Underlay cognitive radio (CR) permits unlicensed secondary users (SUs) to transmit their own data over the licensed spectrum unless the interference from the SUs on the licensed primary user (PU) exceeds an acceptable level. This paper proposes two generalized interference alignment (IA)-based distributed optimization designs for multiple secondary transceivers in the underlay CR case with channel uncertainty under assumption that the actual channel error norm is below a certain bound. One of the designs is an extension to an existing method and the other one is a new design. In these methods, the precoding and power allocation matrices for each SU are either independently or jointly optimized for imperfect channel knowledge to maximize the secondary rates and to hold the secondary interference on the primary receiver under an acceptable limit that is determined by the primary receiver. Numerical results prove the ability of the proposed methods to support significant secondary rates provided that the PU is protected from extra interference from SUs, even in presence of channel uncertainty.

Observed samples in wideband radar are always represented as nonlinear points in high dimensional space. In this paper, we consider the feature selection problem in the scenario of wideband radar target clustering. Inspired by manifold learning, we propose a novel feature selection algorithm, called Local Reconstruction Error Alignment (LREA), to select the features that can best preserve the underlying manifold structure. We first select the features that minimize the reconstruction error in every neighborhood. Then, we apply the alignment technique to extend the local optimal feature sequence to a global unique feature sequence. Experiments demonstrate the effectiveness of our proposed method.

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources.

The multiple-input-multiple-output (MIMO) Gaussian wireless network with K users and an intermediate relay is investigated. In this network, each user with available local channel state information (CSI) intends to convey a multicast message to all other users while receiving all messages from other users via the relay. This model is termed the MIMO K-way relay channel with distributed CSI. For this channel, the sum capacity is shown as MK/(K-1)log(SNR)+o(SNR) where each user and the relay is equipped with M antennas. Achievability is based on the signal space alignment strategy with a K-1 time slot extension. A most general case is then considered, in which each user intends to recover all messages required within T time slots. We provide an improved scheme called fractional signal space alignment which achieves MK/(K-1) degrees of freedom in the general case and the feasibility condition is also explored.

Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in different languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query keywords are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach significantly outperforms previous approaches to the construction of Chinese-English comparable corpora.

In most phrase-based statistical machine translation (SMT) systems, the translation model relies on word alignment, which serves as a constraint for the subsequent building of a phrase table. Word alignment is usually inferred by GIZA++, which implements all the IBM models and HMM model in the framework of Expectation Maximum (EM). In this paper, we present a fully Bayesian inference for word alignment. Different from the EM approach, the Bayesian inference makes use of all possible parameter values rather than estimating a single parameter value, from which we expect a more robust inference. After inferring the word alignment, current SMT systems usually train the phrase table from Viterbi word alignment, which is prone to learn incorrect phrases due to the word alignment mistakes. To overcome this drawback, a new phrase extraction method is proposed based on multiple Gibbs samples from Bayesian inference for word alignment. Empirical results show promising improvements over baselines in alignment quality as well as the translation performance.

In this paper, we propose four different strategies of node pair selection in multiple input multiple output (MIMO) interference channel where interference alignment (IA) is considered as a transceiver design method. In the first scheme, we consider the maximization of the sum rate by selecting node pairs in a brute force way. We also propose a sub-optimal sum rate maximization scheme with lower complexity than the first scheme. In the third scheme, we aim to minimize the number of links among pairs which incurs the outage in MIMO interference channel. In the fourth scheme, we suggest a max-min node pair selection scheme to enhance both the sum rate and the outage probability. Simulation results demonstrate that all our proposed node pair selection schemes can increase the sum rate but also while also reducing the outage probability compared to the scheme with random node pair selection.

As means to control interface reactions between HfO2 and Ge(100), chemical vapor deposition (CVD) of ultrathin Ta-rich oxide using Tri (tert-butoxy) (tert-butylimido) tantalum (Ta-TTT) on chemically-cleaned Ge(100) has been conducted prior to atomic-layer controlled CVD of HfO2 using tetrakis (ethylmethylamino) hafnium (TEMA-Hf) and O3. The XPS analysis of chemical bonding features of the samples after the post deposition N2 annealing at 300 confirms the formation of TaGexOy and the suppression of the interfacial GeO2 layer growth. The energy band structure of HfO2/TaGexOy/Ge was determined by the combination of the energy bandgaps of HfO2 and TaGexOy measured from energy loss signals of O 1s photoelectrons and from optical absorption spectra and the valence band offsets at each interface measured from valence band spectra. From the capacitance-voltage (C-V) curves of Pt-gate MIS capacitors with different HfO2 thicknesses, the thickness reduction of TaGexOy with a relative dielectric constant of 9 is a key to obtain an equivalent SiO2 thickness (EOT) below 0.7 nm.

An SiO2/Si-cap/Si0.55Ge0.45 heterostructure was fabricated on p-type Si(100) and strained silicon on insulator (SOI) substrates by low pressure chemical vapor deposition (LPCVD) and subsequent thermal oxidation in an O2 + H2 gas mixture. Chemical bonding features and valence band offsets in the heterostructures were evaluated by using high-resolution x-ray photoelectron spectroscopy (XPS) measurements and thinning the stack layers with a wet chemical solution.

This paper presents our investigation into improving the performance of our previous automatic reading quality assessment system. The method of the baseline system is calculating the average value of the Phone Log-Posterior Probability (PLPP) of all phones in the voice to be assessed, and the average value is used as the reading quality assessment feature. In this paper, we presents three improvements. First, we cluster the triphones, and then calculate the average value of the normalized PLPP for each classification separately, and use this average values as the multi-dimensional assessment features instead of the original one-dimensional assessment feature. This method is simple but effective, which made the score difference of the machine scoring and manual scoring decrease by 30.2% relatively. Second, in order to assess the reading rhythm, we train Gaussian Mixture Models (GMM), which contain the information of each triphone's relative duration under standard pronunciation. Using the GMM, we can calculate the probability that the relative duration of each phone is conform to the standard pronunciation, and the average value of the probabilities is added to the assessment feature vector as a dimension of feature, which decreased the score difference between the machine scoring and manual scoring by 9.7% relatively. Third, we detect Filled Pauses (FP) by analyzing the formant curve, and then calculate the relative duration of FP, and add the relative duration of FP to the assessment feature vector as a dimension of feature. This method made the score difference between the machine scoring and manual scoring be further decreased by 10.2% relatively. Finally, when the feature vector extracted by the three methods are used together, the score difference between the machine scoring and manual scoring was decreased by 43.9% relatively compared to the baseline system.

Grazing incidence X-ray diffraction experiment proved that the a- and c-axes of PMDA-ODA crystals preferentially aligned in normal and parallel directions to rubbing at surface of rubbed film, and that polymer chains of residual amorphous phase aligned in rubbing direction.

This paper proposes a downlink multi-user transmission scheme for the amplify-and-forward(AF)-based multi-relay cellular network, in which Tomlinson-Harashima precoding(TH precoding) and interference alignment(IA) are jointly applied. The whole process of transmission is divided into two phases: TH precoding is first performed at base-station(BS) to support the multiplexing of data streams transmitted to both mobile-stations(MS) and relay-stations(RS), and then IA is performed at both BS and RSs to achieve the interference-free communication. During the whole process, neither data exchange nor strict synchronization is required among BS and RSs thus reducing the cooperative complexity as well as improving the system performance. Theoretical analysis is provided with respect to the channel capacity of different types of users, resulting the upper-bounds of channel capacity. Our analysis and simulation results show that the joint applications of TH precoding and IA outperforms other schemes in the presented multi-relay cellular network.

This paper presents the evaluation of the L-2L de-embedding method with misalignment of the contact position. The issues of misalignment of the contact position are investigated. The analysis of misalignment in the L-2L de-embedding procedure is performed. Two comparisons are carried out to verify the error of the L-2L de-embedding method. The calculation percent error in quality factor of the transmission line becomes up to 9.0%, while the transistor S-parameter error percentage becomes up to 21% at 60 GHz in the experimental results. The results show that the measurement errors, caused by the misalignment of the contact position, should be considered carefully.

The impact and benefits of channel state information (CSI) are analyzed in terms of degrees-of-freedom (DoFs) in a K-user interference network operating over time-selective channels, where the error variance of CSI estimation is assumed to scale with an exponent of the received signal-to-noise ratio (SNR). The original interference alignment (IA) scheme is used with a slight modification in the network. Then, it is shown that the DoFs promised by the original IA can be fully achieved under the condition that the CSI quality order, represented as a function of the error variance and the SNR, is greater than or equal to 1. Our result is extended to the case where the number of communication pairs, K, scales with the SNR, i.e., infinite K scenario, by introducing the user scaling order. As a result, this letter provides vital information to the system designer in terms of allocating training resources for channel estimation in practical cellular environments using IA.

Depth profile of mass density of vertical alignment film was investigated by X-ray reflectivity, in order to characterize side chains at film surface for vertical alignment of liquid crystals. Existence thin and low density top layer at surface of polyimide film, which was considered to be side chains, was clearly detected. Furthermore, existence of high density layer just below side chain layer was also found, and it is suggested that backbone chain ordering at film surface. Effect of rubbing on VA film was not detected. However, density growth by annealing just below side chain layer of rubbed VA film suggests more ordered backbone chain alignment induced by rubbing.

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.

In this paper, we propose a new interference alignment (IA) scheme that jointly designs the linear transmitter and receiver for the 2-user MIMO X channel system, using minimum total mean square error criterion, subject to each transmitter power constraint. We show that transmitters and receivers under such criteria could be realized through a joint iterative algorithm. Considering the imperfection of channel state information (CSI), we also extend the minimum mean square error interference alignment schemes for the MIMO X channel with CSI estimation error. A robust iterative algorithm which is insensitve to CSI estimation error is proposed. Simulation results are also provided to demonstrate the proposed algorithm.

We detail a self synchronous field programmable gate array (SSFPGA) with dual-pipeline (DP) architecture to conceal pre-charge time for dynamic logic, and its throughput optimization by using pipeline alignment implemented on benchmark circuits. A self synchronous LUT (SSLUT) consists of a three input tree-type structure with 8 bits of SRAM for programming. A self synchronous switch box (SSSB) consists of both pass transistors and buffers to route signals, with 12 bits of SRAM. One common block with one SSLUT and one SSSB occupies 2.2 Mλ2 area with 35 bits of SRAM, and the prototype SSFPGA with 3430 (1020) blocks is designed and fabricated using 65 nm CMOS. Measured results show at 1.2 V 430 MHz and 647 MHz operation for a 3 bit ripple carry adder, without and with throughput optimization, respectively. We find that using the proposed pipeline alignment techniques we can perform at maximum throughput of 647 MHz in various benchmarks on the SSFPGA. We demonstrate up to 56.1 times throughput improvement with our pipeline alignment techniques. The pipeline alignment is carried out within the number of logic elements in the array and pipeline buffers in the switching matrix.

We present a method to constrain a statistical generative word alignment model with the output from a discriminative model. The discriminative model is trained using a small set of hand-aligned data that ensures higher precision in alignment. On the other hand, the generative model improves the recall of alignment. By combining these two models, the alignment output becomes more suitable for use in developing a translation model for a phrase-based statistical machine translation (SMT) system. Our experimental results show that the joint alignment model improves the translation performance. The improvement in average of BLEU and METEOR scores is around 1.0-3.9 points.

This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.