ESCA: Event-Based Split-CNN Architecture with Data-Level Parallelism on UltraScale+ FPGA

Bhowmik, Pankaj; Pantho, Jubaer Hossain; Mbongue, Joel Mandebi; Bobda, Christophe

doi:10.1109/fccm51124.2021.00028

Cited by 7 publications

(2 citation statements)

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“…Chen et al [45] dynamically modulated the sentence generation context in multi-layer feature maps using encoding channel attention and spatial attention. Reference [46] identified spatial saliency associated with image pixels and executed temporal intensity filtering and predictive coding to filter spatiotemporal redundancies from images.…”

Section: Attention Mechanismsmentioning

confidence: 99%

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle–and–fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model’s sequential modeling capability. Finally, the developed shuffle–and–fusion interaction network model is tested using real-world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software.

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Section: Attention Mechanismsmentioning

confidence: 99%

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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“…There are studies on edge sensors with edge intelligence, which can scan images, conduct pre-processing such as edge detection, then use biological vision systems to reduce unnecessary areas of an image [ 22 ], and then apply complicated CNN on the sensors [ 23 ]. These edge sensors when used in conjunction with IR might be able to remove traditional adversarial patches from the scene.…”

Section: Introductionmentioning

confidence: 99%

Surreptitious Adversarial Examples through Functioning QR Code

et al. 2022

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The continuous advances in the technology of Convolutional Neural Network (CNN) and Deep Learning have been applied to facilitate various tasks of human life. However, security risks of the users’ information and privacy have been increasing rapidly due to the models’ vulnerabilities. We have developed a novel method of adversarial attack that can conceal its intent from human intuition through the use of a modified QR code. The modified QR code can be consistently scanned with a reader while retaining adversarial efficacy against image classification models. The QR adversarial patch was created and embedded into an input image to generate adversarial examples, which were trained against CNN image classification models. Experiments were performed to investigate the trade-off in different patch shapes and find the patch’s optimal balance of scannability and adversarial efficacy. Furthermore, we have investigated whether particular classes of images are more resistant or vulnerable to the adversarial QR attack, and we also investigated the generality of the adversarial attack across different image classification models.

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