This letter studies the price-based power control algorithm for the spectrum sharing cognitive radio networks. The primary user (PU) profits from the secondary users (SUs) by pricing the interference power made by them. The SUs cooperate with each other to maximize their sum revenue with the signal-to-interference plus noise ratio (SINR) balancing condition. The interaction between the PU and the SUs is modeled as a Stackelberg game. Closed-form expressions of the optimal price for the PU and power allocation for the SUs are given. Simulation results show the proposed algorithm improves the revenue of both the PU and fairness of the SUs compared with the uniform pricing algorithm.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.

With rapid increase of the number of applications as well as the sizes of data, multi-query processing on the MapReduce framework has gained much attention. Meanwhile, there have been much interest in skyline query processing due to its power of multi-criteria decision making and analysis. Recently, there have been attempts to optimize multi-query processing in MapReduce. However, they are not appropriate to process multiple skyline queries efficiently and they also require modifications of the Hadoop internals. In this paper, we propose an efficient method for processing multi-skyline queries with MapReduce without any modification of the Hadoop internals. Through various experiments, we show that our approach outperforms previous studies by orders of magnitude.

Decentralized channel assignment schemes are proposed to obtain low system-wide spatial overlap regions in wireless local area networks (WLANs). The important point of channel assignment in WLANs is selecting channels with fewer contending stations rather than mitigating interference power due to its medium access control mechanism. This paper designs two potential game-based channel selection schemes, basically each access point (AP) selects a channel with smaller spatial overlaps with other APs. Owing to the property of potential games, each decentralized channel assignment is guaranteed to converge to a Nash equilibrium. In order that each AP selects a channel with smaller overlaps, two metrics are proposed: general overlap-based scheme yields the largest overlap reduction if a sufficient number of stations (STAs) to detect overlaps are available; whereas decomposed overlap-based scheme need not require such STAs, while the performance would be degraded due to the shadowing effect. In addition, the system-wide overlap area is analytically shown to be upper bounded by the negative potential functions, which derives the condition that local overlap reduction by each AP leads to system-wide overlap reduction. The simulation results confirm that the proposed schemes perform better reductions in the system-wide overlap area compared to the conventional interference power-based scheme under the spatially correlated shadowing effect. The experimental results demonstrate that the channel assignment dynamics converge to stable equilibria even in a real environment, particularly when uncontrollable APs exist.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

Field Programmable Gate Array (FPGA) based robust model fitting enjoys immense popularity in image processing because of its high efficiency. This paper focuses on the tradeoff analysis of real-time FPGA implementation of robust circle and ellipse estimations based on the random sample consensus (RANSAC) algorithm, which estimates parameters of a statistical model from a data set of feature points which contains outliers. In particular, this paper mainly highlights implementation alternatives for solvers of simultaneous equations and compares Gauss-Jordan elimination and Cramer's rule by changing matrix size and arithmetic processes. Experimental evaluation shows a Cramer's rule approach coupled with long integer arithmetic can reduce most hardware resources without unacceptable degradation of estimation accuracy compared to floating point versions.

Image sensor communication (ISC), a type of visible light communication, is an emerging wireless communication technology that uses LEDs to transmit a signal and uses an image sensor in a camera to receive the signal. This paper discusses the present status of and future trends in ISC by describing the essential characteristics and features of ISC. Moreover, we overview the products and expected future applications of ISC.

In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to previous methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

This paper studies the supervisory control of partially observed quantitative discrete event systems (DESs) under the fixed-initial-credit energy objective. A quantitative DES is modeled by a weighted automaton whose event set is partitioned into a controllable event set and an uncontrollable event set. Partial observation is modeled by a mapping from each event and state of the DES to the corresponding masked event and masked state that are observed by a supervisor. The supervisor controls the DES by disabling or enabling any controllable event for the current state of the DES, based on the observed sequences of masked states and masked events. We model the control process as a two-player game played between the supervisor and the DES. The DES aims to execute the events so that its energy level drops below zero, while the supervisor aims to maintain the energy level above zero. We show that the proposed problem is reducible to finding a winning strategy in a turn-based reachability game.

In this paper, an accurate experimental noise model to improve the EEHEMT nonlinear model using the Verilog-A language in Agilent ADS is presented for the first time. The present EEHEMT model adopts channel noise to model the noise behavior of pseudomorphic high electron mobility transistor (pHEMT). To enhance the accuracy of the EEHEMT noise model, we add two extra noise sources: gate shot noise and induced gate noise current. Here we demonstrate the power spectral density of the channel noise Sid and gate noise Sig versus gate-source voltage for 0.25 µm pHEMT devices. Additionally, the related noise source parameters, i.e., P, R, and C are presented. Finally, we compare four noise parameters between the simulation and model, and the agreement between the measurement and simulation results shows that this proposed approach is dependable and accurate.

Wi-Fi Direct is a promising and available technology for device-to-device (D2D) proximity communications. To improve the performances of Wi-Fi Direct communication, optimized radio resource allocations are important. This paper proposes network assisted Wi-Fi Direct (NAWD), which operates based on the media independent services framework of IEEE 802.21 standard, for optimizing radio resource allocations. The NAWD is enhanced Wi-Fi Direct with the assistance of infrastructure networks (e.g., cellular network) and allocates radio resources (e.g., frequency channels and transmit power) to reduce radio interferences among Wi-Fi Direct devices (e.g., smart phones and set-top boxes). The NAWD includes mechanisms for gathering configuration information (e.g., location information and network connection information) of Wi-Fi Direct devices and allocating optimized radio resources (e.g., frequency channels and transmit power) to reduce radio interferences among Wi-Fi Direct devices. Simulation results show that the proposed NAWD increases significantly SINR, power efficiency, and areal capacity compared to legacy Wi-Fi Direct, where areal capacity is total traffic throughput per unit area.

Image interpolation is one of the image upsampling technologies from a single input image. This technology obtains high resolution images by fitting functions or models. Although image interpolation methods are faster than other upsampling technologies, they tend to cause jaggies and blurs in edge and texture regions. Multi-surface Fitting is one of the image upsampling techniques from multiple input images. This algorithm utilizes multiple local functions and the weighted means of the estimations in each local function. Multi-surface Fitting obtains high quality upsampled images. However, its quality depends on the number of input images. Therefore, this method is used in only limited situations. In this paper, we propose an image interpolation method with both high quality and a low computational cost which can be used in many situations. We adapt the idea of Multi-surface Fitting for the image upsampling problems from a single input image. We also utilize local functions to reduce blurs. To improve the reliability of each local function, we introduce new weights in the estimation of the local functions. Besides, we improve the weights for weighted means to estimate a target pixel. Moreover, we utilize convolutions with small filters instead of the calculation of each local function in order to reduce the computational cost. Experimental results show our method obtains high quality output images without jaggies and blurs in short computational time.

In this paper, we introduce the new concept of temporal diversity utilization based on asymmetric transmission to minimize network interference in wireless ad-hoc networks with a two-hop half-duplex relaying (HDR) protocol. Asymmetric transmission is an interference-aware backoff technique, in which each communication session (source-relay-destination link) adaptively chooses a certain subset of spectrally-orthogonal data streaming which should be delayed by the duration of one time-slot (i.e., half of one subframe). We design the problem in the HDR scenario by applying the concept of asymmetric transmission, and evaluate the game-theoretical algorithm, called asymmetric transmission game (ATG), to derive the suboptimal solution. We show that ATG is an exact potential game, and derive its convergence and optimality properties. Furthermore, we develop an approximated version of ATG (termed A-ATG) in order to reduce signaling and computational complexity. Numerical results verify that two algorithms proposed show significant synergistic effects when collaborating with the conventional methods in terms of interference coordination. Ultimately, the energy consumption to satisfy the rate requirement is reduced by up to 17.4% compared to the conventional schemes alone.

This paper investigates open-loop Stackelberg games for a class of stochastic systems with multiple players. First, the necessary conditions for the existence of an open-loop Stackelberg strategy set are established using the stochastic maximum principle. Such conditions can be represented as solvability conditions for cross-coupled forward-backward stochastic differential equations (CFBSDEs). Second, in order to obtain the open-loop strategy set, a computational algorithm based on a four-step scheme is developed. A numerical example is then demonstrated to show the validity of the proposed method.

In this letter, we focus on the subcarrier allocation problem for device-to-device (D2D) communication in cellular networks to improve the cellular energy efficiency (EE). Our goal is to maximize the weighted cellular EE and its solution is obtained by using a game-theoretic learning approach. Specifically, we propose a lower bound instead of the original optimization objective on the basis of the proven property that the gap goes to zero as the number of transmitting antennas increases. Moreover, we prove that an exact potential game applies to the subcarrier allocation problem and it exists the best Nash equilibrium (NE) which is the optimal solution to optimize the lower bound. To find the best NE point, a distributed learning algorithm is proposed and then is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

Video quality generally suffers from packet losses caused by an unreliable channel when video is transmitted over an error-prone wireless channel. This quality degradation is the main reason that a video compression encoder uses error-resilient coding to deal with the high packet-loss probability. The use of adequate error resilience can mitigate the effects of channel errors, but the coding efficiency for bit reduction will be decreased. On the other hand, H.264/AVC uses multiple reference frame (MRF) motion compensation for a higher coding efficiency. However, an increase in the number of reference frames in the H.264/AVC encoder has been recently observed, making the received video quality worse in the presence of transmission errors if the cyclic intra-refresh is used as the error-resilience method. This is because the reference-block selection in the MRF chooses blocks on the basis of the rate distortion optimization, irrespective of the intra-refresh coding. In this paper, a new error-resilient reference selection method is proposed to provide error resilience for MRF based motion compensation. The proposed error-resilient reference selection method achieves an average PSNR enhancement up to 0.5 to 2dB in 10% packet-loss-ratio environments. Therefore, the proposed method can be valuable in most MRF-based interactive video encoding system, which can be used for video broadcasting and mobile video conferencing over an erroneous network.

To analyze large-scale data efficiently, studies on Hadoop, one of the most popular MapReduce frameworks, have been actively done. Meanwhile, most of the large-scale data analysis applications, e.g., data clustering, are required to do the same map and reduce functions repeatedly. However, Hadoop cannot provide an optimal performance for iterative MapReduce jobs because it derives a result by doing one phase of map and reduce functions. To solve the problems, in this paper, we propose a new efficient resource management framework for iterative MapReduce processing in large-scale data analysis. For this, we first design an iterative job state-machine for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose an iterative resource management technique for efficiently managing the resources of a Hadoop cluster. Fourthly, we devise a stop condition check mechanism for preventing unnecessary computation. Finally, we show the performance superiority of the proposed framework by comparing it with the existing frameworks.

When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.