For a binary image, the ADC finishes the converting Axitinib mw operation in 2 clock cycles. For a 6-bit gray-scale image, the ADC converts the image in 7 clock cycles. Therefore we can control the quality of the image and the converting time to suit the different vision application. The gray-scale image processors receive the digital image from N ADCs or PE array. Each processor consists of three 6-bit pixel-data registers and an 11-bit ALU, as shown in Figure 3.Figure 3.The interconnection between row-parallel processors.The ALU in row-parallel processor i can access three pixel-data registers Di[j] Di[j?1] Di[j?2] in itself, three pixel-data registers Di?1[j] Di?1[j ? 1] Di?1[j ? 2] in row-parallel processor i ? 1 and three pixel-data registers Di+ 1[j] Di+ 1[j ? 1] Di+ 1[j ? 2] in row-parallel processor i + 1.

We terminal the boundary of the sensor array with low voltage (logic ��0��). This boundary condition is required by mathematical morphology image processing. The ALU can process the data of 3 �� 3 array in the image and perform 8 basic operations Inhibitors,Modulators,Libraries including ��add�� ��subtract�� ��minimum�� ��maximum�� ��comparison�� ��equal�� ��absolution�� and ��shift��. These processors would process one column image data each period. The gray-scale mathematical morphology algorithms can be executed by combination of those operations repeatedly and successively.The core module of the chip is an N �� N PE array. The PE diagram is given in the Figure 4. It consists of nine D-latches, two Multiplexers, an AND gate, an OR gate, an inverter, and eight switches.

One PE is connected directly with its four nearest neighborhood PEs. By selecting the MUX1, we can choose one signal as input Inhibitors,Modulators,Libraries of the PE, which comes from the neighbor PE or the output of itself. When the switch (RS[i]) was turned on, the negative latch (NL[i]) and positive latch (PL) constitute a D-flop
Extracting useful information from the environment has an important effect on the robot navigation process. Simultaneous localization and map building (SLAM), path planning, or even a virtual reconstruction of the scene for supervising the robot navigation are different examples where a detailed description of the environment can usually improve their results. To address this issue, an appropriate Inhibitors,Modulators,Libraries representation of the working environment of the mobile robot must be acquired, which is not trivial.

Many factors and physical constraints affect the reliability of such representation [1].One of the first tasks in the navigation system design is to determine Inhibitors,Modulators,Libraries the type of sensor required Brefeldin_A to selleck compound obtain the desired description in a particular environment. The most appropriate sensor for the application depends on the size of the operation area, the environmental conditions, and the required representation level. Indeed, the most important factor that determines the quality of the representation is this external sensor, and above all, its accuracy.