LEDNet: Deep Learning-Based Ground Sensor Data Monitoring System

Rangappa, Nehul; Prasad, Yogendra; Dubey, Shiv Ram

doi:10.1109/jsen.2021.3129173

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…As for the second term Ψ∆S, it is defined as groups of sparse representation, which are extracted from sparse representation error E. We eliminate the valid information by calculating the groups of sparse representation, which means decomposing E into sparse representation and residual E by using the OMP algorithm iteratively. With several iterations, the residual E is decomposed to achieve groups of sparse representation ΨA [2] , ΨA [3] , • • • , ΨA [C] . The groups of sparse representation can be calculated iteratively by the following mathematical model:…”

Section: The Nssre Structure On Srementioning

confidence: 99%

“…Unmanned aerial vehicles (UAV) [1][2][3] mainly orient to low-altitude remote sensing, which has advantages in complex scenarios exploration. Compared to manual detection, UAV can reach the place where a ground search is difficult to approach by controlling the flight attitude [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Following the data x in (1) being substituted with (2), the mathematical framework of CS is concluded as: y = ΦΨα Dα, (3) in which the matrix D ∈ M×L is the so-called equivalent dictionary. Under the sparsity condition on x, a unique mapping between the measurement y and the original data x [14,15] is established.…”

Section: Introductionmentioning

confidence: 99%

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Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Jiang

Bai

2023

Applied Sciences

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The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS) system, the projected SRE in the compressed measurement will bring a significant challenge to the recovery accuracy of the images. In this work, a new SRE structure is proposed. Following the new structure, a lower sparse representation error (LSRE) is achieved by eliminating groups of sparse representation. With the proposed LSRE modeling, a robust sensing matrix is designed to compress and encrypt the UAV images. Experiments for UAV images are carried out to compare the recovery performance of the proposed algorithm with the existing related algorithms. The results of the proposed algorithm reveal superior recovery accuracy. The new CS framework with the proposed sensing matrix to address the scenario of UAV images with large SRE is dominant.

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Section: The Nssre Structure On Srementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Jiang

Bai

2023

Applied Sciences

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“…Model size is very important as far as performance optimization of on-device system is concerned because larger models mean more memory reference and more energy [26]. [27] 0.37 M 0.7 MB 15mins 383ms LEDNet [28] 1.856 M 3.8 MB --SegNet [29] 29.46 M 56.2 MB 37mins 286ms AlexNet [30], [31] 60 M 232 MB 7,920mins -VGG16 [31], [32] 138 M 528 MB --SqueezNet [25] 0.66 M 4.8 MB --ResNet152 [31] 232 M 60 MB --GoogleNet [31] 6.8 M 28 MB --SGtechNet (Proposed) 128K…”

Section: Model Compressionmentioning

confidence: 99%

Smart power consumption forecast model with optimized weighted average ensemble

Ndife

Rakwichian

Muneesawang

et al. 2022

IJ-AI

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Smart power forecasting enables energy conservation and resource planning. Power estimation through previous utility bills is being replaced with machine intelligence. In this paper, a neural network architecture for demand side power consumption forecasting, called SGtechNet, is proposed. The forecast model applies ConvLSTM-encoder-decoder algorithm designed to enhance the quality of spatial encodings in the input feature to make a 7-day forecast. A weighted average ensemble approach was used, where multiple models were trained but only allow each model’s contribution to the prediction to be weighted proportionally to their level of trust and estimated performance. This model is most suitable for low-powered devices with low processing and storage capabilities like smartphones, tablets and iPads. The power consumption comparison between a manually operated home and a smart home was investigated and the model’s performance was tested on a time-domain household power consumption dataset and further validated using a real time load profile collated from the School of Renewable Energy and Smart Grid Technology, Naresuan University Smart Office. An improved root mean square error (RMSE) of 358 kwh was achieved when validated with holdout validation data from the automated office. Overall performance error, forecast and computational time showed a significant improvement over published research efforts identified in a literature review.

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