Pure WO ₃ and Ag-WO ₃ (mixed solid solutions Ag with WO ₃ ) have been successfully synthesized by sol-gel method and the influences of calcination temperature on the particle size, morphology of the WO ₃ and Ag-WO ₃ nanoparticles were investigated. Powder X-ray diffraction results show that the hexagonal to monoclinic phase transition occurs at calcination temperature varying from 300°C to 500°C. SEM images show that calcination temperature plays an important role in controlling the particle size and morphology of the as-prepared WO ₃ and Ag-WO ₃ nanoparticles. The NO ₂ gas sensing properties of the sensors based on WO ₃ and Ag-WO ₃ nanoparticles calcined at different temperatures were investigated and the experimental results exhibit that the gas sensing properties of the Ag-WO ₃ sensors were superior to those of the pure WO ₃ . Especially, the sensor based on Ag-WO ₃ calcined at 500°C possessed larger response, better selectivity, faster response/recovery and better longer-term stability to NO ₂ than the others at relatively low operating temperature (150°C).

High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas. However, objects of the same category within HRS images generally show significant differences in scale and shape across diverse geographical environments, making it difficult to fit the data distribution. Additionally, a complex background environment causes similar appearances of objects of different categories, which precipitates a substantial number of objects into misclassification as background. These issues make existing learning algorithms sub-optimal. In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and class-agnostic edge aware (CEA) loss. Specifically, we propose a funnel module to downsample, which reduces the computational cost, and extracts high-resolution semantic information from the initial input image. Secondly, we downsample the processed feature images into multi-resolution branches incrementally to capture image features at different scales. Furthermore, with the design of the Window multi-head self-attention, SE attention, and Depth-Wise convolution, the light-efficient IA blocks are utilized to distinguish image features of the same class with variant scales and shapes. Finally, our feature refinement module integrates the CEA loss function, which disambiguates inter-class objects with similar shapes and increases the data distribution distance for correct predictions. With effective pre-training strategies, we demonstrate the superiority of Hi-ResNet over the existing prevalent methods on three HRS segmentation benchmarks.

According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. In the designed parallelism LSTM framework, multiple parallel LSTM sub-networks are trained separately to extract different temporal features from the spectrogram of each frequency and produce corresponding classification probabilities. At the decision level, the probabilities of activity classification from these sub-networks are fused by addition as the recognition output. To validate the proposed method, an experimental data set is collected by using an SFCW radar to monitor 11 participants who continuously perform six activities in sequence with three different transitions and random durations. The validation results demonstrate that the average accuracies of the designed parallelism unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) based on five frequency spectrograms are 85.41% and 96.15%, respectively, outperforming traditional Uni-LSTM and Bi-LSTM networks with only a single-frequency spectrogram by 5.35% and 6.33% at least. Additionally, the recognition accuracy of the parallelism LSTM network reveals an upward trend as the number of multi-frequency spectrograms (namely the number of LSTM subnetworks) increases, and tends to be stable when the number reaches 4.

Through-wall-radar imaging is of interest for mapping the wall layout of buildings and for the detection of stationary targets within buildings. In this paper, we present an easy single-side two-location spotlight imaging method for both wall layout mapping and stationary target detection by utilizing multiple-input multiple-output (MIMO) through-wall-radar. Rather than imaging for building walls directly, the images of all building corners are generated to speculate wall layout indirectly by successively deploying the MIMO through-wall-radar at two appropriate locations on only one side of the building and then carrying out spotlight imaging with two different squint-views. In addition to the ease of implementation, the single-side two-location squint-view detection also has two other advantages for stationary target imaging. The first one is the fewer multi-path ghosts, and the second one is the smaller region of side-lobe interferences from the corner images in comparison to the wall images. Based on Computer Simulation Technology (CST) electromagnetic simulation software, we provide multiple sets of validation results where multiple binary panorama images with clear images of all corners and stationary targets are obtained by combining two single-location images with the use of incoherent additive fusion and two-dimensional cell-averaging constant-false-alarm-rate (2D CA-CFAR) detection.