Vector Search Research Articles

Study of vector boson scattering and search for new physics in events with two same-sign leptons and two jets.

A study of vector boson scattering in pp collisions at a center-of-mass energy of 8 TeV is presented. The data sample corresponds to an integrated luminosity of 19.4 fb(-1) collected with the CMS detector. Candidate events are selected with exactly two leptons of the same charge, two jets with large rapidity separation and high dijet mass, and moderate missing transverse energy. The signal region is expected to be dominated by electroweak same-sign W-boson pair production. The observation agrees with the standard model prediction. The observed significance is 2.0 standard deviations, where a significance of 3.1 standard deviations is expected based on the standard model. Cross section measurements for W(±)W(±) and WZ processes in the fiducial region are reported. Bounds on the structure of quartic vector-boson interactions are given in the framework of dimension-eight effective field theory operators, as well as limits on the production of doubly charged Higgs bosons.

Segregation of MRI brain image using hybrid evolutionary clustering algorithm

The present research is on computer-aided classification of the magnetic resonance brain images. The method proposed congregates on colour-converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on Information Gain and Sequential Forward Floating Search (IGSFFS) and Multi-Class Support Vector Machine (MC-SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The present research acknowledges the colour-converted segmentation by new hybrid evolutionary clustering algorithm which is the mixture of weighted firefly and K-means algorithm to overcome local optima problems in firefly algorithm. Further random cluster initialisation is also modelled. The results of the simulation show that the performance of the proposed algorithm has better segmentation accuracy than the other algorithms such as colour-converted PSO-K-means and K-means clustering algorithm. The performance of the method is evaluated using classification accuracy, sensitivity, specificity and Receiver Operating Characteristic (ROC) curves. The results show that the highest classification accuracy of greater than 98% is obtained for the proposed diagnostic model, and this is very promising compared to the previously reported results.

Early Termination of Block Vector Search for Fast Encoding of HEVC Screen Content Coding

This paper proposes an early termination method of a block vector search for fast encoding of high efficiency video coding (HEVC) screen content coding (SCC). In the proposed algorithm, two blocks indicated by two block vector predictors (BVPs) were first employed as an intra block copy (IBC) search. If the sum of absolute difference (SAD) value of the block is less than a threshold defined empirically, an IBC BV search is terminated early. The initial threshold for early termination is derived by statistical analysis and it can be modified adaptively based on a quantization parameter (QP). The proposed algorithm is evaluated on SCM-2.0 under all intra (AI) coding configurations. Experimental results show that the proposed algorithm reduces IBC BV search time by 29.23% on average while the average BD-rate loss is 0.41% under the HEVC SCC common test conditions (CTC).

Vulnerability assessment and reconfiguration of microgrid through search vector artificial physics optimization algorithm

Taking the mathematical model and reliability parameters of the microgrid into account, this paper has proposed two vulnerability assessment indices (VAI) – weighted complex network structure parameters and comprehensive operational sensitivity to set up a vulnerability assessment system. The microgrid reconfiguration (MR) model is built with consideration of the grid-connected/island mode and the VAI. A novel artificial physics optimization algorithm with a searching vector (SVAPO) is presented and applied to minimize the system vulnerability. The technique has been successfully implemented on CERTS and 38-bus microgrids to demonstrate the performance and effectiveness of the proposed method. The results obtained are encouraging.

Fobe Algorithm for Video Processing Applications

In this paper two existing algorithms named as Cross-Diamond Search algorithm CDS and Fast Objected- Base Efficient Three Step Search algorithm are used. The cross-diamond search algorithm employs two diamond search patterns (a large and small) and a halfway-stop technique. It finds small motion vectors with fewer search points than the DS algorithm while maintaining similar or even better search quality. The efficient Three Step Search (E3SS) algorithm requires less computation and performs better in terms of PSNR. Modified objected block-base vector search algorithm (MOBS) fully utilizes the correlations existing in motion vectors to reduce the computations. Fast Objected- Base Efficient (FOBE) Three Step Search algorithm combines E3SS and MOBS. By combining these two existing algorithms CDS and MOBS, a new algorithm is proposed with reduced computational complexity without degradation in quality.

Fobe Algorithm for Video Processing Applications

In this paper two existing algorithms named as Cross-Diamond Search algorithm CDS and Fast Objected- Base Efficient Three Step Search algorithm are used. The cross-diamond search algorithm employs two diamond search patterns (a large and small) and a halfway-stop technique. It finds small motion vectors with fewer search points than the DS algorithm while maintaining similar or even better search quality. The efficient Three Step Search (E3SS) algorithm requires less computation and performs better in terms of PSNR. Modified objected block-base vector search algorithm (MOBS) fully utilizes the correlations existing in motion vectors to reduce the computations. Fast Objected- Base Efficient (FOBE) Three Step Search algorithm combines E3SS and MOBS. By combining these two existing algorithms CDS and MOBS, a new algorithm is proposed with reduced computational complexity without degradation in quality.

Computationally efficient 2-D DOA estimation for uniform rectangular arrays

A computationally efficient two-dimensional (2-D) direction-of-arrival (DOA) estimation method for uniform rectangular arrays is presented. A preprocessing transformation matrix is first introduced, which transforms both the complex-valued covariance matrix and the complex-valued search vector into real-valued ones. Then the 2-D DOA estimation problem is decoupled into two successive real-valued one-dimensional (1-D) DOA estimation problems with real-valued computations only. All these measures lead to significantly reduced computational complexity for the proposed method.

A New Beamforming Design for Multicast Systems

In this paper, we study a transmit beamforming technique for multiple-input–multiple-output (MIMO) downlink multicast systems. Assuming that channel state information (CSI) is available at a transmitter, we try to maximize the lowest transmit data rate among users for a given transmit power level. Since finding the optimal beamforming vector requires prohibitively high computational complexity, we propose a reduced complexity scheme by applying greedy vector search and a simple power-allocation (PA) algorithm. Simulation results show that the proposed scheme outperforms the conventional semidefinite relaxation (SDR) technique and provides a performance quite close to the optimal performance with much reduced complexity.

Fast transcoding from H.264 to HEVC based on region feature analysis

The new video coding standard, High Efficiency Video Coding (HEVC), achieves much higher coding efficiency than the state-of-the-art H.264. Transcoding H.264 video to HEVC video is important to enable gradual migration to HEVC. Therefore, a fast H.264 to HEVC transcoding algorithm based on region feature analysis is proposed. First, each frame is segmented into three regions in units of coding tree unit (CTU) based on the correlation between image coding complexities and coding bits of the H.264 source stream. Then the searching depth range of each CTU is adaptively decided according to the region type. After that, motion vectors are de-noise filtered and clustered in order to analyze the region features of coding unit (CU). Based on the analysis results, the minimum searching depth of CU and partitions of prediction unit (PU) are optimally selected, and the motion vector predictor and search window size of motion estimation are also optimally decided for further reduction of the computational complexity. Experimental results show that the proposed algorithm achieves a significant improvement on transcoding speed, while maintaining high Rate-Distortion performance.

Applying simulated annealing using different methods for the neighborhood search in forest planning problems

Adjacency constraints along with even flow harvest constraints are important in long term forest planning. Simulated annealing (SA) is previously successfully applied when addressing such constraints. The objective of this paper was to assess the performance of SA under three new methods of introducing biased probabilities in the management unit (MU) selection and compare them to the conventional method that assumes uniform probabilities. The new methods were implemented as a search vector approach based on the number of treatment schedules describing sequences of silvicultural treatments over time and standard deviation of net present value within MUs (Methods 2 and 3, respectively), and by combining the two approaches (Method 4). We constructed three hundred hypothetical forests (datasets) for three different landscapes characterized by different initial age class distributions (young, normal and old). Each dataset encompassed 1600 management units. The evaluation of the methods was done by means of objective function values, first feasible iteration and time consumption. Introducing a bias in the MU selection improves solutions compared to the conventional method (Method 1). However, an increase of computational time is in general needed for the new methods. Method 4 is the best alternative because, for large parts of the datasets, produced the best average and maximum objective function values and had lower time consumption than Methods 2 and 3. Although Method 4 performed very well, Methods 2 and 3 should not be neglected because for a considerable number of datasets the maximum objective function values were obtained by these methods.

Improved Path Planning Algorithm Based on Partial Distance Feature Vector Matching

Robotic path planning algorithm is an important research content of optimization. The improved path planning algorithm, based on distance measurement, use the feature vector for obtaining the location and for identifies different distances in search space. In order to improve the efficient of collision detection, the earth feature vector and taboo search theory were used as tools, combined with partial matching tech and fuzzy logic could get faster and more accurate outcomes than the algorithm only use fuzzy logic.

Identification of hepatic microvascular adhesion‐related genes of human colon cancer cells using random homozygous gene perturbation

Random homozygous gene perturbation (RHGP), in combination with liver sinusoidal endothelial cell (LSEC) adhesion screening of clonal colon cancer cells with perturbed genes, was used to identify genes contributing to the hepatic microvascular adhesion of colon cancer cells. Plasmid vector encoding transactivator and gene search vector were transfected into HT-29 human colorectal cancer cells to create a HT-29 RHGP cell library; the adhesion of these library cells to primary cultured mouse LSEC significantly decreased in the presence of RSL1 ligand (inducer), indicating that most of the genes contributing to HT-29 adhesion to LSEC were altered. Next, HT-29 RHGP cell library fractions with upregulated or silenced LSEC adhesion-related genes were isolated. Around 160 clones having altered expression in LSEC adhesion-related genes were obtained, and nine relevant protein-coding genes were identified. Some were proadhesive genes detected because of their overexpression in adherent HT-29 cells (DGCR8 and EFEMP1 genes) and their silenced status in nonadherent HT-29 cells (DGKE, DPY19L1, KIAA0753, PVR and USP11 genes). Others were antiadhesive genes detected because of their overexpression in nonadherent HT-29 cells (ITPKC gene) and their silenced status in adherent HT-29 cells (PPP6R2 gene). Silencing of PVR, DGCR8 and EFEMP1 genes decreased adhesion to LSEC and hepatic microvascular retention of HT-29 cells. The results conclude that RHGP was a valuable strategy for the discovery of mechanisms regulating microvascular adhesion of circulating colon cancer cells before hepatic metastasis formation. Identified genes may contribute to understand the metastatic process of colon cancer and to discovering molecular targets for hepatic metastasis therapeutics.

Nagata patch interpolation using surface normal vectors evaluated from the IGES file

This paper presents an algorithm to accurately evaluate the surface normal vector in any vertex of a finite element mesh, in order to be able to efficiently apply the Nagata patch interpolation as surface mesh smoothing method when solving contact problems. The proposed algorithm considers that the surface geometry is also described by trimmed NURBS surfaces, with input data available in IGES file format. For each mesh vertex, the proposed approach comprises the following three steps: surface global search, local search, and normal vector evaluation. In the global search procedure, all trimmed NURBS surfaces composing the geometric model are ordered by proximity to the vertex. After that, local search is performed to find both the correct NURBS surface and the local coordinates of the vertex, which are defined by its projection on the selected surface. The vertex normal vector is them determined based on the first derivatives of the NURBS surface at the projection point. To highlight the feasibility of the developed algorithm, a mesh smoothing example is presented, emphasising the influence of the vertex normal vector approximation on the interpolation accuracy.

Efficient Measurement of Service Similarity

The authors define a formal model for information services that incorporates the concept of service similarity. The model places services in metric spaces, and allows for services that have arbitrarily complex inputs and output domains. The authors then address the challenge of service substitution: finding the services most similar to a given service among a group, possibly large, of candidate services. To solve this nearest neighbor problem efficiently the authors embed the space of services into a vector space and search for the nearest neighbors in the target space. The authors report on an extensive experiment that validates both their formalization of similarity and their methods for finding service substitutions.

Performance of derivative free search ANN training algorithm with time series and classification problems

A Manhattan search algorithm to minimize artificial neural network error function is outlined in this paper. From an existing position in Cartesian coordinate, a search vector moves in orthogonal directions to locate minimum function value. The search algorithm computes optimized step length for rapid convergence. This step is performed when consecutive search is successful in minimizing function value. The optimized step length identifies favorable descent direction to minimize function value. The search method is suitable for complex error surface where derivative information is difficult to obtain or when the error surface is nearly flat. The rate of change in function value is almost negligible near the flat surface. Most of the derivative based training algorithm faces difficulty in such scenarios. This algorithm avoids derivative information of an error function. Therefore, it is an attractive search method when derivative based algorithm faces difficulty due to complex ridges and flat valleys. In case the algorithm gets into trapped minimum, the search vector takes steps to move out of a local minimum by exploring neighborhood descent search directions. The algorithm differs from the first and second order derivative based training methods. To measure the performance of the algorithm, estimation of electric energy generation model from Fiji Islands and "L-T" letter recognition problems are solved. Bootstrap analysis shows that the algorithm's predictive and classification abilities are high. The algorithm is reliable when solution to a problem is unknown. Therefore, the algorithm identifies benchmark solution.

GPS 반송파 위상을 사용한 편대비행위성 상대위치결정 연구

The present paper deals with precise relative positioning of formation satellites with long baseline in low Earth orbit making use of L1/L2 dual frequency GPS carrier phase measurements. Kinematic approach means to describe the motion of objects without taking its mass/dynamics model into consideration. The advantage of the kinematic approach is that information about dynamics of the system is not applied, which gives more flexibility and could improve the scientific interest of the observations made by the mission. The ionosphere terms, which are not canceled by double differenced measurement equation in the case of the long baseline, are explicitly estimated as unknown parameters by extended Kalman filter. The estimated float ambiguities by EKF are solved by existing efficient integer vector search strategy under integer least square condition. For the integer vector search, we employ well known MLAMBDA. Finally, The feasibility and accuracy of processing scheme are demonstrated using the GPS measurements for two satellites in low Earth orbit separated by baselines of 100 km.

A novel VQ-based reversible data hiding scheme by using hybrid encoding strategies

Reversible data hiding is a special algorithm that not only guarantees the confidential data will be extracted accurately but also allows the original cover image to be reconstructed without distortion after the confidential data are completely extracted. This paper proposes a new index compression and reversible data hiding scheme based on side-match vector quantization (SMVQ) and search order coding (SOC). In this proposed scheme, the confidential data are embedded into the transformed index table of a cover image. During the extracting phase, simple steps are employed to extract the confidential data and reconstruct the original cover image. The experimental results show that with a codebook size of 256, the average compression rate of the proposed scheme is 0.325bpp, which is superior to that of the methods proposed by Chen and Huang (0.426bpp) and Chang et al. (0.429bpp). Additionally, the embedding and extracting times of our scheme are 5.491s and 0.352s, respectively, demonstrating that the execution time of the proposed scheme is much faster than that of the methods of Chen and Huang and Chang et al. Moreover, our scheme achieves better performance than other selected reversible data hiding schemes with respect to the data embedding and data compression rates.

Fast motion estimation for surveillance video compression

In this article, novel approaches to perform efficient motion estimation specific to surveillance video compression are proposed. These includes (i) selective (ii) tracker-based and (iii) multi-frame-based motion estimation. In selective approach, motion vector search is performed for only those frames that contain some motion activity. In another approach, contrary to performing motion estimation on the encoder side, motion vectors are calculated using information of a surveillance video tracker. This approach is quicker but for some scenarios it degrades the visual perception of the video compared with selective approach. In an effort to speed up multi-frame motion estimation, we propose a fast multiple reference frames-based motion estimation technique for surveillance videos. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategies.

Fast Motion Estimation System Using Dynamic Models for H.264/AVC Video Coding

H.264/AVC offers many coding tools for achieving high compression gains of up to 50% more than other standards. These tools dramatically increase the computational complexity of the block based motion estimation (BB-ME) which consumes up to 80% of the entire encoder's computations. In this paper, computationally efficient accurate skipping models are proposed to speed up any BB-ME algorithm. First, an accurate initial search center (ISC) is decided using a smart prediction technique. Thereafter, a dynamic early stop search termination (DESST) is used to decide if the block at the ISC position can be considered as a best match candidate block or not. If the DESST algorithm fails, a less complex style of the motion estimation algorithm which incorporates dynamic padding window size technique will be used. Further reductions in computations are achieved by combining the following two techniques. First, a dynamic partial internal stop search technique which utilizes an accurate adaptive threshold model is exploited to skip the internal sum of absolute difference operations between the current and the candidate blocks. Second, a dynamic external stop search technique greatly reduces the unnecessary operations by skipping all the irrelevant blocks in the search area. The proposed techniques can be incorporated in any block matching motion estimation algorithm. Computational complexity reduction is reflected in the amount of savings in the motion estimation encoding time. The novelty of the proposed techniques comes from their superior saving in computations with an acceptable degradation in both peak signal-to-noise ratio (PSNR) and bit-rate compared to the state of the art and the recent motion estimation techniques. Simulation results using H.264/AVC reference software (JM 12.4) show up to 98% saving in motion estimation time using the proposed techniques compared to the conventional full search algorithm with a negligible degradation in the PSNR by approximately 0.05 dB and a small increase in the required bits per frame by only 2%. Experimental results also prove the effectiveness of the proposed techniques if they are incorporated with any fast BB-ME technique such as fast extended diamond enhanced predictive zonal search and predictive motion vector field adaptive search technique.

The Improve of HFPS Algorithm in AVS Video Coding

To reduce the complexity of AVS sub-pixel motion vector search, this paper proposes a new improved algorithm based on little diamond window searching policies according to the law of the general video sequence motion vector focusing on the near of the initial searching point at the time of the subpixel motion estimation on the basis of deeping analysis to HFPS algorithm. Comparing with the sub-pixel HFPS algorithm search algorithm, the time of sub-pixel motion estimation can reduce 51.97％ on average and efficiently decrease the computational number of sub-pixel motion estimation on average PSNR lost less than 0.01dB to the video sequences with different motion characteristics.