Underwater image enhancement plays a critical role in marine industry. Various algorithms are applied to enhance underwater images, but their performance in terms of perceptual quality has been little studied. In this paper, we investigate five popular enhancement algorithms and their output image quality. To this end, we have created a benchmark, including images enhanced by different algorithms and ground truth image quality obtained by human perception experiments. We statistically analyse the impact of various enhancement algorithms on the perceived quality of underwater images. Also, the visual quality provided by these algorithms is evaluated objectively, aiming to inform the development of objective metrics for automatic assessment of the quality for underwater image enhancement. The image quality benchmark and its objective metric are made publicly available.

Various image enhancement algorithms are adopted to improve underwater images that often suffer from visual distortions. It is critical to assess the output quality of underwater images undergoing enhancement algorithms, and use the results to optimise underwater imaging systems. In our previous study, we created a benchmark for quality assessment of underwater image enhancement via subjective experiments. Building on the benchmark, this paper proposes a new objective metric that can automatically assess the output quality of image enhancement, namely UWEQM. By characterising specific underwater physics and relevant properties of the human visual system, image quality attributes are computed and combined to yield an overall metric. Experimental results show that the proposed UWEQM metric yields good performance in predicting image quality as perceived by human subjects.

It is common knowledge that the future quadratic programming (FQP) problem is a challenging and widely applicable topic in mathematical science and many engineering fields. In this article, we systematically derive a new specific explicit linear left and right six-step (ELSS) rule with order-7 precision. With the help of the ELSS rule, the ELSS-type discretized Zhang neurodynamics (ELSS-DZNs) algorithm that develops from continuous Zhang neurodynamics (CZNs) model is proposed for tackling the FQP problem subject to linear equation (LE), while the inverse-free continuous hybrid gradient Zhang neurodynamics (IFCHGZNs) model, which combines the Getz–Marsden dynamic system (GMDS) model 2 for time-variant inverse computing (TVIC) and CZN model for tackling the time-variant QP (TVQP) problem subject to LE, develops the ELSS-type inverse-free discretized hybrid gradient Zhang neurodynamics (ELSS-IFDHGZN) algorithm for dealing with the FQP problem subject to LE. Theoretically, three theorems are presented to show the convergent properties of the proposed general CZN and IFCHGZN models by Lyapunov stability theory, while three theorems and five corollaries are presented to show the truncation error pattern of the ELSS-DZN and ELSS-IFDHGZN algorithms by using the stability and convergence theory of linear multistep formulas. Moreover, two numerical experiments are adopted to demonstrate the availability and convergent properties of the proposed general ELSS-DZN and ELSS-IFDHGZN algorithms. In the end, numerical and physical experiments based on two kinds of robot manipulators are additionally performed to verify the applicability and validity of the proposed general ELSS-DZN and ELSS-IFDHGZN algorithms.

Temporal-variant linear equations (TVLEs) are widely acknowledged for their pivotal role in various engineering fields, offering a potent means to model dynamic processes and evolving relationships over time. A conventional approach involves leveraging the zeroing neural network (ZNN) model for tackling TVLE problems. In response to challenges associated with inverse matrix computations and infinite-time convergence constraints, we introduce an innovative single inverse-free finite-time reciprocal ZNN (FRZNN) model constructed to effectively address TVLE problems without using the activation functions. The convergence property and robustness of the FRZNN model are thoroughly examined adopting Lyapunov stability method of the nonlinear system and a comparative approach for nonlinear perturbed systems. Through two numerical experiments and an Angle-of-Arrival (AOA) simulation, the performance of the FRZNN model is thoroughly evaluated, revealing its validity and superior effectiveness when compared to state-of-the-art approaches. In detail, the performance improvement ratio (PIR) of the FRZNN model in addressing the AOA problem is 60.52%, and under a noise environment, the PIR of the FRZNN model is 99.99%.

Although several researches have reported the connection between the transforming growth factor-beta 1 (TGF-β1) gene polymorphisms and chronic hepatitis C virus (HCV) infection, the conclusions of these studies were not always consistent. Here, this paper proposed a meta-analysis to evaluate whether the TGF-ß1 gene polymorphisms, −509C/T (rs1800469), codon 10 T/C (rs1982073) and codon 25G/C (rs1800471), were associated with chronic HCV infection. The summary odds ratios (ORs) of chronic HCV infected patients and controls with all SNPs were obtained by adaptive fixed or random effect model. A series of statistical tools were employed to guarantee the accuracy of related pooling ORs, including the Hardy-Weinberg equilibrium (HWE) test, sensitivity analysis and publication bias test. This paper analyzed 18 case-control studies in 17 articles which totally contains 2718 chronic HCV infection cases corresponding to 1964 controls. The results of the meta-analysis indicated that the −509C/T polymorphism effected an increased risk of chronic HCV infection in all gene models. More specifically by ethnicity stratification, the Egyptians shared the similar association with the above overall study. Moreover, the meta-fusion of healthy control studies showed that − 509 T allele carriers (TT + TA) had nearly 2.00 and 3.36 fold higher risk of chronic HCV infection in the total and Egyptian populations, respectively (OR = 2.004, 95% CI = 1.138–3.528, P = 0.016; OR = 3.363, 95% CI = 1.477–7.655, P = 0.004, respectively). However, our meta-analysis did not find any significant association between the codon 10 T/C or codon 25G/C polymorphisms and chronic HCV infection. Our results suggested that the TGF-ß1–509C/T polymorphism may effect an increased risk of chronic HCV infection, especially in Egyptian population.

As an emerging technique, index modulation (IM) can improve the system bit error rate (BER) performance due to the robustness of index bits over conventional modulated symbol bits. The existing IM aided orthogonal frequency division multiplexing (OFDM) schemes only employ one index modulator at the transmitter, which transmits a small amount of index bits in each transmission. In this paper, a novel IM technique, called cascade IM (CIM), is proposed to increase the proportion of the index bits in the transmission by combining the conventional IM with the multiple‐mode IM together. Subcarrier‐wise and subblock‐wise CIM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next generation wireless communication networks. The optimal subcarrier‐wise maximum likelihood detector is proposed for OFDM‐CIM. To reduce the demodulation complexity, a novel tree search based and an iterative log‐likelihood ratio based detectors, which can avoid illegal index patterns in the search process, are developed for OFDM‐CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM‐IM and appears as a competitive candidate of multi‐carrier transmission techniques for next generation wireless communication networks.