In multi-static sonar systems, the least square (LS) and maximum likelihood (ML) are the typical estimation criteria for target location estimation. The LS localizaiton has the advantage of low computational complexity. On the other hand, the performance of LS can be degraded severely when the target lies on or around the straight line between the source and receiver. We examine mathematically the reason for the performance degradation of LS. Then, we propose a location adaptive — least square (LA-LS) localization that removes the weakness of the LS localizaiton. LA-LS decides the receivers that produce abnormally large measurement errors with a proposed probabilistic measure. LA-LS achieves improved performance of the LS localization by ignoring the information from the selected receivers.

The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.

Pieces of personal information, such as personal names and relationships, are crucial in text mining applications. Obituaries are good sources for this kind of information. This study proposes an effective method for extracting various facts about people from obituary Web pages. Experiments show that the proposed method achieves high performance in terms of recall and precision.

A shift of finite type (SFT) is a set of all bi-infinite sequences over some alphabet which is characterized by a finite set of forbidden words. It is a typical example of sofic shifts and has been used in media storage area, such as CD's or DVD's. The study of sofic shifts is based on graph theory, and the irreducibility of shifts is an important property to be considered for the study. In this paper, we will provide some sufficient conditions for an SFT to be irreducible from the perspective of the antidictionary of a word and the number of forbidden words. We also present a necessary and sufficient condition for an SFT to be irreducible when the number of forbidden words is one less than the alphabet size.

Character projection (CP) is a high-speed mask-less exposure technique for electron-beam direct writing (EBDW). In CP exposure of VIA layers, higher throughput is realized if more VIAs are exposed in each EB shot, but it will result in huge number of VIA characters to cover arbitrary VIA arrangements. We adopt one-dimensional VIA arrays as the basic CP character architecture to increase VIA numbers in an EB shot while saving the stencil area by superposed character arrangement. In addition, CP throughput is further improved by layout constraints on the VIA placement in the detail routing phase. Our experimental results proved the feasibility of our exposure strategy in the practical CP use in 14nm lithography.

A hash function is an important primitive for cryptographic protocols. Since algorithms of well-known hash functions are almost serial, it seems difficult to take full advantage of recent multi-core processors. This paper proposes a multilane hashing (MLH) mode that achieves both of high parallelism and high security. The MLH mode is designed in such a way that the processing speed is almost linear in the number of processors. Since the MLH mode exploits an existing hash function as a black box, it is applicable to any hash function. The bound on the indifferentiability of the MLH mode from a random oracle is beyond the birthday bound on the output length of an underlying primitive.

To analyze the structure of a set of perfect sequences over a composition algebra of the real number field, transforms of a set of sequences similar to the discrete Fourier transform (DFT) are introduced. The discrete cosine transform, discrete sine transform, and generalized discrete Fourier transform (GDFT) of the sequences are defined and the fundamental properties of these transforms are proved. We show that GDFT is bijective and that there exists a relationship between these transforms and a convolution of sequences. Applying these properties to the set of perfect sequences, a parameterization theorem of such sequences is obtained.

Recognizing human action in complex scenes is a challenging problem in computer vision. Some action-unrelated concepts, such as camera position features, could significantly affect the appearance of local spatio-temporal features, and therefore the performance of low-level features based methods degrades. In this letter, we define the action-unrelated concept: the position of camera as high-level features. We observe that they can serve as a prior to local spatio-temporal features for human action recognition. We encode this prior by modeling interactions between spatio-temporal features and camera position features. We infer camera position features from local spatio-temporal features via these interactions. The parameters of this model are estimated by a new max-margin algorithm. We evaluate the proposed method on KTH, IXMAS and Youtube actions datasets. Experimental results show the effectiveness of the proposed method.

In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.

This paper proposes a novel heterodyne multiband multiple-input multiple-output (MIMO) receiver with baseband automatic gain control (AGC) for cognitive radios. The proposed receiver uses heterodyne reception implemented with a wide-passband band-pass filter in the radio frequency (RF) stage to be able to receive signals in arbitrary frequency bands. Even when an RF Hilbert transformer is utilized in the receiver, image-band interference occurs due to the imperfection of the Hilbert transformer. In the receiver, analog baseband AGC is introduced to prevent the baseband signals exceeding the voltage reference of analog-to-digital converters (ADCs). This paper proposes a novel technique to estimate the imperfection of the Hilbert transformer in the heterodyne multiband MIMO receiver with baseband AGC. The proposed technique estimates not only the imperfection of the Hilbert transformer but also the AGC gain ratio, and analog devices imperfection in the feedback loop, which enables to offset the imperfection of the Hilbert transformer. The performance of the proposed receiver is verified by using computer simulations. As a result, the required resolution of the ADC is 9 bits in the proposed receiver. Moreover, the proposed receiver has less computational complexity than that with the baseband interference cancellation unless a frequency band is changed every 9 packets or less.

Weissman introduced a coding problem for channels with action-dependent states. In this coding problem, there are two encoders and a decoder. An encoder outputs an action that affects the state of the channel. Then, the other encoder outputs a codeword of the message into the channel by using the channel state. The decoder receives a noisy observation of the codeword, and reconstructs the message. In this paper, we show an exponential error bound for channels with action-dependent states based on the random coding argument.

Relay selection is a promising technique with which to achieve remarkable gains in multi-relay cooperative networks. Opportunistic relaying (OR) and selection cooperation (SC) are two major relay selection schemes for dual-hop decode-and-forward cooperative networks; they have been shown to be globally outage-optimal under an aggregate power constraint. However, due to channel fluctuations, the channel state information (CSI) used in the selection process may become outdated and differ from the CSI during the actual transmission of data. In this work, we study the effect of outdated CSI on OR and threshold-based SC (TSC) schemes under independent but not necessarily identically distributed Rayleigh fading channels. The source can possibly cooperate with the best relay for data transmission, with the destination performing maximal ratio combining of the signals from the source and the relay. In particular, we analyze the average symbol error probability (ASEP) of OR and TSC with outdated CSI by deriving approximate but tight closed-form expressions for the moment generating function of the end-to-end signal-to-noise ratio. We also investigate the asymptotic behavior of the ASEP. The results show that the diversity orders of OR and TSC reduce to one and two, respectively, due to the outdated CSI. However, TSC achieves full spatial diversity order when the relay-to-destination CSI is perfect. Finally, to verify the analytical results Monte Carlo simulations are performed, in which OR attains better ASEP than TSC in a perfect CSI scenario, while TSC is less susceptible to outdated CSI.

This paper introduces a simple algorithm for pedestrian detection on low resolution images. The main objective is to create a successful means for real-time pedestrian detection. While the framework of the system consists of edge orientations combined with the local binary patterns (LBP) feature extractor, a novel way of selecting the threshold is introduced. Using the mean-variance of the background examples this threshold improves significantly the detection rate as well as the processing time. Furthermore, it makes the system robust to uniformly cluttered backgrounds, noise and light variations. The test data is the INRIA pedestrian dataset and for the classification, a support vector machine with a radial basis function (RBF) kernel is used. The system performs at state-of-the-art detection rates while being intuitive as well as very fast which leaves sufficient processing time for further operations such as tracking and danger estimation.

This paper studies channel sounding for selfish dynamic spectrum control (S-DSC) in which each link dynamically maps its spectral components onto a necessary amount of discrete frequencies having the highest channel gain of the common system band. In S-DSC, it is compulsory to conduct channel sounding for the entire system band by using a reference signal whose spectral components are sparsely allocated by S-DSC. Using nonuniform sampling theory, this paper exploits the finite impulse response characteristic of frequency selective fading channels to carry out the channel sounding. However, when the number of spectral components is relatively small compared to the number of discrete frequencies of the system band, reliability of the channel sounding deteriorates severely due to the ill-conditioned problem and degradation in channel capacity of the next frame occurs as a result. Aiming at balancing frequency selection diversity effect and reliability of channel sounding, this paper proposes an S-DSC which allocates an appropriate number of spectral components onto discrete frequencies with low predicted channel gain besides mapping the rest onto those with high predicted channel gain. A numerical analysis confirms that the proposed S-DSC gives significant enhancement in channel capacity performance.

To mitigate the impact of the frequency selectivity of the wireless channel on the initial ranging (IR) process in 802.16 based WiMax systems, several well known pre-equalization techniques applied in the IR are first analyzed in detail, and the optimal pre-equalization scheme is further improved for the IR by overcoming its weaknesses. A numerical simulation shows that the proposed pre-equalization scheme significantly improves the performance of multiuser detection and parameter estimation in the IR process.

Active measurement is an end-to-end measurement technique that can estimate network performance. The active measurement techniques of PASTA-based probing and periodic-probing are widely used. However, for the active measurement of delay and loss, Baccelli et al. reported that there are many other probing policies that can achieve appropriate estimation if we can assume the non-intrusive context (the load of the probe packets is ignored in the non-intrusive context). While the best policy in terms of accuracy is periodic-probing with fixed interval, it suffers from the phase-lock phenomenon created by synchronization with network congestion. The important point in avoiding the phase-lock phenomenon is to shift the cycle of the probe packet injection by adding fluctuations. In this paper, we analyse the optimal magnitude of fluctuations corresponding to the given autocovariance function of the target process. Moreover, we introduce some evaluation examples to provide guidance on designing experiments to network researchers and practitioners. The examples yield insights on the relationships among measurement parameters, network parameters, and the optimal fluctuation magnitude.

As a first step towards the realization of high-efficiency on-chip antennas for 60GHz-band wireless personal area networks, this paper proposes the fabrication of a patch antenna placed on a 200µm thick dielectric resin and fed through a hole in a silicon chip. Despite the large tan δ of the adopted material (0.015 at 50GHz), the thick resin reduces the conductor loss at the radiating element and a radiation efficiency of 78%, which includes the connecting loss from the bottom is predicted by simulation. This calculated value is verified in the millimeter-wave band by experiments in a reverberation chamber. Six stirrers are installed, one on each wall in the chamber, to create a statistical Rayleigh environment. The manufactured prototype antenna with a test jig demonstrates the radiation efficiency of 75% in the reverberation chamber. This agrees well with the simulated value of 76%, while the statistical measurement uncertainty of our handmade reverberation chamber is calculated as ±0.14dB.

We present an algorithm for simultaneously recognizing and localizing planar textured objects in an image. The algorithm can scale efficiently with respect to a large number of objects added into the database. In contrast to the current state-of-the-art on large scale image search, our algorithm can accurately work with query images consisting of several specific objects and/or multiple instances of the same object. Our proposed algorithm consists of two major steps. The first step is to generate a set of hypotheses that provides information about the identities and the locations of objects in the image. To serve this purpose, we extend Bag-Of-Visual-Word (BOVW) image retrieval by incorporating a re-ranking scheme based on the Hough voting technique. Subsequently, in the second step, we propose a geometric verification algorithm based on A Contrario decision framework to draw out the final detection results from the generated hypotheses. We demonstrate the performance of the algorithm on the scenario of recognizing CD covers with a database consisting of more than ten thousand images of different CD covers. Our algorithm yield to the detection results of more than 90% precision and recall within a few seconds of processing time per image.

This letter improves two adaptive steganographic methods in Refs. [5], [6], which utilize the remainders of two consecutive pixels to record the information of secret data. Through analysis, we point out that they perform mistakenly under some conditions, and the recipient cannot extract the secret data exactly. We correct these by enlarging the adjusting range of the remainders of two consecutive pixels within the block in the embedding procedure. Furthermore, the readjusting phase in Ref. [6] is improved by allowing every two-pixel block to be fully modified, and then the sender can select the best choice that introduces the smallest embedding distortion. Experimental results show that the improved method not only extracts secret data exactly but also reduces the embedding distortion.