Guilin University of Electronic Technology

Abstract High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal elements. Although in the infant stage, the emerging of this new family of materials has brought new opportunities for material design and property tailoring. Distinct from metals, the diversity in crystal structure and electronic structure of ceramics provides huge space for properties tuning through band structure engineering and phonon engineering. Aside from strengthening, hardening, and low thermal conductivity that have already been found in high-entropy alloys, new properties like colossal dielectric constant, super ionic conductivity, severe anisotropic thermal expansion coefficient, strong electromagnetic wave absorption, etc., have been discovered in HECs. As a response to the rapid development in this nascent field, this article gives a comprehensive review on the structure features, theoretical methods for stability and property prediction, processing routes, novel properties, and prospective applications of HECs. The challenges on processing, characterization, and property predictions are also emphasized. Finally, future directions for new material exploration, novel processing, fundamental understanding, in-depth characterization, and database assessments are given.

Metasurfaces are engineered interfaces that contain a thin layer of plasmonic or dielectric nanostructures capable of manipulating light in a desirable manner. Advances in metasurfaces have led to various practical applications ranging from lensing to holography. Metasurface holograms that can be switched by the polarization state of incident light have been demonstrated for achieving polarization multiplexed functionalities. However, practical application of these devices has been limited by their capability for achieving high efficiency and high image quality. Here we experimentally demonstrate a helicity multiplexed metasurface hologram with high efficiency and good image fidelity over a broad range of frequencies. The metasurface hologram features the combination of two sets of hologram patterns operating with opposite incident helicities. Two symmetrically distributed off-axis images are interchangeable by controlling the helicity of the input light. The demonstrated helicity multiplexed metasurface hologram with its high performance opens avenues for future applications with functionality switchable optical devices.

Abstract Sodium‐ion batteries (SIBs) have been considered as the most promising candidate for large‐scale energy storage system owing to the economic efficiency resulting from abundant sodium resources, superior safety, and similar chemical properties to the commercial lithium‐ion battery. Despite the long period of academic research, how to realize sodium‐ion battery commercialization for market applications is still a great challenge. Thus, from the perspective of future practical application, this review will identify the factors that are restricting commercialization, and evaluate the existing active materials and sodium‐ion‐based full‐cell system. The design and development trends that are needed for SIBs to meet the requirements of practical applications in large‐scale energy storage will also be discussed in detail.

In this paper, a double V-shaped metasurface that can efficiently convert linear polarizations of electromagnetic (EM) waves in wideband is proposed. Based on the electric and magnetic resonant features of a single V-shaped particle, four EM resonances are generated in a V-shaped pair, leading to significant bandwidth expansion of cross-polarized reflections. The simulation results show that the proposed metasurface is able to convert linearly polarized waves into cross-polarized waves in ultrawideband from 12.4 to 27.96 GHz, with an average polarization conversion ratio (PCR) of 90%. The experimental results are in good agreement with the numerical simulations. Compared to published designs, the proposed polarization converter has a simple geometry but an ultrawideband and hence can be used in many applications, such as reflector antennas, imaging systems, remote sensors, and radiometers. The method can also be extended to the terahertz band.

Ternary organic solar cells enjoy both the enhanced light absorption by incorporating multiple organic materials in tandem solar cells and the simplicity of processing conditions that are used in single bulk heterojunction solar cells.

The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks. The entire process of feature learning is completed by the deep neural networks automatically; no feature engineering techniques are required. The automatically learned traffic features effectively reduce the FAR. The standard DARPA1998 and ISCX2012 data sets are used to evaluate the performance of the proposed system. The experimental results show that the HAST-IDS outperforms other published approaches in terms of accuracy, detection rate, and FAR, which successfully demonstrates its effectiveness in both feature learning and FAR reduction.

Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient's symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging.

Dual-band bandpass filters using novel stub-loaded resonators (SLRs) are presented in this letter. Characterized by both theoretical analysis and full-wave simulation, the proposed SLR is found to have the advantage that the even-mode resonant frequencies can be flexibly controlled whereas the odd-mode resonant frequencies are fixed. Based on the proposed SLR, a dual-band filter is implemented with three transmission zeros. To further improve the selectivity, a filter with four transmission zeros on either side of both passbands is designed by introducing spur-line. The measured results validate the proposed design.

Highly efficient blue-emitting three-dimensional (3D) lead–free halide perovskites with excellent stability have attracted worldwide attention. Herein, a doping route was adopted to incorporate Sb3+ ions into the Cs2NaInCl6 for decorating the electronic band structure. Due to the moderate electron–phonon coupling, the Sb3+-doped Cs2NaInCl6 double perovskites showed a narrow and relatively unusual blue emission of self-trapped excitons (STEs). Density functional theory (DFT) calculation indicated that the doped Sb3+ ions could break the parity-forbidden transition rule and modulate the density of state (DOS) population effectively to boost the PLQY of STEs drastically. The optimized Sb3+:Cs2NaInCl6 exhibited a PLQY of up to 75.89% and excellent stability under the consecutive illumination of 365 nm UV light for 1000 h. This kind of highly efficient lead-free Sb3+-doped Cs2NaInCl6 double perovskites may overcome the bottlenecks of severe toxicity and insufficient stability and therefore have an extensive application in the scarce blue photonic and optoelectronic fields.

Advances in wireless communications, embedded systems, and integrated circuit technologies have enabled the wireless body area network (WBAN) to become a promising networking paradigm. Over the last decade, as an important part of the Internet of Things, we have witnessed WBANs playing an increasing role in modern medical systems because of its capabilities to collect real-time biomedical data through intelligent medical sensors in or around the patients' body and send the collected data to remote medical personnel for clinical diagnostics. WBANs not only bring us conveniences but also bring along the challenge of keeping data's confidentiality and preserving patients' privacy. In the past few years, several anonymous authentication (AA) schemes for WBANs were proposed to enhance security by protecting patients' identities and by encrypting medical data. However, many of these schemes are not secure enough. First, we review the most recent AA scheme for WBANs and point out that it is not secure for medical applications by proposing an impersonation attack. After that, we propose a new AA scheme for WBANs and prove that it is provably secure. Our detailed analysis results demonstrate that our proposed AA scheme not only overcomes the security weaknesses in previous schemes but also has the same computation costs at a client side.

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.

The widespread application of sewage sludge produced from wastewater treatment plants for agricultural use has been regarded as a primary source of microplastics (MPs) into soils. However, little is known regarding MPs in sludge-based fertilizers and their relevant fate in soils as being applied in agriculture. We comprehensively investigated the abundance, polymer size, type, and morphology of MPs in dewatered sludge, sewage sludge composts, sludge-based fertilizer-amended soils, and earthworms by stereoscopy and micro Fourier transform infrared (μ-FTIR) spectrometry methods. The results clearly showed that the quantity of MPs in soils exhibited a close correlation with the application rate of sludge-based fertilizers. The total abundances of MPs were 545.9 and 87.6 items/kg in soils after annual amendment with 30 (field A) and 15 t/ha (field B) of sludge composts, which is significantly higher than that without compost application (field C, 5.0 items/kg). Correspondingly, MPs were found in earthworms with low quantities of 1.8 and 0.4 items/individual in fields A and B, respectively, while no MP was detected in field C. We speculate that sludge composts may act as a vehicle of MPs into soils and then enter soil biota and in turn influence the spread of MPs in the environment.

Abstract Direct solar steam generation (DSSG) offers a promising, sustainable, and environmentally friendly solution to the energy and water crisis. In the past decades, DSSG has gained tremendous attention due to its potential applications for clean water production, desalination, wastewater treatment, and electric energy harvesting. Even though the solar–thermal conversion efficiency has approached 100% under 1 sun illumination (1 kW m −2 ) using various photothermal materials and systems, the optimization of the materials and system structure remains unclear because of the lack of evaluation methods in unity for the output efficiency. In this review, a few key concerns about different dimensional materials and systems that determine the characteristics of DSSG are explored. Quantitative analysis, including calculations and methods for the solar–thermal conversion efficiency, evaporation rate, and energy loss, is employed to evaluate the materials and systems from the point of view of ultimate utilization. This article focuses on the relationship between the system dimension and energy efficiency and notes opportunities for future system design and commercialization of DSSG.

The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

A critical area of machine learning is Time Series forecasting, as various forecasting problems contain a time component. A series of observations taken chronologically in time is known as a Time Series. In this research, however, we aim to compare three different machine learning models in making a time series forecast. We are going to use the Bitcoin's price dataset as our time series data set and make predictions accordingly. The results show that the ARIMA model gave better results than the deep learning-based regression models. ARIMA gives the best results at 2.76% and 302.53 for MAPE and RMSE respectively. The Gated Recurrent Unit (GRU) however performed better than the Long Short-term Memory (LSTM), with 3.97% and 381.34 of MAPE and RMSE respectively.

Blockchain and AI are promising techniques for next-generation wireless networks. Blockchain can establish a secure and decentralized resource sharing environment. AI can be explored to solve problems with uncertain, time-variant, and complex features. Both of these techniques have recently seen a surge in interest. The integration of these two techniques can further enhance the performance of wireless networks. In this article, we first propose a secure and intelligent architecture for next-generation wireless networks by integrating AI and blockchain into wireless networks to enable flexible and secure resource sharing. Then we propose a blockchain empowered content caching problem to maximize system utility, and develop a new caching scheme by utilizing deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.

Underwater images typically suffer from various quality degradation issues due to the scattering and absorption of light, but these degraded-quality underwater images are unbeneficial for analysis and applications. To effectively solve these quality degradation issues, an underwater image enhancement method via weighted wavelet visual perception fusion is introduced, called WWPF. Concretely, we first present an attenuation-map-guided color correction strategy to correct the color distortion of an underwater image. Subsequently, we employ the maximum information entropy optimized global contrast strategy to the color-corrected image to obtain a global contrast-enhanced image. Meanwhile, we apply a fast integration optimized local contrast strategy to the color-corrected image to get a local contrast-enhanced image. To exploit the complementary of the global contrast-enhanced image and the local contrast-enhanced image, we introduce a weighted wavelet visual perception fusion strategy to obtain a high-quality underwater image by fusing the high-frequency and low-frequency components of images at different scales. Our extensive experiments on three benchmarks validate that our WWPF outperforms the state-of-the-art methods in qualitative and quantitative. Besides, the underwater images processed by our WWPF also benefit practical underwater applications. The code is available <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Li-Chongyi/WWPF_code</uri> .

In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.