Gradient Approach Research Articles

Enhancing security for physical layer communication in RIS-aided MIMO-NOMA systems in the presence of an eavesdropper

This paper investigates the potential of leveraging a reconfigurable intelligent surface (RIS) in the multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) wireless communication systems. In fact, NOMA improves spectral efficiency (SE) through power domain utilization, and RIS enhances the system performance by manipulating the propagation environment. In this paper, we investigate the physical layer security (PLS) of a RIS-aided MIMO-NOMA system serving two users in the presence of an eavesdropper (EV), where the channel state information (CSI) of the EV is unknown for the BS. To address the security challenge, we utilize secrecy outage probability (SOP) and minimize it by optimizing the RIS’s phase shift matrix, the beamforming vector, and the power allocation coefficients. Due to the non-convexity of the main optimization problem, we decompose the initial problem into three subproblems, and then, we propose an iterative approach to determine the unknown parameters alternately. The first subproblem involves obtaining the optimal beamforming vector and phase shift matrix using the semidefinite relaxation (SDR) method, which introduces high complexity. As an alternative, another algorithm is proposed based on the duality of the optimization problem, offering lower complexity and better accuracy for high-dimensional optimization problems. In this algorithm, the successive convex approximation (SCA) technique is employed to achieve the optimum phase shift matrix, and the stochastic gradient (SG) approach is used to determine the beamforming vector. Furthermore, the paper establishes the optimal values of power allocation coefficients by minimizing the difference between the SOPs of the two users. Finally, the simulation results demonstrate that the proposed power allocation coefficients scheme significantly enhances system performance compared to the existing fixed power allocation coefficients scheme.

Optimal Power Allocation in Optical GEO Satellite Downlinks Using Model-Free Deep Learning Algorithms

Geostationary (GEO) satellites are employed in optical frequencies for a variety of satellite services providing wide coverage and connectivity. Multi-beam GEO high-throughput satellites offer Gbps broadband rates and, jointly with low-Earth-orbit mega-constellations, are anticipated to enable a large-scale free-space optical (FSO) network. In this paper, a power allocation methodology based on deep reinforcement learning (DRL) is proposed for optical satellite systems disregarding any channel statistics knowledge requirements. An all-FSO, multi-aperture GEO-to-ground system is considered and an ergodic capacity optimization problem for the downlink is formulated with transmitted power constraints. A power allocation algorithm was developed, aided by a deep neural network (DNN) which is fed channel state information (CSI) observations and trained in a parameterized on-policy manner through a stochastic policy gradient approach. The proposed method does not require the channels’ transition models or fading distributions. To validate and test the proposed allocation scheme, experimental measurements from the European Space Agency’s ARTEMIS optical satellite campaign were utilized. It is demonstrated that the predicted average capacity greatly exceeds other baseline heuristic algorithms while strongly converging to the supervised, unparameterized approach. The predicted average channel powers differ only by 0.1 W from the reference ones, while the baselines differ significantly more, about 0.1–0.5 W.

A gradient model of renal ischemia reperfusion injury to investigate renal interstitial fibrosis.

Background: The progression from acute kidney injury to chronic kidney disease poses a significant health challenge. Nonetheless, a constraint in existing animal models of renal ischemia/reperfusion (I/R) injury is the necessity for a severe injury, almost reaching a life-threatening level, to trigger the subsequent onset of renal fibrosis. Hence, we explored an adapted gradient approach to induce I/R injury, aiming to promote the progression of renal fibrosis while preserving the overall normal functioning of the kidney. Methods: In each group, 6-8 male C57BL/6 mice were used for model construction, with all undergoing sodium pentobarbital anesthesia and left kidney removal. Subsequently, a silk thread was passed beneath the lower renal branch, elevating the right kidney under a 20-g weight's tension via a pulley system for durations of 30, 40, or 60min. Afterwards, we lowered the kidney, sutured the wound, and administered intraperitoneal saline. Mice in different groups were euthanized following reperfusion for 1, 3, 7, or 28days. Results: We observed a complete cessation of blood flow in the lower pole, while an incomplete cessation in the upper pole in the elevated kidney. Significant renal impairment was evident on day 1 with a 60min ischemic period (187.0 ± 65.3 vs 17.9 ± 4.8μmol/L serum creatinine in normal; p < .001), but not with 30 or 40min. On day 1, tubular necrosis and hyaline cast formation was evident in both lower and upper poles. On day 3, renal function returned to normal and remained normal through day 28. Histologic damage resolved in the upper pole over days 3 to 7, resulting in normal histology on day 28. By contrast, there was sustained tubular damage tubular in the lower pole on days 3 and 7, which failed to resolve and led to significant renal fibrosis by day 28. Conclusion: We created a model demonstrating clinically "silent" renal fibrosis.

Anomalous reflection from a two-layered marine sediment.

This paper concerns the theory of acoustic reflection from a two-layered marine sediment, the upper layer of which consists of a fine-grained material (mud). The seawater above and basement below the layer are treated as homogeneous half-spaces. Within the mud layer, the density is taken to be constant, and three sound speed profiles are considered: uniform, linear, and inverse-square. The reflection coefficient exhibits a background component that is similar in all three cases, exhibiting only a weak sensitivity to the gradient of the profile, the frequency, and the depth of the layer. Additionally, the two profiles with a non-zero gradient, linear and inverse-square, exhibit a sequence across grazing angle of narrow spikes of total reflection. The angular distribution of this acoustic glint is highly sensitive to the frequency and depth of the layer, and mildly so to the gradient. As the gradient approaches zero, the glint vanishes and the reflection coefficient reduces identically to the form of a uniform sound speed profile. If it were detectable, the angular distribution of the glint, observed at several frequencies, could constitute a unique, sensitive set of "fingerprints," allowing the depth and sound speed gradient of the mud layer to be inferred.

How Do the Dynamics of Urbanization Affect the Thermal Environment? A Case from an Urban Agglomeration in Lower Gangetic Plain (India)

Urban growth and development has significantly affected urban heat island (UHI) due to urbanization. Particularly in the cities in developing countries, the assessment of UHI has emerged as one of the core research themes as it significantly affects the ecological environment and livability in cities. Thus, the assessment of UHI is crucial for climate mitigation and sustainable urban landscape planning. This study identifies the dynamics of landscape patterns and the impact of composition and configuration on the thermal environment in English Bazar Urban Agglomeration (EBUA), Eastern India, along the urban–rural gradient (URG) approach. Geospatial approaches and spatial metrics were employed to assess the impact of the landscape pattern on the thermal environment. Descriptive and inferential statistics have also been used to find the effects of landscape patterns on the thermal environment. The result has also been validated based on the location and correlation analysis. The built-up area increased by about 63.54%; vegetation covers and water bodies declined by 56.72% and 67.99% from 2001 to 2021. Land surface temperature (LST) decreased with increasing distance from the core of the city. LST declined by about 0.45 °C per kilometer from the core of the city towards the outside. LST had a positive correlation with IS and a negative correlation with green space (GS) and blue space (BS). The mean aggregation of the impervious patches was larger (73.21%) than the GS (43.18%) and BS (49.02%). The aggregation of impervious surface (IS) was positively correlated, and aggregations of GS and BS had a negative correlation with LST. Findings suggest that the spatial composition and configuration of the impervious surface, GS, and BS must be considered in landscape planning and design framework to make the city more livable.

A discrete adjoint gradient approach for equality and inequality constraints in dynamics

The optimization of multibody systems requires accurate and efficient methods for sensitivity analysis. The adjoint method is probably the most efficient way to analyze sensitivities, especially for optimization problems with numerous optimization variables. This paper discusses sensitivity analysis for dynamic systems in gradient-based optimization problems. A discrete adjoint gradient approach is presented to compute sensitivities of equality and inequality constraints in dynamic simulations. The constraints are combined with the dynamic system equations, and the sensitivities are computed straightforwardly by solving discrete adjoint algebraic equations. The computation of these discrete adjoint gradients can be easily adapted to deal with different time integrators. This paper demonstrates discrete adjoint gradients for two different time-integration schemes and highlights efficiency and easy applicability. The proposed approach is particularly suitable for problems involving large-scale models or high-dimensional optimization spaces, where the computational effort of computing gradients by finite differences can be enormous. Three examples are investigated to validate the proposed discrete adjoint gradient approach. The sensitivity analysis of an academic example discusses the role of discrete adjoint variables. The energy optimal control problem of a nonlinear spring pendulum is analyzed to discuss the efficiency of the proposed approach. In addition, a flexible multibody system is investigated in a combined optimal control and design optimization problem. The combined optimization provides the best possible mechanical structure regarding an optimal control problem within one optimization.

Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

The conjugate gradient approach to solve two dimensions linear elliptic boundary value equations as a prototype of the reaction diffusion system

This paper presents a numerical approach to solve the 2-dimensional reaction-diffusion problem, a crucial model in physics and chemistry, with applications ranging from pattern formation to material science. Focusing on addressing a stationary linear elliptic problem within a rectangular domain, boundary conditions are determined through a finite-difference formulation. The Conjugate-Gradient Method is employed for the numerical solution, facilitating efficient computation. Key findings are elucidated: Firstly, the grid size for the symmetric matrix A is intricately linked to a bijective function, enabling the transition of indices to grid points. Notably, the solution to this elliptic problem exhibits a concave-up profile. Secondly, various solvers such as the Conjugate Gradient, Gauss-Seidel, and Jacobi techniques are viable, with the Conjugate Gradient method chosen for its superior accuracy, especially when considering computational efficiency. Moreover, the relationship between grid size and solution accuracy is explored, revealing a proportional dependence. Refinement of the grid leads to increased iteration counts but reduced implementation time, owing to the linearity of the function . The convergence criterion ensures high accuracy in solutions, as demonstrated in the provided figures.

Fair resource allocation and SBS selection for MC-NOMA-based HetNets supporting D2D communications

This paper studies the application of downlink multi-carrier non-orthogonal multiple access (MC-NOMA) and device-to-device (D2D) in heterogeneous networks (HetNets). In the considered model, the small base stations (SBSs) can reuse the sub-channels (SCs) of the macro base stations (MBS). The paper aims to maximize the system sum-rate while managing co-tier and co-channel interference by optimizing joint SBS selection, fair SC allocation, and power allocation, under the constraints of guaranteeing a certain amount of resources to all cellular users (CUs)and D2D pairs and satisfying CUs’ minimum signal-to-interference-plus-noise ratio (SINR) requirements. However, obtaining an efficient exact solution is challenging due to the non-convexity of the mixed integer optimization problem. To deal with this issue, the problem is decoupled into two sub-problems: 1- SBS selection and fair SC allocation problem and 2- power allocation problem. Then, a four-sided many-to-one matching method is proposed to solve the SBS selection and fair SC allocation problem jointly. The performance of the SC fair allocation algorithm is investigated in terms of the system quality of services (QoS) using a proposed mathematical expression. Furthermore, a novel dynamic search operator is introduced to enhance the performance of the matching-based algorithm. Next, the non-convex power allocation problem is transformed into a deep reinforcement learning (DRL) based framework, and the deep deterministic policy gradient (DDPG) approach is employed to solve the problem. Moreover, a joint SBS selection, SC allocation, and power allocation iterative algorithm is proposed to improve the system sum-rate. Simulation results validate our analysis and indicate the superiority of the proposed algorithms in terms of the system sum-rate compared to the benchmark algorithms.

Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks

This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) approach is introduced, where each transmitter node is considered as an agent. Given the definition of a Markov decision process (MDP) model for this problem, the agents learn to collaboratively maximize the channel capacity by deep deterministic policy gradient (DDPG) learning. Specifically, the power allocation of each agent is obtained by a centralized training and distributed execution (CTDE) method. Simulation results show the sum rate achieved by the proposed algorithm approximates that of the fractional programming (FP) algorithm and improves by at least 5% compared with the DQN (deep Q-learning network) -based power allocation algorithm.

A class of derivative free three-term descent Hestenes-Stiefel conjugate gradient algorithms for constrained nonlinear problems

This paper presents a novel method for solving large-scale nonlinear monotone equations with convex constraints. The method builds upon the conjugate gradient approach and incorporates a search direction of three terms, complemented by a modified line search. The proposed method generates a search direction that guarantees sufficient descent property, irrespective of the line search technique used. We establish the global convergence of the method under mild conditions and conduct numerical experiments to compare it with existing algorithms from the literature. The results demonstrate the method’s effectiveness and robustness, highlighting its potential for practical applications.

Design and Field Test of Collision Avoidance Method With Prediction for USVs: A Deep Deterministic Policy Gradient Approach

Design and Field Test of Collision Avoidance Method With Prediction for USVs: A Deep Deterministic Policy Gradient Approach

Intensity Gradients: A Novel Method for Interpreting External Loads in Football.

Global navigation satellite system device-derived metrics are commonly represented by discrete zones with intensity often measured by standardizing volume to per-minute of activity duration. This approach is sensitive to imprecision in duration measurement and can lead to highly variable outcomes-transforming data from zones to a gradient may overcome this problem. The purpose of this study was to critically evaluate this approach for measuring team-sport activity demands. Data were collected from 129 first-team and 73 academy matches from a Scottish Premiership football club. Gradients were calculated for velocity, acceleration, and deceleration zones, along with per-minute values for several commonly used metrics. Means and 95% CIs were calculated for playing level, as well as first-team positional groups. Within-subject coefficients of variation were also calculated for match level, position, and individual groups. The gradient approach showed consistency with per-minute metrics when measuring playing level and position groups. With coefficients of variation of 10.8% to 26.9%, the gradients demonstrated lower variability than most per-minute variables, which ranged from 10.7% to 84.5%. Gradients are a potentially useful way of describing intensity in team sports and compare favorably to existing intensity variables in their ability to distinguish between match types and position groups, providing evidence that gradient variables can be used to monitor match and training intensity in team sports.

A new conjugate gradient for unconstrained optimization problems and its applications in neural networks

We introduce a novel efficient and effective conjugate gradient approach for large-scale unconstrained optimization problems. The primary goal is to improve the conjugate gradient method's search direction in order to propose a new, more active method based on the modified vector , which is dependent on the step size of Barzilai and Borwein. The suggested algorithm features the following traits: (i) The ability to achieve global convergence; (ii) numerical results for large-scale functions show that the proposed algorithm is superior to other comparable optimization methods according to the number of iterations (NI) and the number of functions evaluated (NF); and (iii) training neural networks is done to improve their performance.

The growth of Ge and direct bandgap Ge1-xSnx on GaAs (001) by molecular beam epitaxy.

Germanium tin (GeSn) is a tuneable narrow bandgap material, which has shown remarkable promise for the industry of near- and mid-infrared technologies for high efficiency photodetectors and laser devices. Its synthesis is challenged by the lattice mismatch between the GeSn alloy and the substrate on which it is grown, sensitively affecting its crystalline and optical qualities. In this article, we investigate the growth of Ge and GeSn on GaAs (001) substrates using two different buffer layers consisting of Ge/GaAs and Ge/AlAs via molecular beam epitaxy. The quality of the Ge layers was compared using X-ray diffraction, atomic force microscopy, reflection high-energy electron diffraction, and photoluminescence. The characterization techniques demonstrate high-quality Ge layers, including atomic steps, when grown on either GaAs or AlAs at a growth temperature between 500-600 °C. The photoluminescence from the Ge layers was similar in relative intensity and linewidth to that of bulk Ge. The Ge growth was followed by the growth of GeSn using a Sn composition gradient and substrate gradient approach to achieve GeSn films with 9 to 10% Sn composition. Characterization of the GeSn films also indicates high-quality gradients based on X-ray diffraction, photoluminescence, and energy-dispersive X-ray spectroscopy measurements. Finally, we were able to demonstrate temperature-dependent PL results showing that for the growth on Ge/GaAs buffer, the direct transition has shifted past the indirect transition to a longer wavelength/lower energy suggesting a direct bandgap GeSn material.

SRG: Energy-Efficient Localized Routing to Bypass Void in Wireless Sensor Networks

The Shift Reverse Gradient (SRG) approach presents a void-size-independent hole bypassing scheme for wireless sensor networks. It does not require establishing any chain or hierarchical tree structure to ensure reliable delivery. The proposed Shift Reverse Gradient (SRG) offers an energy-efficient solution with minimal overhead and consumes minimum power. It has a communication overhead equivalent to greedy forwarding. We have shown through the simulation that SRG energy consumption is minimal and is not much affected by an increase in the void size like other existing void bypassing methods.

Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism

AbstractWith the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system.

Harnessing Online Knowledge Transfer for Enhanced Search and Rescue Decisions via Multi-Agent Reinforcement Learning

In the rapidly evolving domain of the Internet of Things (IoT), devices play an instrumental role in high-stakes scenarios like search and rescue (SAR) operations. Traditional decision-making processes within SAR missions often struggle to cope with the dynamic and unpredictable nature of such environments, leading to inefficiencies and delayed responses. This paper aims to explore the potential of multi-agent reinforcement learning (MARL) to improve the decision-making process within SAR operations underpinned by IoT. Functional, current methods are limited by their static decision frameworks and inability to adapt in real time to the chaotic variables present in SAR situations. We introduced a novel MARL framework and compared its performance against benchmark strategies, specifically the multi-agent deep deterministic policy gradient (MADDPG) approach. Uniquely enhanced by online knowledge transfer, the framework leverages the capabilities of the deep deterministic policy gradient (DDPG) method. The preliminary findings underscore the proposed framework’s superior efficiency and speed in SAR contexts. Our research highlights MARL’s transformative potential, positing it as a groundbreaking strategy for IoT-based decision making in high-pressure SAR environments with suggestions for further studies in varied real-world scenarios.

Generalized robust loss functions for machine learning

Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges. Firstly, there is currently a lack of unified frameworks for building robust loss functions in machine learning. Secondly, most of them only care about the occurring noise and pay little attention to those normal points. Thirdly, the resulting performance gain is limited. To this end, we put forward a general framework of robust loss functions for machine learning (RML) with rigorous theoretical analyses, which can smoothly and adaptively flatten any unbounded loss function and apply to various machine learning problems. In RML, an unbounded loss function serves as the target, with the aim of being flattened. A scale parameter is utilized to limit the maximum value of noise points, while a shape parameter is introduced to control both the compactness and the growth rate of the flattened loss function. Later, this framework is employed to flatten the Hinge loss function and the Square loss function. Based on this, we build two robust kernel classifiers called FHSVM and FLSSVM, which can distinguish different types of data. The stochastic variance reduced gradient (SVRG) approach is used to optimize FHSVM and FLSSVM. Extensive experiments demonstrate their superiority, with both consistently occupying the top two positions among all evaluated methods, achieving an average accuracy of 81.07% (accompanied by an F-score of 73.25%) for FHSVM and 81.54% (with an F-score of 75.71%) for FLSSVM.

A NEW THREE -TERM CONJUGATE GRADIENT ALGORITHM FOR SOLVING MINIMIZATION PROBLEMS

The method of optimization is used to determine the most precise value for certain functions within a certain domain; it is mostly studied and employed in the fields of mathematics, computer science, and physics. This work presents a novel three-term conjugate gradient (CG) approach for unconstrained optimization problems. Both the descending criteria and the sufficient descent criterion were met by the new approach. The novel method that has been proposed has been evaluated for global convergence. The outcomes of numerical trials on a few well-known test functions demonstrated how highly successful our new modified method is, depending on the number of iterations (NOI) and the number of functions to be evaluated (NOF).