Circuits, computers, and beyond Boolean logic

Roska, Tamás

doi:10.1002/cta.437

Cited by 28 publications

(15 citation statements)

References 26 publications

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“…Although the currents set up by a resistor can be mimicked in theory by two active elements, such as transconductors, these active networks are less robust to mismatch [15]. Thus, recent implementations of programmable analog image-processing arrays that are based on programmable active elements now also include resistive networks for image smoothing [16,17].…”

Section: Introductionmentioning

confidence: 98%

The effect of mismatch in current‐ versus voltage‐mode resistive grids

Shi

2008

Circuit Theory & Apps

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SUMMARYResistive grids are commonly used in neuromorphic very large-scale integration (VLSI) designs for image smoothing and signal aggregation. Because mismatch is inevitable in VLSI chips, one reason that resistive grids are attractive is that their output is robust to mismatch in the component values. Here, we examine relative sensitivity of different resistive grid designs that differ depending upon whether the input and output are represented by current or voltage. Our analysis reveals that a mixed-mode design, where the input is represented by current but the output is represented by voltage, is less sensitive to mismatch when the amount of smoothing is large. On the other hand, when little smoothing is desired, pure voltage-mode and current-mode designs are preferred. From the point of view of mismatch sensitivity, there is little difference between the two pure-mode designs. Our analysis also reveals that the difference in mismatch sensitivity is due to the vertical resistors, which are the most sensitive to mismatch in all the three designs studied.

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Section: Introductionmentioning

confidence: 98%

The effect of mismatch in current‐ versus voltage‐mode resistive grids

Shi

2008

Circuit Theory & Apps

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“…4 On the other hand, the active waves, generated on the CNN, have become the key for path finding methods of active wave computing based approach. 5 Adamatzky et al 6 and Arena et al 7 used the autowaves on CNN to avoid the obstacles and find the optimum path to the destination. Another approach, generating the wave from the starting point is proposed by Ito et al 8 Actionoriented perception paradigm in robots is again presented by Arena.…”

Section: Introductionmentioning

confidence: 99%

Path planning on cellular nonlinear network using active wave computing technique

Yeniceri

Yalçın

2009

SPIE Proceedings

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This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

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“…The invention of CNN-UM, the first spacetemporal analogical array computer [1], which processes with a trillion operations per second and of the Cellular Wave Computer, a brain inspired computer, capable of performing spatial temporal inference [2], opened new perspectives to the CNN applications.…”

Section: Introductionmentioning

confidence: 99%

Cellular neural networks assisted automatic detection of elements in microscopic medical images. A preliminary study

Botoca

2014

2014 11th International Symposium on Electronics and Telecommunications (ISETC)

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This paper presents a new algorithm for object recognition in medical microscopic images, assisted by a cellular neural network (CNN). The CNN flowchart and its component parts are described based on successions of interconnections templates. The experiments results are shown and they appear to be promising. Our results sustain the usability of CNN as a real time processing tool for assisting the medical act.

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Circuits, computers, and beyond Boolean logic

Cited by 28 publications

References 26 publications

The effect of mismatch in current‐ versus voltage‐mode resistive grids

The effect of mismatch in current‐ versus voltage‐mode resistive grids

Path planning on cellular nonlinear network using active wave computing technique

Cellular neural networks assisted automatic detection of elements in microscopic medical images. A preliminary study

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