ALBERT

Description: Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

Description: Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. In addition, in order to achieve high object detection performance, we propose a new approach to extracting low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset.

Description: This paper addresses the problem of matching common node correspondences among multiple graphs referring to an identical or related structure. This multi-graph matching problem involves two correlated components: i) the local pairwise matching affinity across pairs of graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matchings by different composition orders. Previous studies typically either enforce the matching consistency constraints in the beginning of an iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimization and consistency enforcement into two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function especially when the function is biased due to noises or inappropriate modeling. We propose composition-based multi-graph matching methods to incorporate the two aspects by optimizing the affinity score, meanwhile gradually infusing the consistency. We also propose two mechanisms to elicit the common inliers against outliers. Compelling results on synthetic and real images show the competency of our algorithms.

Description: Standard edge detection operators such as the Laplacian of Gaussian and the gradient of Gaussian can be used to track contours in image sequences. When using edge operators, a contour, which is determined on a frame of the sequence, is simply used as a starting contour to locate the nearest contour on the subsequent frame. However, the strategy used to look for the nearest edge points may not work when tracking contours of non isolated gray level discontinuities. In these cases, strategies derived from the optical flow equation, which look for similar gray level distributions, appear to be more appropriate since these can work with a lower frame rate than that needed for strategies based on pure edge detection operators. However, an optical flow strategy tends to propagate the localization errors through the sequence and an additional edge detection procedure is essential to compensate for such a drawback. In this paper a spatio-temporal intensity moment is proposed which integrates the two basic functions of edge detection and tracking.

Description: Design and development of efficient and accurate feature descriptors is critical for the success of many computer vision applications. This paper proposes a new feature descriptor, referred to as DoN, for the 2D palmprint matching. The descriptor is extracted for each point on the palmprint. It is based on the ordinal measure which partially describes the difference of the neighboring points’ normal vectors. DoN has at least two advantages: 1) it describes the 3D information, which is expected to be highly stable under commonly occurring illumination variations during contactless imaging; 2) the size of DoN for each point is only one bit, which is computationally simple to extract, easy to match, and efficient to storage. We show that such 3D information can be extracted from a single 2D palmprint image. The analysis for the effectiveness of ordinal measure for palmprint matching is also provided. Four publicly available 2D palmprint databases are used to evaluate the effectiveness of DoN, both for identification and the verification. Our method on all these databases achieves the state-of-the-art performance.

Description: Searching for matches to high-dimensional vectors using hard/soft vector quantization is the most computationally expensive part of various computer vision algorithms including the bag of visual word (BoW). This paper proposes a fast computation method, Neighbor-to-Neighbor (NTN) search [1] , which skips some calculations based on the similarity of input vectors. For example, in image classification using dense SIFT descriptors, the NTN search seeks similar descriptors from a point on a grid to an adjacent point. Applications of the NTN search to vector quantization, a Gaussian mixture model, sparse coding, and a kernel codebook for extracting image or video representation are presented in this paper. We evaluated the proposed method on image and video benchmarks: the PASCAL VOC 2007 Classification Challenge and the TRECVID 2010 Semantic Indexing Task. NTN-VQ reduced the coding cost by 77.4 percent, and NTN-GMM reduced it by 89.3 percent, without any significant degradation in classification performance.

Description: Typically, site survey is an inevitable phase for WiFi fingerprint-based localization which is regarded as one of the most promising techniques for indoor localization. However, the site survey can cause poten...

Description: Power control (PC) is a key solution to enable spectrum sharing between secondary users (SUs) and primary users (PUs). However, previous research lacks sensing uncertainties for the status of PUs. In this arti...

Description: With the increasing demands of wireless communication, flexible, complex, and diversified wireless communication applications are required. However, the difficulty of enabling new wireless communication applic...

Description: Developments of wireless body area networks (WBANs) facilitate the pervasive health monitoring with mHealth applications. WBANs can support continuous health monitoring for the human body in convenience and hi...

Description: Wireless personal area networks (WPAN) and wireless local area networks (WLAN) based on IEEE 802.15.4 and 802.11 standards operate in overlapping unlicensed frequency bands; therefore, they create harmful inte...

Description: A time domain sub-sampling technique that can be applied to wired or wireless orthogonal frequency division multiplexing systems is described in this paper. Focus is given on the specific conditions that allow...

Description: Micro-blog has become an emerging application in the Internet in recent years, and affective computing and sentiment analysis for micro-blog have been a vital research project in computer science, natural ling...

Description: Future wireless communication systems are demanding a more flexible physical layer. GFDM is a block filtered multicarrier modulation scheme proposed to add multiple degrees of freedom and to cover other wavefo...

Description: Recently manifold learning has received extensive interest in the community of pattern recognition. Despite their appealing properties, most manifold learning algorithms are not robust in practical applications. In this paper, we address this problem in the context of the Hessian locally linear embedding (HLLE) algorithm and propose a more robust method, called RHLLE, which aims to be robust against both outliers and noise in the data. Specifically, we first propose a fast outlier detection method for high-dimensional datasets. Then, we employ a local smoothing method to reduce noise. Furthermore, we reformulate the original HLLE algorithm by using the truncation function from differentiable manifolds. In the reformulated framework, we explicitly introduce a weighted global functional to further reduce the undesirable effect of outliers and noise on the embedding result. Experiments on synthetic as well as real datasets demonstrate the effectiveness of our proposed algorithm.

Description: A new orthogonal projection method for computing the minimum distance between a point and a spatial parametric curve is presented. It consists of a geometric iteration which converges faster than the existing Newton’s method, and it is insensitive to the choice of initial values. We prove that projecting a point onto a spatial parametric curve under the method is globally second-order convergence.

Description: Full duplex (FD) protocol has been widely used in wireless communications, which could transmit and receive signals at the meantime. In this paper, considering the worst-case channel uncertainty, opportunistic...

Description: This paper presents the problem formulation, development, and use of a robust dynamic genetic algorithm (GA) for channel allocation in cognitive radio. This approach offers an efficient way to access available...

Description: In modern wireless communication system, power amplifier (PA) is an important component which is expected to be operated at the region of high power efficiency, but in this region, PA is inherently nonlinear. ...

Description: Quality of service (QoS) enhancement is one of the prominent research areas in a mobile ad hoc network in which a variety of routing protocols is introduced. However, those routing protocols do not support eff...

Description: This paper presents a new clustering mechanism for the multi-hop relay in vehicular communication. In the proposed mechanism, the cluster-head selection is implemented using the enhanced hybrid wireless mesh p...

Description: To compensate for the adverse effects caused by radio link in worldwide interoperability for Microwave Access (WiMAX) networks, many radio resource management approaches have been present to assign orthogonal ...

Description: Transmission of system-critical control information plays a key role in efficient management of limited wireless network resources and successful reception of payload data information. This paper uses an ortho...

Description: Multi-hop network has received growing attention recently with the widely use of wireless network communication technology. At the same time, the security of multi-hop network is facing more serve challenges. ...

Description: In this paper, we develop an adaptive remote radio head (RRH) control scheme to maximize the network capacity of frequency division duplexing (FDD)-based cloud radio access networks. We focus on a realistic pe...

Description: IPv6 protocol, which should replace the actual IPv4 protocol, brings many new possibilities and improvements considering simplicity, routing speed, quality of service, and security. In comparison to IPv4, IPv6...

Description: We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of very slow convergence. We show that in a special, but important case, it is possible to obtain significantly better bounds on the convergence rate. This special case is when the huge state space can be aggregated into a smaller number of clusters, in which the states behave approximately the same way (but their behavior still may not be identical). A Markov chain with this structure is called quasi-lumpable. This property allows the aggregation of states (nodes) into clusters. Our main contribution is a rigorously proved bound on the rate at which the aggregated state distribution approaches its limit in quasi-lumpable Markov chains. We also demonstrate numerically that in certain cases this can indeed lead to a significantly accelerated way of estimating the measure of subsets. The result can be a useful tool in the analysis of complex networks, whenever they have a clustering that aggregates nodes with similar (but not necessarily identical) behavior.

Description: This paper addresses the problem of how to provide a more flexible service for complex event monitoring in wireless multimedia sensor networks (WMSNs). In particular, we propose C2EM, a cloud-assisted complex event monitoring architecture that involves scalar sensors, camera sensors, cloudlets, and clouds to leverage computation offloading reliability, service response time, coverage efficiency, and energy efficiency. On clouds, we design an opportunistic service access point selection scheme that provides quality of service (QoS) supports for scalar sensor computation offloading. Meanwhile, clouds are responsible for optimizing camera sensor utilization of the whole network. On cloudlets, we design a real-time camera actuation scheme with the objective of minimizing the possible coverage overlaps while providing probabilistic guarantee in residual energy. Through computation division, most complex computations and network environment profilers are executed on cloudlets or clouds. Sensors only need to carry out very simple operations. We evaluate the performance of C2EM through simulations under a complex event scenario. The results demonstrate that C2EM can enhance complex event monitoring performance with optimized energy efficiency, desirable event coverage quality, and potential adaptability to the dynamics of complex events.

Description: Iterative decoding is an effective technique to approach the channel capacity for very large block sizes with enough iterations. However, due to the limitation of bandwidth and delay, small blocks of data are much more commonly applied in practical communications, and low iteration counts are usually preferred for both decoding complexity and delay consideration. In such cases, the design rules of near capacity decoding—which is generally asymptotic with respect to the block size—may cause inferior performance. To overcome this problem for 8-phase shift keying (8PSK) modulated variable length codes (VLCs), an irregular mapping scheme for the transmission system of bit-interleaved coded modulation with iterative decoding (BICM-ID) is studied in this paper. A submapping searching algorithm and an irregular mapping optimization algorithm are proposed aiming at maximizing the extrinsic mutual information after a target number of iterations. Simulation results show that for small data block size with a low iteration count, our scheme has advantages with respect to the existing near capacity systems optimized by the asymptotic tools.

Description: The integration of wireless sensor network (WSN) and cognitive radio (CR) technology enables a new paradigm of communication: cognitive radio sensor networks (CRSN). The existing WSN clustering algorithm cannot consider the advantage of channel resource brought by CR function in CRSN, and the CR network (CRN) clustering algorithm is designed based on the infinite energy nodes; thus both algorithms cannot operate with energy efficiency in CRSN. The paper proposes a low-energy adaptive uneven clustering hierarchy for CRSN, which can not only consider the advantage of the channel resource in reducing the energy consumption but also employ uneven clustering method for balancing the energy consumption among the cluster heads under multiple hops transmission means. Simulation results show that compared with the existing several typical clustering algorithms including WSN and CRSN clustering algorithms, low-energy adaptive clustering hierarchy (LEACH), HEED, energy-efficient unequal clustering (EEUC), cognitive LEACH (CogLEACH), and distributed spectrum-aware clustering (DSAC), the proposed algorithm can not only efficiently balance the energy consumption among cluster heads and network load in CRSN but also remarkably prolong the network lifetime.

Description: Conditional mutual information (CMI) analysis and precoding design for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels are presented in this paper. Although some particular scenarios have been examined in existing publications, this paper investigates a generally correlated transmission system having spatially correlated channel, mutually correlated source symbols, and additive colored Gaussian noise (ACGN). First, without precoding techniques, we derive the optimized source symbol covariances upon mutual information maximization. Secondly, we apply a precoding technique and then design the precoder in two cases: maximizing the mutual information and minimizing the detection error. Since the optimal design for the end-to-end system cannot be analytically obtained in closed form due to the non-monotonic nature, we relax the optimization problem and attain sub-optimal designs in closed form. Simulation results show that without precoding, the average mutual information obtained by the asymptotic design is very close to the one obtained by the optimal design, while saving a huge computational complexity. When having the proposed precoding matrices, the end-to-end mutual information significantly increases while it does not require resources of the system such as transmission power or bandwidth.

Description: The worldwide transition from analog to digital broadcasting has now been completed, and the need to study next-generation standards for ultra-high-definition TV (UHDTV) broadcasting, as well as broadcasting and communication convergence systems is rapidly growing. In particular, high-resolution mobile broadcasting services are needed to satisfy recent consumer demands. Therefore, the development of highly efficient convergence broadcasting systems that provide fixed/mobile broadcasting through a single channel is needed. In this paper, a service scenario and the requirements for providing 4 K UHD and high-definition (HD) convergence broadcasting services through a terrestrial single channel are analyzed by employing the latest transmission and video codec technologies. Optimized transmission parameters for 6- and 8-MHz terrestrial bandwidths are drawn, and receiving performances are measured under additive white Gaussian noise (AWGN) and time-varying typical urban (TU)-6 channel to find the threshold of visibility (TOV). From the results, reliable receiving of HD layer data can be achieved at a 6-MHz bandwidth when the maximum receiver velocity is 140 km/h and no higher due to the limit of bandwidth. When the bandwidth is extended to 8 MHz, reliable receiving of both 4 K UHD and HD layer data can be achieved under a very fast fading multipath channel.

Description: In this paper, we consider a two-tier macrocell/ femtocell overlaid heterogeneous network based on orthogonal frequency division multiple access (OFDMA) technology. Although the co-channel spectrum allocation provides larger bandwidth for both macrocell and femtocells, the resulting cross-tier interference may prevent macrocell users in the vicinity of femtocells to achieve their minimum required signal-to-interference plus noise ratio (SINR) in downlink. Therefore, we propose femtocell power control strategies for mitigating the interference experienced by macrocell users while preventing the femtocell throughput degradation. In particular, the proposed power control schemes make use of femto and macro users’ context information in terms of positioning for setting the appropriate prioritization weights among the current victim macro users and the femto users in outage. System-level simulations show that our schemes enhance the throughput of macrocell users while maintaining a high performance for femtocell users compared to a conventional power allocation. Moreover, we show that the proposed prioritization weights allow to achieve the required level of macrocell/femtocell throughput trade-off.

Description: The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.

Description: In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown.

Description: We propose a linear time algorithm, called G2DLP, for generating 2D lattice L(n1, n2) paths, equivalent to two-item  multiset permutations, with a given number of turns. The usage of turn has three meanings: in the context of multiset permutations, it means that two consecutive elements of a permutation belong to two different items; in lattice path enumerations, it means that the path changes its direction, either from eastward to northward or from northward to eastward; in open shop scheduling, it means that we transfer a job from one type of machine to another. The strategy of G2DLP is divide-and-combine; the division is based on the enumeration results of a previous study and is achieved by aid of an integer partition algorithm and a multiset permutation algorithm; the combination is accomplished by a concatenation algorithm that constructs the paths we require. The advantage of G2DLP is twofold. First, it is optimal in the sense that it directly generates all feasible paths without visiting an infeasible one. Second, it can generate all paths in any specified order of turns, for example, a decreasing order or an increasing order. In practice, two applications, scheduling and cryptography, are discussed.

Description: A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large datasets of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to significantly improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead.

Description: While 3D object-centered shape-based models are appealing in comparison with 2D viewer-centered appearance-based models for their lower model complexities and potentially better view generalizabilities, the learning and inference of 3D models has been much less studied in the recent literature due to two factors: i) the enormous complexities of 3D shapes in geometric space; and ii) the gap between 3D shapes and their appearances in images. This paper aims at tackling the two problems by studying an And-Or Tree (AoT) representation that consists of two parts: i) a geometry-AoT quantizing the geometry space, i.e. the possible compositions of 3D volumetric parts and 2D surfaces within the volumes; and ii) an appearance-AoT quantizing the appearance space, i.e. the appearance variations of those shapes in different views. In this AoT, an And-node decomposes an entity into constituent parts, and an Or-node represents alternative ways of decompositions. Thus it can express a combinatorial number of geometry and appearance configurations through small dictionaries of 3D shape primitives and 2D image primitives. In the quantized space, the problem of learning a 3D object template is transformed to a structure search problem which can be efficiently solved in a dynamic programming algorithm by maximizing the information gain. We focus on learning 3D car templates from the AoT and collect a new car dataset featuring more diverse views. The learned car templates integrate both the shape-based model and the appearance-based model to combine the benefits of both. In experiments, we show three aspects: 1) the AoT is more efficient than the frequently used octree method in space representation; 2) the learned 3D car template matches the state-of-the art performances on car detection and pose estimation in a public multi-view car dataset; and 3) in our new dataset, the learned 3D template solves the joint task of simultaneous object detection, pose/view estimation, and part locali- ation. It can generalize over unseen views and performs better than the version 5 of the DPM model in terms of object detection and semantic part localization.

Description: Semantic segmentation and object detection are nowadays dominated by methods operating on regions obtained as a result of a bottom-up grouping process (segmentation) but use feature extractors developed for recognition on fixed-form (e.g. rectangular) patches, with full images as a special case. This is most likely suboptimal. In this paper we focus on feature extraction and description over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors inside such free-form regions. We introduce second-order generalizations of average and max-pooling that together with appropriate non-linearities, derived from the mathematical structure of their embedding space, lead to state-of-the-art recognition performance in semantic segmentation experiments without any type of local feature coding. In contrast, we show that codebook-based local feature coding is more important when feature extraction is constrained to operate over regions that include both foreground and large portions of the background, as typical in image classification settings, whereas for high-accuracy localization setups, second-order pooling over free-form regions produces results superior to those of the winning systems in the contemporary semantic segmentation challenges, with models that are much faster in both training and testing.

Description: Autoencoders are popular feature learning models, that are conceptually simple, easy to train and allow for efficient inference. Recent work has shown how certain autoencoders can be associated with an energy landscape, akin to negative log-probability in a probabilistic model, which measures how well the autoencoder can represent regions in the input space. The energy landscape has been commonly inferred heuristically, by using a training criterion that relates the autoencoder to a probabilistic model such as a Restricted Boltzmann Machine (RBM). In this paper we show how most common autoencoders are naturally associated with an energy function, independent of the training procedure, and that the energy landscape can be inferred analytically by integrating the reconstruction function of the autoencoder. For autoencoders with sigmoid hidden units, the energy function is identical to the free energy of an RBM, which helps shed light onto the relationship between these two types of model. We also show that the autoencoder energy function allows us to explain common regularization procedures, such as contractive training, from the perspective of dynamical systems. As a practical application of the energy function, a generative classifier based on class-specific autoencoders is presented.

Description: This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.

Description: Random forests works by averaging several predictions of de-correlated trees. We show a conceptually radical approach to generate a random forest: random sampling of many trees from a prior distribution, and subsequently performing a weighted ensemble of predictive probabilities. Our approach uses priors that allow sampling of decision trees even before looking at the data, and a power likelihood that explores the space spanned by combination of decision trees. While each tree performs Bayesian inference to compute its predictions, our aggregation procedure uses the power likelihood rather than the likelihood and is therefore strictly speaking not Bayesian. Nonetheless, we refer to it as a Bayesian random forest but with a built-in safety. The safeness comes as it has good predictive performance even if the underlying probabilistic model is wrong. We demonstrate empirically that our Safe-Bayesian random forest outperforms MCMC or SMC based Bayesian decision trees in term of speed and accuracy, and achieves competitive performance to entropy or Gini optimised random forest, yet is very simple to construct.

Description: Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of an HTML document and consequently boost the performance of a keyword-based search engine. We present a Web-scalable system that exploits a pure text-based search engine to find an initial set of candidate documents for a given query. Then, the candidate set is reranked using visual information extracted from the images contained in the pages. The resulting system retains the computational efficiency of traditional text-based search engines with only a small additional storage cost needed to encode the visual information. We test our approach on one of the TREC Million Query Track benchmarks where we show that the exploitation of visual content yields improvement in accuracies for two distinct text-based search engines, including the system with the best reported performance on this benchmark. We further validate our approach by collecting document relevance judgements on our search results using Amazon Mechanical Turk. The results of this experiment confirm the improvement in accuracy produced by our image-based reranker over a pure text-based system.

Description: This paper proposes a deterministic explanation for mutual-information-based image registration (MI registration). The explanation is that MI registration works because it aligns certain image partitions. This notion of aligning partitions is new, and is shown to be related to Schur- and quasi-convexity. The partition-alignment theory of this paper goes beyond explaining mutual- information. It suggests other objective functions for registering images. Some of these newer objective functions are not entropy-based. Simulations with noisy images show that the newer objective functions work well for registration, lending support to the theory. The theory proposed in this paper opens a number of directions for further research in image registration. These directions are also discussed.

Description: Spectrum aggregation is an emerging technology to satisfy the data rate requirement of broadband services for next-generation wireless communication systems. In dynamic spectrum environment, in which the spectrum availability is time-varying, it is quite challenging to maintain the stability of spectrum aggregation. In this paper, we investigate the spectrum sensing and access schemes to minimize the times of channel switching for achieving stable dynamic spectrum aggregation, taking into consideration the hardware limitations of spectrum sensing and aggregation capability. We develop an analytical framework for the joint spectrum sensing and access problem based on partially observable Markov decision process (POMDP). Especially, we derive the reward function by estimation of the stability of different spectrum sensing and access strategies. Based on the POMDP framework, we propose a rollout-based suboptimal spectrum sensing and access scheme which approximates the value function of POMDP, and propose a differential training method to improve its robustness. It is proved that the rollout policy achieves performance improvement over the basis heuristics. The simulation results show that the proposed POMDP-based spectrum sensing and access scheme improves the system stability significantly and achieves near-optimal performance with a much lower complexity.

Description: Cognitive radio networks (CRNs) have emerged as a promising paradigm that can solve the shortage of spectrum resources, providing the ability to adapt and opportunistically exploit the spectrum holes. The rendezvous process allows cognitive users to find a common channel and establish a communication link. In this paper, we focus on the design of fast blind rendezvous algorithms to guarantee that every node should be able to rendezvous in all common available channels. We follow a systematic approach by first proposing a role-based algorithm that ensures maximum rendezvous diversity and then extending it to a common strategy through the use of multiples radios. Both proposals guarantee rendezvous under symmetric and asymmetric models. Simulation results show that our proposals outperform other recently developed rendezvous protocols with similar approaches in terms of expected time to rendezvous and maximum time to rendezvous.

Description: We consider a system that consists of two sources, a half-duplex relay and a destination. The sources want to transmit their messages reliably to the destination with the help of the relay. We study and analyze the performance of a transmission scheme in which the relay implements a decode-and-forward strategy. We assume that all the channels are frequency selective, and in order to cope with that, we incorporate Orthogonal Frequency-Division Multiplexing (OFDM) transmission into the system. In contrast to previous works, both sources can transmit their messages using all subcarriers and the relay can decide to help none, only one, or both sources. For this scheme, we discuss the design criteria and evaluate the achievable sum-rate. Next, we study and solve the problem of resource allocation aiming at maximizing the achievable sum-rate. We propose an iterative coordinate-descent algorithm that finds a solution that is at least a local optimum. We show through numerical examples the effectiveness of the algorithms and illustrate the benefits of allowing both sources to transmit on all subcarriers.

Description: In this paper, we consider a denoise-and-forward (DNF) two-way relay network (TWRN) with non-coherent differential binary phase-shift keying modulation, where a battery-free relay node harvests energy from the received radio frequency (RF) signals and uses the harvested energy to help the source nodes for information exchange. Based on the power splitting (PS) and time switching (TS) receiver architectures, power splitting relaying (PSR) and time switching relaying (TSR) protocols at relay are studied. In order to investigate the effect of power allocations on two source nodes, power splitting coefficient and time switching factor at relay on performance, the two proposed protocols are analyzed and the bit error rate (BER) expressions of end-to-end system are derived. Based on these expressions, the optimal power of sources, the power splitting ratio and the time switching factor are obtained via the numerical search method. The simulation and numerical results provide practical insights into the effect of various system parameters, such as the power splitting coefficient, the time switching factor, sources to relay distances, the noise power, and the energy harvesting efficiency on the performance of this TWRN. In addition, the results show that the PSR protocol outperforms the TSR protocol in terms of throughput under various network geometries.

Description: In this paper, we validate the availability of direct path in half-duplex-based cooperative relay networks from a practical point of view. Cooperative relaying is a low-complexity technique, which schedules orthogonal transmissions through the divided time slots. By doing so, transmission impairments due to multi-path fading and path loss are mitigated by obtaining a diversity gain. In conventional approaches, most researchers have focused on a role of relay and assumed that the received signal-to-noise ratio in source-to-destination link is doubled when source transmits the same signal twice during the two transmission phases. However, in practical wireless environments, a wireless channel is not static but varies with time. Thus, although the source retransmits the same signal during the second transmission phase instead of forwarding by a relay, the (time) diversity gain may be obtained. As a result, the performance of relaying-aided cooperative communication is not always better than that of the repeated transmission (RT), but the RT scheme may be a better option than a cooperative relaying scheme. To this end, we first show that the RT scheme is comparable to conventional cooperative relaying schemes. We then propose a selection decode-and-forward (DF) relaying scheme, which combines the DF relaying and RT schemes. The proposed selection DF relaying scheme has better outage performance than comparable relaying schemes in time-varying channels. Lastly, all the theoretical results are validated through numerical evaluations and Monte Carlo simulations.

Description: In 4G cellular networks, call admission control (CAC) has a direct impact on quality of service (QoS) for individual connections and overall system efficiency. Reservation-based CAC schemes have been previously proposed for cellular networks where a certain amount of system bandwidth is reserved for high-priority calls, e.g., hand-off calls and real-time new calls. Traditional reservation-based schemes are not efficient for 4G vehicular networks, as the reserved bandwidth may not be utilized effectively in low hand-off rates. We propose a channel borrowing approach in which new best effort (BE) calls can borrow the reserved bandwidth for high-priority calls. Later, if a hand-off call arrives and all the channels are busy, it will pre-empt the service of a borrower BE call if there exists any. The pre-empted BE calls are kept in a queue and resume their service whenever a channel becomes available. The analytical model for this scheme is a mixed loss-queueing system for which it is difficult to calculate call blocking probability (CBP) and call dropping probability (CDP). Our focus in this paper is on the system modeling and performance evaluation of the proposed scheme. We present two system models that approximate the operation of the proposed scheme. For these models, we derive the CBP and CDP analytically. It is shown that our analytical results are very close to the ones obtained from simulations. Furthermore, it is observed that our channel borrowing approach decreases the CBP considerably while increases the CDP slightly over a large range of hand-off rates.

Description: In this work we generate the numerical solutions of Burgers’ equation by applying the Crank-Nicholson method and different schemes for solving nonlinear systems, instead of using Hopf-Cole transformation to reduce Burgers’ equation into the linear heat equation. The method is analyzed on two test problems in order to check its efficiency on different kinds of initial conditions. Numerical solutions as well as exact solutions for different values of viscosity are calculated, concluding that the numerical results are very close to the exact solution.

Description: Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.

Description: Demographic estimation entails automatic estimation of age, gender and race of a person from his face image, which has many potential applications ranging from forensics to social media. Automatic demographic estimation, particularly age estimation, remains a challenging problem because persons belonging to the same demographic group can be vastly different in their facial appearances due to intrinsic and extrinsic factors. In this paper, we present a generic framework for automatic demographic (age, gender and race) estimation. Given a face image, we first extract demographic informative features via a boosting algorithm, and then employ a hierarchical approach consisting of between-group classification, and within-group regression. Quality assessment is also developed to identify low-quality face images that are difficult to obtain reliable demographic estimates. Experimental results on a diverse set of face image databases, FG-NET ( $1K$ images), FERET ( $3K$ images), MORPH II ( $75K$ images), PCSO ( $100K$ images), and a subset of LFW ( $4K$ images), show that the proposed approach has superior performance compared to t- e state of the art. Finally, we use crowdsourcing to study the human perception ability of estimating demographics from face images. A side-by-side comparison of the demographic estimates from crowdsourced data and the proposed algorithm provides a number of insights into this challenging problem.

Description: Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems.

Description: The high complexity of multi-scale, category-level object detection in cluttered scenes is efficiently handled by Hough voting methods. However, the main shortcoming of the approach is that mutually dependent local observations are independently casting their votes for intrinsically global object properties such as object scale. Object hypotheses are then assumed to be a mere sum of their part votes. Popular representation schemes are, however, based on a dense sampling of semi-local image features, which are consequently mutually dependent. We take advantage of part dependencies and incorporate them into probabilistic Hough voting by deriving an objective function that connects three intimately related problems: i) grouping mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Early commitments are avoided by not restricting parts to only a single vote for a locally best correspondence and we learn a weighting of parts during training to reflect their differing relevance for an object. Experiments successfully demonstrate the benefit of incorporating part dependencies through grouping into Hough voting. The joint optimization of groupings, correspondences, and votes not only improves the detection accuracy over standard Hough voting and a sliding window baseline, but it also reduces the computational complexity by significantly decreasing the number of candidate hypotheses.

Description: A proper temporal model is essential to analysis tasks involving sequential data. In computer-assisted surgical training, which is the focus of this study, obtaining accurate temporal models is a key step towards automated skill-rating. Conventional learning approaches can have only limited success in this domain due to insufficient amount of data with accurate labels. We propose a novel formulation termed Relative Hidden Markov Model and develop algorithms for obtaining a solution under this formulation. The method requires only relative ranking between input pairs, which are readily available from training sessions in the target application, hence alleviating the requirement on data labeling. The proposed algorithm learns a model from the training data so that the attribute under consideration is linked to the likelihood of the input, hence supporting comparing new sequences. For evaluation, synthetic data are first used to assess the performance of the approach, and then we experiment with real videos from a widely-adopted surgical training platform. Experimental results suggest that the proposed approach provides a promising solution to video-based motion skill evaluation. To further illustrate the potential of generalizing the method to other applications of temporal analysis, we also report experiments on using our model on speech-based emotion recognition.

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Description: We present a fully automatic system for extracting the semantic structure of a typical academic presentation video, which captures the whole presentation stage with abundant camera motions such as panning, tilting, and zooming. Our system automatically detects and tracks both the projection screen and the presenter whenever they are visible in the video. By analyzing the image content of the tracked screen region, our system is able to detect slide progressions and extract a high-quality, non-occluded, geometrically-compensated image for each slide, resulting in a list of representative images that reconstruct the main presentation structure. Afterwards, our system recognizes text content and extracts keywords from the slides, which can be used for keyword-based video retrieval and browsing. Experimental results show that our system is able to generate more stable and accurate screen localization results than commonly-used object tracking methods. Our system also extracts more accurate presentation structures than general video summarization methods, for this specific type of video.

Description: A robust and effective specular highlight removal method is proposed in this paper. It is based on a key observation—the maximum fraction of the diffuse colour component in diffuse local patches in colour images changes smoothly. The specular pixels can thus be treated as noise in this case. This property allows the specular highlights to be removed in an image denoising fashion: an edge-preserving low-pass filter (e.g., the bilateral filter) can be used to smooth the maximum fraction of the colour components of the original image to remove the noise contributed by the specular pixels. Recent developments in fast bilateral filtering techniques enable the proposed method to run over $200times$ faster than state-of-the-art techniques on a standard CPU and differentiates it from previous work.

Description: We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. First, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Second, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple ”wild” databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources.

Description: The challenges of future wireless networks imply that the physical layer of the next generation mobile communication system needs to be compatible with multiple-input multiple-output and allow a flexible multiple access scheme. Time-reversal space-time coding can be applied to a recent filtered multicarrier modulation scheme, named generalized frequency division multiplexing, to achieve a multiple-input multiple-output non-orthogonal multicarrier modulation with flexibility to address the requirements of future mobile networks. In this paper, the subcarriers of the physical layer block are shared between multiple users, allowing for an efficient and simple multiple access solution. A channel estimation technique that can simultaneously estimate channel frequency response and timing misalignment in generalized frequency division multiple access is described. The paper also shows that the knowledge of channel state information can improve the overall performance when used to schedule the subchannel distribution among the users. Symbol error rate performance analysis shows that the resources of the time-reversal space-time coding generalized frequency division multiple access block can be shared among users that have one or two transmit antennas simultaneously under frequency-selective time-variant channels.

Description: This paper proposes a Decode-and-Forward (DF) relaying scheme for the multi-hop transmission in wireless networks, where the information generated by an independent source has to be sent to a far destination based on multiple-relay cooperation. The proposed DF scheme blends together convolutional channel coding with linear combination of blocks of data over a finite field using very short block lengths (K=13). We provide an extrinsic information transfer (EXIT) chart analysis to understand the good performance behavior of the proposed scheme when compared with other referenced schemes using much larger block lengths. This fact is corroborated by a set of Monte Carlo simulations. Moreover, the proposed DF scheme is suitable for large multi-hop networks since a negligible performance degradation is obtained when adding more hops.

Description: With the rapid development of cognitive radio technologies, spectrum sharing becomes a promising approach to improving the efficiency of spectrum utilization and mitigating the spectrum scarcity problem. Previous research on cognitive networks argues that secondary users can only work under a low-transmission power in an underlay spectrum sharing model, especially when the primary transmitter is far away from the primary receiver. Motivated by the idea of cooperative communications, in this paper, we propose a cooperative framework in which a primary transmitter, being aware of the existence of the secondary network, may select a secondary user that is not in transmitting or receiving mode to relay its traffic. The feasible relay location region and optimal power ratio between the primary network and the secondary network are derived in the underlay spectrum sharing model. Based on the optimal power ratio, we derive the maximum achievable transmission capacity of the secondary network under the outage constraints from both the primary and the secondary network with or without cooperative relaying. Numerical results indicate that secondary users can achieve a higher transmission capacity with cooperative relaying, and that the capacity gain of the cooperative network is significantly affected by the location of the relay and the network system parameters.

Description: We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diffusion (MSD) and a faster Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. The new family of algorithms can be viewed as a generalization of TRW-S from pairwise to higher-order graphical models. We test SRMP on several real-world problems with promising results.

Description: Objects occupy physical space and obey physical laws. To truly understand a scene, we must reason about the space that objects in it occupy, and how each objects is supported stably by each other. In other words, we seek to understand which objects would, if moved, cause other objects to fall. This 3D volumetric reasoning is important for many scene understanding tasks, ranging from segmentation of objects to perception of a rich 3D, physically well-founded, interpretations of the scene. In this paper, we propose a new algorithm to parse a single RGB-D image with 3D block units while jointly reasoning about the segments, volumes, supporting relationships, and object stability. Our algorithm is based on the intuition that a good 3D representation of the scene is one that fits the depth data well, and is a stable, self-supporting arrangement of objects (i.e., one that does not topple). We design an energy function for representing the quality of the block representation based on these properties. Our algorithm fits 3D blocks to the depth values corresponding to image segments, and iteratively optimizes the energy function. Our proposed algorithm is the first to consider stability of objects in complex arrangements for reasoning about the underlying structure of the scene. Experimental results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.

Description: Statistical optimality in multipartite ranking is investigated as an extension of bipartite ranking. We consider the optimality of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories with differential ranking costs. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted conditional probability of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with various ranking algorithms in machine learning. Further, the analysis of multipartite ranking with different costs provides a new perspective on non-smooth listwise ranking measures such as the discounted cumulative gain and preference learning. We illustrate our findings with simulation study and real data analysis.

Description: Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typically mounted high above the ground plane, causing serious perspective changes. Also, most algorithms approach matching across images using the same descriptors, regardless of camera viewpoint or human pose. Here, we introduce a re-identification algorithm that addresses both problems. We build a model for human appearance as a function of pose, using training data gathered from a calibrated camera. We then apply this “pose prior” in online re-identification to make matching and identification more robust to viewpoint. We further integrate person-specific features learned over the course of tracking to improve the algorithm’s performance. We evaluate the performance of the proposed algorithm and compare it to several state-of-the-art algorithms, demonstrating superior performance on standard benchmarking datasets as well as a challenging new airport surveillance scenario.

Description: Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant over time. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellite’s attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets.

Description: An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand its intensity trend and also to perform quantitative comparisons among different images in time and space. The proposed procedure is computationally rapid and stable, executing an initial guess parameter estimation by a multiple regression before the iterative nonlinear fitting. The Gaussian fit was applied to both low and high resolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization shown. As expected, a reduction of the correlation coefficient between the map values and the Gaussian surface was observed for the image with the higher spatial resolution due to the greater variability of the SUHI values. Since the fitting procedure provides a smoothed Gaussian surface, it has better performance when applied to low resolution images, even if the reliability of the SUHI pattern representation can be preserved also for high resolution images.

Description: In this paper, we consider a distributed virtual multiple-input multiple-output (MIMO) coalition formation algorithm. Energy savings are obtained in the reverse link by forming multi-antenna virtual arrays for information transmission. Virtual arrays are formed by finding a stable match between two sets of single antenna devices such as mobile stations (MSs) and relay stations (RSs) based on a game theoretic approach derived from the concept of the college admissions problem. Thus, power savings are obtained through multi-antenna arrays by implementing the concepts of spatial diversity and spatial multiplexing for reverse link transmission. We focus on optimizing the overall consumed power rather than the transmitted power of MSs and RSs. Furthermore, it is shown analytically and by simulation that when the overall consumed power is considered, the energy efficiency of the single antennas devices is not always improved by forming a virtual MIMO array. Hence, single antenna devices may prefer to transmit on their own when channel conditions are favorable. In addition, the simulation results show that the framework we propose provides comparable energy savings and a lower implementation complexity when compared to a centralized exhaustive search approach.

Description: In this paper we propose a new method for generating synthetic handwritten signature images for biometric applications. The procedures we introduce imitate the mechanism of motor equivalence which divides human handwriting into two steps: the working out of an effector independent action plan and its execution via the corresponding neuromuscular path. The action plan is represented as a trajectory on a spatial grid. This contains both the signature text and its flourish, if there is one. The neuromuscular path is simulated by applying a kinematic Kaiser filter to the trajectory plan. The length of the filter depends on the pen speed which is generated using a scalar version of the sigma lognormal model. An ink deposition model, applied pixel by pixel to the pen trajectory, provides realistic static signature images. The lexical and morphological properties of the synthesized signatures as well as the range of the synthesis parameters have been estimated from real databases of real signatures such as the MCYT Off-line and the GPDS960GraySignature corpuses. The performance experiments show that by tuning only four parameters it is possible to generate synthetic identities with different stability and forgers with different skills. Therefore it is possible to create datasets of synthetic signatures with a performance similar to databases of real signatures. Moreover, we can customize the created dataset to produce skilled forgeries or simple forgeries which are easier to detect, depending on what the researcher needs. Perceptual evaluation gives an average confusion of 44.06 percent between real and synthetic signatures which shows the realism of the synthetic ones. The utility of the synthesized signatures is demonstrated by studying the influence of the pen type and number of users on an automatic signature verifier.

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Description: We present a new approach for matching sets of branching curvilinear structures that form graphs embedded in ${mathbb {R}}^2$ or ${mathbb {R}}^3$ and may be subject to deformations. Unlike earlier methods, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformations, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian process regressions to represent the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches considered at each iteration is not selected at random as with many RANSAC-based algorithms. Instead, we introduce a so-called Active Testing Search strategy that performs a priority search to favor the most likely matches and speed-up the process. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.

Description: This article presents a characterization of different LTE-Advanced network deployments with regard to downlink interference and resource usage. The investigation focuses on heterogeneous networks (HetNets) with dedicated spectrum for each layer and, in particular, on cases where small cells are densely deployed. Thus, the main interference characteristics of the macro layer and the dense small cell layer are studied separately. Moreover, the potential benefit of mitigating the dominant interferer in such scenarios is quantified as an upper bound gain and its time variability is discussed and evaluated. A dynamic FTP traffic model is applied, with varying amounts of traffic in the network. The results present an uneven use of resources in all feasible load regions. The interference under the dynamic traffic model shows a strong variability, and the impact of the dominant interferer is such that 30% of the users could achieve at least a 50% throughput gain if said interferer were mitigated, with some users reaching a 300% improvement during certain time intervals. All the mentioned metrics are remarkably similar in the macro and small cell deployments, which suggests that densification does not necessarily imply stricter interference mitigation requirements. Therefore, the conclusion is that the same techniques could be applied in both scenarios to deal with the dominant interferer.

Description: The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.

Description: Direct-sequence optical code division multiple access technology is one efficient and simple way to reduce the multiple access interferences that existed in visible light communication system due to its ease implementation by simply turning the light-emitting diodes (LEDs) on and off. To overcome the multi-path effect and non-perfect clock impairment, an optical zero correlation zone code set is proposed in visible light communication quasi-synchronous optical code division multiple access system. Bit error rate performance is evaluated while taking into account the effects of phase-induced intensity noise, shot noise, and thermal noise. Based on the proposed optical zero correlation zone codes, the multiple access interference and phase-induced intensity noise can be eliminated finally; therefore, a better bit error rate can be obtained.

Description: Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, and analyze the membership of data points falling on the crisp boundary, then give the adjustment strategy. The proposed WFCS is validated both on simulated dataset and real dataset. The experimental results demonstrate that the proposed WFCS has the characteristics of hard clustering and fuzzy clustering, and outperforms many existing clustering algorithms with respect to three metrics: Rand Index, Xie-Beni Index and Within-Between(WB) Index.

Description: Cognitive radio is a promising technology that uses radio spectrum opportunistically and efficiently. In this paper, we present the performance of ‘Always transmit’ strategy, which consists of allowing the cognitive source to transmit over all time slots and in any time slot. Thus, the cognitive source will be able to operate continuously whenever either the primary user is active or absent. The proposed strategy is projected into dual-hop communication systems with decode-and-forward and relay selection over independent but not necessarily identically distributed Nakagami-m fading channels. We derive the closed-form expressions of the outage probability, and we analyze the performance. Simulation results show that the strategy ‘Always transmit + Relay’ outperforms other conventional cognitive radio strategies and the use of relays enhances the performance in deep fading channel.

Description: An efficient data dissemination mechanism is very critical for traffic safety and inquiring path information. All vehicles go straight and do not consider the effect of traffic lights during its running in the motorway. But in view of characteristic of motorway, the fast moving of vehicles will cause the frequent changes of network topology, which is a challenge for data dissemination for motorway environment in Vehicular ad hoc networks (VANETs). This paper proposes a novel data dissemination mechanism for VANETs which apply to broadcasting and on-demand unicast scenarios. We firstly propose a weighted K-nearest neighbors prediction algorithm with self-feedback to predict the current speed of a vehicle and construct a set of forwarders for emergency message broadcasting. To satisfy users’ personal demands, the paper also designs a unicast mechanism based on information urgent level and the improved Greedy Perimeter Stateless Routing (GPSR) protocol. The simulation results show that our proposed method can achieve better performance with higher delivery ratio, lower number of forwarders, and less copies of messages.

Description: This paper reports on a real-world wireless channel measurement campaign for in-vehicle scenarios in the UWB frequency range of 3 to 11 GHz. The effects of antenna placement in the vehicle’s passenger compartment as well as the effects due to the presence of passengers are studied. The measurements have been carried out in the frequency domain, and the corresponding channel impulse responses (CIRs) have been estimated by inverse Fourier transform. The influence of a specific band group selection within the whole UWB range is also given. Statistical analysis of the measured channel transfer functions gives a description of the wireless channel statistics in the form of a generalized extreme value process. The corresponding parameter sets are estimated and documented for all permutations of antenna placement and occupancy patterns inside the vehicle’s passenger compartment. Further, we have carried out a feasibility study of an in-vehicle UWB-based localization system based on the TOA. The positioning performance is evaluated in terms of average error and standard deviation.

Description: Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.

Description: We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.

Description: We seek a practical method for establishing dense correspondences between two images with similar content, but possibly different 3D scenes. One of the challenges in designing such a system is the local scale differences of objects appearing in the two images. Previous methods often considered only few image pixels; matching only pixels for which stable scales may be reliably estimated. Recently, others have considered dense correspondences, but with substantial costs associated with generating, storing and matching scale invariant descriptors. Our work is motivated by the observation that pixels in the image have contexts—the pixels around them—which may be exploited in order to reliably estimate local scales. We make the following contributions. (i) We show that scales estimated in sparse interest points may be propagated to neighboring pixels where this information cannot be reliably determined. Doing so allows scale invariant descriptors to be extracted anywhere in the image. (ii) We explore three means for propagating this information: using the scales at detected interest points, using the underlying image information to guide scale propagation in each image separately, and using both images together. Finally, (iii), we provide extensive qualitative and quantitative results, demonstrating that scale propagation allows for accurate dense correspondences to be obtained even between very different images, with little computational costs beyond those required by existing methods.

Description: A robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics.

Description: In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.

Description: Archetypal analysis is a popular exploratory tool that explains a set of observations as compositions of few ‘pure’ patterns. The standard formulation of archetypal analysis addresses this problem for real valued observations by finding the approximate convex hull. Recently, a probabilistic formulation has been suggested which extends this framework to other observation types such as binary and count. In this article we further extend this framework to address the general case of nominal observations which includes, for example, multiple-option questionnaires. We view archetypal analysis in a generative framework: this allows explicit control over choosing a suitable number of archetypes by assigning appropriate prior information, and finding efficient update rules using variational Bayes’. We demonstrate the efficacy of this approach extensively on simulated data, and three real world examples: Austrian guest survey dataset, German credit dataset, and SUN attribute image dataset.

Description: In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.

Description: Natural images are scale invariant with structures at all length scales.We formulated a geometric view of scale invariance in natural images using percolation theory, which describes the behavior of connected clusters on graphs.We map images to the percolation model by defining clusters on a binary representation for images. We show that critical percolating structures emerge in natural images and study their scaling properties by identifying fractal dimensions and exponents for the scale-invariant distributions of clusters. This formulation leads to a method for identifying clusters in images from underlying structures as a starting point for image segmentation.

Description: Life detection radar that combines radar technology with biomedical engineering detects human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance with non-contact method. I...

Description: 5G-Cloud-VMesh network (5CVN) is nascent as an economical alternative for building federated and community networks. In 5G milieu, the Aggregator Node B (ANB) is the base station with enhanced computing capabi...

Description: The fireworks algorithm (FA) is a new parallel diffuse optimization algorithm to simulate the fireworks explosion phenomenon, which realizes the balance between global exploration and local searching by means of adjusting the explosion mode of fireworks bombs. By introducing the grouping strategy of the shuffled frog leaping algorithm (SFLA), an improved FA-SFLA hybrid algorithm is put forward, which can effectively make the FA jump out of the local optimum and accelerate the global search ability. The simulation results show that the hybrid algorithm greatly improves the accuracy and convergence velocity for solving the function optimization problems.

Description: Fifth generation (5G) technical requirements turned toward end-to-end capacity increase with limited latency and constant network power consumption involve dedicated research topics. Multiple radio access tech...

Description: In this paper, we study the power allocation problem in multi-cell OFDMA networks, where given the tradeoff between user satisfaction and profit of the service provider, maximizing the revenue of the service p...

Description: Sensor networks are often part of a cyber-physical system. A large-scale sensor network often involves big data collection and data fusion. The agent technology has drawn much attention in wireless sensor netw...

Description: In this paper, we present a game-theoretic approach to the problem of joint transmit beamforming and power control in cognitive radio (CR) multiple-input multiple-output broadcast channels (MIMO-BCs), where th...

Description: To mitigate damages due to natural disasters and abruptly changing weather, the importance of a weather radar network system (WRNS) is growing. Because radars in the current form of a WRNS operate in distinct ...

Description: The gravitational search algorithm (GSA) is a kind of swarm intelligence optimization algorithm based on the law of gravitation. The parameter initialization of all swarm intelligence optimization algorithms has an important influence on the global optimization ability. Seen from the basic principle of GSA, the convergence rate of GSA is determined by the gravitational constant and the acceleration of the particles. The optimization performances on six typical test functions are verified by the simulation experiments. The simulation results show that the convergence speed of the GSA algorithm is relatively sensitive to the setting of the algorithm parameters, and the GSA parameter can be used flexibly to improve the algorithm’s convergence velocity and improve the accuracy of the solutions.

Description: Kung-Traub’s conjecture states that an optimal iterative method based on d function evaluations for finding a simple zero of a nonlinear function could achieve a maximum convergence order of 2 d − 1 . During the last years, many attempts have been made to prove this conjecture or develop optimal methods which satisfy the conjecture. We understand from the conjecture that the maximum order reached by a method with three function evaluations is four, even for quadratic functions. In this paper, we show that the conjecture fails for quadratic functions. In fact, we can find a 2-point method with three function evaluations reaching fifth order convergence. We also develop 2-point 3rd to 8th order methods with one function and two first derivative evaluations using weight functions. Furthermore, we show that with the same number of function evaluations we can develop higher order 2-point methods of order r + 2 , where r is a positive integer, ≥ 1 . We also show that we can develop a higher order method with the same number of function evaluations if we know the asymptotic error constant of the previous method. We prove the local convergence of these methods which we term as Babajee’s Quadratic Iterative Methods and we extend these methods to systems involving quadratic equations. We test our methods with some numerical experiments including an application to Chandrasekhar’s integral equation arising in radiative heat transfer theory.