Remote sensing object counting is an important issue in remote sensing analysis. Remote sensing object counting has many challenges, such as large-scale variations and complex backgrounds. The previous counting methods have many shortboards, such as only focusing on local appearance features of crowd scenes and ignoring the self-supervision ability of the network itself. To remedy the above problems, in this paper, we propose a novel remote sensing object counting method, which contains Adaptive Multi-scale Context Aggregation (AMCAM) module and Self Context Distillation (SCDM) module. The AMCAM can model and fuse context information from different receptive fields effectively. And it also keeps detailed information through multiple pixel attention (PA) modules step by step. The SCDM can improve the representation learning without adding any additional supervision information. SCDM uses feature maps from the deeper layer of the network to supervise feature maps from the earlier layer of the network. Our method has achieved good performance on remote sensing object counting dataset RSOC and mainstream crowd counting datasets such as ShanghaiTech and UCF-QNRF datasets.

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

In recent years, deep learning based crowd counting networks have achieved significant progress. However, most of them generate rough crowd density maps due to low-resolution features used for estimating crowd distribution, which affects the performance of crowd counting. To solve this problem, in this paper, we propose a Hierarchical Attention Guided Network (HAGN) for crowd counting. We apply the first 13 layers of VGG-16 to extract base features. Then, the extracted features are processed by the Hierarchical Attention Mechanism (HAM), which guided the extracted features to enlarge step by step via our proposed attention guided branch. Finally, the outputs of HAM are fed to 1×1 convolutional layer for final crowd density estimation. Experiments are performed on ShanghaiTech and UCF-QNRF datasets, and our HAGN achieves promising performance compared with the other state-of-the-art methods on crowd counting and crowd localization, respectively.

CNN-based crowd counting methods have achieved great progress in recent years. However, most of these CNN-based crowd counting methods do not make full use of contextual information, which contains high-level semantic features and low-level detail features from different receptive fields of CNN. But rich contextual information is important to solve the scale variation problem of crowd counting. So the precision of previous CNN-based crowd counting methods is decreased. To solve this problem, we propose an adaptive attention fusion mechanism (AAFM). AAFM can use multi-scale features from different receptive fields of CNN effectively. It integrates the convolution network for feature learning and the attention mechanism for multi-scale features fusion. We apply the first 13 convolution layers of VGG-16 as the encoder module to extract the base features. Then, the base features are fed into the decoder module. The decoder module mainly contains Density Regression Branch (DRB) and Feature Fusion Branch (FFB). DRB uses multiple convolution layers for feature learning and multi-scale feature extraction. FFB uses attention modules for modeling multi-scale features and element-wise multiply for features fusion. Therefore, AAFM can obtain rich contextual information into the encoder-decoder framework for generating high-quality crowd density maps and accurate counting. We perform experiments on ShanghaiTech, UCF-CC-50, and UCF-QNRF datasets, and AAFM achieves promising results.

This paper focuses on the NAND flash memory as a data storage medium in the Internet of things data acquisition system, which plays an important role from beginning to end. The flow process of multi-channel sensor data sampling, holding, conversion, coding, and storage in the data acquisition system is described. The basic working principle and working process of NAND flash memory are introduced. Based on Interleaved Two-plane Program Technology, and the solution of burst massive throughput in high-speed data transmission is proposed. Compared with previously published papers, the temperature dependence of key time parameters of NAND flash memory is studied and analyzed under the condition of temperature change more concretely. The changing trend of three key time parameters of NAND type flash under different temperatures is analyzed by using appropriate test samples. At the same time, the analysis of the causes of reflection from the phenomenon is further expanded and discussed. The combination of theory and practice can provide powerful suggestions for more efficient and accurate application of NAND flash memory.