Career Program 31 Interns. The ACTEDS Intern Program is the Army's primary means of accessing and providing initial occupational and leadership training. Through ACTEDS, the Army is committed to the development. Army Center of Military History Army Civilian Training. Program Participants Roles for the CP 61 Army Centrally-Funded Intern Program 4. This Career Program 61 ACTEDS plan is a living document. The Army Civilian Training, Education, and Development System (ACTEDS) is a requirements-based system that ensures planned development of civilian members of the force through a blending of progressive and. CP- 3. 2 Interns. During the two- year intern program, interns are given an opportunity to expand their knowledge of the Army mission and programs, refine abilities, and develop skills needed to enter a target position at the end of the program. Army Civilian Training, Education, and Development System (ACTEDS). The Army Civilian Training, Education, and Development System (ACTEDS) is a requirements- based system that ensures planned development of civilian members of the force through a blending of progressive and sequential work assignments, formal training, and self- development for individuals as they progress from entry level to key positions. ACTEDS seeks to assure the systematic development and sustainment of Army's civilian workforce, and the development of technically competent and confident civilian leaders so essential to Army readiness. These competency requirements serve as the basis for building a viable ACTEDS plan which includes: the identification of key positions; career ladders showing vertical and horizontal progression paths to key positions; and a Master Training Plan (MTP). For career program occupations, MTPs start with Master Intern Training Plans (MITPs) which apply to all career program interns regardless of whether they are locally funded or centrally funded by DA. MTPs also include the civilian leader development courses. By Mail CP26 Proponency Office Deputy Chief of Staff, G1 ATTN: DAPE-PRM 300 Army Pentagon Washington, DC. Please send CP26 program-related inquiries to our CP 26 Mailbox. Contract Specialist (INTERN). ACTEDS Program eligible positions are open to.Army Civilian Training, Education and Development Systems (ACTEDS) Plan for Career Program, Enterprise Talent Management Program (ETMP). The Department of the Army (DA) Logistics Intern Program mission is to recruit, hire, train, develop, and mentor top quality individuals to perform as premier logisticians throughout the Army. The intern program includes a. Placement of DA ACTEDS career interns to a Permanent Duty Location will be directed by the Regional Training Center's ACPM and/or the Army CP-32 Career Program Office in accordance with the following criteria. Of the Army ACTEDS Interns. ACTEDS Intern Recruitment Cell are specifically related to recruitment and. The ACTEDS Intern program, authorized by 5 Code of Federal Regulations.
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The Professional Doctorate in Counselling Psychology course is designed to equip you with a rigorous training in clinical practice, as well as professional research skills at doctoral level, to enable you to become an. Buy Seismic Tomography: Theory and practice on Amazon.com FREE SHIPPING on qualified orders. Author Title; Joseph Guirguis and Sameh Mehanny: How Effective Are EC8 and Recommended AASHTO-LRFD Criteria for Regular Seismic Behavior of Ductile Bridges with Unequal Height Piers? Mike Mota, Franklin Moon, Ahmet : Shake. It shows the outer surface (red), the surface between compact bone and spongy bone (green) and the surface of the bone marrow (blue). In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super- pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). To be useful, these techniques must typically be combined with a domain's specific knowledge in order to effectively solve the domain's segmentation problems. Thresholding. This method is based on a clip- level (or a threshold value) to turn a gray- scale image into a binary image. There is also a balanced histogram thresholding. The key of this method is to select the threshold value (or values when multiple- levels are selected). Several popular methods are used in industry including the maximum entropy method, Otsu's method (maximum variance), and k- means clustering. Recently, methods have been developed for thresholding computed tomography (CT) images. The key idea is that, unlike Otsu's method, the thresholds are derived from the radiographs instead of the (reconstructed) image. In these works decision over each pixel's membership to a segment is based on multi- dimensional rules derived from fuzzy logic and evolutionary algorithms based on image lighting environment and application. Note that a common technique to improve performance for large images is to downsample the image, compute the clusters, and then reassign the values to the larger image if necessary. The K- means algorithm is an iterative technique that is used to partition an image into K clusters. The difference is typically based on pixel color, intensity, texture, and location, or a weighted combination of these factors. K can be selected manually, randomly, or by a heuristic. This algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters and the value of K. Compression- based methods. The method describes each segment by its texture and boundary shape. Each of these components is modeled by a probability distribution function and its coding length is computed as follows: The boundary encoding leverages the fact that regions in natural images tend to have a smooth contour. This prior is used by Huffman coding to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the shorter coding length it attains. Texture is encoded by lossy compression in a way similar to minimum description length (MDL) principle, but here the length of the data given the model is approximated by the number of samples times the entropy of the model. The texture in each region is modeled by a multivariate normal distribution whose entropy has a closed form expression. An interesting property of this model is that the estimated entropy bounds the true entropy of the data from above. This is because among all distributions with a given mean and covariance, normal distribution has the largest entropy. Thus, the true coding length cannot be more than what the algorithm tries to minimize. For any given segmentation of an image, this scheme yields the number of bits required to encode that image based on the given segmentation. Thus, among all possible segmentations of an image, the goal is to find the segmentation which produces the shortest coding length. This can be achieved by a simple agglomerative clustering method. The distortion in the lossy compression determines the coarseness of the segmentation and its optimal value may differ for each image. This parameter can be estimated heuristically from the contrast of textures in an image. For example, when the textures in an image are similar, such as in camouflage images, stronger sensitivity and thus lower quantization is required. Histogram- based methods. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image. This operation is repeated with smaller and smaller clusters until no more clusters are formed. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable. The histogram can also be applied on a per- pixel basis where the resulting information is used to determine the most frequent color for the pixel location. This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful in video tracking. Edge detection. Region boundaries and edges are closely related, since there is often a sharp adjustment in intensity at the region boundaries. Edge detection techniques have therefore been used as the base of another segmentation technique. The edges identified by edge detection are often disconnected. To segment an object from an image however, one needs closed region boundaries. The desired edges are the boundaries between such objects or spatial- taxons. They are similar to the Gestalt psychological designation of figure- ground, but are extended to include foreground, object groups, objects and salient object parts. Edge detection methods can be applied to the spatial- taxon region, in the same manner they would be applied to a silhouette. This method is particularly useful when the disconnected edge is part of an illusory contour. For that purpose two spaces has to be introduced: one space is the one- dimensional histogram of brightness H = H(B), the second space . The first space allows to measure how compact is distributed the brightness of the image by calculating minimal clustering kmin. Threshold brightness T corresponding to kmin defines the binary (black- and- white) image . The bitmap b is an object in dual space. On that bitmap a measure has to be defined reflecting how compact distributed black (or white) pixels are. So, the goal is to find objects with good borders. For all T the measure MDC =G/(k- L) has to be calculated (where k is difference in brightness between the object and the background, L is length of all borders, and G is mean gradient on the borders). Maximum of MDC defines the segmentation. The common procedure is to compare one pixel with its neighbors. If a similarity criterion is satisfied, the pixel can be set to belong to the cluster as one or more of its neighbors. The selection of the similarity criterion is significant and the results are influenced by noise in all instances. The method of Statistical Region Merging. Initially each pixel forms a single pixel region. SRM then sorts those edges in a priority queue and decide whether or not to merge the current regions belonging to the edge pixels using a statistical predicate. One region- growing method is the seeded region growing method. This method takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The regions are iteratively grown by comparison of all unallocated neighboring pixels to the regions. The difference between a pixel's intensity value and the region's mean, . The pixel with the smallest difference measured in this way is assigned to the respective region. This process continues until all pixels are assigned to a region. Because seeded region growing requires seeds as additional input, the segmentation results are dependent on the choice of seeds, and noise in the image can cause the seeds to be poorly placed. Another region- growing method is the unseeded region growing method. It is a modified algorithm that does not require explicit seeds. It starts with a single region A1. At each iteration it considers the neighboring pixels in the same way as seeded region growing. It differs from seeded region growing in that if the minimum . If not, then the pixel is considered different from all current regions Ai. The mean and scatter of the region and the intensity of the candidate pixel are used to compute a test statistic. If the test statistic is sufficiently small, the pixel is added to the region, and the region. Otherwise, the pixel is rejected, and is used to form a new region. A special region- growing method is called . It is based on pixel intensities and neighborhood- linking paths. A degree of connectivity (connectedness) is calculated based on a path that is formed by pixels. For a certain value of . It is sometimes called quadtree segmentation. This method starts at the root of the tree that represents the whole image. If it is found non- uniform (not homogeneous), then it is split into four child squares (the splitting process), and so on. If, in contrast, four child squares are homogeneous, they are merged as several connected components (the merging process). The node in the tree is a segmented node. This process continues recursively until no further splits or merges are possible. The central idea is to evolve an initial curve towards the lowest potential of a cost function, where its definition reflects the task to be addressed. Ground- penetrating radar - Wikipedia. A ground- penetrating radargram collected on a historic cemetery in Alabama, USA. Hyperbolic arrivals (arrows) indicate the presence of diffractors buried beneath the surface, possibly associated with human burials. Reflections from soil layering are also present (dashed lines). Ground- penetrating radar (GPR) is a geophysical method that uses radar pulses to image the subsurface. This nondestructive method uses electromagnetic radiation in the microwaveband (UHF/VHF frequencies) of the radio spectrum, and detects the reflected signals from subsurface structures. GPR can have applications in a variety of media, including rock, soil, ice, fresh water, pavements and structures. In the right conditions, practitioners can use GPR to detect subsurface objects, changes in material properties, and voids and cracks. A GPR transmitter emits electromagnetic energy into the ground. When the energy encounters a buried object or a boundary between materials having different permittivities, it may be reflected or refracted or scattered back to the surface. A receiving antenna can then record the variations in the return signal. The principles involved are similar to seismology, except GPR methods implement electromagnetic energy rather than acoustic energy, and energy may be reflected at boundaries where subsurface electrical properties change rather than subsurface mechanical properties as is the case with seismic energy. The electrical conductivity of the ground, the transmitted center frequency, and the radiated power all may limit the effective depth range of GPR investigation. Increases in electrical conductivity attenuate the introduced electromagnetic wave, and thus the penetration depth decreases. Because of frequency- dependent attenuation mechanisms, higher frequencies do not penetrate as far as lower frequencies. However, higher frequencies may provide improved resolution. Thus operating frequency is always a trade- off between resolution and penetration. Optimal depth of subsurface penetration is achieved in ice where the depth of penetration can achieve several thousand metres (to bedrock in Greenland) at low GPR frequencies. Dry sandy soils or massive dry materials such as granite, limestone, and concrete tend to be resistive rather than conductive, and the depth of penetration could be up to 1. In moist and/or clay- laden soils and materials with high electrical conductivity, penetration may be as little as a few centimetres. Ground- penetrating radar antennas are generally in contact with the ground for the strongest signal strength; however, GPR air- launched antennas can be used above the ground. Cross borehole GPR has developed within the field of hydrogeophysics to be a valuable means of assessing the presence and amount of soil water. History. A patent for a system using radar pulses rather than a continuous wave was filed in 1. Dr. A glacier's depth was measured using ground penetrating radar in 1. W. Commercial applications followed and the first affordable consumer equipment was sold in 1. In the Earth sciences it is used to study bedrock, soils, groundwater, and ice. It is of some utility in prospecting for gold nuggets and for diamonds in alluvial gravel beds, by finding natural traps in buried stream beds that have the potential for accumulating heavier particles. In environmental remediation, GPR is used to define landfills, contaminant plumes, and other remediation sites, while in archaeology it is used for mapping archaeological features and cemeteries. GPR is used in law enforcement for locating clandestine graves and buried evidence. Military uses include detection of mines, unexploded ordnance, and tunnels. Borehole radars utilizing GPR are used to map the structures from a borehole in underground mining applications. Modern directional borehole radar systems are able to produce three- dimensional images from measurements in a single borehole. Standard electromagnetic induction utility locating tools require utilities to be conductive. These tools are ineffective for locating plastic conduits or concrete storm and sanitary sewers. Since GPR detects variations in dielectric properties in the subsurface, it can be highly effective for locating non- conductive utilities. GPR is often used on the Channel 4 television programme Time Team which uses the technology to determine a suitable area for examination by means of excavations. In 1. 99. 2 GPR was used to recover . GPR can be used to detect and map subsurface archaeological artifacts, features, and patterning. These planview maps show subsurface structures at different depths. Sixty lines of data - individually representing vertical profiles - were collected and assembled as a 3- dimensional data array that can be horizontally . The domed roof of the crypt can be seen between 1 and 2. The concept of radar is familiar to most people. With ground penetrating radar, the radar signal . Subsurface objects and stratigraphy (layering) will cause reflections that are picked up by a receiver. The travel time of the reflected signal indicates the depth. Data may be plotted as profiles, as planview maps isolating specific depths, or as three- dimensional models. GPR can be a powerful tool in favorable conditions (uniform sandy soils are ideal). Like other geophysical methods used in archaeology (and unlike excavation) it can locate artifacts and map features without any risk of damaging them. Among methods used in archaeological geophysics it is unique both in its ability to detect some small objects at relatively great depths, and in its ability to distinguish the depth of anomaly sources. The principal disadvantage of GPR is that it is severely limited by less- than- ideal environmental conditions. Fine- grained sediments (clays and silts) are often problematic because their high electrical conductivity causes loss of signal strength; rocky or heterogeneous sediments scatter the GPR signal, weakening the useful signal while increasing extraneous noise. In the field of cultural heritage GPR with high frequency antenna is also used for investigating historical masonry structures, detecting cracks and decay patterns of columns and detachment of frescoes. In military applications and other common GPR applications, practitioners often use GPR in conjunction with other available geophysical techniques such as electrical resistivity and electromagnetic induction methods. Vehicle Localization. Multiple lines of data systematically collected over an area may be used to construct three- dimensional or tomographic images. Data may be presented as three- dimensional blocks, or as horizontal or vertical slices. Horizontal slices (known as . Time- slicing has become standard practice in archaeological applications, because horizontal patterning is often the most important indicator of cultural activities. Performance is also limited by signal scattering in heterogeneous conditions (e. When looking through stationary items using surface- penetrating or ground- penetrating radar, the equipment needs to be moved in order for the radar to examine the specified area by looking for differences in material composition. While it can identify items such as pipes, voids, and soil, it cannot identify the specific materials, such as gold and precious gems. It can however, be useful in providing subsurface mapping of potential gem- bearing pockets, or . The grid spacing which is scanned is based on the size of the targets that need to be identified and the results required. Typical grid spacings can be 1 meter, 3 ft, 5 ft, 1. The depth to a target is determined based on the amount of time it takes for the radar signal to reflect back to the unit. Radar signals travel at different velocities through different types of materials. It is possible to use the depth to a known object to determine a specific velocity and then calibrate the depth calculations. Power regulation. Systems on the market in 2. Digital signal processing (DSP) to process the data during survey work rather than off- line. A special kind of GPR uses unmodulated continuous- wave signals. This holographic subsurface radar differs from other GPR types in that it records plan- view subsurface holograms. Depth penetration of this kind of radar is rather small (2. Ground Penetrating Radar (2nd ed.). Knoval (Institution of Engineering and Technology). ISBN 9. 78- 0- 8. Retrieved 1. 3 February 2. C.; Henry, G.; Marshall, T. South African Journal of Geology. Geological Society of South Africa. Retrieved 9 December 2. IEEE Transactions on Antennas and Propagation. Ground- penetrating radar and burial practices in western Arnhem Land, Australia. Archaeology in Oceania 4. Some examples of GPR prospecting for monitoring of the monumental heritage. Journal of Geophysics and Engineering 7 (2), 1. S0. 5^Cornick, Matthew; Koechling, Jeffrey; Stanley, Byron; Zhang, Beijia (2. Journal of Field Robotics. And Dean Goodman 1. Ground Penetrating Radar: An Introduction for Archaeologists. Walnut Creek, CA.: Altamira Press^. Retrieved 5 February 2. IET International Radar Conference. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing. Retrieved 2. 6 September 2. Borchert, Olaf: Receiver Design for a Directional Borehole Radar System Dissertation, University of Wuppertal, 2. Ground Penetrating Radar Theory and Applications. A general overview of geophysical methods in archaeology can be found in the following works: Clark, Anthony J. Seeing Beneath the Soil. Prospecting Methods in Archaeology. London, United Kingdom: B. T. Ground- penetrating Radar for Archaeology. Walnut Creek, CA., United States: Alta. Mira Press Ltd. Gaffney, Chris; John Gater (2. Revealing the Buried Past: Geophysics for Archaeologists. Stroud, United Kingdom: Tempus. Bankelsangerlieder Pdf DownloadPart(s); Score; Woodwind - Large Flute Ensemble. Eighth Note Publications. Published by Eighth Note Publications
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