A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

This page shows some examples of multimedia files. It is also available at: http://www.ieee-uffc.org/tr/mexample.pdf. For submission of multimedia manuscripts to TUFFC, please follow “Information for Contributors” at: http://www.ieeeuffc.org/tr/contrib.pdf. Multimedia Example Created by Jian-yu Lu, Editor-in-Chief, 07/07/03 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society 1. Color Figure: The IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC) now accepts color figures online with corresponding grayscale figures in print without additional charges if authors follow the multimedia manuscript submission procedure in the “Information for Contributors”. The “color figures” icon below indicates in the print that a color version of the figure is available online. It also links to the color figure submitted originally by the authors for viewing details. Because of the sizes of color figures and the additional editorial work such as making two sets of PostScript files in which they remain identical after replacing grayscale with color, authors should request color figures online only when it is necessary. The color figure must be in JPEG (Joint Photographic Expert Group) or GIF (Graphics Interchange Format) format to reduce file size and to decrease the bandwidth usage on the TUFFC server. Fig. 1. An eight-layer printed circuit board (PCB) used primarily for multi-channel ultrasound signal reception and storage. The board was designed in the Ultrasound Lab at The University of Toledo, led by Dr. Jian-yu Lu. It is one of the many PCBs designed for a high-frame rate medical ultrasound imaging system [1], which consists of 128 linear, ±144V peak, 0.05-10MHz, and 12-bit arbitrary waveform generators for the production of ultrasound signals or for other research purposes. 2. Movies: Please click on the movie icon to see a movie. The two movies below give you an idea on the file size versus movie quality. “VCD quality” here means 1150kbits/s for video and 224kbits/s for 16-bit MP2 stereo audio at 44.1KHz sampling rate. Fig. 2. An introduction to the Ultrasound Lab at The University of Toledo. 3. Movies and Animations: Please click on the movie icons to see movies or animations. Fig. 3. Circuit design and construction of the imaging system. 4. Movies and Animation: Please click on the movie icons to see movies or animation. Movie [File size: 785KB; Format: MPEG1; Resolution: 320X240; Duration: 9 seconds]

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control

Subsample interpolation bias error in time of flight estimation by direct correlation in digital domain

We have theoretically studied how the presence of a small proportion of energetic beam electrons mixed to a bulk of Maxwellian electrons in a hot plasma affects the temperature-dependent intensity ratio G = (x + y + z)/w of the helium-like triplet intercombination (x, y) and forbidden (z) lines to the singlet resonance line (w). By modelling the electron distribution function as combination of a Maxwellian isotropic component and a monoenergetic beam component, detailed calculations of the G ratio of the Ne8+ lines have been performed for temperatures Te of the Maxwellian component and kinetic energies e0 of the beam component in the ranges 106−107 K and 1.5−25 keV, respectively. A magnetic sublevel-to-magnetic sublevel collisional-radiative model has been used for determining the populations of the upper magnetic sublevels of the four lines at an electron density below 1013 cm−3. Excitations from the ground 1s2 S0 and metastable 1s2s S1 magnetic sublevels to the 1snl (n=2−4) magnetic sublevels as well as inner-shell ionization of the lithiumlike ion in its ground level were taken into account. All basic atomic data, including the radiative transition probabilities and the collisional excitation and ionization cross sections, were computed using the Flexible Atomic Code. It is found that the contribution of a 5% fraction of the beam component can reduce the G ratio by a factor of 30 at Te = 106 K and of 2.4 at Te = 3 × 106 K. Our calculations also indicate that the effect of directionality of the beam component on G is negligible for e0 above ∼10 keV and that for a given Te, G is practically insensitive to variations in e0 above ∼7 keV.

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Republique algerienne democratique et populaire ministere de l'enseignement superieur et de la recherche scientifique

This paper presents a novel climate decision support system for tomatoes in high tunnels using fuzzy logic and adaptive neuro-fuzzy inference system. Three climate decision support systems are developed for high tunnels using fuzzy logic. First climate decision support system takes five inputs—temperature, relative humidity, solar radiations, wind velocity, and weather condition—and controls four outputs—tunnel’s temperature, tunnel’s humidity, fan speed, and shading. Second climate decision support system takes three inputs—temperature, solar radiations, and weather condition—and controls artificial sunlight. Third climate decision support system takes air quality index and controls air purification. We develop and implement the two main algorithms for climate control systems, one algorithm is for fuzzy logic climate decision support system, and other one is for neuro-fuzzy climate control system. We compute time complexity of both algorithms. We use software MATLAB for showing average error between calculated and targeted outputs. We also perform optimization of fuzzy membership functions using particle swarm optimization method and evaluate its results in MATLAB. Our generated results are very much precise and satisfied the desired range of outputs.

Fuzzy Climate Decision Support Systems for Tomatoes in High Tunnels

The present work shows the development of both a pulse-receiver circuit and acquisition circuit prototypes of ultrasonic signals for the measurement of thickness in oil pipelines using a Pipeline Inspection Gauge (PIG). The development parameters count with restrictions of space, sampling velocity and storage capacity, and besides they have to satisfy the norms applied in oil pipeline inspection by the PIG. The prototypes consist of several functional blocks; a pulsed circuit that excites the transducer generating a high voltage pulse of about −200V, a receiver circuit for the reception of the transducer signal, a filter and amplifying block, and finally acquisition and processing step for signals composed by one ADC, a FPGA and a microcontroller.

Development of an ultrasonic thickness measurement equipment prototype

During the last decades, a considerable effort was devoted to develop adequate greenhouse climate and crop models, for driving simulation, control and managing (Guzmán-Cruz, et. Al, Rico-Garcia, et al ). The study and design of greenhouse environmental models implies having a clear understanding of the greenhouse climate processes. These models must be related with the external influences of the outside weather conditions (such as solar radiation, outside air temperature, wind velocity, etc.), and with the control actions performed (such as ventilation, cooling, heating, among others). The practical goal of this work is to model the greenhouse air temperature and humidity using clustering techniques and made an automatically generator of fuzzy rules relations from real data in order to predict the behavior inside the greenhouse.

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Greenhouse Fuzzy and Neuro-Fuzzy Modeling Techniques

In the past 3 decades, there is an idea to extract the 3D information of a scene from its 2D images ant it has been a research interest in many fields. The main idea is to extract the useful depth information from an image or set of images in an efficient and automatic way. The result of the process (depth information) can be used to guide various tasks such as synthetic aperture radar (SAR), magnetic resonance imaging (MRI), automatic inspection, reverse engineering, 3D robot navigation, interferometry and so on. The obtained information can be used to guide various processes such as robotic manipulation, automatic inspection, inverse engineering, 3D depth map for navigation and virtual reality applications (Gokstorp, 1995). Depending on the application, a simple 3D description is necessary to understand the scene and perform the desired task, while in other cases a dense map or detailed information of the object’s shape is necessary. Furthermore, in some cases a complete 3D description of the object may be required. In 3D machine vision, the three-dimensional shape can be obtained by using two different methodologies; Active and Passive Methods, which are also classified as contact and non contact methods. The active methods project energy in the scene and detect the reflected energy; some examples of these methods are sonar, laser ranging, fringe projection and structured method. The fringe processing methods are widely used in non-destructive testing, optical metrology and 3D reconstruction systems. Some of the desired characteristics in these methods are high accuracy, noise-immunity and processing speed. In the spatial and temporal fringe pattern analysis, the main characteristics are the number of fringes, and the intensity variation due temporal and spatial measurements. A few commonly used fringe processing methods are well-known like Fourier Transform Profilometry (FTP) method (Malacara, 2006) and phase-shifting interferometry (Takeda et al., 1992). The main problem to overcome in these methods is the wrapped phase, where the depth information is included.

/pdf/three-dimensional-reconstruction-strategies-using-a-t4lalizpv6.pdf

Three Dimensional Reconstruction Strategies Using a Profilometrical Approach based on Fourier Transform

There are many investigations reported in the scientific literature about Particulate Matter (PM) 2.5 and PM10 in urban and suburban environments [Vega et al 2002, Querol et al 2004, Fuller et al 2004]. In this contribution, the information acquired from PMx monitoring systems is used to accurately forecast particle concentration using diverse soft computing techniques. A number of works have been published in the area of airborne particulates forecasting. For example, Chelani[et al 2001] trained hidden layer neural networks for CO forecasting at India. Caselli [et al 2009] used a feedforward neural network to predict PM10 concentration. Other works such as Kurt’s [et al 2010] have constructed a neural networks model using many input variables (e.g. wind, temperature, pressure, day of the week, Date, concentration, etc) making the model too complex and inaccurate. However, not many scientific literature discuss a number of robust forecasting methods using soft computing techniques. These techniques include neuro-fuzzy inference methods, fuzzy clustering techniques and support vector machines. Each one of these algorithms is discussed separately and the results discussed. Furthermore, a comparison of all methods is made to emphasize their advantages as well as their disadvantages.

/pdf/advances-in-airborne-pollution-forecasting-using-soft-217iiq1wkx.pdf

Advances in Airborne Pollution Forecasting Using Soft Computing Techniques

Six-legged robots are very useful in environments with obstacles of a size comparable to its own. However, the locomotion problem of hexapod robots is complex to solve due to the number of degrees of freedom and unknown environments. Nevertheless, the problem definition of Reinforcement Learning fits naturally for solving the robot locomotion problem. Reinforcement Learning has acquired great relevance in the last decade since it has achieved human-level control for specific tasks. This article presents an overview of Reinforcement Learning methods that have been successfully applied to the six-legged robot locomotion problem. First, a description and some achievements of reinforcement learning will be introduced, followed by examples of hexapod robots throughout history focusing on their locomotion systems. Secondly, the locomotion problem for a six-legged hexapod robot will be defined, with special attention to both, the gait and leg motion planning. Thirdly, the classical framework of reinforcement learning will be introduced and the Q-learning algorithm, which is one of the most used Reinforcement Learning algorithms in this context, will be revised. Finally, reinforcement learning methods applied to six-legged robot locomotion will be extensively discussed followed by open questions.

Ramos-Arreguin Juan Manuel

Papers

Development of an ultrasonic thickness measurement equipment prototype

Greenhouse Fuzzy and Neuro-Fuzzy Modeling Techniques

Three Dimensional Reconstruction Strategies Using a Profilometrical Approach based on Fourier Transform

Advances in Airborne Pollution Forecasting Using Soft Computing Techniques

Reinforcement Learning Applied to Hexapod Robot Locomotion: An Overview