[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Because of the limitations of the Global Positioning System (GPS) in indoor scenarios, various types of indoor positioning or localization technologies have been proposed and deployed. Wireless radio signals have been widely used for both communication and localization purposes due to their popular availability in indoor spaces. However, the accuracy of indoor localization based purely on radio signals is still not perfect. Recently, visible light communication (VLC) has made use of electromagnetic radiation from light sources for transmitting data. The potential for deploying visible light communication for indoor localization has been investigated in recent years. Visible-light-based localization enjoys low deployment cost, high throughput, and high security. In this article, the most recent advances in visible-light-based indoor localization systems have been reviewed. We strongly believe that visible-light-based localization will become a low-cost and feasible complementary solution for indoor localization and other smart building applications.

https://www.mdpi.com/1424-8220/20/5/1382/pdf

Recent Advances in Indoor Localization via Visible Lights: A Survey.

In this letter, a 2 × 2 low-profile broadband triple-mode orbital angular momentum (OAM) array antenna is presented, which can be dynamically controlled with electrostatic method. The antenna element consists of a 4 × 4 metasurface array, a 90° phase-shifting reconfigurable feed network (RFN), and a square driven patch with two switchable feeding ports (SWPs). With the computer-assisted micro-controller unit, the p-i-n diodes direct current (dc) bias voltage on the RFN, and SWPs, the OAM modes can then be dynamically switched between l = +1, l = − 1, and l = 0. The OAM beams of mode l = ±1 and mode l = 0 have been generated in the broad frequency with the impedance band of 4.9–6.5 GHz and 5.0–6.3 GHz, respectively. The experimentally measured peak gains of the triple-mode are 11.17, 11.05, and 15.19 dBi, respectively. Both the simulation and experiment demonstrate that the proposed antenna exhibits several advantages, such as mode-reconfigurable ability, broad frequency band, high directivity, and good helical phase front of the OAM waves.

A Broadband Electronically Mode-Reconfigurable Orbital Angular Momentum Metasurface Antenna

In this communication, an orbital angular momentum (OAM) mode-reconfigurable antenna based on a uniform circular array (UCA) is presented. To realize multiple switchable operating states while maintaining a relatively simple structure, a simplified feeding scheme is developed. By introducing a set of optimal reference phases for different OAM modes, the tunable phase states for the entire array are degenerated, which leads to a reduction in the total number of required phase states. Then, the excitation phases are decomposed into two parts and generated by the reconfigurable radiators and reconfigurable feed network, respectively. In addition, p-i-n diodes are selected as switches to control the operating states of the radiators and feed network. Therefore, OAM-mode switching capability can be obtained by altering the direct current (dc) bias configurations of the diodes. The antenna design is validated by both simulation and measurement. Results indicate that the proposed eight-element array antenna produces six operating states corresponding to OAM modes l = ±1, ±2, and ±3. The measured overlapped band among the states with $\vert \text{S}_{\mathbf {11}}\vert dB covers 2.33–2.73 GHz. Moreover, the proposed antenna also features the advantages of easy fabrication and low cost.

A Mode-Reconfigurable Orbital Angular Momentum Antenna With Simplified Feeding Scheme

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC-ResNet) model for fine-grained disease identification in the system. MDFC-ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC-ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

/pdf/mdfc-resnet-an-agricultural-iot-system-to-accurately-aixg5t6b06.pdf

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

/pdf/deep-learning-in-skin-disease-image-recognition-a-review-1kcyd8abr8.pdf

Deep Learning in Skin Disease Image Recognition: A Review

In this paper, an array antenna for generating multi-mode orbital angular momentum (OAM) vortex waves and having electrically controlled mode reconfigurable function is proposed. The proposed antenna is based on a reconfigurable feed network (RFN) consisting of multiple PIN diodes as reconfigurable switches, a direct current (dc) control board and a $2\times4$ radial uniform circular array. By controlling the coding sequence of the corresponding PIN diode dc bias voltage on the RFN using a programmable microcontroller, the OAM modes generated by the proposed antenna can be dynamically reconfigured between l = 0, ±1, and ±2. Moreover, the OAM beams can be generated in a wide frequency band of 5.5–6.1 GHz and 5.65–6.1GHz for l = ± 1, ±2 modes and l = 0 mode, respectively. The prototype of the proposed antenna is simulated and fabricated to validate the proposed design. Both far-field and near-field experiments were performed to verify the feasibility of the proposed design. The results of the numerical simulation and measurement have good agreement with each other.

An Electronically Mode Reconfigurable Orbital Angular Momentum Array Antenna

This paper proposes an electronically reconfigurable multimode orbital angular momentum (OAM) patch array antenna using single pole double throw radio frequency switch that can complete dynamic switching of three modes (−1,0,1) at 2.4 GHz. As a new type of multiplexing technology, the application value of OAM technology in radar imaging and wireless communication has been continuously verified. Considering that many theoretical algorithms are proposed, a multimode vortex antenna that meets the experimental requirements should be developed. The proposed antenna consists of RF switch, programmable microcontroller, phase-shifted feed network, and $2\times2$ rectangular array. Different feeder networks were selected by controlling the RF switch to generate vortex beams with different modes. The proposed antenna was numerically simulated and physically produced. The feasibility of the design was verified through experiments. The simulation and design results are basically the same. The proposed scheme has simple structure, easy to design and deploy, and has important significance in the design of many mode vortex antenna applications in communication and radar imaging.

/pdf/integrated-multimode-orbital-angular-momentum-antenna-based-3k8h0xo3wo.pdf

Integrated Multimode Orbital Angular Momentum Antenna Based on RF Switch

There is a multipath effect caused by multireflections between the plasma and the vehicle surface, and there are coupling characteristics between array antennas. These two errors will seriously affect the quality of navigation signal. To solve the mutual coupling of uniform linear array (ULA) of plasma, a direction-of-arrival (DOA) estimation algorithm based on mutual coupling correction is proposed. The algorithm is based on the special structure of mutual coupling matrix (MCM) of ULA and combines spatial smoothing algorithms. It solves a quadratic optimization problem under the linear constraint and utilizes peak search technology to obtain DOA. The effectiveness of the proposed algorithm is verified by the simulation results. Numerical simulation is applied in this article to analyze the influence of array antenna from the plasma. In addition, array antenna is calibrated with the mutual coupling calibration information estimated by the algorithm. The result of antenna calibration validates the correctness of the simulation conclusion.

Bao-Shan Li

Papers

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

Deep Learning in Skin Disease Image Recognition: A Review

An Electronically Mode Reconfigurable Orbital Angular Momentum Array Antenna

Integrated Multimode Orbital Angular Momentum Antenna Based on RF Switch

DOA Estimation and Mutual Coupling Calibration Algorithm for Array in Plasma Environment