Computerized design of advanced straight and skew bevel gears produced by precision forging

The computerized design of advanced straight and skew bevel gears produced by precision forging is proposed. Modifications of the tooth surfaces of one of the members of the gear set are proposed in order to localize the bearing contact and predesign a favorable function of transmission errors. The proposed modifications of the tooth surfaces will be computed by using a modified imaginary crown-gear and applied in manufacturing through the use of the proper die geometry. The geometry of the die is obtained for each member of the gear set from their theoretical geometry obtained considering its generation by the corresponding imaginary crown-gear. Two types of surface modification, whole and partial crowning, are investigated in order to get the more effective way of surface modification of skew and straight bevel gears. A favorable function of transmission errors is predesigned to allow low levels of noise and vibration of the gear drive. Numerical examples of design of both skew and straight bevel gear drives are included to illustrate the advantages of the proposed geometry.     

Semantic-based authorization architecture for Grid

There are a few issues that still need to be covered regarding security in the Grid area. One of them is authorization where there exist good solutions to define, manage and enforce authorization policies in Grid scenarios. However, these solutions usually do not provide Grid administrators with semantic-aware components closer to the particular Grid domain and easing different administration tasks such as conflict detection or resolution. This paper defines a proposal based on Semantic Web to define, manage and enforce security policies in a Grid scenario. These policies are defined by means of semantic-aware rules which help the administrator to create higher-level definitions with more expressiveness. These rules also permit performing added-value tasks such as conflict detection and resolution, which can be of interest in medium and large scale scenarios where different administrators define the authorization rules that should be followed before accessing a resource in the Grid. The proposed solution has been also tested providing some reasonable response times in the authorization decision process.     

Superpipelined high-performance optical-flow computation architecture

Optical-flow computation is a well-known technique and there are important fields in which the application of this visual modality commands high interest. Nevertheless, most real-world applications require real-time processing, an issue which has only recently been addressed. Most real-time systems described to date use basic models which limit their applicability to generic tasks, especially when fast motion is presented or when subpixel motion resolution is required. Therefore, instead of implementing a complex optical-flow approach, we describe here a very high-frame-rate optical-flow processing system. Recent advances in image sensor technology make it possible nowadays to use high-frame-rate sensors to properly sample fast motion (i.e. as a low-motion scene), which makes a gradient-based approach one of the best options in terms of accuracy and consumption of resources for any real-time implementation. Taking advantage of the regular data flow of this kind of algorithm, our approach implements a novel superpipelined, fully parallelized architecture for optical-flow processing. The system is fully working and is organized into more than 70 pipeline stages, which achieve a data throughput of one pixel per clock cycle. This computing scheme is well suited to FPGA technology and VLSI implementation. The developed customized DSP architecture is capable of processing up to 170 frames per second at a resolution of 800 × 600 pixels. We discuss the advantages of high-frame-rate processing and justify the optical-flow model chosen for the implementation. We analyze this architecture, measure the system resource requirements using FPGA devices and finally evaluate the system’s performance and compare it with other approaches described in the literature.     

Molecular modeling optimization of anticoagulant pyridine derivatives

Intravascular clotting remains a major health problem in the United States, the most prominent being deep vein thrombosis, pulmonary embolism and thromboembolic stroke. Previous reports on the use of pyridine derivatives in cardiovascular drug development encourage us to pursue new types of compounds based on a pyridine scaffold. Eleven pyridine derivatives (oximes, semicarbazones, N-oxides) previously synthesized in our laboratories were tested as anticoagulants on pooled normal plasma using the prothrombin time (PT) protocol. The best anticoagulant within the oxime series was compound AF4, within the oxime N-oxide series was compound AF4-N-oxide, and within the semicarbazone series, compound MD1-30Y. We also used a molecular modeling approach to guide our efforts, and found that there was good correlation between coagulation data and computational energy scores. Molecular docking was performed to target the active site of thrombin with the DOCK v5.2 package. The results of molecular modeling indicate that improvement in anticoagulant activities can be expected by functionalization at the three-position of the pyridine ring and by N-oxide formation. Results reported here prove the suitability of DOCK in the lead optimization process.     

Characterization of virus replication

New viruses spread faster than ever and current signature based detection do not protect against these unknown viruses. Behavior based detection is the currently preferred defense against unknown viruses. The drawback of behavior based detection is the ability only to detect specific classes of viruses or have successful detection under certain conditions plus false positives. This paper presents a characterization of virus replication which is the only virus characteristic guaranteed to be consistently present in all viruses. Two detection models based on virus replication are developed, one using operation sequence matching and the other using frequency measures. Regression analysis was generated for both models. A safe list is used to minimize false positives. In our testing using operation sequence matching, over 250 viruses were detected with 43 subsequences. There were minimal false negatives. The replication sequence of just one virus detected 130 viruses, 45% of all tested viruses. Our testing using frequency measures detected all test viruses with no false negatives. The paper shows that virus replication can be identified and used to detect known and unknown viruses.     

Lock-in amplifier error prediction and correction in frequency sweep measurements

This article proposes an analytical algorithm for predicting errors in lock-in amplifiers (LIAs) working with time-varying reference frequency. Furthermore, a simple method for correcting such errors is presented. The reference frequency can be swept in order to measure the frequency response of a system within a given spectrum. The continuous variation of the reference frequency produces a measurement error that depends on three factors: the sweep speed, the LIA low-pass filters, and the frequency response of the measured system. The proposed error prediction algorithm is based on the final value theorem of the Laplace transform. The correction method uses a double-sweep measurement. A mathematical analysis is presented and validated with computational simulations and experimental measurements.     

Direct electron crystallographic determination of zeolite zonal structures

The prospect for improving the success of ab initio zeolite structure investigations with electron diffraction data is evaluated. First of all, the quality of intensities obtained by precession electron diffraction at small hollow cone illumination angles is evaluated for seven representative materials: ITQ-1, ITQ-7, ITQ-29, ZSM-5, ZSM-10, mordenite, and MCM-68. It is clear that, for most examples, an appreciable fraction of a secondary scattering perturbation is removed by precession at small angles. In one case, ZSM-10, it can also be argued that precession diffraction produces a dramatically improved 'kinematical' data set. There seems to no real support for application of a Lorentz correction to these data and there is no reason to expect for any of these samples that a two-beam dynamical scattering relationship between structure factor amplitude and observed intensity should be valid. Removal of secondary scattering by the precession mode appears to facilitate ab initio structure analysis. Most zeolite structures investigated could be solved by maximum entropy and likelihood phasing via error-correcting codes when precession data were used. Examples include the projected structure of mordenite that could not be determined from selected area data alone. One anomaly is the case of ZSM-5, where the best structure determination in projection is made from selected area diffraction data. In a control study, the zonal structure of SSZ-48 could be determined from selected area diffraction data by either maximum entropy and likelihood or traditional direct methods. While the maximum entropy and likelihood approach enjoys some advantages over traditional direct methods (non-dependence on predicted phase invariant sums), some effort must be made to improve the figures of merit used to identify potential structure solutions.     

GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.     

Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for binary classification

A classification problem is a decision-making task that many researchers have studied. A number of techniques have been proposed to perform binary classification. Neural networks are one of the artificial intelligence techniques that has had the most successful results when applied to this problem. Our proposal is the use of q-Gaussian Radial Basis Function Neural Networks (q-Gaussian RBFNNs). This basis function includes a supplementary degree of freedom in order to adapt the model to the distribution of data. A Hybrid Algorithm (HA) is used to search for a suitable architecture for the q-Gaussian RBFNN. The use of this type of more flexible kernel could greatly improve the discriminative power of RBFNNs. In order to test performance, the RBFNN with the q-Gaussian basis functions is compared to RBFNNs with Gaussian, Cauchy and Inverse Multiquadratic RBFs, and to other recent neural networks approaches. An experimental study is presented on 11 binary-classification datasets taken from the UCI repository. Moreover, aerial imagery taken in mid-May, mid-June and mid-July was used to evaluate the potential of the methodology proposed for discriminating Ridolfia segetum patches (one of the most dominant and harmful weeds in sunflower crops) in two naturally infested fields in southern Spain.