Pseudo-polynomial Motion Commands for Vibration Suppression of Belt-driven Rotary Platforms

The motion planning technique described in this paper has been developed to eliminate or reduce the residual vibrations of belt-driven rotary platforms, while maintaining unchanged the motion time and the total angular displacement of the platform. The proposed approach is based on a suitable choice of the motion command given to the servomotor that drives the mechanical device; this command is defined by some numerical coefficients which determine the shape of the displacement, velocity and acceleration profiles. Using a numerical optimization technique, these coefficients can be changed without altering the continuity conditions imposed on the displacement and its time derivatives at the initial and final time instants. The proposed technique can be easily and quickly implemented on an actual device, since it requires only a simple modification of the motion command profile mapped in the memory of the electronic motion controller.

Somatic Embryogenesis for Agropyron cristatum on Murashige and Skoog Medium

Agropyron cristatum L. Gaertn. is a native grass of semiarid region in Iran which is quit resistant to cool and drought climate and withstand heavy grazing. This species has close phylogenetic relationship with Triticum and Hordeum. In this research, the effect of seven different concentrations of growth regulator 2,4-D on callus production and somatic embryogenesis of A. cristatum was investigated on Murashige and Skoog medium. The results showed that the rate of callus, embryo and neomorph were highest in 1 mg L-1 2,4-D. Callus production was increased in 1 mg L-1 2,4-D but dramatically decreased at 5.5 and 9 mg L-1 2,4-D. The somatic embryos were observed at 1 and 4 mg L-1 2,4-D but matured embryos and plantlet were only occurred at 1 mg L-1 2,4-D. There were significant differences between 1 mg L-1 2,4-D and other treatments for producing globular and torpedo embryos, plantlet, rooted callus and number of roots (p

Dependence of Particle Initiated PD Characteristics on Size and Position of Metallic Particle Adhering to the Spacer Surface in GIS

It is well known that metallic particles reduce the reliability of Gas-Insulated Substation (GIS) equipments by initiating partial discharge (PDs) that can lead to breakdown and complete failure of GIS. This paper investigates the characteristics of PDs caused by metallic particle adhering to the solid spacer. The PD detection and measurement were carried out by using IEC 60270 method with particles of different sizes and at different positions on the spacer surface. The results show that a particle of certain size at certain position possesses a unique PD characteristic as compared to those caused by particles of different sizes and/or at different positions. Therefore PD characteristics may be useful for the particle size and position identification.

Eigenvalues of Particle Bound in Single and Double Delta Function Potentials through Numerical Analysis

This study employs the use of the fourth order Numerov scheme to determine the eigenstates and eigenvalues of particles, electrons in particular, in single and double delta function potentials. For the single delta potential, it is found that the eigenstates could only be attained by using specific potential depths. The depth of the delta potential well has a value that varies depending on the delta strength. These depths are used for each well on the double delta function potential and the eigenvalues are determined. There are two bound states found in the computation, one with a symmetric eigenstate and another one which is antisymmetric.

Effects of Bay Leaves on Blood Glucose and Lipid Profiles on the Patients with Type 1 Diabetes

Bay leaves have been shown to improve insulin function in vitro but the effects on people have not been determined. The objective of this study was to determine if bay leaves may be important in the prevention and/or alleviation of type 1 diabetes. Methods: Fifty five people with type 1 diabetes were divided into two groups, 45 given capsules containing 3 g of bay leaves per day for 30 days and 10 given a placebo capsules. Results All the patients consumed bay leaves shows reduced serum glucose with significant decreases 27% after 30 d. Total cholesterol decreased, 21 %, after 30 days with larger decreases in low density lipoprotein (LDL) 24%. High density lipoprotein (HDL) increased 20% and Triglycerides also decreased 26%. There were no significant changes in the placebo group. Conclusion, this study demonstrates that consumption of bay leaves, 3 g/d for 30 days, decreases risk factors for diabetes and cardiovascular diseases and suggests that bay leaves may be beneficial for people with type 1 diabetes.

Solving an Extended Resource Leveling Problem with Multiobjective Evolutionary Algorithms

We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.

An Approximate Engineering Method for Aerodynamic Heating Solution around Blunt Body Nose

This paper is devoted to predict laminar and turbulent heating rates around blunt re-entry spacecraft at hypersonic conditions. Heating calculation of a hypersonic body is normally performed during the critical part of its flight trajectory. The procedure is of an inverse method, where a shock wave is assumed, and the body shape that supports this shock, as well as the flowfield between the shock and body, are calculated. For simplicity the normal momentum equation is replaced with a second order pressure relation; this simplification significantly reduces computation time. The geometries specified in this research, are parabola and ellipsoids which may have conical after bodies. An excellent agreement is observed between the results obtained in this paper and those calculated by others- research. Since this method is much faster than Navier-Stokes solutions, it can be used in preliminary design, parametric study of hypersonic vehicles.

Road Extraction Using Stationary Wavelet Transform

In this paper, a novel road extraction method using Stationary Wavelet Transform is proposed. To detect road features from color aerial satellite imagery, Mexican hat Wavelet filters are used by applying the Stationary Wavelet Transform in a multiresolution, multi-scale, sense and forming the products of Wavelet coefficients at a different scales to locate and identify road features at a few scales. In addition, the shifting of road features locations is considered through multiple scales for robust road extraction in the asymmetry road feature profiles. From the experimental results, the proposed method leads to a useful technique to form the basis of road feature extraction. Also, the method is general and can be applied to other features in imagery.

Design of Low-Area HEVC Core Transform Architecture

This paper proposes and implements an core transform architecture, which is one of the major processes in HEVC video compression standard. The proposed core transform architecture is implemented with only adders and shifters instead of area-consuming multipliers. Shifters in the proposed core transform architecture are implemented in wires and multiplexers, which significantly reduces chip area. Also, it can process from 4×4 to 16×16 blocks with common hardware by reusing processing elements. Designed core transform architecture in 0.13um technology can process a 16×16 block with 2-D transform in 130 cycles, and its gate count is 101,015 gates.

A Novel Q-algorithm for EPC Global Class-1 Generation-2 Anti-collision Protocol

This paper provides a scheme to improve the read efficiency of anti-collision algorithm in EPCglobal UHF Class-1 Generation-2 RFID standard. In this standard, dynamic frame slotted ALOHA is specified to solve the anti-collision problem. Also, the Q-algorithm with a key parameter C is adopted to dynamically adjust the frame sizes. In the paper, we split the C parameter into two parameters to increase the read speed and derive the optimal values of the two parameters through simulations. The results indicate our method outperforms the original Q-algorithm.

Syntactic Recognition of Distorted Patterns

In syntactic pattern recognition a pattern can be represented by a graph. Given an unknown pattern represented by a graph g, the problem of recognition is to determine if the graph g belongs to a language L(G) generated by a graph grammar G. The so-called IE graphs have been defined in [1] for a description of patterns. The IE graphs are generated by so-called ETPL(k) graph grammars defined in [1]. An efficient, parsing algorithm for ETPL(k) graph grammars for syntactic recognition of patterns represented by IE graphs has been presented in [1]. In practice, structural descriptions may contain pattern distortions, so that the assignment of a graph g, representing an unknown pattern, to a graph language L(G) generated by an ETPL(k) graph grammar G is rejected by the ETPL(k) type parsing. Therefore, there is a need for constructing effective parsing algorithms for recognition of distorted patterns. The purpose of this paper is to present a new approach to syntactic recognition of distorted patterns. To take into account all variations of a distorted pattern under study, a probabilistic description of the pattern is needed. A random IE graph approach is proposed here for such a description ([2]).

Detection and Classification of Power Quality Disturbances Using S-Transform and Wavelet Algorithm

Detection and classification of power quality (PQ) disturbances is an important consideration to electrical utilities and many industrial customers so that diagnosis and mitigation of such disturbance can be implemented quickly. S-transform algorithm and continuous wavelet transforms (CWT) are time-frequency algorithms, and both of them are powerful in detection and classification of PQ disturbances. This paper presents detection and classification of PQ disturbances using S-transform and CWT algorithms. The results of detection and classification, provides that S-transform is more accurate in detection and classification for most PQ disturbance than CWT algorithm, where as CWT algorithm more powerful in detection in some disturbances like notching

Parametric Study of a Vapor Compression Refrigeration Cycle Using a Two-Phase Constant Area Ejector

There are several ways of improving the performance of a vapor compression refrigeration cycle. Use of an ejector as expansion device is one of the alternative ways. The present paper aims at evaluate the performance improvement of a vapor compression refrigeration cycle under a wide range of operating conditions. A numerical model is developed and a parametric study of important parameters such as condensation (30-50°C) and evaporation temperatures (-20-5°C), nozzle and diffuser efficiencies (0.75-0.95), subcooling and superheating degrees (0-15K) are investigated. The model verification gives a good agreement with the literature data. The simulation results revealed that condensation temperature has the highest effect (129%) on the performance improvement ratio while superheating has the lowest one (6.2%). Among ejector efficiencies, the diffuser efficiency has a significant effect on the COP of ejector expansion refrigeration cycle. The COP improvement percentage decreases from 10.9% to 4.6% as subcooling degrees increases by 15K.

Comparison of BER Performances for Conventional and Non-Conventional Mapping Schemes Used in OFDM

Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for high speed data rate communication with main consideration for 4G and 5G systems. In OFDM, there are several mapping schemes which provide a way of parallel transmission. In this paper, comparisons of mapping schemes used by some standards have been made and also has been discussed about the performance of the non-conventional modulation technique. The Comparisons of Bit Error Rate (BER) performances for conventional and non-conventional modulation schemes have been done using MATLAB software. Mentioned schemes used in OFDM system can be selected on the basis of the requirement of power or spectrum efficiency and BER analysis.

A Parametric Study of an Inverse Electrostatics Problem (IESP) Using Simulated Annealing, Hooke & Jeeves and Sequential Quadratic Programming in Conjunction with Finite Element and Boundary Element Methods

The aim of the current work is to present a comparison among three popular optimization methods in the inverse elastostatics problem (IESP) of flaw detection within a solid. In more details, the performance of a simulated annealing, a Hooke & Jeeves and a sequential quadratic programming algorithm was studied in the test case of one circular flaw in a plate solved by both the boundary element (BEM) and the finite element method (FEM). The proposed optimization methods use a cost function that utilizes the displacements of the static response. The methods were ranked according to the required number of iterations to converge and to their ability to locate the global optimum. Hence, a clear impression regarding the performance of the aforementioned algorithms in flaw identification problems was obtained. Furthermore, the coupling of BEM or FEM with these optimization methods was investigated in order to track differences in their performance.

An Efficient Run Time Interface for Heterogeneous Architecture of Large Scale Supercomputing System

In this paper we propose a novel Run Time Interface (RTI) technique to provide an efficient environment for MPI jobs on the heterogeneous architecture of PARAM Padma. It suggests an innovative, unified framework for the job management interface system in parallel and distributed computing. This approach employs proxy scheme. The implementation shows that the proposed RTI is highly scalable and stable. Moreover RTI provides the storage access for the MPI jobs in various operating system platforms and improve the data access performance through high performance C-DAC Parallel File System (C-PFS). The performance of the RTI is evaluated by using the standard HPC benchmark suites and the simulation results show that the proposed RTI gives good performance on large scale supercomputing system.

Small Sample Bootstrap Confidence Intervals for Long-Memory Parameter

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Here the finite sample performance of different nonparametric residual bootstrap procedures is analyzed when applied to construct confidence intervals. In particular, in addition to the basic residual bootstrap, the local and block bootstrap that might adequately replicate the structure that may arise in the errors of the regression are considered when the series shows weak dependence in addition to the long memory component. Bias correcting bootstrap to adjust the bias caused by that structure is also considered. Finally, the performance of the bootstrap in log periodogram regression based confidence intervals is assessed in different type of models and how its performance changes as sample size increases.

Robust Features for Impulsive Noisy Speech Recognition Using Relative Spectral Analysis

The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.

Intelligent Agents for Distributed Intrusion Detection System

This paper presents a distributed intrusion detection system IDS, based on the concept of specialized distributed agents community representing agents with the same purpose for detecting distributed attacks. The semantic of intrusion events occurring in a predetermined network has been defined. The correlation rules referring the process which our proposed IDS combines the captured events that is distributed both spatially and temporally. And then the proposed IDS tries to extract significant and broad patterns for set of well-known attacks. The primary goal of our work is to provide intrusion detection and real-time prevention capability against insider attacks in distributed and fully automated environments.

Massive Lesions Classification using Features based on Morphological Lesion Differences

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network.