'guten-learn-radio-image', 'class' => 'Guten_Learn_Customize_Control_Radio_Image', 'sanitize' => array( 'Guten_Learn_Customizer', 'sanitize_number' ), )); {"id":272,"date":"2022-07-28T12:00:00","date_gmt":"2022-07-28T09:00:00","guid":{"rendered":"https:\/\/aspida.ee.duth.gr\/?p=272"},"modified":"2022-09-14T13:00:19","modified_gmt":"2022-09-14T10:00:19","slug":"image-shifting-tracking-leveraging-memristive-devices","status":"publish","type":"post","link":"https:\/\/aspida.ee.duth.gr\/en\/image-shifting-tracking-leveraging-memristive-devices\/","title":{"rendered":"Image Shifting Tracking Leveraging Memristive Devices"},"content":{"rendered":"
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\u2924<\/a><\/strong> Image Shifting Tracking Leveraging Memristive Devices<\/h2><\/span>
Unconventional circuits with built-in memory and computing functionalities are becoming the cornerstones of artificial intelligence (AI) at the edge. In the currently deployed systems, sensing and computing occur in separate physical locations, imposing a vast amount of data shuttling between the sensor module and the cloud-computing platforms. Regarding the acceleration of image processing at the edge, in this work, a memristive computing circuit has been designed. By exploiting the non-linear behavior and memory capabilities of memristor devices, a memristive circuit, capable of tracking the shifting of an image is proposed. The presented circuit design can be also combined with an array of sensors, aiming to implement a discrete image tracking module.<\/div><\/div><\/section>\n\n\n\n

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Authors<\/strong><\/span>
Theodoros Panagiotis Chatzinikolaou, Iosif-Angelos Fyrigos, Georgios Ch. Sirakoulis<\/div><\/div><\/section>\n\n\n\n

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Conference<\/strong><\/strong><\/span>
2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)<\/div><\/div><\/section>\n<\/div>\n\n\n\n
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Availability Date<\/strong><\/strong><\/strong><\/span>
July 28th, 2022<\/div><\/div><\/section>\n<\/div>\n<\/div>\n\n\n\n

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