NFT is a kind of virtual token derived from the blockchain. In 2019, the NFT transaction market became a new force in the field of the digital economy, while NFT fraud was also widespread. There is no efficient technology or methods to ensure the authenticity of the source data (which have not been stored on the blockchain yet) on a blockchain traceability system. To solve this problem and to safeguard the rights and interests of members of the blockchain application, we propose a method to measure the user’s credit degree by obtaining the data before it stores on the blockchain. We first analyze some NFT trading markets’ business processes and dealing models. Then, based on the analytic hierarchy process (AHP) in the operational research theory, some indexes of credit rating have been made. A credit rating system has been established by calculating the evaluation matrix and efficacy coefficient of each index. The experimental results show that the credit evaluation system can be used as a method to judge the user’s credit rating on a blockchain traceability system. This method provides a reference for the decision of whether to restrict the transaction of some users with abnormal behavior. Full article

Encoder interfaces should be operated in real time with high precision and fast processing for industrial motor control systems. The continuous bidirectional serial synchronous (BiSS-C) interface is an open-source serial communication protocol designed for motor encoders and is suitable for industrial purposes because of its fast serial communication speed. In this study, we propose a method for developing a BiSS-C interface master for a motor encoder slave, using only the configurable logic block (CLB) peripheral integrated into TI microcontroller units. By analyzing the detailed operation protocol of the BiSS-C interface, we create the truth and state tables for logic circuits and finite state machines, which are required for the BiSS-C interface master. Then, by programming the CLB based on the created truth and state tables, we implement the master clock, serial peripheral interface (SPI) clock, and operational process for the master. This approach is cost-efficient because additional hardware components, such as a field-programmable gate array or a complex programmable logic device, are not required for the master implementations. The developed method can be immediately applied to developing the masters for other BiSS-C encoders with different specifications, which is certainly necessary for a motor drive development and test. By building an AC motor control system with the developed master and performing various experiments, we verify the performance and practical usefulness of the developed BiSS-C interface master. The maximum master clock frequency without any CRC errors is achieved by 6.25 MHz, which can cope with more than 20 kHz motor control cycle frequency. The usefulness is demonstrated by showing the motor speed and position control performance that are acceptable in real applications. Full article

Lithium–sulfur batteries have been considered one of the most promising energy storage batteries in the future of flexible and wearable electronics. However, the shuttling of polysulfides, low sulfur utilization, and bad cycle stability restricted the widespread application of lithium–sulfur batteries. Currently, gradient materials with multiple functions can solve those defects simultaneously and can be applied to various parts of batteries. Herein, an electrospinningtriple−gradient Co−N−C/PVDF/PAN fibrous membrane was prepared and applied to lithium–sulfur batteries. The Co−N−C fibrous membrane provided efficient active sites, excellent electrode conductivity, and boosted polysulfide confinement. At the same time, the PVDF/PAN membrane enhances electron transfer and lithium−ion diffusion. As a result, the integrated S@Co−N−C/PVDF/PAN/Li battery delivered a high initial capacity of 1124.1 mA h g⁻¹. Even under high sulfur loading (6 mg cm⁻²), this flexible Li–S battery still exhibits high areal capacity (846.9 mA h cm⁻²) without apparent capacity attenuation and security issues. Meanwhile, the gradient fibrous membranes can be used in zinc–air batteries, and the same double−gradient Co−N−C/PVDF membranes were also used as a binder−free air cathode with bifunctional catalytic activity and a facile hydrophobic and aerophile membrane, delivering remarkable cycling stability and small voltage gap in aqueous ZABs. The well−tunable structures and materials of the gradient strategy would bring inspiration for excellent performance in flexible and wearable energy storage devices. Full article

In this paper, we propose an adversarial transfer learning method to solve the lack of data resources for named entity recognition (NER) tasks in spoken language understanding. In the framework, we use bi-directional long short-term memory with self-attention and conditional random field (BiLSTM-Attention-CRF) model which combines character and word information as the baseline model to train source domain and target domain corpus jointly. Shared features between domains are extracted by a shared feature extractor. This paper uses two different sharing patterns simultaneously: full sharing mode and private sharing mode. On this basis, an adversarial discriminator is added to the shared feature extractor to simulate generative adversarial networks (GAN) and eliminate domain-dependent features. This paper compares ordinary adversarial discriminator (OAD) and generalized resource-adversarial discriminator (GRAD) through experiments. The experimental results show that the transfer effect of GRAD is better than other methods. The F1 score reaches 92.99% at the highest, with a relative increase of 12.89%. It can effectively improve the performance of NER tasks in resource shortage fields and solve the problem of negative transfer. Full article

FPGA-based cards for data concentration and readout are often used in data acquisition (DAQ) systems for high-energy physics experiments. The DMA engines implemented in FPGA enable efficient data transfer to the processing system’s memory. This paper presents a versatile DMA engine. It may be used in systems with FPGA-equipped PCIe boards hosted in a server and MPSoC-based systems with programmable logic connected directly to the AXI system bus. The core part of the engine is implemented in HLS to simplify further development and modifications. The design is modular and may be easily integrated with the user’s DAQ logic, assuming it delivers the data via a standard AXI-Stream interface. The engine and accompanying software are designed with flexibility in mind. They offer a simple single-packet mode for debugging and a high-performance multi-packet mode fully utilizing the computational power of the processing system. The number of used DAQ cards and the amount of memory used for the DMA buffer may be modified in the runtime without rebooting the system. That is particularly useful in the development and test setups. This paper also presents the development and testing methodology. The whole design is open-source and available in public repositories. Full article

Infrared imaging sensors are frequently used in thermal signature detection applications in industrial, automotive, military and many other areas. However, advanced infrared detectors are generally associated with high costs and complexity. Infrared detectors usually necessitate a thermoelectric heater–cooler for temperature stabilization and various computationally complex preprocessing algorithms for fixed pattern noise (FPN) correction. In this paper, we leverage the benefits of uncooled focal plane arrays and describe a complete digital circuit design for Field Programmable Gate Array (FPGA)-based infrared image acquisition and pre-processing. The proposed design comprises temperature compensation, non-uniformity correction, defective pixel correction cores, spatial image transformation and registration with RGB images. When implemented on Xilinx Ultrascale+ FPGA, the system achieves a throughput of 30 frames per second using the Fraunhofer IMS Digital 17 μm QVGA-IRFPA with a microbolometer array size of 320 × 240 pixels and an RGB camera with a 1024 × 720 resolution. The maximum ratio of the standard deviation to the mean of 0.35% was achieved after FPN correction. Full article

The semantic segmentation and understanding of remote sensing images applying computer technology has become an important component of monitoring mangrove forests’ ecological changes due to the rapid advancement of remote sensing technology. To improve the semantic segmentation capability of various surface features, this paper proposes a semantic understanding method for mangrove remote sensing images based on convolution feature inference. Firstly, the sample data is randomly selected, and next a model of convolution feature extraction is used to obtain the features of the selected sample data and build an initial feature set. Then, the convolution feature space and rule base are generated by establishing the three-dimensional color space distribution map for each class and domain similarity is introduced to construct the feature set and rules for reasoning. Next, a confidence reasoning method based on the convolution feature region growth, which introduces an improved similarity calculation, is put forward to obtain the first-time reasoning results. Finally, this approach adds a correction module, which removes the boundary information and reduces the noise from the results of the first-time reasoning as a new sample to correct the original feature set and rules, and uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. It uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. Experiments show that this algorithm has the benefits of a simple process, a short training time, and easy feature acquisition. The effect has been obviously improved compared to a single threshold segmentation method, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and other image segmentation methods. Full article

There is an increasing demand for high-performance global shutter pixels for CMOS image sensor (CIS) in the high-end imaging field. The existing global pixels have disadvantages, such as low sensitivity, high dark current, and large pixel area. In the present work, we developed a novel 9T global shutter CIS pixel with a much smaller pixel pitch of 2.8 μm. To our knowledge, it is the most miniature reported 9T global shutter CIS pixel. The developed 9T global shutter CIS pixel shows an excellent low dark current by the strategy of merging active area, which is 38 e⁻/s. Moreover, the cross-talk, sensitivity, read noise, dynamic range and full well capacity show performance. The current strategy is expected to be helpful in further improving the performance of other kinds of CIS pixels. Full article

Lately, skeleton-based action recognition has drawn remarkable attention to graph convolutional networks (GCNs). Recent methods have focused on graph learning because graph topology is the key to GCNs. We propose to align graph learning on the channel level by introducing graph convolution with enriched topology based on careful channel-wise correlations, namely the attentive channel-wise correlation graph convolution (ACC-GC). For the model to learn channel-wise enriched topologies, ACC-GC learns a shared graph topology spanning many channels and enhances it with careful channel-wise correlations. Encoding the intra-correlation between various nodes within each channel, boosting informative channel-wise correlations, and suppressing trivial ones generates attentive channel-wise correlations. Our enhanced ACC-GCN is created by substituting our ACC-GC for the GC in a standard GCN. Extensive experiments on NTURGB60 and Northwestern-UCLA datasets demonstrate that our proposed ACC-GCN performs comparably to state-of-the-art methods while reducing the computational cost. Full article

Object detection in road scenes is a task that has recently become popular and it is also an important part of intelligent transportation systems. Due to the different locations of cameras in the road scenes, the size of the traffic objects captured varies greatly, which imposes a burden on the network optimization. In addition, in some dense traffic scenes, the size of the traffic objects captured is extremely small and it is easy to miss detection and to encounter false detection. In this paper, we propose an improved multi-scale YOLOv5s algorithm based on the YOLOv5s algorithm. In detail, we add a detection head for extremely small objects to the original YOLOv5s model, which significantly improves the accuracy in detecting extremely small traffic objects. A content-aware reassembly of features (CARAFE) module is introduced in the feature fusion part to enhance the feature fusion. A new SPD-Conv CNN Module is introduced instead of the original convolutional structure to enhance the overall computational efficiency of the model. Finally, the normalization-based attention module (NAM) is introduced, allowing the model to focus on more useful information during training and significantly improving detection accuracy. The experimental results demonstrate that compared with the original YOLOv5s algorithm, the detection accuracy of the multi-scale YOLOv5s model proposed in this paper is improved by 7.1% on the constructed diverse traffic scene datasets. The improved multi-scale YOLOv5s algorithm also maintains the highest detection accuracy among the current mainstream object detection algorithms and is superior in accomplishing the task of detecting traffic objects in complex road scenes. Full article

Deep neural networks (DNNs) and Convolutional neural networks (CNNs) have improved accuracy in many Artificial Intelligence (AI) applications. Some of these applications are recognition and detection tasks, such as speech recognition, facial recognition and object detection. On the other hand, CNN computation requires complex arithmetic and a lot of memory access time; thus, designing new hardware that would increase the efficiency and throughput without increasing the hardware cost is much more critical. This area in hardware design is very active and will continue to be in the near future. In this paper, we propose a novel 8T XNOR-SRAM design for Binary/Ternary DNNs (TBNs) directly supporting the XNOR-Network and the TBN DNNs. The proposed SRAM Computing-in-Memory (CIM) can operate in two modes, the first of which is the conventional 6T SRAM, and the second is the XNOR mode. By adding two extra transistors to the conventional 6T structure, our proposed CIM showed an improvement up to 98% for power consumption and 90% for delay compared to the existing state-of-the-art XNOR-CIM. Full article

The fluctuations in the heating value of an underground coal gasification (UCG) process limit its application in electricity generation, where a desired composition of the combustible gases is required to operate gas turbines efficiently. This shortcoming can be addressed by designing a robust control scheme for the process. In the current research work, a model-based, chattering-free sliding mode control (CFSMC) algorithm is developed to maintain a desired heating value trajectory of the syngas mixture. Besides robustness, CFSMC yields reduced chattering due to continuous control law, and the tracking error also converges in finite time. To estimate the unmeasurable states required for the controller synthesis, a state-dependent Kalman filter (SDKF) based on the quasi-linear decomposition of the nonlinear model is employed. The simulation results demonstrate that despite the external disturbance and measurement noise, the control methodology yields good tracking performance. A comparative analysis is also made between CFSMC, a conventional SMC, and an already designed dynamic integral SMC (DISMC), which shows that CFSMC yields $71.2\%$ and $69.9\%$ improvement in the root mean squared tracking error with respect to SMC and DISMC, respectively. Moreover, CFSMC consumes $97\%$ and $23.2\%$ less control energy as compared to SMC and DISMC, respectively. Full article

In the production process of strip tandem cold rolling mills, the flatness control system is important for improving the flatness quality. The control efficiency of actuators is a pivotal factor affecting the flatness control accuracy. At present, the data-driven methods to intelligently identify the flatness control efficiency have become a research hotspot. In this paper, a wavelet transform longitudinal denoising method, combined with a genetic algorithm (GA-WT), is proposed to handle the big noise of the measured data from each signal channel of the flatness meter, and Legendre orthogonal polynomial fitting is employed to extract the effective flatness features. Based on the preprocessed actual production data, the adaptive moment estimation (Adam) optimization algorithm is applied, to intelligently identify the flatness control efficiency. This paper takes the actual production data of a 1420 mm tandem cold mill as an example, to verify the performance of the new method. Compared with the control efficiency determined by the empirical method, the flatness residual MSE 0.035 is 5.4% lower. The test results indicate that the GA-WT-Legendre-Adam method can effectively reduce the noise, extract the flatness features, and achieve the intelligent determination of the flatness control efficiency. Full article

The Internet of Things (IoT) and artificial intelligence (AI) have promoted teaching reform while improving people’s lives. Under the new teaching environment, the position of physical education (PE) teaching in the teaching work has become increasingly prominent. At present, there are some problems in the PE teaching mode of most colleges and universities, such as poor teaching environment, unstable teaching data, and lack of technical support for the teaching system. This also leads to the low quality of PE teaching and unsatisfactory teaching results. In this paper, IoT and AI are combined to study the application mode of innovative practical teaching in college PE. This paper first constructs a physical education teaching system based on the Internet of Things, then summarizes the necessity of artificial intelligence technology participating in the reform of physical education classroom teaching, and gives a specific teaching application model. Finally, based on the golden sine algorithm-optimization neural network, the application model of college physical education in this paper is evaluated. Through experiments and investigations, the new teaching mode improves the teaching efficiency by 14.7%, improves the teaching quality, and provides reference for the next development of IoT and AI in teaching. Full article

Nonstoichiometric GeSi_xO_y glass films and many-layer structures based on them were obtained by high-vacuum electron beam vapor deposition (EBVD). Using EBVD, the GeO₂, SiO, SiO₂, or Ge powders were co-evaporated and deposited onto a cold (100 °C) p⁺-Si(001) substrate with resistivity ρ = 0.0016 ± 0.0001 Ohm·cm. The as-deposited samples were studied by Fourier-transformed infrared spectroscopy, atomic force microscopy, X-ray photoelectron spectroscopy, and Raman spectroscopy. A transparent indium–tin–oxide (ITO) contact was deposited as the top electrode, and memristor metal–insulator–semiconductor (MIS) structures were fabricated. The current–voltage characteristics (I–V), as well as the resistive switching cycles of the MIS, have been studied. Reversible resistive switching (memristor effect) was observed for one-layer GeSi_0.9O_2.8, two-layer GeSi_0.9O_1.8/GeSi_0.9O_2.8 and GeSi_0.9O_1.8/SiO, and three-layer SiO₂/a–Ge/GeSi_0.9O_2.8 MIS structures. For a one-layer MIS structure, the number of rewriting cycles reached several thousand, while the memory window (the ratio of currents in the ON and OFF states) remained at 1–2 orders of magnitude. Intermediate resistance states were observed in many-layer structures. These states may be promising for use in multi-bit memristors and for simulating neural networks. In the three-layer MIS structure, resistive switching took place quite smoothly, and hysteresis was observed in the I–V characteristics; such a structure can be used as an “analog” memristor. Full article