Journal of Electronic Science and Technology最新文献

This study presents an overview of the current status of hydrogen production in relation to the global requirement for energy and resources. Subsequently, it symmetrically outlines the advantages and disadvantages of various production routes including fossil fuel/biomass conversion, water electrolysis, microbial fermentation, and photocatalysis (PC), in terms of their technologies, economy, energy consumption, and costs. Considering the characteristics of hydrogen energy and the current infrastructure issues, it highlights that onsite production is indispensable and convenient for some special occasions. Finally, it briefly summarizes the current industrialization situation and presents future development and research directions, such as theoretical research strengthening, renewable raw material development, process coupling, and sustainable energy use.

In this paper, the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered. An adaptive control strategy is proposed to smooth the agent’s trajectory, and the neural network is constructed to estimate the system’s unknown components. The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties. Then, the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’ models. Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control. Finally, the theoretical results are verified by numerical simulations, and a comparative experiment is conducted, showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.

In this paper, we present a comparison of the Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora. These four features include linear predictive coding (LPC), linear prediction cepstrum coefficient (LPCC), perceptual linear prediction (PLP), and Mel frequency cepstral coefficient (MFCC). The 10-h speech data was used for training and 3-h data for testing. For each spectral feature, different hidden Markov model (HMM) based recognizers with variations in HMM states and different Gaussian mixture models (GMMs) were built. The performance was evaluated by using the word error rate (WER). The experimental results showed that MFCC provides a better representation for Khasi speech compared with the other three spectral features.

Perovskite single crystals have gained enormous attention in recent years due to their facile synthesis and excellent optoelectronic properties including the long carrier diffusion length, high carrier mobility, low trap density, and tunable absorption edge ranging from ultra-violet (UV) to near-infrared (NIR), which offer potential for applications in solar cells, photodetectors (PDs), lasers, etc. In this review, we summarized the synthesis, properties, and applications of organic-inorganic mixed and all-inorganic perovskite single crystals, particularly through the solution synthesis approach. Challenges towards the crystal growth and stability with future perspectives were also briefly described at the end of this manuscript.

The biological features of the valvular heart disease with atrial fibrillation (AF-VHD) remain unknown when involving long non-coding RNAs (lncRNAs). This study performed system analysis on lncRNA and messenger RNA (mRNA) expression profiles constructed by using bioinformatics methods and tools for biological features of AF-VHD. Fold change and t-test were used to identify differentially expressed (DE) lncRNAs and mRNAs. The enrichment analysis of DE mRNAs was performed. The subgroups formed by lncRNAs and nearby mRNAs were screened, and a transcriptional regulation network among lncRNAs, mRNAs, and transcription factors (TFs) was constructed. The interactions between mRNAs related to lncRNAs and drugs were predicted. The 620 AF-VHD-related DE lncRNAs and 452 DE mRNAs were identified. The 3 lncRNA subgroups were screened. The 665 regulations mediated by lncRNAs and TFs were identified. The 9 mRNAs related to lncRNAs had 1 or more potential drug interactions, totaling 37 drugs. Of these, 9 drugs targeting 3 genes are already known to be able to control or trigger atrial fibrillation (AF) or other cardiac arrhythmias. The found biological features of AF-VHD provide foundations for further biological experiments to better understand the roles of lncRNAs in development from the valvular heart disease (VHD) to AF-VHD.

A nonlinearity-compensation-free optical frequency domain reflectometry (OFDR) scheme is proposed and experimentally demonstrated based on the electrically-controlled optical frequency sweep. In the proposed scheme, the linear frequency sweep light is generated by propagating an ultra-narrow-linewidth continuous-wave (CW) light through an electro-optic frequency shifter which consists of a dual-parallel Mach-Zehnder modulator (DPMZM) and an electronic 90° hybrid, where the electro-optic frequency shifter is driven by a linear frequency modulated signal generated by a direct digital synthesizer (DDS). Experimental results show that the spatial resolution and signal-to-noise ratio (SNR) of the proposed OFDR scheme without the nonlinear phase compensation are comparable to those of OFDR employing a commercial tunable laser source (TLS), an auxiliary interferometer, and a software-based nonlinear phase compensation method. The proposed OFDR scheme is helpful to reduce the complexity of the optical structure and eliminate the difficulty of developing the nonlinear phase compensation algorithm.

A kind of signal acquisition circuit and the related signal processing method of the capacitance electromagnetic flowmeter were introduced. The circuit can eliminate the influence of distributed capacitance on the input impedance of the operational amplifier, and greatly improve the input impedance of the detection circuit to overcome the disadvantage of high signal source impedance. The rotating capacitor filter is a signal processing method based on the phase-sensitive detection technology. It can extract the weak signal from the strong and wide-band background noise, so it is very suitable for the processing of capacitive electromagnetic flow signals. Through the comparison of the signal amplitude obtained at different flow rates and the comparison of signal spectrum components before and after the filter, the effectiveness of the bootstrap signal acquisition circuit and rotating capacitor filtering method is verified.

In light of the COVID-19 outbreak caused by the novel coronavirus, companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion. Employees, however, have been exposed to different security risks because of working from home. Moreover, the rapid global spread of COVID-19 has increased the volume of data generated from various sources. Working from home depends mainly on cloud computing (CC) applications that help employees to efficiently accomplish their tasks. The cloud computing environment (CCE) is an unsung hero in the COVID-19 pandemic crisis. It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data. Despite the increase in the use of CC applications, there is an ongoing research challenge in the domains of CCE concerning data, guaranteeing security, and the availability of CC applications. This paper, to the best of our knowledge, is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE. Additionally, this paper also highlights the security risks of working from home during the COVID-19 pandemic.

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.

This paper presents an effective image classification algorithm based on superpixels and feature fusion. Differing from classical image classification algorithms that extract feature descriptors directly from the original image, the proposed method first segments the input image into superpixels and, then, several different types of features are calculated according to these superpixels. To increase classification accuracy, the dimensions of these features are reduced using the principal component analysis (PCA) algorithm followed by a weighted serial feature fusion strategy. After constructing a coding dictionary using the nonnegative matrix factorization (NMF) algorithm, the input image is recognized by a support vector machine model. The effectiveness of the proposed method was tested on the public Scene-15, Caltech-101, and Caltech-256 datasets, and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.