Orthogonal frequency division multiplexing has emerged as a promising air interface scheme for wireless broadband communications. For OFDM systems, frame synchronization has received much attention in the literature, though simple correlators are still widely used in real systems. In this letter, we present the analytical expression of the optimal frame synchronizer for OFDM systems. Frame synchronization is posed as a maximum a posteriori probability estimation. We show that the resulting frame synchronizer consists of a correlation term and a correction term. The correction term accounts for the random data surrounding a synchronization word. Numerical results show the performance gain of the proposed frame synchronizer over a correlation scheme.

The Voronoi game is a two-person perfect information game modeling a competitive facility location. The original version of the game is played on a continuous domain. Only two special cases (1-dimensional case and 1-round case) have been extensively investigated. Recently, the discrete Voronoi game of which the game arena is given as a graph was introduced. In this note, we give a complete analysis of the discrete Voronoi game on a path. There are drawing strategies for both the first and the second players, except for some trivial cases.

This paper presents efficient frame-synchronous beam pruning for HMM-based automatic speech recognition. In the conventional beam pruning, a few hypotheses that have greater potential to reach various words on a lexical tree are likely to be pruned out by a number of hypotheses that have limited potential, since all hypotheses are treated equally without considering this potential. To make the beam pruning less restrictive for hypotheses with greater potential and vice versa, the proposed method adds to the likelihood of each hypothesis a tentative reward as a monotonically increasing function of the number of reachable words from the HMM state where the hypothesis stays in a lexical tree. The reward is designed not to collapse the ASR probabilistic framework. The proposed method reduced 84% of the processing time for a grammar-based 10k-word short sentence recognition task. For a language-model-based dictation task, it also resulted in an additional 23% reduction in processing time from the beam pruning with the language model look-ahead technique.

This paper presents a pedestrian detection framework using a top-view camera. The paper contains two novel contributions for the pedestrian detection task: 1. Using shape context method to estimate the pedestrian directions and normalizing the pedestrian regions. 2. Based on the locations of the extracted head candidates, system chooses the most adaptive classifier from several classifiers automatically. Our proposed methods may solve the difficulties on top-view pedestrian detection field. Experimental was performed on video sequences with different illumination and crowed conditions, the experimental results demonstrate the efficiency of our algorithm.

In this paper, we propose a robust state estimation method using a particle filter (PF) for a class of nonlinear systems which have stochastic parameter uncertainties. A robust PF was designed using prediction and correction structure. The proposed PF draws particles from a simple proposal density function and corrects the particles with particle-wise correction gains. We present a method to obtain an error variance of each particle and its upper bound, which is minimized to determine the correction gain. The proposed method is less restrictive on system nonlinearities and noise statistics; moreover, it can be applied regardless of system stability. The effectiveness of the proposed robust PF is illustrated via an example based on Chua's circuit.

We find necessary and sufficient conditions for the (shifted) oversampling expansions to hold in wavelet subspaces. In particular, we characterize scaling functions with the (shifted) oversampling property. We also obtain L2 and L∞ norm estimates for the truncation and aliasing errors of the oversampling expansion.

In this letter, an AGV based relay selection mechanism is developed to ensure relays reporting true information in wireless relay networks. The source selects relays based on the channel state information (CSI) of relay-destination links. Selfish relays may report fake CSI in order to obtain a better chance of being selected, whereas the source is not able to tell the reported in real or in false. In the proposed scheme, a relay node receives some payoffs from the destination with respect to the achievable data rate and also some compensations from the others in terms of the reported CSI of all relays. This mechanism not only enforces truth-telling upon relay nodes with maximum payoff but also ensures fairness among them. The equilibrium of payoff is attained when relay nodes report their true CSI. Simulation results demonstrate the theoretical solutions.

In this article, we present a new image-stabilization technology for still images based on blind deconvolution and introduce it to a consumer digital still camera. This technology consists of three features: (1)double-exposure-based PSF detection, (2)efficient image deblurring filter, and (3)edge-based ringing reduction. Without deteriorating the deblurring performance, the new technology allows us to reduce processing time and ringing artifacts, both of which are common problems in image deconvolution.

Recently, IEEE 802.15.4 has been standardized for WSNs (Wireless Sensor Networks). This paper proposes an enhanced CCA scheme which involves the data transmission device sending a notifyBusyChannel (nBC) signal in the backoff period when the Channel Using Quotient (CUQ) exceeds 0.5. The CUQ stands for the rate of channel utilization in the previous slot duration. In a single CCA operation, the device nodes are made aware of the busy status of the channel by the nBC signal. We implement the ECCA scheme in a hardware chip for a performance evaluation. The results show that the proposed scheme has short queuing times and less energy consumption than IEEE 802.15.4 CCA. And the scheme is compatible with conventional IEEE 802.15.4 devices.

In this paper, we propose a reduced complexity algorithm for blind frame synchronization based on code-constraints in a quasi-cyclic low density parity check (QC-LDPC) coded system. It can be used for both hard and soft synchronizers. For soft synchronizers, we present a modified algorithm that achieves better performance at high signal to noise ratio (SNR). Analysis indicates that the proposed algorithm has low complexity for hardware implementation.

The excessive memory access required to perform motion compensation when decoding compressed video is one of the main limitations in improving the performance of an H.264/AVC decoder. This paper proposes an H.264/AVC decoder that employs three techniques to reduce external memory access events: efficient distribution of reference frame data, on-chip cache memory, and frame memory recompression. The distribution of reference frame data is optimized to reduce the number of row activations during SDRAM access. The novel cache organization is proposed to simplify tag comparisons and ease the access to consecutive 4×4 blocks. A recompression algorithm is modified to improve compression efficiency by using unused storage space in neighboring blocks as well as the correlation with the neighboring pixels stored in the cache. Experimental results show that the three techniques together reduce external memory access time by an average of 90%, which is 16% better than the improvements achieved by previous work. Efficiency of the frame memory recompression algorithm is improved with a 32×32 cache, resulting in a PSNR improvement of 0.371 dB. The H.264/AVC decoder with the three techniques is fabricated and implemented as an ASIC using 0.18 µm technology.

In nearest neighbor distance ratio (NNDR) matching the fixed distance ratio threshold sometimes results in an insufficient number of inliers or a huge number of outliers, which is not good for robust tracking. In this letter, we propose adjusting the distance ratio threshold based on maximizing the number of inliers while maintaining the ratio of the number of outliers to that of inliers. By applying the proposed method to a model-based camera tracking system, its effectiveness is verified.

We describe a parameter estimation method for a target object in an area that sensors monitor. The parameters to be estimated are the perimeter length, size, and parameter determined by the interior angles of the target object. The estimation method does not use sensor location information, only the binary information on whether each sensor detects the target object. First, the sensing area of each sensor is assumed to be line-segment-shaped, which is a model of an infrared distance measurement sensor. Second, based on the analytical results of assuming line-segment-shaped sensing areas, we developed a unified equation that works with general sensing areas and general target-object shapes to estimate the parameters of the target objects. Numerical examples using computer simulation show that our method yields accurate results.

The characteristics of hole-assisted fiber (HAF) are investigated both numerically and experimentally in terms of its applicability as a bending-loss insensitive fiber (BIF). We show that HAF with the desired mode-field diameter (MFD), bending-loss and cutoff wavelength characteristics can be roughly designed by taking a few specific structural parameters into consideration. We also show that an optical cord composed of adequately designed HAF realizes satisfactory transmission performance with respect to its multi-path interference (MPI) characteristics. These results reveal that a hole-assisted type BIF will be beneficial for realizing easy and economical installation and maintenance in future access networks.

This paper addresses an application of the potential game theory to a power-aware mobile sensor coverage problem where each sensor tries to maximize a probability of target detection in a convex mission space. The probability of target detection depends on a sensing voltage of each mobile sensor as well as its current position. While a higher sensing voltage improves the target detection probability, this requires more power consumption. In this paper, we assume that mobile sensors have different sensing capabilities of detecting a target and they can adaptively change sensing areas by adjusting their sensing voltages. We consider an objective function to evaluate a trade-off between improving the target detection probability and reducing total power consumption of all sensors. We represent a sensing voltage and a position of each mobile sensor using a barycentric coordinate over an extended strategy space. Then, the sensor coverage problem can be formulated as a potential game where the power-aware objective function and the barycentric coordinates correspond to a potential function and players' mixed strategies, respectively. It is known that all local maximizers of a potential function in a potential game are equilibria of replicator dynamics. Based on this property of potential games, we propose decentralized control for the power-aware sensor coverage problem such that each mobile sensor finds a locally optimal position and sensing voltage by updating its barycentric coordinate using replicator dynamics.

It has been shown that power control is an efficient approach to achieve the quality of service (QoS) requirement in wireless networks. In this letter, we present a utility function that denotes the QoS and energy efficiency of each wireless terminal. We propose a framework of power control based on the game theory for the QoS of wireless mesh networks (WMNs). In this framework, all players do their best to maximize their own utility. Furthermore, we present a Nash Equilibrium (NE) point of the power control game. We prove that the equilibrium is inefficient, and we propose a distributed power control algorithm that improves the total utility with respect to the NE point. Finally, the proposed framework is evaluated by a numerical experiment, the results of which show the rationality of our system model and the efficiency of our power control algorithm.

We fabricated and evaluated a prototype imaging system using the Simultaneous Frequency-Encoding technique, which is an active imaging technique that is potentially capable of fast frame-frequency imaging using a frequency-scanning antenna with only a single transceiver. The prototype performed simultaneous acquisition of pixels in elevation using Simultaneous Frequency-Encoding and performed a mechanical scan in azimuth. We also studied a ranging technique and incorporated it into the prototype. The ranging technique for Simultaneous Frequency-Encoding must take into account the characteristics of the frequency-scanning antenna, which are fundamental to Simultaneous Frequency-Encoding. We verified that ordinary range processing can be performed before frequency analysis with Simultaneous Frequency-Encoding, giving both range and angular profiles. The prototype was evaluated based on the radiation patterns of a receiver antenna comprising the frequency-scanning antenna and a reflector, on which both the image quality and ranging performance depend. Finally we conducted actual imaging tests and confirmed the capability of through-obstacle imaging. The frame frequency was only 0.1 Hz, which was due to the use of a slow mechanical scan in azimuth. However, assuming electronic beam forming is used instead of the mechanical scan, the frame frequency can be improved to several Hertz.

Model-based reinforcement learning uses the gathered information, during each experience, more efficiently than model-free reinforcement learning. This is especially interesting in multiagent systems, since a large number of experiences are necessary to achieve a good performance. In this paper, model-based reinforcement learning is developed for a group of self-interested agents with sequential action selection based on traditional prioritized sweeping. Every single situation of decision making in this learning process, called extensive Markov game, is modeled as n-person general-sum extensive form game with perfect information. A modified version of backward induction is proposed for action selection, which adjusts the tradeoff between selecting subgame perfect equilibrium points, as the optimal joint actions, and learning new joint actions. The algorithm is proved to be convergent and discussed based on the new results on the convergence of the traditional prioritized sweeping.

As a basic tool for deriving sparse representation of a color image from its atomic-decomposition with a redundant dictionary, the authors have recently proposed a new kind of shrinkage technique, viz. color shrinkage, which utilizes inter-channel color dependence directly in the three primary color space. Among various schemes of color shrinkage, this paper particularly presents the soft color-shrinkage and the hard color-shrinkage, natural extensions of the classic soft-shrinkage and the classic hard-shrinkage respectively, and shows their advantages over the existing shrinkage approaches where the classic shrinkage techniques are applied after a color transformation such as the opponent color transformation. Moreover, this paper presents the applications of our color-shrinkage schemes to color-image processing in the redundant tight-frame transform domain, and shows their superiority over the existing shrinkage approaches.

The traditional spectrum auctions require a central auctioneer. Then, the secondary users (SUs) can bid for spectrum in multiple auction or sealed auction way. In this paper, we address the problem of distributed spectrum sharing in the cognitive networks where multiple owners sell their spare bands to multiple SUs. Each SU equips multi-interface/multi-radio, so that SU can buy spare bands from multiple owners. On the other hand, each owner can sell its spare bands to serval SUs. There are two questions to be addressed for such an environment: the first one is how to select bands/the owners for each SU; the second one is how to decide the competitive prices for the multiple owners and multiple SUs. To this end, we propose a two-side multi-band market game theoretic framework to jointly consider the benefits of all SUs and owners. The equilibrium concept in such games is named core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all SUs and owners. We show that the core in our model is always non-empty. When the measurement of price takes discrete value, the core of the game is defined as discrete core. The Dynamic Multi-band Sharing algorithm (DMS) is proposed to converge to the discrete core of the game. With small enough measurement unit of price, the algorithm can achieve the optimal performance compared with centralized one in terms of the system utility.