Journal of Electronic Science and Technology最新文献

In this paper, an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar (SAR) target detection. By extending the fractional Gabor transform (FrGT) into two dimensions, the fractional time-frequency spectrum feature of an image can be obtained. In the achievement process, we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT. Finally, the energy attenuation gradient (EAG) feature of the optimal time-frequency spectrum is extracted for high-frequency detection. The simulation results show that the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.

Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function. Though progress has been made to elucidate the process, molecular mechanisms of different classes of cardiomyopathies remain elusive. This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM). We firstly detected the co-expressed modules using the weighted gene co-expression network analysis (WGCNA). Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient (PCC) between the modules and the phenotype of DCM/ICM. The differentially expressed genes in the modules were selected to perform functional enrichment. The potential transcription factors (TFs) prediction was conducted for transcription regulation of hub genes. Apoptosis and cardiac conduction were perturbed in DCM and ICM, respectively. TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different, which helps make more accurate discrimination between them at molecular levels. In conclusion, comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression. Thus, this understanding may promote the development of innovative diagnoses and treatments.

In this study, we investigated the abatement of volatile organic compounds (VOCs) by the atmospheric pressure microwave plasma torch (AMPT). To study the treatment efficiency of AMPT, we used the toluene and water-based varnish to simulate VOCs, respectively. By measuring the compounds and contents of the mixture gas before/after the microwave plasma process, we have calculated the treatment efficiency of AMPT. The experiment results show that the treatment efficiency of AMPT for toluene with a concentration of 17.32×10⁴ ​ppm is up to 60 ​g/kWh with the removal rate of 86%. For the volatile compounds of water-based varnish, the removal efficiency is up to 97.99%. We have demonstrated the higher potential for VOCs removal of the AMPT process.

Omnidirectional photodetectors attract enormous attention due to their prominent roles in optical tracking systems and omnidirectional cameras. However, it is still a challenge for the construction of high-performance omnidirectional photodetectors where the incident light can be effectively absorbed in multiple directions and the photo-generated carriers can be effectively collected. Here, a high-performance omnidirectional self-powered photodetector based on the CsSnBr₃/indium tin oxide (ITO) heterostructure film was designed and demonstrated. The as-fabricated photodetector exhibited excellent self-powered photodetection performance, showing responsivity and detectivity up to 35.1 ​mA/W and 1.82 ​× ​10¹⁰ Jones, respectively, along with the smart rise/decay response time of 4 ​ms/9 ​ms. Benefitting from the excellent photoelectric properties of the CsSnBr₃ film as well as the ability of the CsSnBr₃/ITO heterostructure to efficiently separate and collect photo-generated carriers, the as-fabricated photodetector also exhibited excellent omnidirectional self-powered photodetection performance. All the results have certified that this work finds an efficient way to realize high-performance omnidirectional self-powered photodetectors.

The viability of the indium phosphide (InP) Gunn diode as a source for low-THz band applications is analyzed based on a notch-δ-doped structure using the Monte Carlo modeling. The presence of the δ-doped layer could enhance the current harmonic amplitude (A₀) and the fundamental operating frequency (f₀) of the InP Gunn diode beyond 300 ​GHz as compared with the conventional notch-doped structure for a 600-nm length device. With its superior electron transport properties, the notch-δ-doped InP Gunn diodes outperform the corresponding gallium arsenide (GaAs) diodes with up to 1.35 times higher in f₀ and 2.4 times larger in A₀ under DC biases. An optimized InP notch-δ-doped structure is estimated to be capable of generating 0.32-W radio-frequency (RF) power at 361 ​GHz. The Monte Carlo simulations predict a reduction of 44% in RF power, when the device temperature is increased from 300 ​K to 500 ​K; however, its operating frequency lies at 280 ​GHz which is within the low-THz band. This shows that the notch-δ-doped InP Gunn diode is a highly promising signal source for low-THz sensors, which are in a high demand in the autonomous vehicle industry.

At present, knowledge embedding methods are widely used in the field of knowledge graph (KG) reasoning, and have been successfully applied to those with large entities and relationships. However, in research and production environments, there are a large number of KGs with a small number of entities and relations, which are called sparse KGs. Limited by the performance of knowledge extraction methods or some other reasons (some common-sense information does not appear in the natural corpus), the relation between entities is often incomplete. To solve this problem, a method of the graph neural network and information enhancement is proposed. The improved method increases the mean reciprocal rank (MRR) and Hit@3 by 1.6% and 1.7%, respectively, when the sparsity of the FB15K-237 dataset is 10%. When the sparsity is 50%, the evaluation indexes MRR and Hit@10 are increased by 0.8% and 1.8%, respectively.

Due to the complexity of joint structures and the diversity of disorders in the joints, the diagnosis of joint diseases is challenging. Current clinical diagnostic techniques for evaluating joint diseases, such as arthritis, have strengths and weaknesses. New imaging techniques need to be developed for the diagnosis or auxiliary diagnosis of arthritis. As an emerging nonintrusive low-cost imaging method, microwave-induced thermoacoustic imaging (TAI) can present tissue morphology while providing the tissue microwave energy absorption density distribution related to dielectric properties. TAI is currently in development to potentially visualize joint anatomic structures and to detect arthritis. Here, we offer a mini review to summarize the status of research on TAI of joints and present an outlook to the future development of TAI in the detection of joint diseases.

Quantum-dot cellular automata (QCA) is an emerging computational paradigm which can overcome scaling limitations of the existing complementary metal oxide semiconductor (CMOS) technology. The existence of defects cannot be ignored, considering the fabrication of QCA devices at the molecular level where it could alter the functionality. Therefore, defects in QCA devices need to be analyzed. So far, the simulation-based displacement defect analysis has been presented in the literature, which results in an increased demand in the corresponding mathematical model. In this paper, the displacement defect analysis of the QCA main primitive, majority voter (MV), is presented and carried out both in simulation and mathematics, where the kink energy based mathematical model is applied. The results demonstrate that this model can also be valid for the displacement defect in QCA MV.

For the anti-interference/denoise purpose, it usually requires minimizing the sidelobe level (SLL) of a wide-beam pattern with a desired low nulling level (NL) in the nulling region. To realize such an objective, the shaped-beam pattern synthesis (SBPS) is the most commonly used approach. However, since the SBPS problem focuses on synthesizing a predetermined beam shape, the minimum SLL via this approach cannot ensure to obtain the maximum power gain. Conversely, it cannot obtain the lowest SLL with a certain power gain requirement. Based on such consideration, this paper tries to further minimize SLL of a wide-beam pattern with a desired low NL nulling region, by solving the power gain pattern synthesis (PGPS) problem. The PGPS problem selects the array excitation by directly optimizing the power gain. Hence, it has the potential to reduce SLL, when achieving the equal mainlobe power gain constraint via SBPS. An iterative algorithm which converts the primal optimization problem into convex sub-problems is proposed, resulting in an effective problem-solving scheme. Numerical simulations demonstrate the proposed algorithm can obtain about 10-dB lower SLL than the existing algorithms.