916 resultados para Industrial automation, Programmable logic controllers.


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应用传统现场总线的工业控制网络无法实现办公室自动化与工业自动化的无缝结合 .由于以太网在确定性、速度和优先法则等方面性能的提高 ,阻碍以太网应用于实时控制环境的难点已被解决 .以太网早已成为商业管理网络的首要选择 ,那么它应用于企业现场设备控制层是控制网络发展的趋势 ,将极大地促进信息从传感器到管理层的集成

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Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

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© 2005-2012 IEEE.Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies.

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Architectures and methods for the rapid design of silicon cores for implementing discrete wavelet transforms over a wide range of specifications are described. These architectures are efficient, modular, scalable, and cover orthonormal and biorthogonal wavelet transform families. They offer efficient hardware utilization by exploiting a number of core wavelet filter properties and allow the creation of silicon designs that are highly parameterized, including in terms of wavelet type and wordlengths. Control circuitry is embedded within these systems allowing them to be cascaded for any desired level of decomposition without any interface glue logic. The time to produce chip designs for a specific wavelet application is typically less than a day and these are comparable in area and performance to handcrafted designs. They are also portable across a wide range of silicon foundries and suitable for field programmable gate array and programmable logic data implementation. The approach described has also been extended to wavelet packet transforms.

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Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

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In Run Time Reconfiguration (RTR) systems, the amount of reconfiguration is considerable when compared to the circuit changes implemented. This is because reconfiguration is not considered as part of the design flow. This paper presents a method for reconfigurable circuit design by modeling the underlying FPGA reconfigurable circuitry and taking it into consideration in the system design. This is demonstrated for an image processing example on the Xilinx Virtex FPGA.

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In DSP applications such as fixed transforms and filtering, the full flexibility of a general-purpose multiplier is not required and only a limited range of values is needed on one of the multiplier inputs. A new design technique has been developed for deriving multipliers that operate on a limited range of multiplicands. This can be used to produce FPGA implementations of DSP systems where area is dramatically improved. The paper describes the technique and its application to the design of a poly-phase filter on a Virtex FPGA. A 62% area reduction and 7% speed increase is gained when compared to an equivalent design using general purpose multipliers. It is also compared favourably to other known fixed coefficient approaches.

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Matrix algorithms are important in many types of applications including image and signal processing. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix algorithms such as matrix multiplication. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using a novel custom coprocessor system for MATrix algorithms based on Reconfigurable Computing (RCMAT). The proposed RCMAT architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.

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Exploiting the underutilisation of variable-length DSP algorithms during normal operation is vital, when seeking to maximise the achievable functionality of an application within peak power budget. A system level, low power design methodology for FPGA-based, variable length DSP IP cores is presented. Algorithmic commonality is identified and resources mapped with a configurable datapath, to increase achievable functionality. It is applied to a digital receiver application where a 100% increase in operational capacity is achieved in certain modes without significant power or area budget increases. Measured results show resulting architectures requires 19% less peak power, 33% fewer multipliers and 12% fewer slices than existing architectures.