Low power CMOS vision sensor for foreground segmentation

This thesis focuses on the design of a top-ranked algorithm for background subtraction, the Pixel Adaptive Based Segmenter (PBAS), for its mapping onto a CMOS vision sensor on the focal plane processing. The redesign of PBAS into its hardware oriented version, HO-PBAS, has led to a less number of memories per pixel, along with a simpler overall model, yet, resulting in an acceptable loss of accuracy with respect to its counterpart on CPU. This thesis features two CMOS vision sensors. The first one, HOPBAS1K, has laid out a 24 x 56 pixel array onto a miniasic chip in standard 180 nm CMOS technology. The second one, HOPBAS10K, features an array of 98 x 98 pixels in standard 180 nm CMOS technology too. The second chip fixes some issues found in the first chip, and provides good hardware and background performance metrics

Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Wireless Sensor Network With Perpetual Motes for Terrestrial Snail Activity Monitoring

Wireless sensor networks (WSNs) are increasingly adopted in agriculture to monitor environmental variables to predict the presence of pests. Differently from these approaches, this paper introduces aWSN to detect the presence of snails in the field. The network can be used to both trigger an alarm of early pest presence and to further elaborate statistical models with the addition of environmental data as temperature or humidity to predict snail presence. In this paper we also design our own WSN simulator to account for real-life conditions as an uneven spacing of motes in the field or different currents generated by solar cells at the motes. This allows achieving more realistic network deployment in the field. Experimental tests are included in this paper, showing that our motes are perpetual in terms of energy consumptionConsellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019); European Regional Development Fund; Ministerio de Economía, Industria y Competitividad (TEC2015-66878-C3-3-R)S