With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary progress on the design Roger Woods flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.
The Field Programmable Gate Array (FPGA) implementation of the commonly used Histogram of Oriented Gradients (HOG) algorithm is explored. The HOG algorithm is employed to extract features for object detection. A key focus has been to explore the use of a new FPGA-based processor which has been targeted at image processing. The paper gives details of the mapping and scheduling factors that influence the performance and the stages that were undertaken to allow the algorithm to be deployed on FPGA hardware, whilst taking into account the specific IPPro architecture features. We show that multi-core IPPro performance can exceed that of against state-ofthe-art FPGA designs by up to 3.2 times with reduced design and implementation effort and increased flexibility all on a low cost, Zynq programmable system.
Increasing evidence suggests that SARS-CoV-2, the virus responsible for the COVID-19 pandemic, is associated with increased risk of developing neurological or psychiatric conditions such as depression, anxiety or dementia. While the precise mechanism underlying this association is unknown, aberrant activation of toll-like receptor (TLR)3, a viral recognizing pattern recognition receptor, may play a key role. Synthetic cannabinoids and enhancing cannabinoid tone via inhibition of fatty acid amide hydrolase (FAAH) has been demonstrated to modulate TLR3-induced neuroimmune responses and associated sickness behaviour. However, the role of individual FAAH substrates, and the receptor mechanisms mediating these effects, are unknown. The present study examined the effects of intracerebral or systemic administration of the FAAH substrates
N
-oleoylethanolamide (OEA),
N
-palmitoylethanolamide (PEA) or the anandamide (AEA) analogue meth-AEA on hyperthermia and hypothalamic inflammatory gene expression following administration of the TLR3 agonist, and viral mimetic, poly I:C. The data demonstrate that meth-AEA does not alter TLR3-induced hyperthermia or hypothalamic inflammatory gene expression. In comparison, OEA and PEA attenuated the TLR3-induced hyperthermia, although only OEA attenuated the expression of hyperthermia-related genes (
IL-1β, iNOS, COX2
and
m-PGES
) in the hypothalamus. OEA, but not PEA, attenuated TLR3-induced increases in the expression of all IRF- and NFκB-related genes examined in the hypothalamus, but not in the spleen. Antagonism of PPARα prevented the OEA-induced attenuation of IRF- and NFκB-related genes in the hypothalamus following TLR3 activation but did not significantly alter temperature. PPARα agonism did not alter TLR3-induced hyperthermia or hypothalamic inflammatory gene expression. These data indicate that OEA may be the primary FAAH substrate that modulates TLR3-induced neuroinflammation and hyperthermia, effects partially mediated by PPARα.
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