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Ground-penetrating radar (GPR) has been widely applied to the detection and delineation of buried targets in the subsurface. Compared with conventional single-channel GPR, polarimetric GPR has been proven to possess an improved ability to detect and characterize an elongated object in the subsurface. This letter proves that the scattering signals from a cylinder in two orthogonal polarization channels have a phase difference of about 90° when its diameter-to-wavelength ratio is about 0.05–0.33. Consequently, a polarization-difference imaging method, which shifts the VV component by −90° and subtracts it from the HH component, is proposed for the improved detection and imaging of a subsurface elongated object. Its effectiveness is verified by numerical, laboratory, and field tests on buried rebars and pipes. The signal-to-clutter ratios of the reconstructed GPR images can be improved by up to 4.5 dB by considering the phase difference between the dual-polarization components. [ABSTRACT FROM AUTHOR]

To meet the demand for simultaneously supporting the microwave and millimeter-wave technologies in emerging industrial wireless systems, research on dual-band components with a large frequency ratio has been conducted recently. However, it is challenging to implement independent frequency tunability on bandpass filters with both compactness and high performance. This article presents a dual-band bandpass filter with a large frequency ratio, independently tunable frequencies, and an ultrawide stopband. This is achieved by artfully incorporating a harmonic-controllable varactor-loaded microstrip BPF and a wide-stopband dielectric-loaded cavity BPF with the bandgap-embedded aperture coupling mechanism. Extremely compact size can be achieved without additional filtering elements. Two pairs of transmission zeros can be allocated beside two passbands for good selectivity due to the separate path and cross-coupling in the resonators. For validation, a prototype is designed, fabricated and measured. It can provide two independent tunable frequency ranges (3.22–3.52 GHz and 25–27.06 GHz), exhibiting a large frequency ratio up to 8.3. Mid-band suppression better than 20 dB and ultra-wide stopband up to 32.5 GHz with 20 dB suppression level are achieved. Low insertion loss and good selectivity can also be observed. [ABSTRACT FROM AUTHOR]

This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based on the decomposition for solving problems in which the solutions are initially far from the Pareto-optimal set. Subsequently, a tree is constructed by a modified $k$ -means algorithm on $N$ uniform weight vectors, and each node of the tree contains a weight vector. Each node is associated with a subproblem with the help of its weight vector. Consequently, a subproblem tree can be established. It is easy to find that the descendant subproblems are refinements of their ancestor subproblems. The proposed algorithm approaches the Pareto front (PF) by solving a few subproblems in the first few levels to obtain a rough PF and gradually refining the PF by involving the subproblems level-by-level. This strategy is highly favorable for solving problems in which the solutions are initially far from the Pareto set. Moreover, the proposed algorithm has lower time complexity. Theoretical analysis shows the complexity of dealing with a new candidate solution is $\mathcal {O}(M \log N)$ , where $M$ is the number of objectives. Empirical studies demonstrate the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Head pose estimation (HPE) has wide industrial applications, such as online education, human–robot interaction, and automatic manufacturing. In this article, we address two key problems in HPE based on label learning and asymmetric relation cues: 1) how to bridge the gap between the better prediction performance of networks and incorrectly label pose images in the HPE datasets and 2) how to take full advantage of the adjacent poses information around the centered pose image. We reconstruct all the incorrect labels as a two-dimensional Lorentz distribution to tackle the first problem. Instead of directly adopting the angle values as hard labels, we assign part of the probability values (soft labels) to adjacent labels for learning discriminative feature representations. To address the second problem, we reveal the asymmetric relation nature of HPE datasets. The yaw direction and pitch direction are assigned different weights by introducing the half at half-maximum of the Lorentz distribution. Compared with the traditional end-to-end frameworks, the proposed one can leverage the asymmetric relation cues for predicting the head pose angle in the incorrect label scenarios. Extensive experiments on two public datasets and our infrared dataset demonstrate that the proposed ARHPE network significantly outperforms other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Although product design has been considered as a collaborative activity, progress in developing methods to facilitate the design process has been hampered by difficulties in building common bases among various stakeholders. Customers may only have general needs of the product in layman's terms instead of the sufficient domain knowledge to identify the product specifications. Thus, there is a great need in design research to translate the expressed needs in natural language to design specifications and bridge this semantic gap. By leveraging the massive amount of online user-generated content, we develop a deep learning-based method to automatically identify product design parameters or product specifications. Specifically, we crawl product review data and the corresponding product metadata from e-commerce websites. A convolutional neural network based solution is provided to map product reviews to product specifications. Experimental results show that the method can be well adapted to the mapping from customer needs in the natural language to product specifications. The method facilitates product design by addressing the semantic gap between general customer needs and detailed product specifications. In addition, the automated mapping greatly improves the efficiency and reduces labor in the design process. [ABSTRACT FROM AUTHOR]

This paper describes a proof-of-concept sensor (1) that can detect and record (without battery) a semiconductor-chip tamper activity (i.e., de-soldering followed by mechanical banging) which a counterfeiter does to scavenge semiconductor chips from a printed circuit board and (2) that can be wirelessly interrogated without need to open semiconductor packages. The sensor is based on a High-overtone-Bulk-Acoustic-Resonator (HBAR) working as a Radio-Frequency Identification (RFID) tag, which can be permanently broken down by the voltage and charge generated by a pyroelectric-energy-converter (PEC). The concept is demonstrated through connecting a 7.5 GHz HBAR (based on a 350 nm-thick ZnO thin film deposited on sapphire) to a $5\times 5\times0.15$ mm3 lithium niobate-based PEC, via a Kapton-copper cantilever switch. When the switch is turned on (due to a mechanical shock emulating a counterfeiter’s mechanical banging), the accumulated charge on the PEC (produced by 250 °C temperature rise emulating a counterfeiter’s de-soldering) generates an electrical pulse with 9.6 V peak voltage and 1.36 nC charge transfer to an 8 $\text{M}\Omega $ load (close to the HBAR’s resistance). The PEC’s voltage and charge are shown to permanently breakdown the ZnO film on the HBAR ($0.1\times 0.1\times0.33$ mm3) having a quality factor (Q) of about 2,500, so that the Q drops to near zero, drastically changing the RF absorption/scatter characteristics. A wireless interrogation is demonstrated by having a pair of $2\times 2\times0.2$ cm3 microstrip patch antennas, one of which is connected to HBAR while the other one is connected to a network analyzer working as a wireless interrogator. [ABSTRACT FROM AUTHOR]

Recommendation accuracy is a fundamental problem in the quality of the recommendation system. In this article, we propose an efficient deep matrix factorization (EDMF) with review feature learning for the industrial recommender system. Two characteristics in user’s review are revealed. First, interactivity between the user and the item, which can also be considered as the former’s scoring behavior on the latter, is exploited in a review. Second, the review is only a partial description of the user’s preferences for the item, which is revealed as the sparsity property. Specifically, in the first characteristic, EDMF extracts the interactive features of onefold review by convolutional neural networks with word-attention mechanism. Subsequently, ${L}_{0}$ norm is leveraged to constrain the review considering that the review information is a sparse feature, which is the second characteristic. Furthermore, the loss function is constructed by maximum a posteriori estimation theory, where the interactivity and sparsity property are converted as two prior probability functions. Finally, the alternative minimization algorithm is introduced to optimize the loss functions. Experimental results on several datasets demonstrate that the proposed methods, which show good industrial conversion application prospects, outperform the state-of-the-art methods in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-N, N-to-1, and N-to-N, in knowledge graphs. To overcome these issues, we introduce a multi-scale dynamic convolutional network (M-DCN) model for knowledge graph embedding. This model features topnotch performance and an ability to generate richer and more expressive feature embeddings than its counterparts. The subject entity and relation embeddings in M-DCN are composed in an alternating pattern in the input layer, which helps extract additional feature interactions and increase the expressiveness. Multi-scale filters are generated in the convolution layer to learn different characteristics among input embeddings. Specifically, the weights of these filters are dynamically related to each relation to model complex relations. The performance of M-DCN on the five benchmark datasets is tested via experiments. Results show that the model can effectively handle complex relations and achieve state-of-the-art link prediction results on most evaluation metrics. [ABSTRACT FROM AUTHOR]

To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments. [ABSTRACT FROM AUTHOR]

The terahertz (THz)-based thickness measurement of thermal barrier coatings (TBCs) demands smart determination of THz peaks instead of human intervention for efficiency and accuracy, especially in the case of uneven microstructure due to imperfect spraying conditions. To this end, in this article, a long short-term memory (LSTM) framework driven by a theoretical model of THz signals is proposed to locate peaks automatically and location errors are eliminated by finding the maximum around the originally located results. Then, the local extrema are used to extract peaks for calculating refractive index online to remove the influence of the change in refractive index on thickness measurement. Finally, the simulations and experiments were carried out to verify the proposed method. The results show that our method generated identical results with those from the manual intervention, while the efficiency is significantly improved. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have been recognized as a promising approach for bilevel optimization. However, the population-based characteristic of EAs largely influences their efficiency and effectiveness due to the nested structure of the two levels of optimization problems. In this article, we propose a transfer learning-based parallel EA (TLEA) framework for bilevel optimization. In this framework, the task of optimizing a set of lower level problems parameterized by upper level variables is conducted in a parallel manner. In the meanwhile, a transfer learning strategy is developed to improve the effectiveness of each lower level search (LLS) process. In practice, we implement two versions of the TLEA: the first version uses the covariance matrix adaptation evolutionary strategy and the second version uses the differential evolution as the evolutionary operator in lower level optimization. The experimental studies on two sets of widely used bilevel optimization benchmark problems are conducted, and the performance of the two TLEA implementations is compared to that of four well-established evolutionary bilevel optimization algorithms to verify the effectiveness and efficiency of the proposed algorithm framework. [ABSTRACT FROM AUTHOR]

Migration can reconstruct the geometric structure of a subsurface object from the ground penetrating radar (GPR) data. However, a GPR antenna is usually simplified as an ideal point/line source of normal migration algorithms, which ignore the influence of the antenna radiation pattern in subsurface soil. In this letter, the back-propagation algorithm is corrected with the analytical half-space far-field radiation pattern of an infinite line source. The superiority of this modified migration algorithm is verified through numerical, laboratory, and field experiments. The results show that the undesired diffractive artifacts at the target edges can be suppressed, while reserving the reflection amplitude in the migrated images with antenna pattern correction, compared with the conventional back-propagation and Kirchhoff algorithms. [ABSTRACT FROM AUTHOR]

When solving constrained multiobjective optimization problems (CMOPs), the most commonly used way of measuring constraint violation is to calculate the sum of all constraint violations of a solution as its distance to feasibility. However, this kind of constraint violation measure may not reflect the distance of an infeasible solution from feasibility for some problems, for example, when an infeasible solution closer to a feasible region does not have a smaller constraint violation than the one farther away from a feasible region. Unfortunately, no set of artificial benchmark problems focusing on this area exists. To remedy this issue, a set of CMOPs with deceptive constraints is introduced in this article. It is the first attempt to consider CMOPs with deceptive constraints (DCMOPs). Based on our previous work, which designed a set of directed weight vectors to solve CMOPs, this article proposes a cooperative framework with an improved version of directed weight vectors to solve DCMOPs. Specifically, the cooperative framework consists of two switchable phases. The first phase uses two subpopulations—one to explore feasible regions and the other to explore the entire space. The two subpopulations provide useful information about the optimal direction of objective improvement to each other. The second phase aims mainly at finding Pareto-optimal solutions. Then an infeasibility utilization strategy is used to improve the objective function values. The two phases are switchable based on the information found to date at any time in the evolutionary process. The experimental results show that this method significantly outperforms the algorithms with which it is compared on most of the DCMOPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions. [ABSTRACT FROM AUTHOR]

A high-sensitive multi-parameter gas sensor composed of symmetric dual side-coupled ring resonators in a metallic-dielectric-metallic (MDM) structure is investigated by the finite difference time domain (FDTD) method. The dual side-coupled resonators have lower transmission dip and faster response due to the superposition of the outgoing fields’ amplitude of the symmetric dual resonators, which is very beneficial for detection. Two independently tunable transmission dips are achieved by expanding the ring resonator in ${x}$ -direction, making it possible for dual-parameter sensing. The electric and magnetic distribution characteristics at the two resonant wavelengths are analyzed. Another plasmonic structure expanded along both sides in a central resonator-straight waveguide way is also investigated, and the former structure expanded in ${x}$ -direction works better in detection difficulty and structural utilization. After optimizing the radius and position of ring resonators, the refractive index sensitivities of cavity A and B can reach 2427 nm/RIU and 2431.9 nm/RIU, respectively. The methane and hydrogen sensitivities are −9.213 nm/% and −1.65 nm/% with an extremely high linearity, after coating the methane and hydrogen gas-sensitive film in cavity A and B respectively. This investigation provides an effective way for the design of multi-parameter high-sensitive independently tunable sensors. [ABSTRACT FROM AUTHOR]

This brief proposes a novel generalized Fractional Folding (FF) architecture for digital signal processing integrated circuits. With this new structure, a Fractional Folding based enhanced Parallel Inversionless Berlekamp-Massey (FF-ePIBM) Reed-Solomon Decoder is presented of which the number of processing element (PE) can be reduced to only one, resulting in ultra-low hardware complexity. The FF-ePIBM VLSI architecture can greatly reduce the hardware cost by about 60% compare to the fully expanded parallel ePIBM architecture. With FF-ePIBM architecture, syndrome calculation (SC) block and Chien search & error evaluation (CSEE) block are able to operate at a lower frequency resulting in about 14.8% power saving. A polyphase clock signal is needed in order to achieve fractional folding function according to specific fractional-factor. It is generated from Delay Locked Loop (DLL) at little or no extra cost in different SoCs. To maximize the throughput of decoder, pipelined architecture is adopted to optimize critical path delay. The RS (255, 239) decoder with FF-ePIBM architecture is finally implemented with 40nm CMOS technology. The synthesized results demonstrate that the decoder has a gate count of 12.9k and can operate at 3.1GHz to achieve a highest throughput of 24.8Gb/s and a best TSNT FoM value of 592 to this date. [ABSTRACT FROM AUTHOR]

An assessment of shoulder muscle coordination patterns is important to gain insight into muscle fatigue during wheelchair propulsion. The objective of the present study was to quantify muscle coordination changes over time during fatiguing wheelchair propulsion, as the muscles go through distinct levels of fatigue, a) non-fatigued, b) transiting to fatigue and c) fatigued to exhaustion. We recorded surface electromyography (sEMG) signals of the anterior deltoid (AD), middle deltoid (MD), posterior deltoid (PD), infraspinatus (IS), upper trapezius (UT), sternal head of the pectoralis major (PM), biceps brachii (BB), and triceps brachii (TB) during a wheelchair incremental exercise test. Nine wheelchair users with a diagnosis of spina bifida or T6-T12 spinal cord injury volunteered for the study. Oxygen uptake and SmartWheel kinetic parameters were also recorded during the test. EMG signals were processed by wavelet and principal component analysis (PCA), allowing for an assessment of how wheelchair users modify their muscle coordination patterns over time. Analyses of covariance (ANCOVA) were conducted to identify the main effect of fatigue levels on muscle coordination patterns by controlling for the effect of increased workload as covariate. A significant effect of fatigue levels on the PC1 and PC3 loading scores was found after controlling for the effect of increasing workloads (with ${p} < 0.05$ both cases). In addition, PC3 reflects the most dominant fatigue effect on muscle coordination patterns which are not affected by increased ergometer workload. PC3 indicates muscle imbalance when muscles are fully fatigued and muscle co-contraction when muscles are beginning to fatigue. We conclude that fatigue-related changes in neuromuscular activity during wheelchair propulsion contribute to muscle imbalance and reflect a strategy of stiffening the shoulder joint. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been widely applied to the nondestructive inspection of concrete structures such as tunnel lining, bridge deck, and retaining wall, which are usually reinforced by steel bars. The scattering of electromagnetic (EM) waves caused by the dense steel rebar embedded in the concrete structures has a severe influence on the penetration capacity of GPR waves. In this letter, the scattering and penetration characteristics of EM waves propagating through rebar net are investigated via both numerical and laboratory experiments, with an aim to select the antenna nominal frequency for a different reinforcement density. The results show that the rebar, which is perpendicular to the polarization direction of GPR waves and has a very small diameter compared with the wavelength, is almost transparent to the impinged GPR waves. The scattering and interaction of GPR waves caused by the rebar that is parallel to the polarization direction result in a shielding effect, which is manifested as a blind band in the low-frequency range in the transmitted spectrum. This result violates the rule of thumb commonly used in the GPR community, i.e., the lower frequency has a deeper GPR penetration depth. In the end, a low cutoff frequency is recommended for selecting a GPR antenna with an appropriate nominal frequency when it is used in the detection of an anomaly inside and behind a reinforced concrete structure, in which the spacing of the rebar net is known. [ABSTRACT FROM AUTHOR]

In order to precisely predict 3-D gaze points, calibration is needed for each subject prior to first use the mobile gaze tracking system. However, traditional calibration methods normally expect the user to stare at predefined targets in the scene, which is troublesome and time-consuming. In this study, we proposed a novel method to remove the explicit user calibration and achieve robust 3-D gaze estimation in the room-scale area. Our proposed framework treats salient regions in the scene as possible 3-D locations of gaze points. To improve the efficiency of predicting 3-D gaze from visual saliency, the bag-of-word algorithm is adopted for eliminating redundant scene image data based on their similarities. After the elimination, saliency maps are generated from those scene images, and the geometrical relationship among the scene and eye cameras is obtained through aggregating 3-D salient targets with eye visual directions. Finally, we calculate the 3-D point of regard (PoR) by utilizing 3-D structures of the scene. The experimental results indicate that our method enhances the reliability of saliency maps and achieves promising performances on 3-D gaze estimation with different subjects. [ABSTRACT FROM AUTHOR]

Multiobjective multitasking optimization (MTO), which is an emerging research topic in the field of evolutionary computation, was recently proposed. MTO aims to solve related multiobjective optimization problems at the same time via evolutionary algorithms. The key to MTO is the knowledge transfer based on sharing solutions across tasks. Notably, positive knowledge transfer has been shown to facilitate superior performance characteristics. However, how to find more valuable transferred solutions for the positive transfer has been scarcely explored. Keeping this in mind, we propose a new algorithm to solve MTO problems. In this article, if a transferred solution is nondominated in its target task, the transfer is positive transfer. Furthermore, neighbors of this positive-transfer solution will be selected as the transferred solutions in the next generation, since they are more likely to achieve the positive transfer. Numerical studies have been conducted on benchmark problems of MTO to verify the effectiveness of the proposed approach. Experimental results indicate that our proposed framework achieves competitive results compared with the state-of-the-art MTO frameworks. [ABSTRACT FROM AUTHOR]

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments. [ABSTRACT FROM AUTHOR]

This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solution evaluation among these indicators. Accordingly, a promising-region-based evolutionary many-objective algorithm with the ratio-based indicator is proposed. In our proposed algorithm, a promising region is identified in the objective space using the ratio-based indicator with infinite norm. Since the individuals outside the promising region are of poor quality, we can discard these solutions from the current population. To ensure the diversity of population, a strategy based on the parallel distance is introduced to select individuals in the promising region. In this strategy, all individuals in the promising region are projected vertically onto the normal plane so that crowded distances between them can be calculated. Afterward, two solutions with a smaller distance are selected from the candidate solutions each time, and the solution with the smaller indicator fitness value is removed from the current population. Empirical studies on various benchmark problems with 3–20 objectives show that the proposed algorithm performs competitively on all test problems. Compared with a number of other state-of-the-art evolutionary algorithms, the proposed algorithm is more robust on these problems with various Pareto fronts. [ABSTRACT FROM AUTHOR]

A hybrid method is proposed to reconstruct the subsurface 3-D objects with electromagnetic fields. The frequency-domain reverse time migration (FRTM) is first used to determine the approximate locations and sizes of the objects. Then, based on these results, the full-wave inversion, the variational Born iteration method (VBIM) is used to reconstruct both the shapes and dielectric parameters of the objects. The Monte Carlo method (MCM) is adopted to further refine the reconstructed shapes. Numerical simulations show that the proposed hybrid method can be effectively used for the subsurface imaging and detection. [ABSTRACT FROM AUTHOR]

A gas-mixture sensor based on local surface plasmon resonance (LSPR) behavior in metal-dielectric-metal (MDM) periodic array is proposed. The comparisons of gas sensing performance between metal-dielectric (MD) and MDM structure are discussed for single-gas and gas-mixture separately. The influence of MDM structural parameters on the extinction spectrum and gas sensitivity are analyzed to obtain optimal parameters. By replacing the dielectric with suitable gas-sensitive films, the gas mixture of methane and hydrogen can be accurately measured without interfering with each other. The methane and hydrogen sensitivities can reach to −3.435nm /% and −0.72nm /%, respectively. The results also verify the feasibility of multi-channel gas detection and provide an effective method for gas component analysis. [ABSTRACT FROM AUTHOR]

This article presents active noise cancelation (ANC) based on MEMS resonant microphone array (RMA) which offers very high sensitivities (and thus very low noise floors) near resonance frequencies and also provides filtering in acoustic domain. The ANC is targeted to actively cancel out any sound between 5–9 kHz (above the speech range of 300 – 3,400 Hz). The ANC works best around the resonance frequencies of the resonant microphones where the sensitivities are high. The ANC has been implemented with analog inverter, digital phase compensator, digital adaptive filter, and deep learning, and shown to perform better with a digital adaptive filter for both RMA-based and flat-band-microphone-based ANC. At the same time, when the sound intensity over 5 – 9 kHz is low, RMA-based ANC with adaptive filter works the best among different approaches tested. Automatic speech recognition under different noises (of different intensity levels) has been tested with ANC. In all the tested cases, word error rate improves with ANC. [2020-0212] [ABSTRACT FROM AUTHOR]

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Erasure codes have been used extensively in large-scale storage systems to reduce the storage overhead of triplication-based storage systems. One key performance issue introduced by erasure codes is the long time needed to recover from a single failure, which occurs constantly in large-scale storage systems. We present ESetStore, a prototype erasure-coded storage system that aims to achieve fast recovery from failures. ESetStore is novel in the following aspects. We proposed a data placement algorithm named ESet for our ESetStore that can aggregate adequate I/O resources from available storage servers to recover from each single failure. We designed and implemented efficient read and write operations on our erasure-coded storage system via effective use of available I/O and computation resources. We evaluated the performance of ESetStore with extensive experiments on a cluster with 50 storage servers. The evaluation results demonstrate that our recovery performance can obtain linear performance growth by harvesting available I/O resources. With our defined parameter recovery I/O parallelism under some mild conditions, we can achieve optimal recovery performance, in which ESet enables minimal recovery time. Rather than being an alternative to improve recovery performance, our work can be an enhancement for existing solutions, such as Partial-parallel-repair (PPR), to further improve recovery performance. [ABSTRACT FROM AUTHOR]

The failure of the transistor could make an amplifier useless, bringing the wireless system to a sudden halt. Therefore, an amplifier which can handle this type of fault without introducing additional components is highly demanded in industrial applications. For this purpose, a distributed amplification topology is proposed in this paper. The robustness of the proposed structure was theoretically verified by analyzing the ideal models of the amplifier under normal and failed modes. The possibility for further performance enhancement using tunable components is also theoretically investigated. For validation, a wideband amplifier which covers a bandwidth from 0.75 to 2.4 GHz is designed, fabricated, and measured. To verify the robustness, the cases with one and two field effect transistors (FETs) failed are evaluated. For the case with one failed FET, the implemented amplifier maintained a comparable performance over the operating frequency band. For the case with two failed FETs, the same bandwidth can be maintained with the unavoidable minor degradation in gain and gain flatness. The unique fault-tolerant property of the proposed topology is demonstrated. As a beneficial supplement to the proposed fault-tolerant architecture, the proposed optimization approach is applied to achieve a better tradeoff among return loss, gain, and gain flatness. [ABSTRACT FROM AUTHOR]

In this study, an ionization gas sensor device assembled by a nanoneedle array and a single nanowire is reported. Each nanoneedle is hybridized and consists of the Si nanorod with platinum nanocrystallites on the top. In the device, nanoneedle arrays serve as one electrode and the single silicon nanowire as the counter electrode to form a unique asymmetric discharge space. We believe that gas between this electrode pair is polarized and directly ionized in an extremely high electrical field, which is confirmed by the microstructure characterization, gas-sensing test and theoretic calculation. The advantage of ultralow operation voltage, power consumption with trace sensing ability renders the device an extraordinary ionization gas sensor. [ABSTRACT FROM AUTHOR]

This letter presents the joint reconstruction of unknown subsurface structures by the full-wave inversion (FWI) and reverse time migration (RTM) imaging. In the FWI, the 1-D distorted Born iteration method (DBIM) is employed to retrieve the dielectric parameters of the layered subsurface medium by minimizing the difference between measured fields and calculated fields via Fréchet derivatives. Based on the inversion results, the RTM is directly performed in the frequency domain to image the buried objects using the ground-penetrating radar (GPR) data. Numerical and laboratory experiments show that the proposed joint method can be used to reconstruct the subsurface structures reliably and efficiently. [ABSTRACT FROM AUTHOR]

This paper addresses a class of optimization problems in which either part of the objective function is differentiable while the rest is nondifferentiable or the objective function is differentiable in only part of the domain. Accordingly, we propose a dual-decomposition-based approach that includes both objective decomposition and domain decomposition. In the former, the original objective function is decomposed into several relatively simple subobjectives to isolate the nondifferentiable part of the objective function, and the problem is consequently formulated as a multiobjective optimization problem (MOP). In the latter decomposition, we decompose the domain into two subdomains, that is, the differentiable and nondifferentiable domains, to isolate the nondifferentiable domain of the nondifferentiable subobjective. Subsequently, the problem can be optimized with different schemes in the different subdomains. We propose a population-based optimization algorithm, called the simulated water-stream algorithm (SWA), for solving this MOP. The SWA is inspired by the natural phenomenon of water streams moving toward a basin, which is analogous to the process of searching for the minimal solutions of an optimization problem. The proposed SWA combines the deterministic search and heuristic search in a single framework. Experiments show that the SWA yields promising results compared with its existing counterparts. [ABSTRACT FROM AUTHOR]

This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas. [ABSTRACT FROM AUTHOR]

To meet the stringent requirements of the emerging wireless communication system, the components should exhibit high flexibility in the control of operating frequency, amplitude characteristics, and also phase characteristics. Among them, the reconfigurability in phase is important but difficult to implement. The existing phase-reconfigurable coupler can only provide a tuning range between 45° and 135°, resulting in limited applications. To solve this problem, a tunable unit consisting of open and shorted stubs and tunable capacitors is introduced between two eighth wavelength coupled lines to construct a compact phase-tunable coupler. For the first time, the continuous tuning range of a phase-reconfigurable coupler is extended to 0°–180°. The corresponding theoretical analysis has been conducted. For the demonstration, two couplers operating at 2.4 GHz were fabricated and measured. Good agreement between simulation and measurement can be observed. Coupler I achieved a phase tuning range from 45° to 135°, maintaining equal power division characteristics. The amplitude bandwidth defined by 10-dB return loss and 1-dB amplitude imbalance was larger than 16.3% for all states. Coupler II provided a tunable phase from 0° to 180° over an amplitude bandwidth larger than 12.5%. These results validate the effectiveness of the proposed structures. [ABSTRACT FROM AUTHOR]

In this brief, a high-isolation and wide-stopband diplexer with controllable transmission zero (TZ) nearby the passband, is implemented using substrate integrated waveguide elements. The TZ is generated by the mixed electric and magnetic coupling scheme of $ {TE}_{ {101}}$ mode. It can be flexibly controlled by changing the length of rectangular apertures while keeping good wide-stopband performance unchanged. An ${X}$ -band diplexer with lower and upper channels centered at 9.5 GHz and 10.5 GHz was designed, fabricated, and measured to verify the proposed concept. Good agreement between simulation and measurement can be observed. The designed diplexer exhibits a high isolation level larger than 45 dB over the whole band and a wide stopband up to 20 GHz. [ABSTRACT FROM AUTHOR]

Infrared (IR) spectral imaging sensing is a powerful visual technique for industrial material recognition in robot vision systems. However, the imaging sensing data have issues of random noise and band overlap. Resolution enhancement is usually the first step in the preprocessing procedure of industrial robot vision sensing. In this article, we develop a resolution-enhancement algorithm with total variation (TV) constraints for the degraded Fourier transform IR (FTIR) spectrum due to overlap and noise degradation in the robot vision sensing. The kernel function is calculated using the spectrometer imaging systems and Fourier optical theory. The proposed model not only can remove noises effectively but also can estimate the kernel function because of the adaptive TV as constraint regularization. This model is examined by a set of simulated FTIR spectra with the Poisson noises and a series of real FTIR spectra. The proposed model is compared with the other state-of-the-art methods in terms of performance. Experimental results demonstrate that the proposed approach can split the overlap band effectively while the spectral structure details are retained satisfactorily. The enhanced high-resolution imaging spectrum data can raise the robot vision sensing accuracy in industrial intelligent systems. [ABSTRACT FROM AUTHOR]

Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Over the past two decades, Evolutionary Multi-objective Optimization (EMO) algorithms have demonstrated their ability to find and maintain multiple trade-off solutions in two and three-objective problems, making EMO as one of the most emergent and exciting fields of research and application within Computational Intelligence (CI) area. The main reason for EMO's success is that the population-based EMO operators are able to establish an implicit parallel search within an evolving population to find multiple Pareto-optimal regions of the search space parallelly. For many-objective optimization problems involving a largedimensional objective space, the extent of implicit parallelism is argued here to be too generic, compared to the same in a lower-dimensional objective space. Decomposition-based EMO algorithms - a recent trend in EMO literature - which divide the overall computing task into a number of sub-tasks of focusing within a region of the search space have found to be successful in solving many-objective problems. In this paper, we study the effect of explicit control of an algorithm's implicit parallelism mechanism for achieving an enhanced performance of decomposition-based EMO algorithms. We consider three decomposition-based many-objective evolutionary algorithms (EAs) - MOEA/D, MOEA/D-M2M, and NSGA-III - for this purpose. We also investigate another explicit control strategy of suitably choosing a normalization method of objectives for improving the performance of MOEA/D and MOEA/ D-M2M methods, and report much improved performance than their original counterparts. The principles of this study are valid for any population-based search and optimization algorithms and can be extended to improve the performance other single-objective EA, EMO, and other relevant CI methods. [ABSTRACT FROM AUTHOR]

When sounding pavement layers using ground penetrating radar (GPR), antenna effects including dispersion and multiple reflections usually degrade the vertical resolution. In far-field conditions, these effects can be analytically filtered out by a linear method. However, for near-field operation, the antenna model tends to be nonlinear, and thus, these unwanted effects cannot be analytically removed anymore. In this letter, a method based on the radial basis function (RBF) neural network is proposed to filter out antenna effects under near-field conditions. A well-developed GPR model is used to simulate the input training data, and the corresponding zero-offset Green’s function is calculated as the desired output for each training data. The trained RBF network is applied to the simulated and measured data. The results show that the proposed method is effective in filtering out the antenna effects and increasing the vertical resolution of GPR. [ABSTRACT FROM AUTHOR]

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) has been widely applied to the detection of subsurface elongated targets, such as underground pipes, concrete rebars, and subsurface fractures. The orientation angle of a subsurface elongated target can hardly be delineated by a commercial single-polarization GPR system. In this paper, a hybrid dual-polarimetric GPR system, which consists of a circularly polarized transmitting antenna and two linearly polarized receiving antenna, is employed to detect buried elongated objects. A polarimetric calibration experiment using a gridded trihedral is carried out to correct the imbalances and cross talk between the two receiving channels. A full-polarimetric scattering matrix is extracted from the double-channel GPR signals reflected from a buried elongated object. An improved Alford rotation method is proposed to estimate the orientation angle of the elongated object from the extracted scattering matrix, and its accuracy is validated by a numerical test. A laboratory experiment was further conducted to detect five metal rebar buried in dry sand at different orientation angle relative to the GPR scan direction. The maximum relative error of the estimated angles of the buried rebars in the migrated GPR images is less than 5%. It is concluded that radar polarimetry can provide not only richer information than single-polarization GPR, but also a reliable approach for orientation estimation of a subsurface elongated object. [ABSTRACT FROM AUTHOR]

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system. [ABSTRACT FROM AUTHOR]

In this paper, the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method is used to solve the electromagnetic scattering problem for 3-D inhomogeneous uniaxial anisotropic objects embedded in a layered uniaxial medium. The optical axis of the background medium is perpendicular to the layer boundaries. However, the optical axis of the scatterer can be rotated through any angles. The electric field integral equation (EFIE) is formulated in the layered uniaxial anisotropic background medium. The volumetric roof-top function is used for the basis function as well as the testing function. The dyadic Green’s functions (DGFs) for both the electric field and the magnetic vector potential are used to solve the EFIE, and the results as well as the performance are compared. Several numerical simulations are carried out to validate the accuracy and efficiency of the proposed method. The major new contribution of this paper is to extend the BCGS-FFT method to accommodate the uniaxial anisotropy of the layered background medium. Therefore, the DGFs for the layered uniaxial medium are evaluated before solving the EFIE. In addition, the DGF for the electric field is also used to solve the 3-D scattering problem through BCGS-FFT iterations, which is different from the previous work. [ABSTRACT FROM AUTHOR]

Infrared (IR) spectrometers, particularly the aging ones, often suffer from the band overlap and random noise. In this paper, a blind estimation method based on discrete cosine transform (DCT) regularization is proposed for IR spectrum measured from an aging spectrometer instrument. Motivated by the observation that the DCT coefficient distribution of the ground-truth spectrum is sparser than that of the observed spectrum, an IR spectral deconvolution model is formulated in our method to regularize the distribution of the observed spectrum by total variation regularization. Then, the split Bregman method is exploited to solve the resulting optimization problem. The experimental results demonstrate an encouraging performance of the proposed approach to suppress noise and preserve spectral details. The novelty of our method lies on its ability to estimate instrument function and latent spectrum in a joint framework; thus, mitigating the effects of instrument aging to a large extent. The recovered IR spectra can efficiently capture the spectral features and interpret the unknown chemical mixture in industrial applications. [ABSTRACT FROM AUTHOR]

Collective motion is a fundamental operation of robot swarms by which a group of robots move from a source to a destination in a cohesive way (i.e., connectivity is preserved during these movements). However, the collective motion of robot swarms along preplanned paths has not been well studied. In this paper, we propose self-adaptive collective motion algorithms for swarm robots in 3-D space. Using the proposed collective motion algorithms, robots are able to move along a preplanned path from a source to a destination while satisfying the following requirements: 1) the robots use only one-hop neighbor information; 2) the robots maintain connectivity of the network topology for information exchange; 3) the robots maintain a desired neighboring distance; and 4) the robots are capable of bypassing obstacles without partitioning the robot swarm (i.e., member loss). Our basic idea is to introduce a guidance force and a topology force into the system. The guidance force is used to guide the robots to their destination along the preplanned path. It ensures that the robots continue to move until they reach their destination. The topology force is used to maintain a “good” topology of the robot swarm, such as maintaining connectivity of the network topology and the desired distance between neighboring robots. The resultant of the guidance and topology forces determines the movement of a robot. We develop collective motion algorithms for three cases: 1) no obstacles or leaders; 2) no obstacles with a leader; and 3) with obstacles (with and without a leader). Extensive simulations are conducted to evaluate performance of the proposed algorithms. The simulation results show that: 1) our algorithms meet all the requirements; 2) our algorithms are resistant to GPS errors and robot failures; and 3) self-adaptive control of our algorithms makes network topologies more stable and significantly saves travel time of swarm robots. Note to Practitioners—We propose self-adaptive collective motion algorithms that enable swarm robots to move along a preplanned path from a source to a destination in 3-D space. Our algorithms use only one-hop neighbor information and operate without central controllers. The algorithms are designed to be self-adaptive in the sense that robots are able to dynamically determine proper moving parameters, based on their environments and statuses. With the proposed algorithms, swarm robots are able to: 1) maintain connectivity and a desired neighboring distance during movement; 2) bypass obstacles without member loss (i.e., the robot swarm is partitioned); and 3) be resistant to GPS errors and robot failures. We address both cases of with a leader and without a leader. Simulation results show effectiveness of our algorithms which can be applied in applications of surveillance, search and rescue, mining, agricultural foraging, autonomous military units, and distributed sensing in micromachinery or human bodies. [ABSTRACT FROM AUTHOR]

A frequency-domain reverse-time migration (RTM) algorithm based on the layered medium dyadic Green's function (DGF) is proposed for high-resolution and efficient subsurface imaging using ground penetrating radar (GPR). Different from the conventional RTM, which is performed by the finite-difference time domain (FDTD) method, both the forward and backward extrapolation wavefields in this frequency-domain RTM are computed by the multiplication between the frequency spectra of the excitation source or the recorded GPR data and the layered medium DGFs. The final image is reconstructed by the direct summation of all the wavefields for all the sampling frequencies in the frequency domain, instead of transforming them back to the time domain and processing them. Two-dimensional (2-D) or 3-D numerical and laboratory experiments show that the proposed frequency-domain RTM algorithm can yield almost the same imaging results as the conventional time domain RTM algorithm but requires less than two orders of magnitude in computational costs. The proposed GPR imaging method is verified to be competent for the fast 3-D imaging of potential larva rocks under the lunar surface, which can provide critical information for the regolith-drilling task of the Chang-E 5 lunar exploration mission of China. Real-time subsurface imaging by RTM could be fulfilled and be widely applied to engineering geophysics, such as urban utility detection, through-wall imaging, and ice monitoring by ground-coupled or air-borne GPR. [ABSTRACT FROM AUTHOR]

An effective allocation of search effort is important in multiobjective optimization, particularly in many-objective optimization problems (MaOPs). This paper presents a new adaptive search effort allocation strategy for multiobjective evolutionary algorithm based on decomposition MOEA/D-M2M, a recent MOEA/D algorithm for challenging MaOPs. This proposed method adaptively adjusts the subregions of its subproblems by detecting the importance of different objectives in an adaptive manner. More specifically, it periodically resets the subregion setting based on the distribution of the current solutions in the objective space such that the search effort is not wasted on unpromising regions. The basic idea is that the current population can be regarded as an approximation to the Pareto front (PF) and thus one can implicitly estimate the shape of the PF and such estimation can be used for adjusting the search focus. The performance of proposed algorithm has been verified by comparing it with eight representative and competitive algorithms on a set of degenerated MaOPs with disconnected and connected PFs. Performances of the proposed algorithm on a number of nondegenerated test instances with connected and disconnected PFs are also studied. [ABSTRACT FROM AUTHOR]

The hardware instability of a ground penetrating radar (GPR) system has a severe impact on the quantitative analysis of GPR data, which is aimed for material characterization and subsurface monitoring. In this letter, an instability index is proposed to quantify the stability performance of a GPR system and the influences of the GPR system type, warm-up time, environmental noise, and the antenna vibration on it are evaluated through a series of laboratory experiments on a sandbox model. It is found that the GPR signal recorded by a stepped-frequency GPR system based on a vector network analyzer is much more stable than that by a commercial impulse GPR system at a cost of more sweep time. A warm-up time of several minutes is enough for an impulse GPR system. Environmental noise has a negligible influence on the stability performance of a GPR system. Mechanical vibrations of GPR antennas have a severe impact on the stability performance of the GPR system, and the instability index and timing jitter can be increased by more than one order of magnitude in a vibrating condition over those in a static condition. The instability index of the direct signal has a negligible difference with that of the reflection signal from a metal plate; thus, a simple measurement of direct signal on the ground surface is suggested for the evaluation of the instability of a GPR system in field in the future. [ABSTRACT FROM PUBLISHER]

Compared to general vehicular systems, bus systems have advantages including wide coverage, fixed routes, and regular service. Inspired by these unique features of the bus systems, we propose to use the bus systems as routing backbones of VANETs. In this work, we present a Community-based Bus System (CBS) which consists of two components: a community-based backbone and a routing scheme over the backbone. The backbone construction is a one-off operation which is done offline while the routing is done online in individual buses. We build a community-based backbone by applying community detection techniques and propose a two-level routing scheme which operates over the backbone. The proposed routing scheme performs sequentially in the inter-community level and the intra-community level, and is able to support message delivery to both buses and specific locations/areas. We develop a probabilistic model to analyze the message delivery latency of CBS. The average error of the analytically-derived latency is shown to be 8.9 percent of the latency derived from the real traces. Extensive experiments are conducted on real-world traces from the Beijing bus system and the Dublin bus system and the results show that CBS can significantly lower the delivery latency and improve the delivery ratio, compared to the existing solutions. CBS is a general solution which is applicable to any bus-based VANETs. [ABSTRACT FROM AUTHOR]

The variational Born iterative method (VBIM) is employed here to reconstruct 3-D objects with permittivity contrast buried in spherically multilayered media. The nonlinear inverse problem is solved iteratively via the conjugate-gradient method, and in each iteration, the scattered field is linearized by using the Born approaximation. The forward solver is provided by the method of moments combined with a Krylov subspace method. The dyadic Green’s function for spherically layered media is constructed in terms of the spherical vector wave functions by using the scattering superposition in the spherical coordinate system and then transformed into the Cartesian coordinate system. Thus, the inversion region is discretized into $N$ uniform cubic cells and the reconstructed result can be obtained in the Cartesian coordinate system by employing VBIM. Numerical results with high resolution are presented to validate the capability of our method in reconstructing 3-D multiple objects of arbitrary shapes buried in spherically multilayered media. [ABSTRACT FROM PUBLISHER]

There are two main tasks involved in addressing a multiobjective optimization problem (MOP) by evolutionary multiobjective (EMO) algorithms: 1) make the population converge close to the Pareto-optimal front and 2) maintain adequate population diversity. However, most state-of-the-art EMO algorithms are designed based on the “convergence first and diversity second” principle. It has been observed that although these EMO algorithms have been successful in optimizing many real-world MOPs, they fail to solve certain problems that feature a severe imbalance between diversity preservation and achieving convergence. This paper characterizes an imbalanced MOP by clearly defining properties and indicating the reasons for the existing EMO algorithms’ difficulties in solving them. We then present 14 imbalanced problems, with and without constraints. Computational results using four existing EMO algorithms—elitist non-dominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), strength Pareto evolutionary algorithm 2 (SPEA2), and S metric selection EMO algorithm (SMS-EMOA) and a proposed generalized vector-evaluated genetic algorithm are then presented. It is seen that these EMO algorithms cannot solve these imbalanced problems, but they are able to solve the problems when augmented by multiobjective to multiobjective (M2M), an approach that decomposes the population into several interacting subpopulations. These results and the successful application of the EMO methods with the M2M approach even on standard so-called balanced problems indicate the usefulness of using the M2M approach. [ABSTRACT FROM AUTHOR]

We apply the forward scattering and inverse scattering algorithms to a cold unmagnetized plasma region within a multilayered background medium. Each layer has a different plasma frequency. The disturbed region in the plasma has an arbitrary shape, so it is an electromagnetic wave scatterer and can exist in any layer. The stabilized biconjugate-gradient fast Fourier transform (BCGS-FFT) algorithm is used to compute the scattered field. The scattered fields calculated by the BCGS-FFT yield excellent agreement with simulated results from the commercial software. In the inverse scattering process, the variational Born iterative method is used to reconstruct the relative permittivity, and thus the plasma frequency of the disturbed region. Multiple frequencies are adopted to determine the dispersive property of the plasma medium. [ABSTRACT FROM PUBLISHER]