Henan Polytechnic University

Pei Y., Zhang X., Hui Z., Zhou J., Huang X., Sun G., Huang W.

Sensors are becoming increasingly significant in our daily life because of the rapid development in electronic and information technologies, including Internet of Things, wearable electronics, home automation, intelligent industry, etc. There is no doubt that their performances are primarily determined by the sensing materials. Among all potential candidates, layered nanomaterials with two-dimensional (2D) planar structure have numerous superior properties to their bulk counterparts which are suitable for building various high-performance sensors. As an emerging 2D material, MXenes possess several advantageous features of adjustable surface properties, tunable bandgap, and excellent mechanical strength, making them attractive in various applications. Herein, we particularly focus on the recent research progress in MXene-based sensors, discuss the merits of MXenes and their derivatives as sensing materials for collecting various signals, and try to elucidate the design principles and working mechanisms of the corresponding MXene-based sensors, including strain/stress sensors, gas sensors, electrochemical sensors, optical sensors, and humidity sensors. In the end, we analyze the main challenges and future outlook of MXene-based materials in sensor applications.

Wang J., Lu S., Wang S., Zhang Y.

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Li X., Li J., Liu Y., Ding Z., Nallanathan A.

This paper investigates the impact of residual transceiver hardware impairments (RTHIs) on cooperative nonorthogonal multiple access (NOMA) networks, where generic α - μ fading channel is considered. To be practical, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) are taken into account. More particularly, two representative NOMA scenarios are proposed, namely non-cooperative NOMA and cooperative NOMA. For the non-cooperative NOMA, the base station (BS) directly performs NOMA with all users. For the cooperative NOMA, the BS communicates with NOMA users with the aid of an amplify-and-forward (AF) relay, and the direct links between BS and users are existent. To characterize the performance of the proposed networks, new closed-form and asymptotic expressions for the outage probability (OP), ergodic capacity (EC) and energy efficiency (EE) are derived, respectively. Specifically, we also design the relay location optimization algorithms from the perspectives of minimize the asymptotic OP. For non-cooperative NOMA, it is proved that the OP at high signal-to-noise ratios (SNRs) is a function of threshold, distortion noises, estimation errors and fading parameters, which results in 0 diversity order. In addition, high SNR slopes and high SNR power offsets achieved by users are studied. It is shown that there are rate ceilings for the EC at high SNRs due to estimation error and distortion noise, which cause 0 high SNR slopes and ∞ high SNR power offsets. For cooperative NOMA, similar results can be obtained, and it also demonstrates that the outage performance of cooperative NOMA scenario exceeds the non-cooperative NOMA scenario in the high SNR regime.

Li W., Chai Y., Khan F., Jan S.R., Verma S., Menon V.G., Kavita, Li X.

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

Jin Y., Zhang C., Dong X., Zang S., Mak T.C.

This tutorial review focuses on the modification and assembly of atomically-precise silver clusters by changing shell layers for more stability and functionalities, especially for brighter luminescence.

Han Z., Dong X., Luo P., Li S., Wang Z., Zang S., Mak T.C.

Atomically precise chiral silver clusters luminesce brightly from thermally activated delayed fluorescence.

Guo H., Liang W.

This paper is concerned with the existence of chaos for a type of partial difference equations. We establish four chaotification schemes for partial difference equations with tangent and cotangent functions, in which the systems are shown to be chaotic in the sense of Li–Yorke or of both Li–Yorke and Devaney. For illustration, we provide three examples are provided.

Li X., Zhao M., Zeng M., Mumtaz S., Menon V.G., Ding Z., Dobre O.A.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication have been envisioned as two promising technologies for the Internet-of-things due to their high spectral efficiency and energy efficiency. Motivated by this fact, we consider an ambient backscatter NOMA system in the presence of a malicious eavesdropper. Under the realistic assumptions of residual hardware impairments (RHIs), channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC), we investigate the physical layer security (PLS) of the ambient backscatter NOMA systems with emphasis on reliability and security. In order to further improve the security of the considered system, an artificial noise scheme is proposed where the radio frequency (RF) source acts as a jammer that transmits interference signals to the legitimate receivers and eavesdropper. On this basis, the analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived. To gain more insights, the asymptotic analysis and corresponding diversity orders for the OP in the high signal-to-noise ratio (SNR) regime are carried out, and the asymptotic behaviors of the IP in the high main-to-eavesdropper ratio (MER) region are explored as well. Finally, the correctness of the theoretical analysis is verified by the Monte Carlo simulation results. These results show that compared with the non-ideal conditions, the reliability of the considered system is high under ideal conditions, but the security is low.

Zeng M., Li X., Li G., Hao W., Dobre O.A.

An intelligent reflecting surface (IRS) consists of a large number of low-cost reflecting elements, which can steer the incident signal collaboratively by passive beamforming. This way, IRS reconfigures the wireless environment to boost the system performance. In this letter, we consider an IRS-assisted uplink non-orthogonal multiple access (NOMA) system. The objective is to maximize the sum rate of all users under individual power constraint. The considered problem requires a joint power control at the users and beamforming design at the IRS, and is non-convex. To handle it, semidefinite relaxation is employed, which provides a near-optimal solution. Presented numerical results show that the proposed NOMA-based scheme achieves a larger sum rate than orthogonal multiple access (OMA)-based one. Moreover, the impact of the number of reflecting elements on the sum rate is revealed.

Lu S., Wang S., Zhang Y.

Computer-aided diagnosis system is becoming a more and more important tool in clinical treatment, which can provide a verification of the doctors’ decisions. In this paper, we proposed a novel abnormal brain detection method for magnetic resonance image. Firstly, a pre-trained AlexNet was modified with batch normalization layers and trained on our brain images. Then, the last several layers were replaced with an extreme learning machine. A searching method was proposed to find the best number of layers to be replaced. Finally, the extreme learning machine was optimized by chaotic bat algorithm to obtain better classification performance. Experiment results based on 5 × hold-out validation revealed that our method achieved state-of-the-art performance.