Weight perturbation learning was proposed as a learning rule in which perturbation is added to the variable parameters of learning machines. The generalization performance of weight perturbation learning was analyzed by statistical mechanical methods and was found to have the same asymptotic generalization property as perceptron learning. In this paper we consider the difference between perceptron learning and AdaTron learning, both of which are well-known learning rules. By applying this difference to weight perturbation learning, we propose adaptive weight perturbation learning. The generalization performance of the proposed rule is analyzed by statistical mechanical methods, and it is shown that the proposed learning rule has an outstanding asymptotic property equivalent to that of AdaTron learning.

One of the efficient methods to build the index of continuous window queries over moving objects is by means of region quadtree index. In this paper, we present an optimal algorithm to search for the optimal position translation of query windows, where the total number of decomposed quadtree blocks for those windows in quadtree representation is minimal. We exploit the branch-and-bound concept to prune the particular paths of recursions in the search space. Evaluation proves that our optimal algorithm reduces search time greatly and the quadtree index based on optimal position translation works efficiently for continuous window queries. To the best of our knowledge, the algorithms and experiments reported in this paper are novel.

In this paper, a novel visual signal reliability (VSR) measure is proposed to consider video degradation at the signal level in audio-visual speaker identification (AVSI). The VSR estimation is formulated using a~ Gaussian fuzzy membership function (GFMF) to measure lighting variations. The variance parameters of GFMF are optimized in order to maximize the performance of the overall AVSI. The experimental results show that the proposed method outperforms the score-based reliability measuring technique.

This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.

In multiple-input multiple-output (MIMO) systems, the multiuser MIMO (MU-MIMO) systems have the potential to provide higher channel capacity owing to multiuser and spatial diversity. Block diagonalization (BD) is one of the techniques to realize MU-MIMO systems, where multiuser interference can be completely cancelled and therefore several users can be supported simultaneously. When the number of multiantenna users is larger than the number of simultaneously receiving users, it is necessary to select the users that maximize the system capacity. However, computation complexity becomes prohibitive, especially when the number of multiantenna users is large. Thus simplified user scheduling algorithms are necessary for reducing the complexity of computation. This paper proposes a simplified capacity-based user scheduling algorithm, based on analysis of the capacity-based user selection criterion. We find a new criterion that is simplified by using the properties of Gram-Schmidt orthogonalization (GSO). In simulation results, the proposed algorithm provides higher sum rate capacity than the conventional simplified norm-based algorithm; and when signal-to-noise power ratio (SNR) is high, it provides performance similar to that of the conventional simplified capacity-based algorithm, which still requires high complexity. Fairness of the users is also taken into account. With the proportionally fair (PF) criterion, the proposed algorithm provides better performance (sum rate capacity or fairness of the users) than the conventional algorithms. Simulation results also shows that the proposed algorithm has lower complexity of computation than the conventional algorithms.

We present a scalable approach to automatically discovering particular objects (as opposed to object categories) from a set of images. The basic idea is to search for local image features that consistently appear in the same images under the assumption that such co-occurring features underlie the same object. We first represent each image in the set as a set of visual words (vector quantized local image features) and construct an inverted file to memorize the set of images in which each visual word appears. Then, our object discovery method proceeds by searching the inverted file and extracting visual word sets whose elements tend to appear in the same images; such visual word sets are called co-occurring word sets. Because of unstable and polysemous visual words, a co-occurring word set typically represents only a part of an object. We observe that co-occurring word sets associated with the same object often share many visual words with one another. Hence, to obtain the object models, we further cluster highly overlapping co-occurring word sets in an agglomerative manner. Remarkably, we accelerate both extraction and clustering of co-occurring word sets by Min-Hashing. We show that the models generated by our method can effectively discriminate particular objects. We demonstrate our method on the Oxford buildings dataset. In a quantitative evaluation using a set of ground truth landmarks, our method achieved higher scores than the state-of-the-art methods.

In this paper, we propose a method capable of shortening the distance from a noise detection microphone to a loudspeaker, which is one of important issues in the field of active noise control (ANC). In the ANC system, the secondary noise provided by the loudspeaker is required arriving at an error microphone simultaneously with the primary noise to be cancelled. However, the reverberation involved in the secondary path from the loudspeaker to the error microphone increases the secondary noise components arriving later than the primary noise. The late components are not only invalid for canceling the primary noise but also impede the cancellation. To reduce the late components, the distance between the noise detection microphone and the loud speaker is generally extended. The proposed method differently reduces the late components by forming the noise control filter, which produces the secondary noise, with the cascade connection of a non-recursive and a recursive filters. The distance can be thus shortened. On the other hand, the recursive filter is required to work stably. The proposed method guarantees the stable work by forming the recursive filter with the lattice filter whose coefficients are restricted to less than unity.

Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.

We consider how to accurately estimate the position of targets that exist in closed areas such as a room. In the past, arranging the sensors in a straight line would trigger large position estimation errors in the same direction of the straight line. However, this arrangement is useful because of easy setting, wirings, and space limitations. In this paper, we show a novel algorithm which can reduce the error using signals from reflection objects such as walls. The algorithm uses ellipse relations among sensors, targets and reflection points. Using ellipse relations, the algorithm estimates the positions of the reflection points which are assumed to be the locations of the virtual sensors. So in spite of the straight sensor arranging, the sensors are virtually distributed such as surrounding the targets. In this paper, we show the algorithm and error reduction performances as determined from computer simulations.

In this letter, two new network coding (NC) diversity enhancement schemes are introduced for wireless relay systems. Conventional diversity enhancement approaches for relay systems suffer from error propagation at each relay and exhibit second order diversity performance. In the proposed schemes, when a relay experiences a decoding failure, the relay makes a request to have the source transmit the NC frames to the destination in its time slot. Due to this operation, the proposed schemes prevent error propagation and achieve near third order diversity performance. The proposed schemes are compared to conventional schemes based on the derived mathematical error bounds and simulation performance, both of which demonstrate the superiority of the proposed schemes.

This study focused on three simplified models, namely (1) one set of single-phase DC-DC converter, (2) two sets of parallel connection single-phase DC-DC converter, and (3) two sets of series connection single-phase DC-DC converter. The purposes are: (1) to propose the simplification conditions and procedures for the three-phase AC-DC converter; (2) propose a set of new simplification steps for modeling, and present the examples of different three-phase AC-DC circuit topologies, detailed discussion on the simplification steps for modeling of a three-phase AC-DC converter is offered, to help people simplify and analyze the simplified model easily; (3) according to three types of simplified modeling in the three-phase AC-DC converter, this study established a useful reference for the design and analysis of the control systems of the three-phase AC-DC converter simply; (4) to acquire PWM control strategy beforehand based on PFC-Controlled property; (5) to reduce the switching loss for the PWM control strategy of the simplified model; (6) to maintain the original circuit topology and verify that the theory can extensively apply the knowledge of single-phase DC-DC converter to the simplified modeling of three-phase AC-DC converter.

This letter has retrained an MQDF classifier on the retraining set, which is constructed by samples locating near classification boundary. The method is evaluated on HCL2000 and HCD Chinese handwriting sets. The results show that the retrained MQDF outperforms MQDF and cascade MQDF on all test sets.

A MB-OFDM UWB transmitter with on-chip transformer and LO leakage calibration for WiMedia bandgroup 1 is presented. The measurements show a gain-flatness of 1 dB, an LOLRR of -53 dBc/-43 dBc (wi/o cali), an EVM of 2.2% with a power consumption of 22 mW and an area of 1.26 mm2.

We present a simple constellation precoding technique for a coherent MIMO transmission scheme. Significant coding/diversity gains can be achieved with or without full channel state information. It is shown that the proposed scheme outperforms the conventional space-time block codes in the presence of continuous fading.

Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, that is, readings from sensor nodes are aggregated at intermediate nodes to reduce the communication cost. However, the previous optimally secure in-network aggregation protocols against multiple corrupted nodes require two round-trip communications between each node and the base station, including the result-checking phase whose congestion is O(log n) where n is the total number of sensor nodes. In this paper, we propose an efficient and optimally secure sensor network aggregation protocol against multiple corrupted nodes by a random-walk adversary. Our protocol achieves one round-trip communication to satisfy optimal security without the result-checking phase, by conducting aggregation along with the verification, based on the idea of TESLA technique. Furthermore, we show that the congestion complexity, communication complexity and computational cost in our protocol are constant, i.e., O(1).

With the increasing demand of high video quality and large image size, adaptive interpolation filter (AIF) addresses these issues and conquers the time varying effects resulting in increased coding efficiency, comparing with recent H.264 standard. However, currently most AIF algorithms are based on either frame level or macroblock (MB) level, which are not flexible enough for different video contents in a real codec system, and most of them are facing a severe time consuming problem. This paper proposes a content based coarse to fine AIF algorithm, which can adapt to video contents by adding different filters and conditions from coarse to fine. The overall algorithm has been mainly made up by 3 schemes: frequency analysis based frame level skip interpolation, motion vector modeling based region level interpolation, and edge detection based macroblock level interpolation. According to the experiments, AIF are discovered to be more effective in the high frequency frames, therefore, the condition to skip low frequency frames for generating AIF coefficients has been set. Moreover, by utilizing the motion vector information of previous frames the region level based interpolation has been designed, and Laplacian of Gaussian based macroblock level interpolation has been proposed to drive the interpolation process from coarse to fine. Six 720p and six 1080p video sequences which cover most typical video types have been tested for evaluating the proposed algorithm. The experimental results show that the proposed algorithm reduce total encoding time about 41% for 720p and 25% for 1080p sequences averagely, comparing with Key Technology Areas (KTA) Enhanced AIF algorithm, while obtains a BDPSNR gain up to 0.004 and 3.122 BDBR reduction.

In this paper we give a simple algorithm to generate all st-orientations of a given biconnected plane graph G with a designated edge (s,t) on the outer face of G. Our algorithm generates all st-orientations of G in O(n) time for each without duplications, where n is the number of vertices.

In this paper, we show that the covariance-eigenvalue approach converges much faster than using cumulative distribution function (CDF) for determining diversity gain from channel measurements in reverberation chamber. The covariance-eigenvalue approach can be used for arbitrary multi-port antennas, but it is limited to Maximum Ratio Combining (MRC).

Microwave/millimeter-wave phase and amplitude characteristics of the optically controlled phased array antenna with a different SMF for each antenna feed were measured. Suitable phases for the beam steering can be realized by the adjustment of the LD wavelength independently with multiple SMFs. In addition to the phase, amplitude of each antenna feed can be controlled stably using LD current without phase variation. Furthermore, effectiveness of the calibration method of the phased array using multiple SMFs by LD wavelength adjustment is experimentally verified. Excellent microwave/millimeter-wave phase characteristics using 2- and 3-element optically controlled phased array feed were experimentally demonstrated with calibration of the phases. Phase characteristics of the array using multiple SMFs were also compared with that using a single SMF experimentally.

We consider a model composed of nonlinear perceptrons and analytically investigate its generalization performance using correlated examples in the framework of on-line learning by a statistical mechanical method. In Hebbian and AdaTron learning, the larger the number of examples used in an update, the slower the learning. In contrast, Perceptron learning does not exhibit such behaviors, and the learning becomes fast in some time region.