agent by additionally assuming that agents on the same node are synchronized, $f

Since the owner's data might be leaked from the centralized server storage, the distributed storage schemes with the server storage have been investigated. To ensure the owner's data in those schemes, they use Reed Solomon code. However, those schemes occur the burden of data capacity since the parity data are increased by how much the disconnected data can be restored. Moreover, the calculation time for the restoration will be higher since many parity data are needed to restore the disconnected data. In order to reduce the burden of data capacity and the calculation time, we proposed the server-based distributed storage using Secret Sharing with AES-256 for lightweight safety restoration. Although we use Secret Sharing, the owner's data will be safely kept in the distributed storage since all of the divided data are divided into two pieces with the AES-256 and stored in the peer storage and the server storage. Even though the server storage keeps the divided data, the server and the peer storages might know the pair of divided data via Secret Sharing, the owner's data are secure in the proposed scheme from the inner attack of Secret Sharing. Furthermore, the owner's data can be restored by a few parity data. The evaluations show that our proposed scheme is improved for lightweight, stability, and safety.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

The spectral efficiency of photonic networks can be enhanced by the use of higher modulation orders and narrower channel bandwidth. Unfortunately, these solutions are precluded by the margins required to offset uncertainties in system performance. Furthermore, as recently highlighted, the disaggregation of optical transport systems increases the required margin. We propose here highly spectrally efficient networks, whose margins are minimized by transmission-quality-aware adaptive modulation-order/channel-bandwidth assignment enabled by optical performance monitoring (OPM). Their effectiveness is confirmed by experiments on 400-Gbps dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM) signals with the application of recently developed Q-factor-based OPM. Four-subcarrier 32-Gbaud DP-QPSK signals within 150/162.5/175GHz and two-subcarrier 32-Gbaud DP-16QAM signals within 75/87.5/100GHz are experimentally analyzed. Numerical network simulations in conjunction with the experimental results demonstrate that the proposed scheme can drastically improve network spectral efficiency.