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Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: Information Visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization. [ABSTRACT FROM AUTHOR]

The information and communications technology (ICT) industry is making important contributions to economic growth both locally and globally. There is a high demand for ICT professionals that higher education institutions are still struggling to meet. An effective literature review is an important part of understanding the existing findings and issues in the education of future ICT professionals, and for planning future research directions. This comprehensive study presented here includes a review of 761 papers on the level of summary analysis and 155 papers in depth, according to ten research questions related to: curriculum design and delivery, knowledge and skills of future ICT professionals, teaching methods, collaboration between academia and industry, and future employment and career development of ICT professionals in the labor market. Based on the findings from a content and cluster analysis, the results indicate the need for a more holistic and strategic approach to the education of future ICT professionals, including career development support within formal processes of higher education. [ABSTRACT FROM PUBLISHER]

We address the problem of reducing the size of Craig’s interpolants used in SAT-based model checking. Craig’s interpolants are AND-OR circuits, generated by post-processing refutation proofs of SAT solvers. Being highly redundant, their compaction is typically tackled by reducing the proof graph and/or by exploiting standard logic synthesis techniques. In this paper, we propose a set of ad-hoc logic synthesis functions that, revisiting known logic synthesis approaches, specifically address speed and scalability. Though general and not restricted to interpolants, these techniques target the main sources of redundancy in combinational circuits. This paper includes an experimental evaluation, showing the benefits of the proposed techniques, on a set of benchmark interpolants arising from hardware model checking problems. [ABSTRACT FROM AUTHOR]

Presents the front cover for this issue of the publication. [ABSTRACT FROM PUBLISHER]

Subspace clustering aims to cluster a collection of data points lying in a union of subspaces. Based on the assumption that each point can be approximately represented as a linear combination of other points, extensive efforts have been made to compute an affinity matrix in a self-expressive framework for describing the similarity between points. However, the existing clustering methods consider the average feature solutions, which would not be powerful enough to capture the intrinsic relationship between points. In this paper, we present the $k$ -support norm subspace clustering (KSC) method by utilizing k-$ support norm regularization. The k-$ support norm trades off the sparsity of norm and the uniform shrinkage of \ell _{2} norm to yield better predictive performance on the data connection. The theoretical analysis of KSC makes up a large proportion of paper. In the noise-free case, we provide the kEBD condition, which ensures the coefficient matrix to be block diagonal. If the data are corrupted, we prove the incompletion-grouping effect for KSC. Moreover, we provide the statistical recovery guarantee for both noise-free and noise cases. The theory analyses show the validity and feasibility of KSC, and the experimental results on multiple challenging databases demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We analyze the completion time of wafer lots in single-armed cluster tools with parallel processing modules (PMs) by considering the lot switching operation. To effectively assign wafer lots and dispatch overhead hoist transports (OHTs) to manufacturing tools, it is crucial to obtain the completion time of wafer lots. However, estimating the completion time is not straightforward, due to the concurrent processing of two consecutive wafer lots during lot switching operation, which often increases wafer sojourn times in PMs. In this paper, we derive closed-form expressions of the completion time of wafer lots in single-armed cluster tools with parallel PMs. We assume that the robot unloads wafers in the order of their loading sequence. We then experimentally show that the formulas derived can be used even when processing time variation exists or another robot task sequence, which is of first-in first-out (FIFO), is assumed.

Note to Practitioners—Due to the larger wafer size and circuit width reduction, cluster tools often perform the lot switching operation with each pair of consecutive wafer lots. In addition, since most tools are operated with parallel chambers, concurrent processing with two different wafer lots occurs frequently. Such transient periods in operating tools make it hard to estimate the completion time of wafer lots. In this paper, we derive closed-form expressions to obtain the completion time of wafer lots in single-armed cluster tools with parallel chambers. We further show that the formulas can be used with processing time variation or the FIFO rule. With the formulas, OHTs can be sent just-in-time to tools to load or unload wafer cassettes, and wafer lots can be assigned while minimizing the transient periods. In addition, the estimated completion time can be utilized in the planning and scheduling of wafer fabrication processes. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new method for estimating the visual focus of attention (VFOA) in a video stream captured by a single distant camera and showing several persons sitting around a table, like in formal meeting or video conferencing settings. The visual targets for a given person are automatically extracted online using an unsupervised algorithm that incrementally learns the different appearance clusters from low-level visual features computed from face patches provided by a face tracker without the need of an intermediate error-prone step of head pose estimation as in classical approaches. The clusters learned in that way can then be used to classify the different visual attention targets of the person during a tracking run, without any prior knowledge on the environment and the configuration of the room or the visible persons. The experiments on public datasets containing almost 2 h of annotated videos from meetings and video conferencing show that the proposed algorithm produces state-of-the-art results and even outperforms a traditional supervised method that is based on head orientation estimation and that classifies VFOA using Gaussian mixture models. [ABSTRACT FROM AUTHOR]

Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, but they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. Experiments on real-life networks validate the performance of our algorithm. [ABSTRACT FROM AUTHOR]

The NK hybrid genetic algorithm (GA) for clustering is proposed in this paper. In order to evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 (NKCV2). NKCV2 uses information about the disposition of ${N}$ small groups of objects. Each group is composed of ${K+1}$ objects of the dataset. Experimental results show that density-based regions can be identified by using NKCV2 with fixed small ${K}$. In NKCV2, the relationship between decision variables is known, which in turn allows us to apply gray box optimization. Mutation operators, a partition crossover (PX), and a local search strategy are proposed, all using information about the relationship between decision variables. In PX, the evaluation function is decomposed into ${q}$ independent components; PX then deterministically returns the best among ${2^{q}}$ possible offspring with computational complexity ${O(N)}$. The NK hybrid GA allows the detection of clusters with arbitrary shapes and the automatic estimation of the number of clusters. In the experiments, the NK hybrid GA produced very good results when compared to another GA approach and to state-of-art clustering algorithms. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Binning after volume production is a widely accepted technique to classify fabricated integrated circuits (ICs) into different clusters depending on different degrees of specification compliance. This allows the manufacturer to sell nonoptimal devices at lower rates, so adapting to customer’s quality-price requirements. The binning procedure can be carried out by measuring every single circuit performances, but this approach is costly and time-consuming. On the contrary, if alternate measurements are used to characterize the bins, the procedure is considerably enhanced. In such a case, the specification bin boundaries become arbitrary shape regions due to the highly nonlinear mappings between the specifications space and the alternate measurements space. The binning strategy proposed in this paper functions with the same efficiency regardless of these shapes. The digital encoding of the bins in the alternate measurements space using octrees is the key idea of the proposal. The strategy has two phases: 1) the training phase and 2) the binning phase. In the training phase, the specification bins are encoded using octrees. This first phase requires sufficient samples of each class to generate the octree under realistic variations, but it only needs to be performed once. The binning phase corresponds to the actual production binning of the fabricated ICs. This is achieved by evaluating the alternate measurements in the previously generated octree. The binning phase is fast due to the inherent sparsity of the octree data structure. In order to illustrate the proposal, the method has been applied to a band-pass Butterworth filter considering three specification bins as a proof of concept. Successful simulation results are reported showing considerable advantages as compared to a support vector machine (SVM)-based classifier. Similar bin misclassifications are obtained with both methods, 1.68% using octrees and 1.83% using SVM, while binning time is $5\times $ times faster using octrees than using the SVM-based classifier. [ABSTRACT FROM AUTHOR]

The stochastic block model with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability $p$ within clusters and q$ across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower bounds on the scaling of |p-q|$ to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if \alpha =pn/\log (n)$ and \beta =qn/\log (n)$ are constant (with and is impossible if ({\alpha +\beta }/{2}) - \sqrt {\alpha \beta }<1 . In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest that it may achieve the threshold. An efficient algorithm that succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the cluster synchronization in finite time for a class of complex networks with nonlinear coupling strengths and probabilistic coupling delays. The complex networks consist of several clusters of nonidentical discontinuous systems suffered from uncertain bounded external disturbance. Based on the Takagi–Sugeno (T–S) fuzzy interpolation approach, we first obtain a set of T–S fuzzy complex networks with constant coupling strengths. By developing some novel Lyapunov functionals and using the concept of Filippov solution, some new analytical techniques are established to derive sufficient conditions ensuring the cluster synchronization in a setting time. In particular, this paper extends the pinning control strategies for networks with continuous-time dynamics to discontinuous networks. Numerical simulations demonstrate that the theoretical results are effective and the T–S fuzzy approach is important for relaxed results. [ABSTRACT FROM AUTHOR]

The kinematic redundancy of the human arm enables the elbow position to rotate about the axis going through the shoulder and wrist, which results in infinite possible arm postures when the arm reaches to a target in a 3-D workspace. To infer the control strategy the human motor system uses to resolve redundancy in reaching movements, this paper compares five redundancy resolution criteria and evaluates their arm posture prediction performance using data on healthy human motion. Two synthesized criteria are developed to provide better real-time arm posture prediction than the five individual criteria. Of these two, the criterion synthesized using an exponential method predicts the arm posture more accurately than that using a least squares approach, and therefore is preferable for inferring the contributions of the individual criteria to motor control during reaching movements. As a methodology contribution, this paper proposes a framework to compare and evaluate redundancy resolution criteria for arm motion control. A cluster analysis which associates criterion contributions with regions of the workspace provides a guideline for designing a real-time motion control system applicable to upper-limb exoskeletons for stroke rehabilitation. [ABSTRACT FROM AUTHOR]

This paper proposes mechanisms to efficiently address critical tasks in the operation of cluster-based target tracking, namely: (1) measurement integration, (2) inclusion/exclusion in the cluster, and (3) cluster head rotation. They all employ distributed probabilistic tools designed to take into account wireless camera networks (WCNs) capabilities and constraints. They use efficient and distribution-friendly representations and metrics in which each node contributes to the computation in each mechanism without requiring any prior knowledge of the rest of the nodes. These mechanisms are integrated in two different distributed schemes so that they can be implemented in constant time regardless of the cluster size. Their experimental validation showed that the proposed mechanisms and schemes significantly reduce energy consumption ($>$ 55 percent) and computational burden with respect to existing methods. [ABSTRACT FROM PUBLISHER]

In this paper, an approximate method is proposed to derive the probability density function of the sum's power of multiple independent fading random variables. It is revealed that the product of two independent Rayleigh distributed random variables could be approximated by a sum of two Gamma variables. Moreover, by employing the proposed analysis, the same approximation could be derived as that of the Nakagami approximation when the involved variables' statistical moments are the same. Then the approximate analysis is utilized to obtain the performance estimate of hybrid automatic repeat request with code combining over the ideally interleaved Rayleigh fading channel. Both the lower and upper estimates of pairwise error probability are derived to enable suitable performance evaluation, and are validated by both the numerical and simulation results. In addition, it is shown that the previous known reliability estimate of the forward error correction scheme could be regarded as a special case of the performance estimates derived in this paper. [ABSTRACT FROM AUTHOR]

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one’s experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which ${K}$ -means is utilized to accelerate particles’ convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, ${K}$ -means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search. [ABSTRACT FROM AUTHOR]

Wind farms commonly cluster in regions rich in wind resources. Thus, correlation of wind speeds from different wind farms should not be ignored when modeling a power system with large wind energy penetration. This paper proposes a probabilistic optimal power flow (POPF) technique based on the quasi-Monte Carlo simulation (QMCS) considering the correlation of wind speeds using copula functions. In this paper, a copula function is used to model the dependent structure of random wind speeds and their forecast errors. QMCS is employed in the sampling procedure to reduce computation burden. The proposed method is applied in probabilistic power flow (PPF). Furthermore, the PPF is used in the POPF problem that aims at minimizing the expectation and downside risk of fuel cost simultaneously. Simulation studies are conducted on a modified IEEE 118-bus power system with wind farms integrated in two areas, and the results show that the accuracy and efficiency are improved by the proposed method. [ABSTRACT FROM AUTHOR]

Clustered unit commitment (CUC) formulations have been proposed to provide accurate and fast approximations to the unit commitment (UC) problem. In these formulations, identical or similar plants are grouped into clusters. This way, the binary commitment variables of all the plants within a cluster can be replaced by a single integer variable. This approach has recently been mainly used for tractably integrating flexibility constraints in generation expansion planning problems. However, a thorough general validation is still missing. In addition, these formulations do not provide commitment schedules on a plant-by-plant level and hence cannot be used directly for operating actual systems or markets. A first contribution of this paper is to show that errors can be introduced both due to the problem formulation and the grouping of nonidentical units. A case study is presented in which these errors are quantified under different conditions. Overall, the error in approximating the total cost does not exceed 0.06%. A second contribution of this paper is the development of a hybrid approach, which sequentially uses a CUC and a traditional UC model. This approach allows to reduce the computational cost of solving the UC problem while providing a guaranteed feasible and near-optimal solution. [ABSTRACT FROM AUTHOR]

In this paper, we report, through a comparative analysis of static and dynamic measurements, on the impact of mode hopping on the overall intensity noise dynamics of InGaN edge emitting lasers. Mode clustering usually observed in InGaN lasers enhances the mode competition and then the laser intensity fluctuation. Variations as large as 20 dB have been observed. The coexistence of longitudinal modes induces mode competition and then unstable bimodal lasing. This mode competition is mediated by the temporal fluctuations of spontaneous emission through the nonlinear cross saturation of the optical gain. The subsequent enhancement of the relative intensity noise can be detrimental for applications, where the intensity stability of InGaN lasers is a key parameter. Our experimental observations are supported by a simple rate equation model. We point out the major importance of the different modal gain in the bimodal regime on the overall intensity noise of the laser. [ABSTRACT FROM AUTHOR]

Journal impact indicators are widely accepted as possible measurements of academic journal quality. However, much debate has recently surrounded their use, and alternative journal impact evaluation techniques are desirable. Aggregation of multiple indicators offers a promising method to produce a more robust ranking result, avoiding the possible bias caused by the use of a single impact indicator. In this paper, fuzzy aggregation and fuzzy clustering, especially the ordered weighted averaging (OWA) operators are exploited to aggregate the quality scores of academic journals that are obtained from different impact indicators. Also, a novel method for linguistic term-based fuzzy cluster grouping is proposed to rank academic journals. The paper allows for the construction of distinctive fuzzy clusters of academic journals on the basis of their performance with respect to different journal impact indicators, which may be subsequently combined via the use of the OWA operators. Journals are ranked in relation to their memberships in the resulting combined fuzzy clusters. In particular, the nearest-neighbor guided aggregation operators are adopted to characterize the reliability of the indicators, and the fuzzy clustering mechanism is utilized to enhance the interpretability of the underlying ranking procedure. The ranking results of academic journals from six subjects are systematically compared with the outlet ranking used by the Excellence in Research for Australia, demonstrating the significant potential of the proposed approach. [ABSTRACT FROM PUBLISHER]

The terahertz (THz) waves with enormous bandwidth can be used along with the existing sub-6GHz and millimeter wave (mmWave) bands to achieve the ever evolving ecosystem of applications that need to be supported by the modern wireless networks. This paper investigates a user-centric dynamic base station (BS) clustering design for a hybrid network where THz, mmWave, and sub-6GHz BSs coexist. Invoking the proposed clustering model, the BS cooperation within each band is made possible by considering long term channel variations and all the surrounding BSs within a cluster with tunable size. A typical user is associated with the best BS cluster, from either a sub-6GHz, mmWave or THz tier based on the maximum signal-to-interference-plus-noise-ratio (SINR) criterion or the maximum rate criterion. Using tools from stochastic geometry, we assess the performance of the proposed user-centric hybrid system in terms of SINR and rate coverage performances, while accounting for: band specific propagation models, directional beamfroming, and BSs random locations. The accuracy of the analytical results is validated with Monte-Carlo simulations. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz BSs improves the rate significantly but may degrade the coverage performance. Thus, with carefully planned networks, enabling user-centric BS cooperation for hybrid wireless systems can achieve ultra-high rates while maintaining sufficient coverage in sixth-generation (6G) networks. [ABSTRACT FROM AUTHOR]

In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near-nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution. [ABSTRACT FROM AUTHOR]

Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively. [ABSTRACT FROM AUTHOR]

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reliability-aware hard real-time task scheduling method for multicore systems along with a quantitative reliability model. The proposed method uses a heuristic clustered replication to maintain the desired reliability threshold with both minimum replication overhead and latency increase. It also minimizes intercore communication overhead of tasks. Both single and multiple soft errors are considered in this method. Simulation results show that the efficiency of our proposed approach improves with larger network-on-chip sizes, higher reliability thresholds, and higher number of tolerating errors. The proposed method achieves near optimal replica overhead (up to $\text{7.3}\%$ higher than optimal replica overhead) with up to $\text{2500}\%$ time complexity improvement compared to exhaustive exploration. Experimental results also show that the feasibility of the proposed method is higher than the conventional replication method up to $\text{9.3}\%$. All experiments are performed on both synthetic random task graphs and PARSEC real application benchmarks. Obtained task mapping solutions with communication volume reduction and near optimal replica overhead impose negligible latency increase (up to $\text{6.3}\%$) in comparison with the space exploration approach. [ABSTRACT FROM AUTHOR]

This paper introduces a new evidential clustering method based on the notion of “belief peaks” in the framework of belief functions. The basic idea is that all data objects in the neighborhood of each sample provide pieces of evidence that induce belief on the possibility of such sample to become a cluster center. A sample having higher belief than its neighbors and located far away from the other local maxima is then characterized as cluster center. Finally, a credal partition is created by minimizing an objective function with the fixed cluster centers. An adaptive distance metric is used to fit for unknown shapes of data structures. We show that the proposed evidential clustering procedure has very good performance with an ability to reveal the data structure in the form of a credal partition, from which hard, fuzzy, possibilistic, and rough partitions can be derived. Simulations on synthetic and real-world datasets validate our conclusions. [ABSTRACT FROM AUTHOR]

When spectral clustering analysis is applied, a similarity matrix of data plays a vital role in both clustering performance and stability of clustering results. In order to enhance the clustering performance and maintain the stability of the clustering results, a new method to jointly learn the similarity matrix and the clustering structure, called the joint learning method (FSCM) of spectral clustering structure and fuzzy similarity matrix of data, is proposed in this paper. In FSCM, the capability of a double-index fuzzy C-means clustering algorithm is used to determine an appropriate fuzzy similarity between any pair of data points. A fuzzy similarity matrix of data is also determined by adaptively assigning fuzzy neighbors of data points so the spectral clustering structure of data can be found and the clustering stability of FSCM can be assured. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Radio channel modeling has been an important research topic, since the performance of any communication system depends on channel characteristics. So far, most existing clustering algorithms are conducted based on the multipath components (MPCs) extracted by using a high-resolution parameter estimation approach, e.g., SAGE or MUSIC, etc. However, most of the estimation approaches require prior information to extract MPCs. Moreover, the high-resolution estimation approaches usually result in relatively high complexity, and thus, the clusters can only be identified by using an offline approach after the measurements. Therefore, a power-angle-spectrum (PAS) based clustering and tracking algorithm (PASCT) is proposed in this paper. First, a PAS is extracted from measurement data by using a Bartlett beamformer. For each PAS, the potential targets are selected from the background and separated into clusters by using image processing approaches. The recognized clusters are characterized by the following three attributes: size, position, and shape feature, where an orientation histogram is developed to describe the shape feature of the clusters. Moreover, a cost minimizing tracking approach based on Kuhn–Munkres method is proposed to accurately identify the clusters in non-stationary channels. The proposed PASCT algorithm is validated based on both simulations and measurements. It is found that the dominating clusters in both line-of-sight and non-line-of-sight environments can be well recognized and tracked with the proposed algorithm. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurements, e.g., number, birth–death process, and size of the clusters, can be well observed. Through the experiments, the proposed algorithm can achieve fairly good accuracy on the cluster identification with lower complexity compared to the conventional solution. [ABSTRACT FROM AUTHOR]

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) conventional image into an HR HSI has become a prevalent HSIs super-resolution scheme. However, in most previous works, little attention has been paid on exploiting the underlying manifold structure in the spatial domain of the latent HR HSI. In this paper, we advance a provable prior knowledge that the clustering manifold structure of the latent HSI can be well preserved in the spatial domain of the input conventional image. Inspired by this, we first conduct clustering in the spatial domain of the input conventional image and adopt the intra-cluster self-expressiveness model to implicitly depict the clustering manifold structure, which enables learning the complicated manifold structure via solving a constrained ridge regression model without knowing the exact form of the manifold. Then, we incorporate the learned structure into a variational super-resolution framework to regularize the latent HSI. The resulted framework can be effectively optimized by a standard alternating direction method of multipliers. Since the learned structure can well depict the underlying spatial manifold of the latent HSI, the proposed method shows the state-of-the-art super-resolution performance on two benchmark data sets. [ABSTRACT FROM AUTHOR]

Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems for optimization. There are two general goals for solving MMOPs. One is to maintain population diversity so as to locate global optima as many as possible, while the other is to increase the accuracy of the solutions found. To achieve these two goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) is proposed in this paper. The novelties and advantages of DSDE include the following three aspects. First, a dual-strategy mutation scheme is designed to balance exploration and exploitation in generating offspring. Second, an adaptive selection mechanism based on APC is proposed to choose diverse individuals from different optimal regions for locating as many peaks as possible. Third, an archive technique is applied to detect and protect stagnated and converged individuals. These individuals are stored in the archive to preserve the found promising solutions and are reinitialized for exploring more new areas. The experimental results show that the proposed DSDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms when evaluated on the benchmark problems from CEC2013, in terms of locating more global optima, obtaining higher accuracy solution, and converging with faster speed. [ABSTRACT FROM AUTHOR]

In wireless communications, cochannel interference is usually characterized by a sum of lognormal random variables. Since the characteristic function of a lognormal distribution lacks explicit expression, and numerical calculation of a lognormal sum distribution is very challenging, lognormal distributions are often used to approximate lognormal sum distributions. However, it has been shown that a lognormal distribution can only capture a certain part of the body of a lognormal sum distribution. To improve the accuracy of approximation of lognormal sum distributions, one must resort to nonlognormal approximations. In this paper, we propose the use of power lognormal distributions to approximate lognormal sum distributions. To illustrate the superiority of the proposed model, some numerical experimental results are provided. [ABSTRACT FROM AUTHOR]

In this paper, two optimization models for improvement of the boiler-turbine system performance are formulated. The models are constructed using a data-mining approach. Historical process data is clustered and the discovered patterns are selected for performance improvement of the boiler-turbine system. The first model optimizes a widely used performance index, the unit heat rate. The second model minimizes the total fuel consumption while meeting the electricity demand. The strengths and weaknesses of the two models are discussed. An industrial case study illustrates the concepts presented in the paper. [ABSTRACT FROM AUTHOR]

This paper presents an automatic speech–speaker recognition (ASSR) system implemented in a chip which includes a built-in extraction, recognition, and training (ERT) core. For VLSI design (here, ASSR system), the hardware cost and time complexity are always the important issues which are improved in this proposed design in two levels: 1) algorithmic and 2) architecture. At the algorithm level, a newly binary-halved clustering (BHC) is proposed to achieve low time complexity and low memory requirement. In addition, at the architecture level, a new ERT core is proposed and implemented based on data dependence and reuse mechanism to reduce the time and hardware cost as well. Finally, the chip implementation is synthesized, placed, and routed using TSMC 90-nm technology library. To verify the performance of the proposed BHC method, a case study is performed based on nine speakers. Moreover, the validation of the ASSR system is examined in two parts: 1) speech recognition and 2) speaker recognition. The results show that the proposed system can achieve 93.38% and 87.56% of recognition rates during speech and speaker recognition, respectively. Furthermore, the proposed ASSR chip includes 396k gate counts, and consumes power in 8.74 mW. Such results demonstrate that the performance of the proposed ASSR system is superior to the conventional systems. [ABSTRACT FROM PUBLISHER]

This paper investigates the synthetic aperture radar (SAR) image segmentation in terms of feature analysis and fusion and develops a new algorithm based on multifeature ensemble accordingly. This paper is characterized by two aspects. First, multiple heterogeneous features are extracted to accurately describe the objects in SAR images. These features are then integrated in the feature level and the similarity level, respectively, to avoid the mutual influences between different kinds of features and maximize the discriminability of the similarity measure between objects. Second, a two-stage algorithm consisting of a coarse merging stage and a fine classification stage is proposed. In the coarse merging stage, a context-based region iterative merging algorithm is designed to merge most of the unambiguous superpixels in image domain at a high speed. In the fine classification stage, a fuzzy clustering algorithm incorporating hybrid optimization is developed to balance the efficiency and the robustness of the algorithm by simultaneously searching heuristically in the complete high-dimension feature space and searching along the direction of the gradient steepest descent in each feature subspace. The effectiveness of the proposed method has been successfully validated on synthetic and real SAR images. [ABSTRACT FROM PUBLISHER]

This paper presents a complete perception system of moving point detection and target tracking for robustly following target humans in an unknown indoor dynamic environment. To detect moving points under grid-based formulation, a modified inverse observation model is proposed to overcome several frequently happened detection limitations. Next, related human-extraction techniques are proposed to filter out less possible clusters for detecting potential human target from these moving points. Finally, the multiple hypothesis tracking algorithm is implemented to deal with the data association problem for enhancing the reliability and robustness of the human tracking when measurements are noisy. Different levels of experiments have been performed to evaluate the effectiveness of the proposed algorithm framework. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that, due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response, and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over cluster-sparse channels. [ABSTRACT FROM PUBLISHER]

Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers’ life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods. [ABSTRACT FROM PUBLISHER]

This paper presents a new robust EM algorithm for the finite mixture learning procedures. The proposed Spatial-EM algorithm utilizes median-based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. It is robust to outliers and initial values. Compared with many robust mixture learning methods, the Spatial-EM has the advantages of simplicity in implementation and statistical efficiency. We apply Spatial-EM to supervised and unsupervised learning scenarios. More specifically, robust clustering and outlier detection methods based on Spatial-EM have been proposed. We apply the outlier detection to taxonomic research on fish species novelty discovery. Two real datasets are used for clustering analysis. Compared with the regular EM and many other existing methods such as K-median, X-EM and SVM, our method demonstrates superior performance and high robustness. [ABSTRACT FROM AUTHOR]

For clustered wireless networks, the conventional dual-hop cooperation technique is inefficient since relays with imbalanced channels to the source and the destination may become the bottleneck of the overall transmission. In this paper, a novel cooperative framework called the three-stage relaying (TSR) scheme is proposed to address this problem. In TSR, relays are divided into two clusters/groups, and a virtual multiple-input–multiple-output (MIMO) antenna array is formed by introducing the transmission between the two relay groups. This “relay-to-relay” communication enables transmissions over shorter distances, which naturally breaks the bottleneck emerging in the dual-hop cooperation scheme. An optimization problem is formulated to deal with the relay selection for these two groups, aiming at maximizing the received signal-to-noise ratio (SNR) at the destination, subject to the number-of-relay constraint. This problem turns out to be nontrivial due to the coupling of the relays between two groups. We tackle this challenging problem by decomposing it into two subproblems: One deals with the selection of transmitting relays, and the other deals with the selection of receiving relays. We then propose two heuristic algorithms that achieve the tradeoff between the optimality of the solution and the computational complexity. Through extensive numerical simulations, we show the superiority of TSR over the dual-hop cooperation schemes in both the clustered and centered wireless networks in terms of symbol error rate and throughput. [ABSTRACT FROM PUBLISHER]

Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios. [ABSTRACT FROM PUBLISHER]

Customers of different contract types have different shapes in daily load profiles in the manner of different characteristics. Therefore, maximally capture local and global shape variability is essential in load profiling which exhibits the customers' different behaviors and characteristics. Existing approaches are focusing on the global property by considering all dimensions in the data set. However, the load shapes are determined by subspace of dimensions in most of the time. In this paper, we use subspace projection methods (subspace clustering and projected clustering) to capture such subspaces of load diagrams which maximize the difference between particular load shapes in different groups of customers. Also, we have treated clustering as classification to select most appropriate cluster numbers. The contribution of our study is that we have interpreted the strength and weakness of subspace projection method in load profiling. The result shows that subspace projection based method outperforms traditional clustering algorithms. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) remote sensing images provide detailed geometric information about land cover. As a result, it is possible to detect more subtle changes with the help of HSR images. However, due to the increased spatial resolution and the limited spectral information, it is difficult to identify the real changes only through the spectral feature of the image. To fully explore the spectral–spatial information and improve the change detection performance for HSR images, this paper proposes the hybrid conditional random field (HCRF) model, which combines the traditional random field method with an object-based technique. In the proposed method, the spectral discriminative information of a single pixel is extracted by the unary potential, which is modeled using a soft clustering method to make an initial separation of changed and unchanged pixels. The pairwise potential then considers the contextual information of adjacent pixels to favor spatial smoothing. An object term is also introduced in the HCRF model to keep the homogeneity of changed objects. By the use of these approaches, the oversmoothing problem of the random field-based methods and the detection error caused by the segmentation strategy in the object-based methods can be relieved. The proposed method was tested on three HSR image data sets and outperformed the compared state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With an unprecedented amount of sensitive private data generated by mobile user equipment (UE), securing the emerging ultra-dense networks (UDNs) becomes critical. Although involving more access points (APs) is potentially capable of enhancing both the UE’s throughput and security, the energy consumption becomes significant. In this paper, we investigate secure UDNs in the context of the user-centric clustering of UDNs from a secrecy energy efficiency perspective, while satisfying both the throughput and the security of each UE. We first propose a secure user-centric clustering architecture by introducing both a dedicated jamming strategy and an embedded jamming strategy, both of which degrade the overheard signals of the eavesdroppers and guarantee secure transmission relying on the different APs’ involvement status. We formulate the secure user-centric clustering design for both known and unknown eavesdropper channel state information (CSI), whilst maximizing the secrecy energy efficiency with the aid of various secure transmission schemes. Since the problem formulated is a non-convex mixed integer non-linear programming problem, we develop a set of heuristic greedy secure user-centric clustering algorithms for diverse operating scenarios. Finally, our numerical results reveal the quantitative benefits of the proposed secure user-centric clustering architectures as a function of the network densities (i.e., AP, UE, and eavesdropper) and of both the throughput and the security constraints on the secrecy energy efficiency trade-off in different scenarios. [ABSTRACT FROM AUTHOR]