Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

In recent years, there has been an increasing demand with regard to available elemental services provided by independent firms for compositing new services. Currently, however, whenever it is difficult to maintain the required level of quality of a new composite web service, assignment of the new computer's resources as provisioning at the data center is not always effective, especially in the area of performance for composite web service providers. Thus, a new approach might be required. This paper presents a new control method aiming to maintain the performance requirements for composite web services. There are three aspects of our method that are applied: first of all, the theory of constraints (TOC) proposed by E.M. Goldratt ; secondly, an evaluation process in the non-linear feed forward controlling method: and finally multiple trials in applying policies with verification. In particular, we will discuss the architectural and theoretical aspects of the method in detail, and will show the insufficiency of combining the feedback controlling approach with TOC as a result of our evaluation.

We propose a reduced complexity maximum likelihood sequence detection (MLSD) equalizer for wireless communications using bidirectional decision feedback equalizers (DFEs). We apply reduced-length two-level estimates produced by a bidirectional DFE. Therefore, the computationally expensive MLSD algorithm is applied sparingly for two-level signals with the effective channel length shorter than the original channel, regardless of the original constellation size of the symbols. Simulation results show that the proposed algorithm outperforms existing combination schemes based on bidirectional DFEs, especially for large constellations.

In order to improve the detection performance in passive millimeter-wave (PMMW) imaging, a new method forwarding a null in the direction of human body and objects is proposed. The forward-nulling PMMW imaging using a dielectric tube occupied by cooling water placed near the focus line of a parabolic cylinder are performed. It is shown experimentally that the contrast between human body and conducting objects such as a conducting plate and a conducting sphere is improved by the presence of the cooling dielectric tube and parabolic cylinder.

This letter deals with a system identification problem with unknown model order, which can be formulated as the matrix rank minimization problem by applying the subspace identification method. A sequential rank minimization algorithm is provided by modifying the null space based alternating optimization (NSAO) algorithm, and a model order identification algorithm is proposed. Numerical examples show that the proposed sequential algorithm can adaptively identify the model order of switched systems whose model order changes.

Linear Discriminant Analysis (LDA) is one of the most popular dimensionality reduction techniques in existing handwritten Chinese character (HCC) recognition systems. However, when used for unconstrained handwritten Chinese character recognition, the traditional LDA algorithm is prone to two problems, namely, the class separation problem and multimodal sample distributions. To deal with these problems,we propose a new locally linear discriminant analysis (LLDA) method for handwritten Chinese character recognition.Our algorithm operates as follows. (1) Using the clustering algorithm, find clusters for the samples of each class. (2) Find the nearest neighboring clusters from the remaining classes for each cluster of one class. Then, use the corresponding cluster means to compute the between-class scatter matrix in LDA while keeping the within-class scatter matrix unchanged. (3) Finally, apply feature vector normalization to further improve the class separation problem. A series of experiments on both the HCL2000 and CASIA Chinese character handwriting databases show that our method can effectively improve recognition performance, with a reduction in error rate of 28.7% (HCL2000) and 16.7% (CASIA) compared with the traditional LDA method.Our algorithm also outperforms DLA (Discriminative Locality Alignment,one of the representative manifold learning-based dimensionality reduction algorithms proposed recently). Large-set handwritten Chinese character recognition experiments also verified the effectiveness of our proposed approach.

This paper presents a new 8T (8-transistor) SRAM cell layout mitigating multiple-bit upset (MBU) in a divided wordline structure. Because bitlines along unselected columns are not activated, the divided wordline structure eliminates a half-select problem and achieves low-power operation, which is often preferred for low-power/low-voltage applications. However, the conventional 8T SRAM with the divided wordline structure engenders MBUs because all bits in the same word are physically adjoining. Consequently, it is difficult to apply an error correction coding (ECC) technique to it. In this paper, we propose a new 8T cell layout pattern that separates internal latches in SRAM cells using both an n-well and a p-substrate. We saw that a SEU cross section of nMOS is 3.5–4.5 times higher than that of pMOS (SEU: single event upset; a cross section signifies a sensitive area to soft error effects). By using a soft-error simulator, iRoC TFIT, we confirmed that the proposed 8T cell has better neutron-induced MBU tolerance. The simulator includes soft-error measurement data in a commercial 65-nm process. The MBU in the proposed 8T SRAM is improved by 90.70% and the MBU soft error rate (SER) is decreased to 3.46 FIT at 0.9 V when ECC is implemented (FIT: failure in time). Additionally, we conducted Synopsys 3-D TCAD simulation, which indicates that the linear energy transfer (LET) threshold in SEU is also improved by 66% in the proposed 8T SRAM by a common-mode effect.

A novel online speaker clustering method based on a generative model is proposed. It employs an incremental variant of variational Bayesian learning and provides probabilistic (non-deterministic) decisions for each input utterance, on the basis of the history of preceding utterances. It can be expected to be robust against errors in cluster estimation and the classification of utterances, and hence to be applicable to many real-time applications. Experimental results show that it produces 50% fewer classification errors than does a conventional online method. They also show that it is possible to reduce the number of speech recognition errors by combining the method with unsupervised speaker adaptation.

In camera-based object recognition and classification, surface color is one of the most important characteristics. However, apparent object color may differ significantly according to the illumination and surface conditions. Such a variation can be an obstacle in utilizing color features. Geusebroek et al.'s color invariants can be a powerful tool for characterizing the object color regardless of illumination and surface conditions. In this work, we analyze the estimation process of the color invariants from RGB images, and propose a novel invariant feature of color based on the elementary invariants to meet the circular continuity residing in the mapping between colors and their invariants. Experiments show that the use of the proposed invariant in combination with luminance, contributes to improve the retrieval performances of partial object image matching under varying illumination conditions.

In the letter, we propose an improved histogram shifting (HS) based reversible data hiding scheme for small payload embedding. Conventional HS based schemes are not suitable for low capacity embedding with relatively large distortion due to the inflexible side information selection. From an analysis of the whole HS process, we develop a rate-distortion model and provide an optimal adaptive searching approach for side information selection according to the given payload. Experiments demonstrate the superior performance of the proposed scheme in terms of performance curve for low payload embedding.

This paper presents a simple 3-D array configuration for high-resolution 2-D Direction-Of-Arrival (DOA) estimation. Planar array structures like Uniform Rectangular Array (URA) or Uniform Circular Array (UCA) often well estimate azimuth angle but cannot well estimate elevation angle because of short antenna aperture in elevation direction. One may put more number of array elements to improve elevation angle estimation accuracy, however it will require very large hardware and software cost. This paper presents a simple 3-D array structure for high-resolution 2-D DOA estimation only by modifying the height of some array elements in a planar array. Based on the analysis of Cramer-Rao Lower Bound (CRLB) formulation and its dependency on the height of array elements, we develop a simple 3-D array structure which improves elevation angle estimation accuracy while preserving azimuth angle estimation accuracy.

In this letter we propose a practical sensing-based opportunistic spectrum sharing scheme for cognitive radio (CR) downlink MIMO systems. Multi-antennas are exploited at the secondary transmitter to opportunistically access the primary spectrum and effectively achieve a balance between secondary throughput maximization and mitigation of interference probably caused to primary radio link. We first introduce a brief secondary frame structure, in which a sensing phase is exploited to estimate the effective interference channel. According to the sensing result and taking the interference caused by the primary link into account, we propose an enhanced signal-to-leakage-and-noise ratio (SLNR)-based precoding scheme for the secondary transmitter. Compared to conventional schemes where perfect knowledge of the channels over which the CR transmitter interferes with the primary receiver (PR) is assumed, our proposed scheme shows its superiority and simulation results validate this.

This paper aims at channel modeling and bit error rate (BER) performance improvement with diversity reception for in-body to on-body ultra wideband (UWB) communication for capsule endoscope application. The channel characteristics are firstly extracted from 3.4 to 4.8 GHz by using finite difference time domain (FDTD) simulations incorporated with an anatomical human body model, and then a two-path impulse response channel model is proposed. Based on the two-path channel model, a spatial diversity reception technique is applied to improve the communication performance. Since the received signal power at each receiver location follows a lognormal distribution after summing the two path components, we investigate two methods to approximate the lognormal sum distribution in the combined diversity channel. As a result, the method matching a short Gauss-Hermite approximation of the moment generating function (MGF) of the lognormal sum with that of a lognormal distribution exhibits high accuracy and flexibility. With the derived probability density function (PDF) for the combined diversity signals, the average BER performances for impulse-radio (IR) UWB with non-coherent detection are investigated to clarify the diversity effect by both theoretical analysis and computer simulation. The results realize an improvement around 10 dB on Eb/No at BER of 10-3 for two-branch diversity reception.

For an adaptive system identification filter with a stochastic input signal, a coefficient vector updated with an NLMS algorithm converges in the sense of ensemble average and the expected convergence vector has been revealed. When the input signal is periodic, the convergence of the adaptive filter coefficients has also been proved. However, its convergence vector has not been revealed. In this paper, we derive the convergence vector of adaptive filter coefficients updated with the NLMS algorithm in system identification for deterministic sinusoidal inputs. Firstly, we derive the convergence vector when a disturbance does not exist. We show that the derived convergence vector depends only on the initial vector and the sinusoidal frequencies, and it is independent of the step-size for adaptation, sinusoidal amplitudes, and phases. Next, we derive the expected convergence vector when the disturbance exists. Simulation results support the validity of the derived convergence vectors.

Compressing a JPEG image twice will greatly decrease the values of some of its DCT coefficients. This effect can be easily detected by statistics methods. To defend this forensic method, we establish a model to evaluate the security and image quality influenced by the re-compression. Base on the model, an optimized adjustment of the DCT coefficients is achieved by Genetic Algorithm. Results show that the traces of double compression are removed while preserving image quality.

One of promising application offered by implant body area networks (BANs) is a capsule endoscope localization system. To begin with, this paper performs finite-difference time-domain (FDTD) simulations on implant BAN propagation with a numerical human model, and investigates the propagation characteristics of implant BAN signals at 400 MHz medical implant communication service (MICS) band. Then, the paper presents a capsule endoscope localization system which utilizes only received signal strength indicator (RSSI) and two estimation methods, such as a maximum likelihood (ML) estimation method and a least squares (LS) method. Furthermore, we evaluate the two localization methods by two computer simulation scenarios. Our computer simulation results demonstrate that the ML localization can improve the location estimation accuracy as compared with the LS localization, that is, our performance comparison reveals that a careful consideration the propagation characteristics of implant BANs signals is efficient in terms of estimation performance improvement in capsule endoscope localization.

In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.

This paper describes very compact MIC magic-Ts and their integration with planar array antennas to realize the advanced antenna modules. The orthogonal transmission modes are effectively used to arrange the preferable port layout of magic-Ts. This flexible port layout of magic-Ts is a practical feature for integration with planar array antennas. The integration of magic-Ts and planar array antennas can easily create advanced functions. A couple of array antennas based on the integration advantages are introduced to materialize this technical concept. This integration approach is of big worth to originate various kinds of advanced antennas and the wireless modules in the ubiquitous society.

High-performance FFT processor is indispensable for real-time OFDM communication systems. This paper presents a CORDIC based design of variable-length FFT processor which can perform various FFT lengths of 64/128/256/512/1024/2048/4096/8192-point. The proposed FFT processor employs memory based architecture in which mixed radix 4/2 algorithm, pipelined CORDIC, and conflict-free parallel memory access scheme are exploited. Besides, the CORDIC rotation angles are generated internally based on the transform of butterfly counter, which eliminates the need of ROM making it memory-efficient. The proposed architecture has a lower hardware complexity because it is ROM-free and with no dedicated complex multiplier. We implemented the proposed FFT processor and verified it on FPGA development platform. Additionally, the processor is also synthesized in 0.18 µm technology, the core area of the processor is 3.47 mm2 and the maximum operating frequency can be up to 500 MHz. The proposed FFT processor is better trade off performance and hardware overhead, and it can meet the speed requirement of most modern OFDM system, such as IEEE 802.11n, WiMax, 3GPP-LTE and DVB-T/H.

In this letter, we adopt a new factor analysis of neighborhood-preserving embedding (NPE) for speaker verification. NPE aims at preserving the local neighborhood structure on the data and defines a low-dimensional speaker space called neighborhood-preserving embedding space. We compare the proposed method with the state-of-the-art total variability approach on the telephone-telephone core condition of the NIST 2008 Speaker Recognition Evaluation (SRE) dataset. The experimental results indicate that the proposed NPE method outperforms the total variability approach, providing up to 24% relative improvement.