Recovery of noisy pyroelectric-detector signals through neural-network processing

2005 ◽  
Vol 76 (5) ◽  
pp. 053104 ◽  
Author(s):  
Martín G. González ◽  
Alejandro L. Peuriot ◽  
Verónica B. Slezak ◽  
Guillermo D. Santiago
Keyword(s):  
Neural Network ◽  
Pyroelectric Detector ◽  
Network Processing

scholarly journals A review of recommendation system research based on bipartite graph

2021 ◽  
Vol 336 ◽  
pp. 05010
Author(s):  
Ziteng Wu ◽  
Chengyun Song ◽  
Yunqing Chen ◽  
Lingxuan Li
Keyword(s):  
Neural Network ◽  
Bipartite Graph ◽  
Recommendation System ◽  
Research Priorities ◽  
Research Progress ◽  
Full Paper ◽  
Data Sparseness ◽  
The Past ◽  
Interaction History ◽  
Network Processing

The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.

scholarly journals Deep neural network processing of DEER data

2018 ◽  
Vol 4 (8) ◽  
pp. eaat5218 ◽  
Author(s):  
Steven G. Worswick ◽  
James A. Spencer ◽  
Gunnar Jeschke ◽  
Ilya Kuprov
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Processing

Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design

2021 ◽  
Vol 40 (2) ◽  
pp. 1-19
Author(s):  
Ethan Tseng ◽  
Ali Mosleh ◽  
Fahim Mannan ◽  
Karl St-Arnaud ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Optical Design ◽  
Neural Nets ◽  
Fine Tuning ◽  
Optical Systems ◽  
Design Tools ◽  
End To End ◽  
Application Specific ◽  
Network Processing

Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.

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