Robust Iterative Algorithm Research Articles

Robust Task Offloading and Trajectory Optimization for UAV-Mounted Mobile Edge Computing

Mobile edge computing (MEC) deployed in unmanned aerial vehicles (UAVs) has shown special strength by enhancing computational capacity and prolonging the battery lives of terrestrial user equipment (UE). Nevertheless, current research lacks studies of robust offloading scheme scheduling and trajectory planning using terrestrial random channels. The state-of-the-art joint task-offloading and trajectory-planning optimization techniques for UAV-mounted MEC are focused on scenarios where only air–ground channels exist rather than time-varying terrestrial channels. By contrast, this paper considers the scenario where both the time-varying/random terrestrial channels and the line-of-sight air–ground channels occur. Aiming at robust resource scheduling for energy-efficient UAV-assisted MEC, we formulate a novel joint optimization of UAV trajectory planning and task offloading, which, however, is highly nonconvex. As a countermeasure, the original optimization is recast as subproblems related to task offloading and trajectory planning and solved by a novel robust iterative optimization algorithm that combines the methods of weighted minimum mean square error, S-procedure, successive convex approximation, etc. Numerical results indicate that, compared to various baselines, the proposed algorithm can effectively reduce energy consumption and optimize the trajectory in the presence of a large number of input tasks. In addition, in terms of stability and effectiveness, the proposed robust iterative optimization algorithm can reduce energy consumption more stably in time-varying/random channels compared to non-robust schemes.

A robust iterative algorithm for SINS/USBL integrated navigation based on dual hydrophone differential model

Aiming at the complex underwater environment which leads to large measurement errors and outliers in ultrashort baseline, a robust iterative Kalman filtering algorithm based on the differential model of dual hydrophones is proposed in this paper. Firstly, to achieve accurate navigation under large initial misalignment angles, the algorithm builds an inertial navigation error equation based on the right invariant error definition of Lie groups. Then a dual hydrophone differential model is proposed based on SINS/USBL tightly coupled. It successfully improves the accuracy of integrated navigation and significantly mitigating the issue of SINS/USBL positioning accuracy degradation caused by USBL slant distance noise that grows with distance. Finally, a robust iterative Kalman filter based on the dual hydrophone differential model is proposed to counter the threat to the vehicle’s safe operation and the accuracy loss caused by outliers. The algorithm’s superiority and effectiveness are verified through simulation and sea trial.

Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub–Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views. Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub–Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration. Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach. Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals.

Motion prediction of tumbling uncooperative spacecraft during proximity operations.

The relative attitude estimation between chasers and uncooperative targets is an important prerequisite for executing in orbit service (OOS) tasks. Only by efficiently obtaining relative pose parameters can chasers design close-range rendezvous trajectories close to uncooperative targets. The focus of this article is on active systems, such as TOF cameras or LIDAR. This paper proposes an attitude estimation scheme to obtain relative attitude parameters between uncooperative targets. This scheme utilizes LIDAR to obtain three-dimensional point clouds of non-cooperative targets, extracts key points and simplifies the number of point clouds through joint farthest point sampling and point cloud feature analysis, and then uses point fast feature histograms (FPFHs) and robust iterative closest point algorithms to achieve point cloud registration between every two frames. Finally, a filtering framework was designed, whose scheme is an extended Kalman filter designed for updating measurements of relative position, velocity, attitude, and angular velocity estimation. The experimental results show that this method can effectively achieve point cloud registration for close range rotation and translation motion, and can estimate the motion state of the target.

Learning with Euler Collaborative Representation for Robust Pattern Analysis

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.

Resource Allocation of UAV-Assisted IoT Node Secure Communication System

To balance the information security and energy harvest for massive internet-of-things (IoT) devices, an unmanned aerial vehicle (UAV)–assisted secure communication model is proposed in this paper. We extend the secure transmission model with physical layer security (PLS) to simultaneous wireless information and power transfer (SWIPT) technology and optimize the UAV trajectory, transmission power, and power splitting ratio (PSR). The nonconvex object function is decomposed into three subproblems. Then a robust iterative suboptimal algorithm based on the block coordinate descent (BCD) method is proposed to solve the subproblems. Numerical simulation results are provided to show the effectiveness of the proposed method. These results clearly illustrate that our resource allocation schemes surpass baseline schemes in terms of both transmit power and ratio of harvesting energy, while maintaining an approximately instantaneous secrecy rate.

Water thermal enhancement in a porous medium via a suspension of hybrid nanoparticles: MHD mixed convective Falkner's-Skan flow case study

Keeping in mind the whispered applications of Falkner's-Skan Flows, the present hydrothermal scrutinization intended to explore the two-dimensional flow pattern and thermal characteristics featuring the steady mixed convective motion of a radiating homogeneous hybrid nanofluidic mixture (Ag+TiO2)−H2O (i.e., a homogeneous aquatic mixture containing spherical silver and titanium dioxide nanoparticles) over a non-movable wedge surface. By combining Darcy's-Brinkman and single-phase models, the conservation equations are stated properly in the case where the hydrothermal impacts of specific variable heat and magnetic sources are taken into account effectively in the present flow problem. After many simplifications and rearrangements, the derived boundary layer equations are handled numerically with the help of a robust iterative GDQM-NRT algorithm, whose accurate results are presented graphically and tabularly. As the main findings, it is evidenced that the thermal buoyancy forces, as well as Lorentz's and Darcy's forces, act as assisting factors, which exert a cooling impact throughout the hybrid nanofluidic mixture (Ag+TiO2)−H2O due to the imposed axial pressure gradient. Energetically, it is demonstrated that the silver nanoparticles play a noteworthy role in the enhancement of the heat transfer rate at the wedge surface with a strengthening in the resulting surface viscous drag forces.

Robust iterative adaptive filtering against intrapulse Doppler shifts

Recently, a two-dimensional joint iterative adaptive filtering (2-D JIAF) has been proposed to address the masking effect of large targets on adjacent small targets in multi-target scenarios. However, its performance is impaired for the cases with fast moving targets due to intrapulse Doppler mismatch. In this paper, we consider the intrapulse Doppler shifts in filtering design and propose a robust iterative adaptive filtering algorithm based on matched filter outputs, termed robust iterative adaptive filtering against intrapulse Doppler shifts (RIAF-IDS), to suppress the range sidelobes induced by intrapulse Doppler mismatch and improve the filtering performance. The proposed algorithm can jointly suppress range-Doppler sidelobes and obtain accurate estimation of range-Doppler image even in cases with fast moving targets. The derivation of RIAF-IDS is provided in detail, and its filtering performance is validated through several simulations, which demonstrate that RIAF-IDS has superior Doppler tolerance over 2-D JIAF at the cost of more computation.

Robust Trajectory and Resource Optimization in UAV-Enabled IoT Networks under Probabilistic LoS Channel in Presence of Jammers

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

Forex Investment Optimization Using Instantaneous Stochastic Gradient Ascent—Formulation of an Adaptive Machine Learning Approach

In the current complex financial world, paper currencies are vulnerable and unsustainable due to many factors such as current account deficit, gold reserves, dollar reserves, political stability, security, the presence of war in the region, etc. The vulnerabilities not limited to the above, result in fluctuation and instability in the currency values. Considering the devaluation of some Asian countries such as Pakistan, Sri Lanka, Türkiye, and Ukraine, there is a current tendency of some countries to look beyond the SWIFT system. It is not feasible to have reserves in only one currency, and thus, forex markets are likely to have significant growth in their volumes. In this research, we consider this challenge to work on having sustainable forex reserves in multiple world currencies. This research is aimed to overcome their vulnerabilities and, instead, exploit their volatile nature to attain sustainability in forex reserves. In this regard, we work to formulate this problem and propose a forex investment strategy inspired by gradient ascent optimization, a robust iterative optimization algorithm. The dynamic nature of the forex market led us to the formulation and development of the instantaneous stochastic gradient ascent method. Contrary to the conventional gradient ascent optimization, which considers the whole population or its sample, the proposed instantaneous stochastic gradient ascent (ISGA) optimization considers only the next time instance to update the investment strategy. We employed the proposed forex investment strategy on forex data containing one-year multiple currencies’ values, and the results are quite profitable as compared to the conventional investment strategies.

Secure Information Transmission for B5G HetNets: A Robust Game Approach

This article investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

FPGA Implementation of Reconfigurable CORDIC Algorithm and a Memristive Chaotic System With Transcendental Nonlinearities

Coordinate Rotation Digital Computer (CORDIC) is a robust iterative algorithm that computes many transcendental mathematical functions. This paper proposes a reconfigurable CORDIC hardware design and FPGA realization that includes all possible configurations of the CORDIC algorithm. The proposed architecture is introduced in two approaches: multiplier-less and single multiplier approaches, each with its advantages. Compared to recent related works, the proposed implementation overpasses them in the included number of configurations. Additionally, it demonstrates efficient hardware utilization and suitability for potential applications. Furthermore, the proposed design is applied to a memristive chaotic system with different transcendental functions computed using the proposed reconfigurable block. The memristive system design is realized on the Artix-7 FPGA board, yielding throughputs of 0.4483 and 0.3972 Gbit/s for the two approaches of reconfigurable CORDIC.

A registration-based stitching method for obtaining high-accuracy material removal distribution in the sub-aperture polishing process

In the research of rapidly developed deterministic sub-aperture polishing technologies, the analysis of material removal distribution is vital for promoting the precision, efficiency, and stability of the polishing process. To obtain a high-accuracy material removal distribution over a large-scale freeform surface with a high lateral resolution, sub-aperture stitching measurements are indispensable. Due to the limitation of traditional stitching methods, it remains a great challenge to obtain precise freeform surface topographies. This paper proposes a registration-based stitching method, which minimizes the negative impact of stitching error, to yield a material removal distribution with both high accuracy and fine lateral resolution. Firstly, to obtain an initial estimate of the rigid transformation for each sub-aperture, conventional stitching and registration based on the least-square stitching method and the robust iterative closest point algorithm are implemented. Secondly, a registration-based stitching model considering the invariance characteristic of the unpolished region is developed to cancel out the inconsistent stitching errors for obtaining a more precise material removal distribution. Finally, a polishing experiment has been conducted to validate the effectiveness of the proposed method. The registration-based stitching method and a commercial stitching program are utilized individually to obtain the material removal distribution. Comparison results show that the proposed method can largely improve the measuring accuracy of material removal distribution, which promotes the further study of the deterministic sub-aperture polishing process.

Robust trajectory and communication design for angle-constrained multi-UAV communications in the presence of jammers

This paper studies a multi-unmanned aerial vehicle (UAV) enabled wireless communication system, where multiple UAVs are employed to communicate with a group of ground terminals (GTs) in the presence of potential jammers. We aim to maximize the throughput overall GTs by jointly optimizing the UAVs' trajectory, the GTs' scheduling and power allocation. Unlike most prior studies, we consider the UAVs' turning and climbing angle constraints, the UAVs' three-dimensional (3D) trajectory constraints, minimum UAV-to-UAV (U2U) distance constraint, and the GTs' transmit power requirements. However, the formulated problem is a mixed-integer non-convex problem and is intractable to work it out with conventional optimization methods. To tackle this difficulty, we propose an efficient robust iterative algorithm to decompose the original problem be three sub-problems and acquire the suboptimal solution via utilizing the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Extensive simulation results show that our proposed robust iterative algorithm offers a substantial gain in the system performance compared with the benchmark algorithms.

Asymmetric interferometric frequency resolved optical gating for complete unambiguous ultrashort pulse characterisation

We propose a modification of the second-harmonic generation frequency resolved optical gating method (SHG-FROG) utilizing asymmetric spectral interferometry (aiFrog) for complete unambiguous reconstruction of ultrashort optical pulse intensity and phase. The large amount of information encoded in the aiFrog trace allows a straightforward calculation of the fundamental pulse spectrum and a direct reconstruction of the pulse envelope and phase. A simple and robust iterative algorithm with fast convergence is developed for pulse reconstruction from the aiFrog trace, taking advantage of large data redundancy and noise averaging. Due to the asymmetry of the setup, our method is free from the known challenges of a standard SHG-FROG (direction-of-time ambiguity, almost identical FROG traces for well separated pulses with different phases and for asymmetric well separated pulses). The method is highly sensitive to the weak satellite pulse of the main pulse, which can be used to track pre- or post-pulses even without full processing of the measured aiFrog trace.

Adjoint-based sensitivity analysis of periodic orbits by the Fourier–Galerkin method

Sensitivity of periodic solutions of time-dependent partial differential equations is commonly computed using time-consuming direct and adjoint time integrations. Particular attention must be provided to the periodicity condition in order to obtain accurate results. Furthermore, stabilization techniques are required if the orbit is unstable. The present article aims to propose an alternative methodology to evaluate the sensitivity of periodic flows via the Fourier–Galerkin method. Unstable periodic orbits are directly computed and continued without any stabilizing technique. The stability of the periodic state is determined via Hill's method: the frequency-domain counterpart of Floquet analysis. Sensitivity maps, used for open-loop control and physical instability identification, are directly evaluated using the adjoint of the projected operator. Furthermore, we propose an efficient and robust iterative algorithm for the resolution of underlying linear systems. First of all, the new approach is applied on the Feigenbaum route to chaos in the Lorenz system. Second, the transition to a three-dimensional state in the periodic vortex-shedding past a circular cylinder is investigated. Such a flow case allows the validation of the sensitivity approach by a systematic comparison with previous results presented in the literature. Finally, the transition to a quasi-periodic state past two side-by-side cylinders is considered. These last two cases also served to test the performance of the proposed iterative algorithm.

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

AbstractReal-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

An optimal learning algorithm contributes to the quality of the neuron states in the form of 3 Satisfiability logical representation during the retrieval phase of the Discrete Hopfield Neural Network. Based on that basis, we proposed a modified bipolar Grey Wolf Optimization algorithm with a Discrete Hopfield Neural Network for Boolean 3 Satisfiability analysis by manipulating the different levels of complexities. This work concerns the improvement in the learning phase which requires a robust iterative metaheuristic algorithm in minimizing the cost function of 3 Satisfiability logical representation with less iteration. Under some reasonable conditions, the proposed hybrid network will be assessed by employing several performance measures, in terms of learning errors, minimum energy evaluations, variability, and similarity analysis. To verify the compatibility of the Grey Wolf Optimization algorithm as a learning paradigm, the comparison was made with the hybrid model with an Exhaustive search. Thus, the results proved the capability of the proposed learning algorithm in optimizing the learning and generating global minimum solutions for 3 Satisfiability logic based on the analysis obtained via various performance metrics evaluation.

WiFi-RITA Positioning: Enhanced Crowdsourcing Positioning Based on Massive Noisy User Traces

Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.

A novel method for reconstructing general 3D curves from stereo images

Three-dimensional (3D) reconstruction of objects and scenes from camera images is of great interests due to its wide applications. Reconstruction based on feature point correspondence is an established approach. Existing research on curve-based reconstruction is limited to certain type of curves and constrained by case-dependent reconstruction accuracy. In view of that, this paper developed a new method to reconstruct general 3D curves from stereo images. Under proposal, a B-spline curve fitting is applied to sets of 2D edge points extracted from acquired stereo images. Derived approximating parametric curves are then used to construct conic surfaces. Further, robust iterative algorithms are developed to get intersection of corresponding conic surfaces to recover 3D curve. Due to the method design, proposed approach can reconstruct general 3D curves including both open and closed curves. The curve fitting technique and developed robust algorithms can meet accuracy requirement of many real applications. Validity of the proposed method is verified through experiments on a cylinder and teacup in laboratory and a real forging within a workshop.