This paper proposes a fast safe-region generation method for several kinds of vicinity queries including set k nearest neighbor (NN) queries, ordered kNN queries, reverse kNN queries, and distance range queries. When a user is driving a car on a road network, he/she wants to know about objects located in the vicinity of the car. However, the result changes according to the movement of the car, and therefore, the user needs to request up-to-date result to the server. On the other hand, frequent requests for up-to-date results cause heavy loadings on the server. To cope with this problem efficiently, the idea of the safe-region has been proposed, however, it takes long processing time in existing works. This paper proposes a fast generation method of the safe-region applicable to several types of vicinity queries. Through experimental evaluations, we demonstrate that the proposed method outperforms the existing algorithms in the processing time by one or two orders of magnitude.

In this letter, we study a scenario based on decoupled RF energy harvesting networks (DRF-EHNs) that separate energy sources from information sources to overcome the doubly near-far problem and improve harvesting efficiency. We propose an algorithm to maximize energy efficiency (EE) while satisfying constraints on the maximum transmit power of the hybrid access point (H-AP) and power beacon (PB), while further satisfying constraints on the minimum quality of service and minimum amount of harvested power in multi-user Rayleigh fading channel. Using nonlinear fractional programming and Lagrangian dual decomposition, we optimize EE with four optimization arguments: the transmit power from the H-AP and PB, time-splitting ratio, and power-splitting ratio. Numerical results show that the proposed algorithm is more energy-efficient compared to baseline schemes.

Many text entry methods are available in the use of touch interface devices when using a screen reader, and blind smartphone users and their supporters are eager to know which one is the easiest to learn and the fastest. Thus, we compared the text entry speeds and error counts for four combinations of software keyboards and character-selecting gestures over a period of five days. The split-tap gesture on the Japanese numeric keypad was found to be the fastest across the five days even though this text entry method produced the most errors. The two entry methods on the QWERTY keyboard were slower than the two entry methods on the numeric keypad. This difference in text entry speed was explained by the differences in key pointing and tapping times and their repitition numbers among different methods.

In this paper, we consider a similarity control problem for nondeterministic discrete event systems, which requires us to synthesize a nonblocking supervisor such that the supervised plant is simulated by a given specification. We assume that a supervisor can observe not only the event occurrence but also the current state of the plant. We present a necessary and sufficient condition for the existence of a nonblocking supervisor that solves the similarity control problem and show how to verify it in polynomial time. Moreover, when the existence condition of a nonblocking supervisor is satisfied, we synthesize such a supervisor as a solution to the similarity control problem.

In this work, the metal-oxide-metal (MOM) capacitor in the scaled CMOS process has been modeled at high frequencies using an EM simulator, and its layout has been optimized. The modeled parasitic resistance consists of four components, and the modeled parasitic inductance consists of the comb inductance and many mutual inductances. Each component of the parasitic resistance and inductance show different degrees of dependence on the finger length and on the number of fingers. The substrate network parameters also have optimum points. As such, the geometric dependence of the characteristics of the MOM capacitor is investigated and the optimum layout in the constant-capacitance case is proposed by calculating the results of the model. The proposed MOM capacitor structures for 50fF at f =60GHz are L =5μm with M =3, and, L =2μm with M =5 and that for 100fF at f =30GHz are L =9μm with M =3, and L =4μm with M =5. The target process is 65-nm CMOS.

In this study, we first developed a simultaneous measurement system for accommodation and convergence eye movement and evaluated its precision. Then, using a stuffed animal as the target, whose depth should be relatively easy to perceive, we measured convergence eye movement and accommodation at the same time while a tablet displaying a 3D movie was moved in the depth direction. By adding the real 3D display depth movement to the movement of the 3D image, subjects showed convergence eye movement that corresponds appropriately to the dual change of parallax in the 3D movie and real display, even when a subject's convergence changed very little. Accommodation also changed appropriately according to the change in depth.

This research addresses improvements in the efficiency of spectrum utilization by defending against jamming attacks and corrupting the communications of the adversary network by executing its own jamming strategy. The proposed scheme, based on game theory, selects the best operational strategy (i.e., communications and jamming strategies) to maximize the successful communications and jamming rates of the network. Moreover, an estimation algorithm is investigated to predict the behavior of the adversary network in order to improve the efficiency of the proposed game theory-based scheme.

A compact uniplanar antenna design for tablet/laptop applications is proposed. The main design principle of this antenna is the use of the coupling-feed mechanism. The proposed antenna is composed of an inverted L-shaped parasitic element, T-shaped feeding strip, parasitic shorted strip, and a step tuning stub. With its small size of 55mm × 15mm × 0.8mm, the proposed antenna is able to excite dual wideband transmission over the full LTE/WWAN operation ranges of 698-960MHz and 1710-2690MHz. Furthermore, the proposed antenna also exhibits reduced ground effects, such that reducing the ground size of the proposed antenna will not affect its performance.

Precise determination of antenna phase centers is crucial to reduce the uncertainty in gain when employing the three-antenna method, particularly when the range distances are short-such as a 3-m radio anechoic chamber, where the distance between the phase centers and the open ends of an aperture antenna (the most commonly-used reference) is not negligible compared with the propagation distance. An automatic system to determine the phase centers of aperture antennas in a radio anechoic chamber is developed. In addition, the absolute gain of horn antennas is evaluated using the three-antenna method. The phase centers of X-band pyramidal horns were found to migrate up to 18mm from the open end. Uncertainties in the gain were evaluated in accordance with ISO/IEC Guide 93-3: 2008. The 95% confidence interval of the horn antenna gain was reduced from 0.57 to 0.25dB, when using the phase center location instead of the open end. The phase centers, gains, polarization, and radiation patterns of space-borne antennas are measured: low and medium-gain X-band antennas for an ultra small deep space probe employing the polarization pattern method with use of the horn antenna. The 95% confidence interval in the antenna gain decreased from 0.74 to 0.47dB.

In this paper, we propose an indoor localization method that uses only the Received Signal Strength Indicator (RSSI) of signals transmitted from wireless beacons. The beacons use three-element array antennas, and the position of the receiving terminal is estimated by using multiple DOD information. Each beacon transmits four beacon signals with different directivities by feeding signals to the three-element array antennas via 180-degree and 90-degree hybrids. The correlation matrix of the propagation channels is estimated from just the strength of the signals, and the DOD is estimated from the calculated correlation matrix. For determining the location of the receiving terminal, the existence probability function is introduced. Experiments show that the proposed method attains lower position estimation error than the conventional method.

Three-dimensional (3-D) scattering center models use a finite number of point scatterers to efficiently represent complex radar target signature. Using the CLEAN algorithm, 3-D scattering center model is extracted from the inverse synthetic aperture radar (ISAR) image, which is generated based on the shooting and bouncing ray (SBR) technique. The conventional CLEAN extracts the strongest peak iteratively based on the assumption that the scattering centers are isolated. In a realistic target, however, both interference from the closely spaced points and additive noise distort the extraction process. This paper proposes a matched filter-based CLEAN algorithm to improve accuracy efficiently. Using the matched filtering of which impulse response is the known point spread function (PSF), a point most correlated with the PSF is extracted. Thus, the proposed method optimally enhances the accuracy in the presence of massive distortions. Numerical simulations using canonical and realistic targets demonstrate that the extraction accuracy is improved without loss of time-efficiency compared with the existing CLEAN algorithms.

This paper proposes a novel method of reducing channel state information (CSI) feedback by using transmit antenna selection for downlink multiuser multiple input multiple output (DL-MU-MIMO) transmission in dense distributed antenna systems. It is widely known that DL-MU-MIMO transmission achieves higher total bit-rate by mitigating inter-user interference based on pre-coding techniques. The pre-coding techniques require CSI between access point (AP) and multiple users. However, overhead for CSI acquisition degrades the transmission efficiency of DL-MU-MIMO transmission. In the proposed CSI feedback reduction method, AP first selects the antenna set that maximizes the received power at each user, second it skips the sequence of CSI feedback for users whose signal to interference power ratio is larger than a threshold, and finally it performs DL-MU-MIMO transmission to multiple users by using the selected antenna set. To clarify the proposed method, we evaluate it by computer simulations in an indoor scenario. The results show that the proposed method can offer higher transmission efficiency than the conventional DL-MU-MIMO transmission with the usual CSI feedback method.

In-band full-duplex (FD) Multiple-Input and Multiple-Output (MIMO) communication performs uplink and downlink transmission at the same time using the same frequency. In this system, the spectral efficiency is theoretically double that of conventional duplex schemes, such as Time Division Duplex (TDD) and Frequency Division Duplex (FDD). However, this system suffers interference because the uplink and downlink streams coexist within the same channel. Especially at the terminal side, it is quite difficult for the terminal to eliminate the interference signals from other terminals since it has no knowledge about the contents of the interference signals. This paper presents an inter-terminal interference suppression method between the uplink and downlink signals assuming the multi-user environment. This method uses eigen-beamforming at the transmitting terminal to direct the null to the other terminal. Since this beamforming technique reduces the degrees of freedom available, the interference suppression performance and transmitting data-rate have a trade-off relation. This study investigates the system capacity characteristics in multi-user full-duplex MIMO communication using the propagation channel information measured in an actual outdoor experiment and shows that the proposed communication scheme offers higher system capacity than the conventional scheme.

The Transmission Control Protocol (TCP) with Network Coding (TCP/NC) was proposed to introduce packet loss recovery ability at the sink without TCP retransmission, which is realized by proactively sending redundant combination packets encoded at the source. Although TCP/NC is expected to mitigate the goodput degradation of TCP over lossy networks, the original TCP/NC does not work well in burst loss and time-varying channels. No apparent scheme was provided to decide and change the network coding-related parameters (NC parameters) to suit the diverse and changeable loss conditions. In this paper, a solution to support TCP/NC in adapting to mentioned conditions is proposed, called TCP/NC with Loss Rate and Loss Burstiness Estimation (TCP/NCwLRLBE). Both the packet loss rate and burstiness are estimated by observing transmitted packets to adapt to burst loss channels. Appropriate NC parameters are calculated from the estimated probability of successful recoverable transmission based on a mathematical model of packet losses. Moreover, a new mechanism for coding window handling is developed to update NC parameters in the coding system promptly. The proposed scheme is implemented and validated in Network Simulator 3 with two different types of burst loss model. The results suggest the potential of TCP/NCwLRLBE to mitigate the TCP goodput degradation in both the random loss and burst loss channels with the time-varying conditions.

Wireless Sensor Networks (WSNs) are randomly deployed in a hostile environment and left unattended. These networks are composed of small auto mouse sensor devices which can monitor target information and send it to the Base Station (BS) for action. The sensor nodes can easily be compromised by an adversary and the compromised nodes can be used to inject false vote or false report attacks. To counter these two kinds of attacks, the Probabilistic Voting-based Filtering Scheme (PVFS) was proposed by Li and Wu, which consists of three phases; 1) Key Initialization and assignment, 2) Report generation, and 3) En-route filtering. This scheme can be a successful countermeasure against these attacks, however, when one or more nodes are compromised, the re-distribution of keys is not handled. Therefore, after a sensor node or Cluster Head (CH) is compromised, the detection power and effectiveness of PVFS is reduced. This also results in adverse effects on the sensor network's lifetime. In this paper, we propose a Fuzzy Rule-based Key Redistribution Method (FRKM) to address the limitations of the PVFS. The experimental results confirm the effectiveness of the proposed method by improving the detection power by up to 13.75% when the key-redistribution period is not fixed. Moreover, the proposed method achieves an energy improvement of up to 9.2% over PVFS.

Hadoop is a popular open-source MapReduce implementation. In the cases of jobs, wherein huge scale of output files of all relevant Map tasks are transmitted into Reduce tasks, such as TeraSort, the Reduce tasks are the bottleneck tasks and are I/O bounded for processing many large output files. In most cases, including TeraSort, the intermediate data, which include the output files of the Map tasks, are large and accessed sequentially. For improving the performance of these jobs, it is important to increase the sequential access performance. In this paper, we propose methods for improving the performance of Reduce tasks of such jobs by considering the following two things. One is that these files are accessed sequentially on an HDD, and the other is that each zone in an HDD has different sequential I/O performance. The proposed methods control the location to store intermediate data by modifying block bitmap of filesystem, which manages utilization (free or used) of blocks in an HDD. In addition, we propose striping layout for applying these methods for virtualized environment using image files. We then present performance evaluation of the proposed method and demonstrate that our methods improve the Hadoop application performance.

Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Secant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational efficiency.

Two different types of representations, such as an image and its manually-assigned corresponding labels, generally have complex and strong relationships to each other. In this paper, we represent such deep relationships between two different types of visible variables using an energy-based probabilistic model, called a deep relational model (DRM) to improve the prediction accuracies. A DRM stacks several layers from one visible layer on to another visible layer, sandwiching several hidden layers between them. As with restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs), all connections (weights) between two adjacent layers are undirected. During maximum likelihood (ML) -based training, the network attempts to capture the latent complex relationships between two visible variables with its deep architecture. Unlike deep neural networks (DNNs), 1) the DRM is a totally generative model and 2) allows us to generate one visible variables given the other, and 2) the parameters can be optimized in a probabilistic manner. The DRM can be also fine-tuned using DNNs, like deep belief nets (DBNs) or DBMs pre-training. This paper presents experiments conduced to evaluate the performance of a DRM in image recognition and generation tasks using the MNIST data set. In the image recognition experiments, we observed that the DRM outperformed DNNs even without fine-tuning. In the image generation experiments, we obtained much more realistic images generated from the DRM more than those from the other generative models.

We propose new CAPTCHA image generation systems by using generative adversarial network (GAN) techniques to strengthen against CAPTCHA solvers. To verify whether a user is human, CAPTCHA images are widely used on the web industry today. We introduce two different systems for generating CAPTCHA images, namely, the distance GAN (D-GAN) and composite GAN (C-GAN). The D-GAN adds distance values to the original CAPTCHA images to generate new ones, and the C-GAN generates a CAPTCHA image by composing multiple source images. To evaluate the performance of the proposed schemes, we used the CAPTCHA breaker software as CAPTCHA solver. Then, we compared the resistance of the original source images and the generated CAPTCHA images against the CAPTCHA solver. The results show that the proposed schemes improve the resistance to the CAPTCHA solver by over 67.1% and 89.8% depending on the system.

Graph sampling is an effective method to sample a representative subgraph from a large-scale network. Recently, researches have proven that several classical sampling methods are able to produce graph samples but do not well match the distribution of the graph properties in the original graph. On the other hand, the validation of these sampling methods and the scale of a good graph sample have not been examined on weighted graphs. In this paper, we propose the weighted graph sampling problem. We consider the proper size of a good graph sample, propose novel methods to verify the effectiveness of sampling and test several algorithms on real datasets. Most notably, we get new practical results, shedding a new insight on weighted graph sampling. We find weighted random walk performs best compared with other algorithms and a graph sample of 20% is enough for weighted graph sampling.