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Targets and Interference
Published in Habibur Rahman, Fundamental Principles of Radar, 2019
Surface clutter is a signal echo from approximately planner surfaces whose area exceeds the radar resolution cell on the clutter surface. The radar cross section of surface clutter depends on the clutter characteristics and the area of clutter within a radar resolution cell. Three distinct regions of clutter behavior are recognized separated by grazing angles: the low angle grazing region, the plateau region, and the near vertical incidence region as discussed by Long9 and shown in Figure 4.8. Within each of the regions the dependence of σ° on grazing angle and wavelength can be described in a general way, but the borders of the three regions change with wavelength, surface irregularities, and polarization.
Computed tomography
Published in Ken Holmes, Marcus Elkington, Phil Harris, Clark's Essential Physics in Imaging for Radiographers, 2021
There are actually many more benefits and ways we can use our multi-slice detectors, we are not just restricted to grouping them into channels. The following list gives an idea of the benefits of these systems and how we might use them;Very fine narrow slices (as fine as the detector) for smaller body areas but have to be aware of low S/N ratios, so this is more applicable where we have high subject contrast.Multiple axial slices or spirals obtained in a single rotation, in our example we produced 8 × 5 mm slices simultaneously.Fused axial slices into thicker slices, provides better CR, as signals from individual detectors can be added together boosting S/N ratios.Examination time reduced as collimation can be as wide as the detector bank per rotation of the tube increasing z axis coverage.Reduced artefacts as there is less chance of partial voluming, in particular, due to the finer slices.Isotropic voxels are possible with 64 slice (and above) enabling 3D and mult-planner reconstructions of the same resolution in any direction
Natural Language Processing Associated with Expert Systems
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
We can mention here briefly some important technical features of SHRDLU: In this system, the procedural approach was systematically followed, and realized by using the deductive properties of the MICRO-PLANNER programming language. For example, instead of representing a linguistic notion saying that a sentence is composed of a noun phrase and a verb phrase under the form of a rule in a grammar, “S → NP VP,” see subsection 3.4.1, this notion was directly represented as a MICRO-PLANNER procedure that called other procedures under the form of (PARSE NP) and (PARSE VP).Coherently with the above, SHRDLU was characterized by an “imperative” conception of NL, derived from the “speech act” theories. According to this, the meaning of an utterance is not represented declaratively as a fact about the world, but as a command addressed to the system in order to do something. For example, a simple assertion like “the pyramid is on the table,” is translated as a command (a program) for adding information to the database.The problem-solver component of the system (i.e., the modules that know about how to accomplish tasks in the block world) contained some form of explicit representation of the cognitive context. For example, SHRDLU has embedded the notion of “focus” (see, below, Section 3.4.4.2) that, in front of an assertion relating to “the red block,” is able to link this assertion with one of all the possible red blocks of the world, the block which is more in focus of the others because of having been mentioned or acted upon recently.
Hierarchical and parameterized learning of pick-and-place manipulation from under-specified human demonstrations
Published in Advanced Robotics, 2020
Kun Qian, Huan Liu, Jaime Valls Miro, Xingshuo Jing, Bo Zhou
Programming Domain Description Language (PDDL) [45,46] separates the model of the planning problem into domain description and the related problem description, which are eventually the input of a planner. The domain description consists of the definition of requirements, class, predicates, and also actions, which are formulated in the PDDL.Domain file. The class model describes the abstraction of common targets such as object, arm and goal. The predicate model describes the existence of relations or states of classes. In addition, the action model describes a potential action by its action name, parameters, preconditions, effect and duration. An example of the PDDL.Domain file for the , and tasks are shown in Figure 3. As shown in Figure 3, five actions (R, G, M, O, P) and three object-types classes (arm, object, goal) that are necessary for the three tasks are defined. Table 1 is an example of the all possible predicate description for the three tasks. Actions are defined by their parameters, preconditions and effects. Pseudocode 2 and Pseudocode 3 give the description of actions PlaceOnTop(P) and MoveObject(M), respectively. In the action definition, the precondition of a subsequent action should be the same with its former action's effect, so that the two successive actions can be linked in correct order.