Using graph theory to identify essential proteins is a hot topic at present. These methods are called network-based methods. However, the generalization ability of most network-based methods is not satisfactory. Hence, in this paper, we consider the identification of essential proteins as a multi-objective optimization problem and use a novel multi-objective optimization method to solve it. The optimization result is a set of Pareto solutions. Every solution in this set is a vector which has a certain number of essential protein candidates and is considered as an independent predictor or voter. We use a voting strategy to assemble the results of these predictors. To validate our method, we apply it on the protein-protein interactions (PPI) datasets of two species (Yeast and Escherichia coli). The experiment results show that our method outperforms state-of-the-art methods in terms of sensitive, specificity, F-measure, accuracy, and generalization ability.

The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks. A massive MIMO-NOMA system can deliver remarkable spectral improvements and reduced communication latency. Nevertheless, the uncontrollable stochastic behavior of the wireless channels can still degrade its performance. In this context, intelligent reflecting surface (IRS) has arisen as a promising technology for smartly overcoming the harmful effects of the wireless environment. The disruptive IRS concept of controlling the propagation channels via software can provide attractive performance gains to the communication networks, including higher data rates, improved user fairness, and, possibly, higher energy efficiency. In this article, in contrast to the existing literature, we demonstrate the main roles of IRSs in MIMO-NOMA systems. Specifically, we identify and perform a comprehensive discussion of the main performance gains that can be achieved in IRS-assisted massive MIMO-NOMA (IRS-NOMA) networks. We outline exciting futuristic use case scenarios for IRS-NOMA and expose the main related challenges and future research directions. Furthermore, throughout the article, we support our in-depth discussions with representative numerical results.

Group sparse representation (GSR) based method has led to great successes in various image recovery tasks, which can be converted into a low-rank matrix minimization problem. As a widely used surrogate function of low-rank, the nuclear norm based convex surrogate usually leads to over-shrinking problem, since the standard soft-thresholding operator shrinks all singular values equally. To improve traditional sparse representation based image compressive sensing (CS) performance, we propose a generalized CS framework based on GSR model, leading to a nonconvex nonsmooth low-rank minimization problem. The popular -norm and M-estimator are employed for standard image CS and robust CS problem to fit the data respectively. For the better approximation of the rank of group-matrix, a family of nuclear norms are employed to address the over-shrinking problem. Moreover, we also propose a flexible and effective iteratively-weighting strategy to control the weighting and contribution of each singular value. Then we develop an iteratively reweighted nuclear norm algorithm for our generalized framework via an alternating direction method of multipliers framework, namely, GSR-ADMM-IRNN. Experimental results demonstrate that our proposed CS framework can achieve favorable reconstruction performance compared with current state-of-the-art methods and the RCS framework can suppress the outliers effectively.

Millimeter wave (mmWave) technology can meet the requirements of large amount of data communications among intelligent devices for indoor scenario. With high requirement of throughput, reducing interference in an enclosed region is still a challenge. For indoor scenario, the human bodies are considered as the main blockages besides conventional blockages. In this paper, we present a new system model by exploiting the self-blockage model to capture the effects of human body, while using multi-ball Line of Sight (LOS) link state model to describe the conventional blockage. The combination of the two components provides a more comprehensive and accurate expression of indoor obstruction. We give a closed expression of coverage rate to analyze the system performance. Simulation results show that the proposed model is highly accurate to describe the distribution of blockages for indoor case. Then, we formulate the resource allocation as an optimization problem under this scenario. The target of this optimization is to achieve the maximum throughput by minimizing interference. To reach this goal, an improved Powell Multi Vertex Coloring algorithm (PMVC) is proposed. The throughput raises apparently when we modify this scheme to put more flows in different time-slots evenly. Numerical experiments show that the improved algorithm can achieve higher system throughput than traditional greedy algorithm and Powell Vertex Coloring (PVC) scheme.

Wireless fidelity (WiFi) and long term evolution (LTE) coexistence system has attracted significant attention recently. However, most studies on coexistence only consider the issues about downlink performance and fairness between WiFi network and LTE network. In this paper, we take a comprehensive consideration of both the performance of WiFi and LTE, to propose a downlink/uplink decoupling (DUDe) scheme for the fairness of the coexistence system. Through the process of modeling construction, problem formulation with reformulation, solution and simulations, we illustrate that DUDe can achieve proportional fairness in terms of sum throughput and load balancing between uplink and downlink. In our decoupling model for coexistence between WiFi and LTE, resource allocation is under adaptive spectrum partitioning and users can access each network of WiFi and LTE. Moreover, to make our proposal more realistic, we adopt support vector machine (SVM) to predict the total number of users in WiFi and LTE coexistence system, where the predictive simulation shows its high prediction accuracy with low complexity. We further apply the prediction results to the optimization problem of DUDe. Simulation results manifest that the proposed DUDe scheme has better performance in terms of sum throughput. The sum throughput of DUDe scheme can come up to almost twice more than that of coupled association scheme. The result shows that DUDe can also achieve load balance between uplink and downlink in the coexistence system.

This paper summarizes and synthetically evaluates a method proposed for metasurface design. The method takes as input a set of desired far-field (FF) performance specifications and produces an effective susceptibility distribution that, when illuminated with a known incident field, produces a FF radiation pattern exhibiting the desired specifications. To this end, electromagnetic inversion (inverse source) is used to solve for the desired tangential fields on the output side of the metasurface. A finite-difference frequency-domain solver, recently developed for simulation of metasurfaces, is used to synthetically evaluate the proposed method using a two-dimensional (2D) example. It should be noted that microscopic metasurface design (i.e., the design of the physical metasurface implementation) is beyond the scope of this paper.

Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well,constraint satisfaction is emphasized over objective minimization.Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems.

Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide. With recent advances in gait analysis, clinical assessment of such a knee-related condition has been improved. Although motion capture (mocap) technology is deemed the gold standard for gait analysis, it heavily relies on adequate data processing to yield clinically significant results. Moreover, gait data is non-linear and high-dimensional. Due to missing data involved in a mocap session and typical statistical assumptions, conventional data processing methods are unable to reveal the intrinsic patterns to predict gait abnormalities. Research question Albeit studies have demonstrated the potential of Artificial Intelligence (AI) algorithms to address these limitations, these algorithms have not gained wide acceptance amongst biomechanists. The most common AI algorithms used in gait analysis are based on machine learning (ML) and artificial neural networks (ANN). By comparing the predictive capability of such algorithms from published studies, we assessed their potential to augment current clinical gait diagnostics when dealing with knee OA. Methods Thus, an evidence-based review and analysis were conducted. With over 188 studies identified, 8 studies met the inclusion criteria for a subsequent analysis, accounting for 78 participants overall. Results The classification performance of ML and ANN algorithms was quantitatively assessed. The test classification accuracy (ACC), sensitivity (SN), specificity (SP) and area under the curve (AUC) of the ML-based algorithms were clinically valuable, i.e., all higher than 85%, differently from those obtained via ANN. Significance This study demonstrates the potential of ML for clinical assessment of knee disorders in an accurate and reliable manner.

High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based simulated data. This paper proposes metrics to quantify the generator dynamic model validation results based on the response of generators to each system mode, which includes both local and inter-area, using modal analysis approach. The metrics provide information on the inaccuracy associated with the model in terms of the characteristics of each mode. Initial results obtained using the real-world data validates the effectiveness of the proposed metrics. In this paper, modal analysis was carried out using Prony method.

Memory effects of radio frequency power amplifiers (PAs) can interact with dynamic transmitting signals, dynamic operations, and dynamic environment, resulting in complicated nonlinear problems of the PAs. Recently, deep learning based schemes have been proposed to deal with the memory effects. Although these schemes are powerful in constructing complex nonlinear structures, they are still direct learning-based and are relatively static. In this paper, we propose an adaptive deep learning aided digital predistortion (DL-DPD) model by optimizing a deep regression neural network. Thanks to the sequence structure of the proposed DL-DPD, we then make the linearization architecture more adaptive by using multiple sub-DPD modules and an ensemble predicting process. The results show the effectiveness of the proposed adaptive DL-DPD, and reveals that the online system handovers the sub-DPD modules more frequently than expected.

Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support. The simultaneous use of colour (RGB) and depth (D) data allows to improve segmentation accuracy, especially in case of colour camouflage, illumination changes and occurrence of shadows. Three GPUs were used to accelerate calculations: embedded NVIDIA Jetson TX2 (Maxwell architecture), mobile NVIDIA GeForce GTX 1050m (Pascal architecture) and efficient NVIDIA RTX 2070 (Turing architecture). Segmentation accuracy comparable to previously published works was obtained. Moreover, the use of a GPU platform allowed to get real-time image processing. In addition, the system has been adapted to work with two RGB-D sensors: RealSense D415 and D435 from Intel.

This is a pre-print for the submission to IEEE LISS 2020 Conference, Budapest, Hungary.

Matching fluids used in microwave imaging are often lossy to reduce the reflections from the system enclosure. To enable the use of low-loss matching fluids, we investigate the use of absorbing metasurfaces as the enclosure. This has the potential to enhance the signal-to-noise ratio of the data, thus, enhancing the achievable image accuracy. The presented example has the following limitation: as opposed to design and simulation of the physical structure of the metasurface enclosure, it uses two impedance boundary conditions on top of a metallic-backed substrate. Thus, the angular dependency of a practical absorber is not completely taken into account by this model. At the conference, we’ll also consider a microwave imaging metasurface enclosure via design and simulation of a physical structure.

A method based on electromagnetic inversion is extended to facilitate the design of passive, lossless, and reciprocal metasurfaces. More specifically, the inversion step is modified to ensure that the field transformation satisfies local power conservation, using available knowledge of the incident field. This paper formulates a novel cost functional to apply this additional constraint, and describes the optimization procedure used to find a solution that satisfies both the user-defined field specifications and local power conservation. Lastly, the method is demonstrated with a two-dimensional (2D) example.

Preprint of paper Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses, published in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 109-119, Jan. 2018. doi: 10.1109/TVCG.2017.2745178 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8019883&isnumber=8165924

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto- correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS, and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach the performance of ML after several repetitions for a lower overall complexity.