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.

As a core component in intelligent edge computing, deep neural networks (DNNs) will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain, like smart factories and autonomous driving. Due to the requirement for a large amount of storage space and computing resources, DNNs are unfavorable for resource-constrained edge computing devices, especially for mobile terminals with scarce energy supply. Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing. Field-programmable gate array (FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit (CPU) and graphics processing unit (GPU). This paper gives a brief overview of binary neural networks (BNNs) and the corresponding hardware accelerator designs on edge computing environments, and analyzes some significant studies in detail. The performances of some methods are evaluated through the experiment results, and the latest binarization technologies and hardware acceleration methods are tracked. We first give the background of designing BNNs and present the typical types of BNNs. The FPGA implementation technologies of BNNs are then reviewed. Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted. Finally, certain interesting directions are also illustrated as future work.

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.