Infrared and Visible Image Fusion Method Based on a Principal Component Analysis Network and Image Pyramid

The aim of infrared (IR) and visible image fusion is to generate a more informative image for human observation or some other computer vision tasks. The activity-level measurement and weight assignment are two key parts in image fusion. In this paper, we propose a novel IR and visible fusion method based on the principal component analysis network (PCANet) and an image pyramid. Firstly, we use the lightweight deep learning network, a PCANet, to obtain the activity-level measurement and weight assignment of IR and visible images. The activity-level measurement obtained by the PCANet has a stronger representation ability for focusing on IR target perception and visible detail description. Secondly, the weights and the source images are decomposed into multiple scales by the image pyramid, and the weighted-average fusion rule is applied at each scale. Finally, the fused image is obtained by reconstruction. The effectiveness of the proposed algorithm was verified by two datasets with more than eighty pairs of test images in total. Compared with nineteen representative methods, the experimental results demonstrate that the proposed method can achieve the state-of-the-art results in both visual quality and objective evaluation metrics

Observing Individuals and Behavior of Hainan Gibbons (<i>Nomascus hainanus</i>) Using Drone Infrared and Visible Image Fusion Technology

The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world. Infrared and visible images taken by drones are an important and effective way to observe Hainan gibbons. However, a single infrared or visible image cannot simultaneously observe the movement tracks of Hainan gibbons and the appearance of the rainforest. The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. We propose a fusion method of infrared and visible images of the Hainan gibbon for the first time, termed Swin-UetFuse. The Swin-UetFuse has a powerful global and long-range semantic information extraction capability, which is very suitable for application in complex tropical rainforest environments. Firstly, the hierarchical Swin Transformer is applied as the encoder to extract the features of different scales of infrared and visible images. Secondly, the features of different scales are fused through the l1-norm strategy. Finally, the Swing Transformer blocks and patch-expanding layers are utilized as the decoder to up-sample the fusion features to obtain the fused image. We used 21 pairs of Hainan gibbon datasets to perform experiments, and the experimental results demonstrate that the proposed method achieves excellent fusion performance. The infrared and visible image fusion technology of drones provides an important reference for the observation and protection of the Hainan gibbons

A High-Performance Elliptic Curve Cryptographic Processor of SM2 over GF(<i>p</i>)

Elliptic curve cryptography (ECC) is widely used in practical applications because ECC has far fewer bits for operands at the same level of security than other public-key cryptosystems such as RSA. The performance of an ECC processor is usually determined by modular multiplication (MM) and point multiplication (PM) operations. For recommended prime field, MM operation can consist of multiplication and fast reduction operations. In this paper, a 256-bit multiplication operation is implemented by a 129-bit (half-word) multiplier using Karatsuba&#8211;Ofman multiplication algorithm. The fast reduction is a modulo operation, which gets 512-bit input data from multiplication and outputs a 256-bit result ( 0 &#8804; Z &lt; p ) . We propose a two-stage fast reduction algorithm (TSFR) over SCA-256 prime field, which can obtain an intermediate result of 0 &#8804; Z &lt; 2 p instead of 0 &#8804; Z &lt; 14 p in traditional algorithm, avoiding a lot of repetitive subtraction operations. The PM operation is implemented in width nonadjacent form (NAF) algorithm and its operational schedules are improved to increase the parallelism of multiplication and fast reduction operations. Synthesized with a 0.13 &#956; m complementary metal oxide semiconductor (CMOS) standard cell library, the proposed processor costs an area of 280 k gates and PM operation takes 0.057 ms at the frequency of 250 MHz. The design is also implemented on Xilinx Virtex-6 platform, which consumes 27.655 k LUTs and takes 0.37 ms to perform one 256-bit PM operation, attaining six times speed-up over the state-of-the-art. The processor makes a tradeoff between area and performance, thus it is better than other methods

Distorted Janus transition metal dichalcogenides: Stable two-dimensional materials with sizable band gap and ultrahigh carrier mobility

Transition metal dichalcogenides (TMDs) are ideal layered materials to fabricate field effect transistors (FETs) due to their sizable band gaps and high stability, however, the low carrier mobility limits the response speeds. Here, based on recent experimental progress, we employed first principle calculations to reveal a distorted phase of the Janus TMD, 1T′ MoSSe, which is highly stable, exhibiting a moderate band gap and ultrahigh carrier mobility. We show that 1T′ MoSSe can be obtained via structural transition from the synthesized 2H phase after overcoming an energy barrier of 1.10 eV, which can be significantly reduced with alkali metal adsorption, thus proposing a feasible approach for experimental fabrications. 1T′ MoSSe is predicted to be a semiconductor with a trivial band gap of 0.1 eV (based on Heyd-Scuseria-Ernzerhof calculations), which can be closed to form Dirac nodes and then reopened under strain deformation. Due to the almost linear dispersion of the band states, an ultrahigh electron (hole) mobility of up to 1.21 × 105 (7.24 × 104) cm2/V/s is predicted for the new phase, which is 3 orders of magnitudes higher than traditional counterparts and close to the value of graphene. The high stability, sizable band gap, and ultrahigh carrier mobility in the new Janus systems are expected to be used in high-performance electronics applications

Calibration and Testing of Discrete Element Simulation Parameters for Sandy Soils in Potato Growing Areas

To improve the accuracy of discrete element simulation in the process of separating potato–soil mixtures, the contact parameters of sandy soil with 3, 6, 9, and 12% water content were calibrated in DEM simulation using EDEM software simulation. The error of the rest angle between them was used as an index, and the approach of performing only one simulation and multiple Box–Behnken response surface analyses was proposed to determine the optimal parameter combinations. Meanwhile, unconfined compression and direct shear tests were conducted to obtain the parameters of polymer bonds for soil with different water content, and a simulation was carried out using EDEM. The test results show that the significant parameters affecting the rest angle are JKR surface energy, soil interparticle recovery coefficient, and rolling friction factor. The numerical simulation of the rest angle was compared with the physical test, and the maximum relative error between them was 4.72%. The bond parameters of soil with different water content and firmness were obtained and compared with the simulation test, the maximum error was 6.53% for the direct shear test and 8.07% for the unconfined compression test, which proved that the bonding parameters are reliable and provide an effective parametric and theoretical basis for the discrete element simulation of soil particles

Thermo-mechanical behavior of energy diaphragm wall: physical and numerical modelling

International audienceThe paper presents a study of the thermo-mechanical behavior of energy diaphragm wall. A physical model, which consists of a small-scale concrete diaphragm wall equipped with a heating exchange pipe, was used. A heating test was performed where hot water (at 50 °C) was circulated through a heat exchange pipe for 75 h. The results show that the temperatures in the wall and in the soil increased quickly during the first 20 h and reached stabilization at the end of the experiment. The temperature increase induced increase of axial strain in the wall and earth pressure at the soil/wall interface. In addition to the experiment, a numerical model, using finite element analysis, was used to predict the behavior of the wall during this experiment. The good agreement between the numerical and the experimental results allows the main phenomena that took place to be explained; heating induces thermal expansion of the wall that results in the modification in stress in the wall and at the soil/wall interface. In addition, since the pipe was located closer to one side of the wall, the thermal expansion of the wall was not homogenous, and the wall bent during heating

The Data-Driven Modeling of Pressure Loss in Multi-Batch Refined Oil Pipelines with Drag Reducer Using Long Short-Term Memory (LSTM) Network

Due to the addition of the drag reducer in refined oil pipelines for increasing the pipeline throughput as well as reducing energy consumption, the classical method based on the Darcy-Weisbach Formula for precise pressure loss calculation presents a large error. Additionally, the way to accurately calculate the pressure loss of the refined oil pipeline with the drag reducer is in urgent need. The accurate pressure loss value can be used as the input parameter of pump scheduling or batch scheduling models of refined oil pipelines, which can ensure the safe operation of the pipeline system, achieving the goal of energy-saving and cost reduction. This paper proposes the data-driven modeling of pressure loss for multi-batch refined oil pipelines with the drag reducer in high accuracy. The multi-batch sequential transportation process and the differences in the physical properties between different kinds of refined oil in the pipelines are taken into account. By analyzing the changes of the drag reduction rate over time and the autocorrelation of the pressure loss sequence data, the sequential time effect of the drag reducer on calculating pressure loss is considered and therefore, the long short-term memory (LSTM) network is utilized. The neural network structure with two LSTM layers is designed. Moreover, the input features of the proposed model are naturally inherited from the Darcy-Weisbach Formula and on adaptation to the multi-batch sequential transportation process in refined oil pipelines, using the particle swarm optimization (PSO) algorithm for network hyperparameter tuning. Case studies show that the proposed data-driven model based on the LSTM network is valid and capable of considering the multi-batch sequential transportation process. Furthermore, the proposed model outperforms the models based on the Darcy-Weisbach Formula and multilayer perceptron (MLP) from previous studies in accuracy. The MAPEs of the proposed model of pipelines with the drag reducer are all less than 4.7% and the best performance on the testing data is 1.3627%, which can provide the calculation results of pressure loss in high accuracy. The results also indicate that the model’s capturing sequential effect of the drag reducer from the input data set contributed to improving the calculation accuracy and generalization ability

Distorted Janus Transition Metal Dichalcogenides: Stable Two-Dimensional Materials with Sizable Band Gap and Ultrahigh Carrier Mobility

Transition metal dichalcogenides (TMDs) are ideal layered materials to fabricate field effect transistors (FETs) due to their sizable band gaps and high stability, however, the low carrier mobility limits the response speeds. Here, based on recent experimental progress, we employed first principle calculations to reveal a distorted phase of the Janus TMD, 1T′ MoSSe, which is highly stable, exhibiting a moderate band gap and ultrahigh carrier mobility. We show that 1T′ MoSSe can be obtained via structural transition from the synthesized 2H phase after overcoming an energy barrier of 1.10 eV, which can be significantly reduced with alkali metal adsorption, thus proposing a feasible approach for experimental fabrications. 1T′ MoSSe is predicted to be a semiconductor with a trivial band gap of 0.1 eV (based on Heyd–Scuseria–Ernzerhof calculations), which can be closed to form Dirac nodes and then reopened under strain deformation. Due to the almost linear dispersion of the band states, an ultrahigh electron (hole) mobility of up to 1.21 × 10⁵ (7.24 × 10⁴) cm²/V/s is predicted for the new phase, which is 3 orders of magnitudes higher than traditional counterparts and close to the value of graphene. The high stability, sizable band gap, and ultrahigh carrier mobility in the new Janus systems are expected to be used in high-performance electronics applications