Journal of Electronic Science and Technology最新文献

This paper proposed a high-sensitivity phase imaging eddy current magneto-optical (PI-ECMO) system for carbon fiber reinforced polymer (CFRP) defect detection. In contrast to other eddy current-based detection systems, the proposed system employs a fixed position excitation coil while enabling the detection point to move within the detection region. This configuration effectively mitigates the interference caused by the lift-off effect, which is commonly observed in systems with moving excitation coils. Correspondingly, the relationship between the defect characteristics (orientation and position) and the surface vertical magnetic field distribution (amplitude and phase) is studied in detail by theoretical analysis and numerical simulations. Experiments conducted on woven CFRP plates demonstrate that the designed PI-ECMO system is capable of effectively detecting both surface and internal cracks, as well as impact defects. The excitation current is significantly reduced compared with traditional eddy current magneto-optical (ECMO) systems.

A silicon (Si)/silicon carbide (4H–SiC) heterojunction double-trench metal-oxide-semiconductor field effect transistor (MOSFET) (HDT-MOS) with the gate-controlled tunneling effect is proposed for the first time based on simulations. In this structure, the channel regions are made of Si to take advantage of its high channel mobility and carrier density. The voltage-withstanding region is made of 4H–SiC so that HDT-MOS has a high breakdown voltage (BV) similar to pure 4H–SiC double-trench MOSFETs (DT-MOSs). The gate-controlled tunneling effect indicates that the gate voltage (V_G) has a remarkable influence on the tunneling current of the heterojunction. The accumulation layer formed with positive V_G can reduce the width of the Si/SiC heterointerface barrier, similar to the heavily doped region in an Ohmic contact. This narrower barrier is easier for electrons to tunnel through, resulting in a lower heterointerface resistance. Thus, with similar BV (approximately 1770 ​V), the specific on-state resistance (R_ON-SP) of HDT-MOS is reduced by 0.77 ​mΩ‧cm² compared with that of DT-MOS. The gate-to-drain charge (Q_GD) and switching loss of HDT-MOS are 52.14 ​% and 22.59 ​% lower than those of DT-MOS, respectively, due to the lower gate platform voltage (V_GP) and the corresponding smaller variation (ΔV_GP). The figure of merit (Q_GD ​× ​R_ON-SP) of HDT-MOS decreases by 61.25 ​%. Moreover, the heterointerface charges can reduce R_ON-SP of HDT-MOS due to trap-assisted tunneling while the heterointerface traps show the opposite effect. Therefore, the HDT-MOS structure can significantly reduce the working loss of SiC MOSFET, leading to a lower temperature rise when the devices are applied in the system.

The extraction and understanding of text knowledge become increasingly crucial in the age of big data. One of the current research areas in the field of natural language processing (NLP) is how to accurately understand the text and collect accurate linguistic information because Chinese vocabulary is diverse and ambiguous. This paper mainly studies the candidate entity generation module of the entity link system. The candidate entity generation module constructs an entity reference expansion algorithm to improve the recall rate of candidate entities. In order to improve the efficiency of the connection algorithm of the entire system while ensuring the recall rate of candidate entities, we design a graph model filtering algorithm that fuses shallow semantic information to filter the list of candidate entities, and verify and analyze the efficiency of the algorithm through experiments. By analyzing the related technology of the entity linking algorithm, we study the related technology of candidate entity generation and entity disambiguation, improve the traditional entity linking algorithm, and give an innovative and practical entity linking model. The recall rate exceeds 82%, and the link accuracy rate exceeds 73%. Efficient and accurate entity linking can help machines to better understand text semantics, further promoting the development of NLP and improving the users’ knowledge acquisition experience on the text.

This paper proposes an improved exponential curvature-compensated bandgap reference circuit to exploit the exponential relationship between the current gain β of the bipolar junction transistor (BJT) and the temperature as well as reduce the influence of resistance-temperature dependency. Considering the degraded circuit performance caused by the process deviation, the trimmable module of the temperature coefficient (TC) is introduced to improve the circuit stability. The circuit has the advantages of simple structure, high linear stability, high TC accuracy, and trimmable TC. It consumes an area of 0.09 ​mm² when fabricated by using the 0.25-μm complementary metal-oxide-semiconductor (CMOS) process. The proposed circuit achieves the simulated power supply rejection (PSR) of about −78.7 ​dB@1 ​kHz, the measured TC of ∼4.7 ​ppm/°C over a wide temperature range from −55 ​°C to 125 ​°C with the 2.5-V single-supply voltage, and the tested line regulation of 0.10 ​mV/V. Such a high-performance bandgap reference circuit can be widely applied in high-precision and high-reliability electronic systems.

The mortar pumpability is essential in the construction industry, which requires much labor to estimate manually and always causes material waste. This paper proposes an effective method by combining a 3-dimensional convolutional neural network (3D CNN) with a 2-dimensional convolutional long short-term memory network (ConvLSTM2D) to automatically classify the mortar pumpability. Experiment results show that the proposed model has an accuracy rate of 100% with a fast convergence speed, based on the dataset organized by collecting the corresponding mortar image sequences. This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.

The microwave-induced thermoacoustic imaging (TAI) technology has both the advantages of high contrast of microwave imaging and high resolution of ultrasound imaging (UI), so it has carried out exploratory application research in various areas, such as the early detection of breast tumors and cerebrovascular diseases. However, the microwave generator used in the traditional TAI technology is huge and expensive, and the temporal resolution is also too low due to the single-element scanning mechanism. Thus, it is difficult to meet the needs of clinical applications. In this paper, the iterative process and the analysis of related application scenarios from single-element scanning to portable and array-based TAI, such as the miniaturized microwave generator, handheld antenna, multi-channel data acquisition, and UI/TAI dual-modality imaging, are reviewed, and the future trends of this technology are discussed. This review helps researchers in the field of TAI learn the technological development process and future trends. It also deepens clinicians’ understanding of TAI so as to put forward more application requirements.

Automatic segmentation of early esophagus cancer (EEC) in gastrointestinal endoscopy (GIE) images is a critical and challenging task in clinical settings, which relies primarily on labor-intensive and time-consuming routines. EEC has often been diagnosed at the late stage since early signs of cancer are not obvious, resulting in low survival rates. This work proposes a deep learning approach based on the U-Net++ method to segment EEC in GIE images. A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center, West China Hospital of Sichuan University, China, have been utilized. The experimental result shows that our proposed method achieved promising results. Furthermore, the comparison has been made between the proposed and other U-Net-related methods using the same dataset. The mean and standard deviation (SD) of the dice similarity coefficient (DSC), intersection over union (IoU), precision (Pre), and recall (Rec) achieved by the proposed framework were DSC (%) ​= ​94.62 ​± ​0.02, IoU (%) ​= ​90.99 ​± ​0.04, Pre ​(%) = ​94.61 ​± ​0.04, and Rec (%) ​= ​95.00 ​± ​0.02, respectively, outperforming the others. The proposed method has the potential to be applied in EEC automatic diagnoses.

Kernel adaptive filters (KAFs) have sparked substantial attraction for online non-linear learning applications. It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion. Concerning this, the logarithmic hyperbolic cosine (lncosh) criterion with better robustness and convergence has drawn attention in recent studies. However, existing lncosh loss-based KAFs use the stochastic gradient descent (SGD) for optimization, which lack a trade-off between the convergence speed and accuracy. But recursion-based KAFs can provide more effective filtering performance. Therefore, a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article. Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness, accuracy performance, and computational cost.

Microwave-induced thermoacoustic imaging (MTAI) has advantages including the large imaging depth, high imaging resolution, high imaging contrast, and fast imaging speed. The thermoacoustic (TA) group of South China Normal University has dedicated to developing TA imaging for more than a decade and has made many breakthroughs. This review introduces these breakthroughs from two aspects including the improvement in techniques and the exploration of applications. On the technological level, there are ultrashort microwave pulse (USMP)-induced TA imaging that can improve the imaging resolution, nonlinear thermoacoustic imaging (NTAI) that can improve the imaging contrast, polarized microwave-induced thermoacoustic imaging (P-MTAI) that can obtain cellular-level alignment information, and more convenient and accurate handheld and multimodal probes. On the application side, the optimization and expansion have been carried out, mainly concentrating on breast and myocardial imaging. Finally, several current research directions are introduced, including the application of P-MTAI on joint imaging and research on whole-body imaging of small animals.