Journal of Electronic Science and Technology最新文献

The marine biological sonar system evolved in the struggle of nature is far superior to the current artificial sonar. Therefore, the development of bionic underwater concealed detection is of great strategic significance to the military and economy. In this paper, a generative adversarial network (GAN) is trained based on the dolphin vocal sound dataset we constructed, which can achieve unsupervised generation of dolphin vocal sounds with global consistency. Through the analysis of the generated audio samples and the real audio samples in the time domain and the frequency domain, it can be proven that the generated audio samples are close to the real audio samples, which meets the requirements of bionic underwater concealed detection.

To explore the potential of conventional image processing techniques in the classification of cervical cancer cells, in this work, a co-occurrence histogram method was employed for image feature extraction and an ensemble classifier was developed by combining the base classifiers, namely, the artificial neural network (ANN), random forest (RF), and support vector machine (SVM), for image classification. The segmented pap-smear cell image dataset was constructed by the k-means clustering technique and used to evaluate the performance of the ensemble classifier which was formed by the combination of the above considered base classifiers. The result was also compared with that achieved by the individual base classifiers as well as that trained with color, texture, and shape features. The maximum average classification accuracy of 93.44% was obtained when the ensemble classifier was applied and trained with co-occurrence histogram features, which indicates that the ensemble classifier trained with the co-occurrence histogram features is more suitable for the classification of cervical cancer cells.

Magnetism in two-dimensional (2D) materials has attracted much attention recently. However, intrinsic magnetic 2D materials are rare and mostly unstable in ambient. Although heteroatom doping can introduce magnetism, the basic property especially the electrical-magnetic coupling property has been rarely revealed. Herein, both iron (Fe)-doped and vanadium (V)-doped MoS₂ films were grown by chemical vapor deposition. Through studying the structure and electrical property of Fe-doped and V-doped MoS₂, it was found that both Fe and V doping would decrease the electron concentration, exhibiting a p-type doping effect. Significantly, V-doped MoS₂ displays a p-type conduction behavior. Although the carrier mobility decreases after heteroatom doping, both Fe and V doping could endow MoS₂ with magnetism, in which the transfer curves of both MoS₂ transistors exhibit a strong magnetic-dependent behavior. It is found that the magnetic response of Fe-doped MoS₂ can be tuned from ~0.2 ​nA/T to ~1.3 ​nA/T, with the tunability much larger than that of V-doped MoS₂. At last, the magnetic mechanism is discussed with the local magnetic property performed by magnetic force microscopy. The typical morphology-independent magnetic signal demonstrates the formed magnetic domain structure in Fe-doped MoS₂. This study opens new potential to design novel magnetic-electrical devices.

To explore the influence of the fusion of different features on recognition, this paper took the electromyogram (EMG) signals of rectus femoris under different motions (walk, step, ramp, squat, and sitting) as signals, linear features (time-domain features (variance (VAR) and root mean square (RMS)), frequency-domain features (mean frequency (MF) and mean power frequency (MPF)), and nonlinear features (EMD) of the signals were extracted. Two feature fusion algorithms, the series splicing method and complex vector method, were designed, which were verified by a double hidden layer error back propagation (BP) neural network. Results show that with the increase of the types and complexity of feature fusions, the recognition rate of the EMG signal to actions is gradually improved. When the EMG signal is used in the series splicing method, the recognition rate of time-domain ​+ ​frequency-domain ​+ ​empirical mode decomposition (TD ​+ ​FD ​+ ​EMD) splicing is the highest, and the average recognition rate is 92.32%. And this value is raised to 96.1% by using the complex vector method, and the variance of the BP system is also reduced.

Clean graphene transfer has received widespread research attention, where most methods are focused on cleaning the upper surface of graphene to improve the transfer technique. However, the residue formation on the bottom surface of graphene is also inevitable; therefore, cleaning the bottom surface is crucial. In this study, we proposed an improved graphene wet transfer method using an ultrasonic processing (UP) step for etching copper (Cu). Using this method, the bottom surface can be cleaned efficiently. The results of atomic force microscopy (AFM) and Raman spectroscopy mapping revealed that the graphene films transferred with UP had smoother and cleaner surfaces, less contamination, and higher quality than those transferred without UP.

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprise of, end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset which is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves 98.98% accuracy (ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99% precision (Pr), 97.88% F₁- score, and 1.8974-second computational time.

Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset (SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification. In this investigation, well-known and accurate learning models are used, including naive Bayes, random forest, K-nearest neighbor, support vector machines, and logistic regression models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performance of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the support vector machine model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time, with the accuracy of 82%.

A coherent optical frequency comb is a kind of broad-spectrum light sources delivering equidistant frequencies, and correspondingly its temporal waveform appears as a sequence of ultrashort pulses. Being the cornerstone technology of today's laser and time-frequency disciplines, it effectively links the optical frequency and the microwave frequency, and has promoted the development of diverse applications, such as precision spectroscopy, optical measurement, coherent optical communications, and optical clocks in the past two decades. In this review, we comprehensively introduce the development path, physical principle, generation/tuning methods, and advanced applications of optical frequency combs.

This paper explores a way of deploying the classical algorithm named genetic algorithm (GA) with the memristor. The memristor is a type of circuit device with both characteristics of storage and computing, which provides the similarity between electronic devices and biological components, such as neurons, and the structure of the memristor-based array is similar to that of chromosomes in genetics. Besides, it provides the similarity to the image gray-value matrix that can be applied to image restoration with GA. Thus, memristor-based GA is proposed and the experiment about image restoration using memristor-based GA is carried out in this paper. And parameters, such as the size of initial population and the number of iterations, are also set different values in the experiment, which demonstrates the feasibility of implementing GA with memristors.¹

In this paper, a salp swarm algorithm (SSA) is proposed for solving the optimal power flow (OPF) problem of a power system with the incorporation of the thyristor-controlled series capacitor (TCSC). The proposed methodology is implemented for determining the optimal setting of control variables for the OPF problem, which includes the real power of generators buses, voltages of generator buses, reactive power injected by shunt compensators, and tap changing transformer ratios. The performance of the proposed approach is validated and tested on the standard IEEE-30 bus system and single-objective functions, including transmission line losses. The severity factor has been minimized and the result obtained is compared with the existing algorithms. Simulation results achieved with the proposed SSA approach demonstrate that it results in an effective and better solution for the OPF problem.