A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications

Yao, Fang; Ding, Yulong; Hong, Shengguang; Yang, Shuang–Hua

doi:10.53941/ijndi0101002

Cited by 49 publications

(18 citation statements)

References 75 publications

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“…Moreover, DCP-A can be extended into multi-task pruning. In the future, we will 1) consider more different guidance mechanisms with layer information [32,46,47,49,56,57,66], 2) introduce control strategies to enhance the model robustness [3,27,33,50,51,58], and 3) extend our approach to other complicated multi-task learning problems [1,23,34,59,65,67].…”

Section: Discussionmentioning

confidence: 99%

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Cheng

Wang²,

Ma³

et al. 2023

Complex Intell. Syst.

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Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices. Neural architecture search (NAS) provides an efficient way to automatically seek appropriate neural architecture design for compressed model. It is observed that, for existing NAS-based pruning methods, there is usually a lack of layer information when searching the optimal neural architecture. In this paper, we propose a new NAS approach, namely, differentiable channel pruning method guided via attention mechanism (DCP-A), where the adopted attention mechanism is able to provide layer information to guide the optimization of the pruning policy. The training process is differentiable with Gumbel-softmax sampling, while parameters are optimized under a two-stage training procedure. The neural network block with the shortcut is dedicatedly designed, which is of help to prune the network not only on its width but also on its depth. Extensive experiments are performed to verify the applicability and superiority of the proposed method. Detailed analysis with visualization of the pruned model architecture shows that our proposed DCP-A learns explainable pruning policies.

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Section: Discussionmentioning

confidence: 99%

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Cheng

Wang²,

Ma³

et al. 2023

Complex Intell. Syst.

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“…The detection of lung nodules using low-dose CT techniques is critical for the early detection of lung cancer [6]. However, this process can be quite burdensome for radiologists due to the large number of CT images that need to be reviewed.…”

Section: Lung Nodule Detectionmentioning

confidence: 99%

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

et al. 2023

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In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global search module. A multi-scale cascade module has also been introduced to enhance the model detection using attention enhancement, global information search, and contextual feature fusion. The experimental results showed that the proposed network achieved accurate detection of lung nodule information, and our method achieves a high sensitivity of 88.08% of the average FROC score on the LUNA16 dataset. In addition, ablation experiments also demonstrated the effectiveness of our method.

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“…It is used in machine monitoring, asset tracking, energy management, and environmental monitoring [8][9][10][11][12]. However, the quality of data transmission via LoRa can be affected by environmental constraints [13]. Multiple obstacles, high temperatures, Humidity, excessive dust, and particles, as well as the presence of metallic equipment, make it difficult to transmit data over long or medium distances [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

Aarif,

Tabaa,

Hachimi

2023

JSAN

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LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.

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A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications

Cited by 49 publications

References 75 publications

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

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