We propose two types of active directional couplers to assure high TX cancellation: an asymmetric type and a symmetric type. For attaining low receiving through loss, coupling capacitors used in conventional couplers are replaced by amplifiers in the proposed active directional couplers. The asymmetric active directional coupler is composed of a small number of components and simple structure. The symmetric active directional coupler has wide-bandwidth TX cancellation. Measurement results show that receiving through loss of -5.3 dB and the TX cancellation of -67.6 dB are obtained in the asymmetric active directional coupler, and receiving through loss of -6.7 dB and the TX cancellation of -66.4 dB are obtained in the symmetric active directional coupler. Compared to the asymmetric active directional coupler, the symmetric active directional coupler has advantage of wider bandwidth of 1.25 MHz to reduce TX leakage of less than -55 dB. Both the proposed active directional couplers achieve high TX cancellation, and the symmetric active directional coupler can be applied in a UHF RFID system with 10-m communication range.

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.

In this paper, we propose an SNMP-aware web cache design that has two main objectives: (1) to avoid overload of network devices by SNMP requests, and (2) guaranteeing the monitoring time granularity of SNMP Object Identifiers (OID) for a large scale network such as the Internet. To meet these objectives, a cache is built into an RESTful active proxy, called Tambourine, which is the gateway for accessing management information through the Internet. Tambourine changes the landscape of traditional SNMP monitoring by allowing the Internet users to monitor closed-domain network devices through translating requests in HTTP into SNMP. However, the typical web cache algorithm can not be used in Tambourine due to two main reasons: (1) SNMP is not a cache-aware protocol and therefore can not provide Tambourine with the caching rules that need to be applied, and (2) the cache in Tambourine needs to accommodate two SNMP monitoring patterns: periodic and on-demand polling. In order for efficient periodic polling, SNMP traffic is reduced by a multi-TTL cache and user (or Manager)-side aggregation. For efficient on-demand polling, four-state transition is used to categorize OIDs into dynamic and static objects, each of which is allocated an optimum TTL. To provide users with a proper time stamp, the cache time stamp is included in the response to the users' request. Our experiments show that our cache design gives the staleness of 0 and a bounded number of SNMP requests even when the number of users' requests goes to infinity.

We propose multiscale bagging as a modification of the bagging procedure. In ordinary bagging, the bootstrap resampling is used for generating bootstrap samples. We replace it with the multiscale bootstrap algorithm. In multiscale bagging, the sample size m of bootstrap samples may be altered from the sample size n of learning dataset. For assessing the output of a classifier, we compute bootstrap probability of class label; the frequency of observing a specified class label in the outputs of classifiers learned from bootstrap samples. A scaling-law of bootstrap probability with respect to σ2=n/m has been developed in connection with the geometrical theory. We consider two different ways for using multiscale bagging of classifiers. The first usage is to construct a confidence set of class labels, instead of a single label. The second usage is to find inputs close to decision boundaries in the context of query by bagging for active learning. It turned out, interestingly, that an appropriate choice of m is m =-n, i.e., σ2=-1, for the first usage, and m =∞, i.e., σ2=0, for the second usage.

We revisit the problem with generic object recognition from the point of view of human-computer interaction. While many existing algorithms for generic object recognition first try to detect target objects before features are extracted and classified in processing, our work is motivated by the belief that solving the task of detection by computer is not always necessary in many practical situations, such as those involving mobile recognition systems with touch displays and cameras. It is natural for these systems to ask users to input the segmentation data for targets through their touch displays. Speaking from the perspective of usability, such systems should involve rough segmentation to reduce the user workload. In this situation, different people would provide different segmentation data. Here, an interesting question arises – if multiple training samples are generated from a single image by using various segmentation data created by different people, what would happen to the accuracy of classification? We created “20 wild bird datasets” that had a large number of rough segmentation datasets made by 383 people in an attempt to answer this question. Our experiments revealed two interesting facts: (i) generating multiple training samples from a single image had positive effects on classification accuracies, especially when image features including spatial information were used and (ii) augmenting training samples with artificial segmentation data synthesized with a morphing technique also had slightly positive effects on classification accuracies.

In this paper, we propose a two dimensional (2D) non-separable adaptive directional lifting (ADL) structure for discrete wavelet transform (DWT) and its image coding application. Although a 2D non-separable lifting structure of 9/7 DWT has been proposed by interchanging some lifting, we generalize a polyphase representation of 2D non-separable lifting structure of DWT. Furthermore, by introducing the adaptive directional filteringingto the generalized structure, the 2D non-separable ADL structure is realized and applied into image coding. Our proposed method is simpler than the 1D ADL, and can select the different transforming direction with 1D ADL. Through the simulations, the proposed method is shown to be efficient for the lossy and lossless image coding performance.

In this paper, a highly linear and low noise CMOS active RF tracking filter for a digital TV tuner is presented. The Gm cell of the Gm-C filter is based on a dynamic source degenerated differential pair with an optimized transistor size ratio, thereby providing good linearity and high-frequency operation. The proposed RF tracking filter architecture includes two complementary parallel paths, which provide harmonic rejection in the low band and unwanted signal rejection in the high band. The fabricated tracking filter based on a 0.13 µm CMOS process shows a 48860 MHz tracking range with 30–32 dB 3rd order harmonic rejection, a minimum input referred noise density of 2.4 nV/, and a maximum IIP3 of 0 dBm at 3 dB gain while drawing 39 mA from a 1.2-V supply. The total chip area is 1 mm0.9 mm.

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.

To reduce the error of channel estimation caused by noise, a novel noise suppression method based on the degree of confidence is proposed in this paper. The false alarm and false dismissal probabilities, corresponding to noise being taken as part of channel impulse response (CIR) and part of the CIR being mis-detected as noise, respectively, are also investigated. A false alarm reduction method is therefore presented to reduce the false alarms in the estimated CIR while the mis-detection ratio still remains low. Simulation results show the effectiveness of the proposed method.

In this letter, a novel physical layer signaling transmission scheme is proposed, where the signaling information is conveyed by a pair of training sequences located in the odd and even subcarriers of an orthogonal frequency division multiplexing (OFDM) training symbol. At the receiver side, only a single correlator is required to detect the signaling information. Computer simulations verify the proposed signaling could outperform the S1 signaling and achieve similar robustness as the S2 signaling of the DVB-T2 standard.

A new direction of arrival (DOA) estimation method is introduced with arbitrary array geometry when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are first estimated via subspace-based high resolution DOA estimation technique. Then a matrix that only contains the information of coherent signals can be formulated by eliminating the contribution of uncorrelated signals. Finally a subspace block sparse reconstruction approach is taken for DOA estimations of the coherent signals.

Histogram-based image features such as HoG, SIFT and histogram of visual words are generally represented as high-dimensional, non-negative vectors. We propose a supervised method of reducing the dimensionality of histogram-based features by using non-negative matrix factorization (NMF). We define a cost function for supervised NMF that consists of two terms. The first term is the generalized divergence term between an input matrix and a product of factorized matrices. The second term is the penalty term that reflects prior knowledge on a training set by assigning predefined constants to cannot-links and must-links in pairs of training data. A multiplicative update rule for minimizing the newly-defined cost function is also proposed. We tested our method on a task of scene classification using histograms of visual words. The experimental results revealed that each of the low-dimensional basis vectors obtained from the proposed method only appeared in a single specific category in most cases. This interesting characteristic not only makes it easy to interpret the meaning of each basis but also improves the power of classification.

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 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.

This letter presents a novel user scheduling algorithm that provides a maximum sum-rate based on zero-forcing beamforming (ZFBF) in multiple-input multiple-output (MIMO) systems. The proposed technique determines primary user pairs in which the sum-rate exceeds a predetermined threshold. To determine the threshold, we define the maximum-sum-rate criterion (MSRC) derived from the extreme value theory (EVT). Applying the MSRC in ZFBF-based user scheduling, we find that the performance of the proposed method is comparable to that of the exhaustive searching scheme which has a greater computational load. Through computer simulations, we show that the proposed method outperforms the very well-known correlation-based method, semi-orthogonal user selection (SUS), yielding a sum rate that is about 0.57 bps/Hz higher when the transmit SNR is 10 dB with perfect CSI at BS and the numbers of users and transmit antennas in a cell are 100 and 4, respectively.

We propose a committee-based method of active learning for large vocabulary continuous speech recognition. Multiple recognizers are trained in this approach, and the recognition results obtained from these are used for selecting utterances. Those utterances whose recognition results differ the most among recognizers are selected and transcribed. Progressive alignment and voting entropy are used to measure the degree of disagreement among recognizers on the recognition result. Our method was evaluated by using 191-hour speech data in the Corpus of Spontaneous Japanese. It proved to be significantly better than random selection. It only required 63 h of data to achieve a word accuracy of 74%, while standard training (i.e., random selection) required 103 h of data. It also proved to be significantly better than conventional uncertainty sampling using word posterior probabilities.

We propose frequency-domain optical code-division-multiplexing (CDM) employing quadrature-amplitude-modulation (QAM) using two of multi-level (M-ary) data generated based on electrical-domain spatial code spreading. Its spectral efficiency is enhanced compared to the conventional scheme with amplitude-shift-keying (ASK) using only one of M-ary data. Although it demands the recovery of amplitude and optical phase information, the practicality of the receiver is retained with self-homodyne detection using a phase-shift-keying (PSK) pilot light. Performance is theoretically evaluated and the optimal parameters are derived. Finally, the feasibility of the proposed technique is experimentally confirmed.

This paper presents novel hybrid push-pull protocols for peer-to-peer video streaming. Our approaches intend to reap the best of push- and pull-based schemes by adaptively switching back and forth between the two modes according to video chunk distributions. The efficacy of the proposed protocols is validated through an evaluation study that demonstrates substantial performance gains.

This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.

The measure of the goodness, or inversely the cost, of concatenating synthesis units plays an important role in concatenative speech synthesis. In this paper, we present a probabilistic approach to concatenation modeling in which the goodness of concatenation is measured by the conditional probability of observing the spectral shape of the current candidate unit given the previous unit and the current phonetic context. This conditional probability is modeled by a conditional Gaussian density whose mean vector has a form of linear transform of the past spectral shape. Decision tree-based parameter tying is performed to achieve robust training that balances between model complexity and the amount of training data available. The concatenation models are implemented for a corpus-based speech synthesizer, and the effectiveness of the proposed method was confirmed by an objective evaluation as well as a subjective listening test. We also demonstrate that the proposed method generalizes some popular conventional methods in that those methods can be derived as the special cases of the proposed method.