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Crop diseases, as one of the major problems in global agricultural production, lead to crop yield reduction, death, and even total extinction, with serious impacts on farmers and the food supply. Traditionally, crop diseases are identified by visual inspection and based on the experience of farmers and agricultural experts, a method that not only consumes human resources but also has a certain degree of subjectivity and inaccuracy. The development of artificial intelligence technology successfully achieves real-time monitoring, automatic identification, and intelligent decision by combining the Internet of Things (IoT) technology and cloud computing technology. Herein, we proposed an EfficientNet Convolutional Group-Wise Transformer (EGWT) architecture. The local features of crop leaf images are extracted by EfficientNet convolution and then input into a group-wise transformer architecture. In the group-wise transformer process, the input features are divided into multiple groups. An attention mechanism is used within each group to calculate correlations between features. After calculating the intra-group attention, the output features of each group are stitched together to form the final output features. Our proposed model achieves 99.8% accuracy on the PlantVillage dataset, 86.9% accuracy on the cassava dataset, and 99.4% accuracy on the Tomato leaves dataset, with the least number of parameters 23.04M in the state-of-the-art convolutional combinatorial transformer hybrid model. The experimental results indicate that the proposed model has the best accuracy and optimal model complexity so far compared to other neural networks based on CNN, transformer, and the hybrid structure of CNN and transformer.

The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm’s performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.

Traditional single ground-based ionospheric detection methods often fail to obtain accurately ionospheric parameter information due to the measurement errors of detection systems and inverse algorithms. With the development of high frequency communication and radar sounding techniques, the ionospheric combined detection technology with multi-means is the most effective way to obtain the more accurate ionospheric characteristics. In this study, combined with ionospheric backscatter detection and oblique detection methods, an integrated ionospheric detection technology (quasi-backscatter detection technology) is proposed. The ionospheric parameters are obtained based on the minimum mean square error criterion and global searching method. The experimental results show that the inversion accuracy of ionospheric parameters is significantly improved by using quasi-backscatter detection system(42.3°N, GeoM). In addition, we simulate the propagation characteristics of the quasi-backscatter detection system by using ray tracing technique. And the variation characteristics between the propagation mode (and the propagation energy) and the angle of the transmitting-receiving beam are obtained.

The Internet of Things establishes the intimacy between the Internet and the physical world. Due to portable size, most IoT devices have limited computing and storage capabilities and are vulnerable to various malicious intrusions. Therefore, it is vital to have efficient approaches to distinguish the true IoT data from fake one, we term such methods as anomaly detection (AD). To detect anomalies accurately and efficiently, in this article a 3-hierarchy joint local and global anomaly detection framework, HADIoT, is proposed, in which IoT devices generate and transmit sensory data to their local edge servers for local AD after data refinement which includes re-framing, normalization, complexity reduction via Principal Component Analysis, and symbol mapping. High detection accuracy is achieved by jointly local and global ADs. The local AD focuses on the data pattern consistency of individual devices via the Gated Recurrent Unit, and the processed data is then forwarded from edge servers to the cloud server for global AD. The global AD focuses on the analysis of the data pattern correlations between different IoT devices, using the Conditional Random Fields. For the maintenance of cyber-security, the proposed anomaly detection framework HADIoT enables to provide an accurate and faster anomaly detection for IoT applications, compared with existing anomaly detection methods. The performance of the proposed method is also empirically evaluated through simulations, using a real dataset - the Information Security Center of Excellence (ISCX) 2012 dataset. Simulation results demonstrate the effectiveness of the proposed framework in terms of True Positive Rate, False Positive Rate, Precision, Accuracy and F_score, compared with three benchmark schemes.

In this paper, we have demonstrated an efficient perovskite light-emitting device (PeLED) by improving perovskite crystallization. The improved perovskite crystallization has been achieved by pretreating the underneath poly (3, 4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) layer with N, N-dimethylformamide (DMF) solvent before single-step spin-coating the perovskite, which results in a prolonged wet environment during the perovskite thermal annealing process to promote the grain growth. The improved perovskite crystallization induces a perovskite thin film with higher surface coverage, fewer defects, and larger grain size with less scattering of grain boundaries. As a result, the PeLEDs exhibit maximum external quantum efficiency of 0.728% and maximum luminance of 2088 cd/m2, which represent a significant improvement over the control devices. Moreover, perovskite films with improved crystalline quality have proved its beneficial effect on the device stability.

A flexible efficient top-emitting organic light-emitting device (TOLED) on an off-the-shelf silk substrate has been demonstrated by planarizing the silk substrate with photopolymer NOA63. The flexibility of the bare silk substrates was retained in the planarized silk substrates due to ductile characteristics of cured NOA63. The planarized silk substrate has shown superiority on surface morphology, which is beneficial to the performances of OLEDs. Their maximum luminance and current efficiency are 45545 ${\rm{cd/ m}}^{2}$ and 37.7 cd/A, respectively. Moreover, our devices show not only high luminance and efficiency but also high flexibility and mechanical robustness. Emission of operating devices is uniform and free of defects under a very small bending radius and the luminance and efficiency do not deteriorate obviously after repeated bending. TOLEDs on silk substrate are a potential alternative to wearable displays.

Surface morphology of perovskite film is critical for highly efficient perovskite-based solar cells. The solution processed perovskite films tend to have voids and pin-holes between the crystals, which has been cited as very detrimental to device performance. The voids and pin-holes not only cause electrical shorting but deleteriously impact charge dissociation/transport/recombination because of the defects existed on the surface and grain boundaries of the perovskite films as well. Here, we demonstrate passivation of the perovskite surface and grain boundaries by small molecules through infiltration of subphthalocyanines (SubPc) into the perovskite film. Reduced surface defects have been obtained, which was verified by XRD and time-resolved dynamics measurements. As a result, the power conversion efficiency for the solution-processed planar heterojunction solar cells has been enhanced from 9.96% for the contrastive perovskite solar cells to 13.6% for the SubPc passivated perovskite solar cells.

Annealing-insensitive black silicon with high absorption below the silicon bandgap has been achieved by femtosecond laser direct writing. Spike microstructures with sizes ranging from 4 to 25 μm are formed on the surface layer of silicon substrate, and a large amount of phosphorous impurities (1021 cm-3) is doped during the resolidification process. The infrared absorption of phosphorus-doped black silicon decreases slightly with both the annealing temperature and duration. Excitingly, the largest decrease is less than 10% at 2 μm (annealing 240 min at 873K). This thermostable infrared absorption is related to free carrier absorption. After laser irradiation, the phosphorus-doped layer maintains a relatively high crystallinity that can be improved further during thermal annealing. The density of the electrically activated impurities is approximately 1019 cm-3.

The indium–tin-oxide-free organic solar cells (OSCs) have been fabricated using a semitransparent Au film as anode. A comparative study between the OSCs with different metal cathodes shows that formation of an ideal ohmic contact at low-workfunction Ca and the organic interface reduces series resistance and increases the carrier collection. Moreover, a periodic corrugation was introduced into the device structure, which leads to an enhanced light absorption in the OSCs by elongating the optical path of the incident light inside the absorber material. The highest power conversion efficiency of an optimal OSC with a Au anode and a corrugated Ca/Ag cathode is 1.4%, which is significantly improved in contrast to 0.61% for the OSC with a planar Al cathode.

An ~ 12-μm-long Bragg grating was engraved in an ~ 5-μm-diameter optical fiber tip by focused ion beam (FIB) milling. An ~ 10-dB extinction was achieved at 1570 nm with only 11 indentations. The grating was used for temperature sensing, and it exhibited a temperature sensitivity of ~ 22 pm°C.

Magnetic field effects on tris-(8-hydroxyquinoline) aluminum-based organic light-emitting devices (OLEDs) by employing Fe3O4 as a magnetic dopant in the hole-transport layer (HTL) have been studied. The magnetic doped OLEDs exhibit efficient injection and transport of holes, and its performances are further enhanced after a magnetic field is applied. The enhancement of luminance and current efficiency of 20% and 24% has been obtained from the magnetic doped devices, while they are only 8% and 9%, respectively, for the nondoped devices under an applied magnetic field of 500 mT. Organic magnetoresistance induced by the magnetic doped HTL is the main origin of increased electroluminescence for the magnetic doped OLEDs.

We propose a photoelastic pressure sensor using a liquid crystal (LC) tunable fiber polarizer. The polarizer contains an etched single-mode fiber sealed in an LC cell. A low-index LC is used as a tunable birefringent cladding of the fiber. Any electric-field-induced LC director reorientation may thus affect the fiber's light-guiding characteristics. Different polarization states experience different LC cladding indices. The transmittance contrast between the x- and y-polarized modes reaches 31.9 dB, which is sensitive enough to monitor any polarization state change in the fiber. A pressure sensor with the sensitivity of 0.25 rad/N is thus demonstrated. The corresponding stress-optic coefficient of the fiber is measured at 1550-nm telecomm band. The advantages and applications of the LC tuning and sensing technologies are discussed.